Acknowledgement

In the beginning, I would like to express my special gratitude to my Ph. D.

supervisor Dr. Simona NICULESCU, who gave me this opportunity and precise advice for my work. The thesis could not have been accomplished without her guidance throughout this research. I am also grateful for her kindness and empathy.

I would like to express my sincere appreciation to the members of my thesis jury, Prof. Christophe CLARAMUNT, Dr. Thomas CORPETTI, Prof. Vincent RODIN, and the rapporteurs, Dr. Antoine COLLIN and Prof. Sébastien GADAL, not only for taking the time to assess my work, but also for being comprehensive when I had to reschedule the defense because of my health issues.

I would like to extend my sincere thanks to Dr. Thomas CORPETTI and Dr. Dino IENCO for being on my thesis committee, evaluating my works, and providing advice and guidance during these four years of study.

I thank my dear work colleagues of UMR LETG Brest, thank you for taking me in and all the beautiful times. In particular, Soumia, Chi-Nguyen, Nina, and Laurianne, thank you so much for bringing me joy over these four years; I could not have undertaken this journey without you.

I would like to acknowledge the medical team of Cavale Blanche hospital for being professional and performing the surgeries quickly every time so that I could go back to work and submit the thesis on time (with some delays, although).

I am also very grateful to my dear partner Hassan and my lovely family-in-law for supporting and taking care of me when I was under the stress of writing, moreover, a special thanks to Daniel for the help in the layout of my thesis. I am so lucky to call you my family, and I will never forget your support and kindness.

At last, to my beloved parents and family, whom I have not seen for three years,

List of tables

General introduction

General context

"Land cover" and "land use" refer to the physical ground cover information of the land [START_REF] Meyer | Changes in Land Use and Land Cover: A Global Perspective[END_REF]) and the socio-economic and cultural utility of the land occupied by humans [START_REF] Turner | The Sustainability Principle in Global Agendas: Implications for Understanding Land-Use/Cover Change[END_REF], respectively. Land use and land cover (LULC)

information have long been considered the key element to understanding the Earth's surface and the purpose the land serves. Moreover, land use and land cover change (LULCC) driven by various factors (e.g., economic, biophysical, political, social) [START_REF] Verburg | Modeling Land-Use and Land-Cover Change[END_REF] has emerged as a fundamental component of global environmental change and sustainability research [START_REF] Turner | The emergence of land change science for global environmental change and sustainability[END_REF]. In recent years, LULCC has attracted significant attention from the scientific community, government, and decision-makers because LULCC reflects the pattern of human use in a region (Y. [START_REF] Chang | Review of Land Use and Land Cover Change research progress[END_REF], which significantly affects the function of the Earth's system [START_REF] Lambin | The causes of land-use and land-cover change: Moving beyond the myths[END_REF].

Coastal areas-due to the interactions between land, water, and atmosphere that are constantly being altered by natural and human forces [START_REF] Beatley | An Introduction to Coastal Zone Management: Second Edition[END_REF]-have always played an essential role in LULC studies for their high concentration of functions, socio-economic activities and population, and natural resources [START_REF] Mossinger | Coastal regions[END_REF].

Global economic, social, and environmental changes have been observed on different scales since the late 1990s; coastal LULC issues have become of increasing scientific interest nowadays, considering that more than half of the world's population lives in coastal regions [START_REF] Neumann | Future Coastal Population Growth and Exposure to Sea-Level Rise and Coastal Flooding-A Global Assessment[END_REF], of which only 15% remain in their natural state.

In particular, urbanization, the development of infrastructure and associated facilities at high speed, growing intense agriculture production, and the increasing exploitation of natural resources are the main driving factors of dramatically rising LULCC in coastal areas in recent decades.

In the meantime, remote sensing technologies have brought a significant breakthrough in this new scientific discipline known as "land change science" [START_REF] Turner | The emergence of land change science for global environmental change and sustainability[END_REF] with automatic classification algorithms and high spatial, spectral, and temporal resolution, and radiometric satellite imagery. It has become possible to map LULC accurately and monitor the changes that cannot be observed in ground research. In addition, remote sensing has become an essential tool for LULC studies because it facilitates global, recurrent, and long-term observation. Moreover, the latest generation of satellite imagery from Europe's Copernicus Programme-which provides free access, high-quality multispectral sensors-as well as the development of different digital data, have brought LULC classification and LULCC detection into a new era.

Our study area, Pays de Brest, located on the west coast of France, is the most important metropolitan and agglomeration in the northwestern Finistère department in Brittany region, where around 80% of the population live in coastal areas (i.e., within 30 km of a coastline). The LULC of the Pays de Brest coastline, however, appears to be relatively fragmented due to its particular geographical configuration (e.g., fragmented peninsula, uneven topography) and a long history of land reclamation.

Like the majority of coastland worldwide, Pays de Brest has experienced considerable LULCC since the 1990s and today faces increasing and varied types of pressure, including natural, demographic, residential, economic, and environmental [START_REF] Adeupa | La lettre des observatoires de l'ADEUPa-Octobre[END_REF]. The coastline changes in Pays de Brest are driven mostly by rising population, development of tourism, urban expansion, and intense agricultural activities [START_REF] Adeupa | La lettre des observatoires de l'ADEUPa-Octobre[END_REF][START_REF] Sparfel | Etude des changements d'occupation des sols dans la zone côtière à partir de données hétérogènes: Application au pays de Brest[END_REF]. Those factors have led to environmental degradation and conflicts of interest among the private sector, public services, housing, local planning, tourist sectors, and public authorities seeking protection for the natural landscape (ADEUPa Brest, n.d.;[START_REF] Sparfel | Etude des changements d'occupation des sols dans la zone côtière à partir de données hétérogènes: Application au pays de Brest[END_REF]. For example, the conflict between permanent or vacation housing construction on coastlines with aquaculture and croplands and natural landscape, which are often encroached upon by urban expansion and the artificialization of coastland.

Thus, the use of high spatial and temporal resolution satellite data could offer fresh perspectives for LULC studies in Pays de Brest. In particular, near-real-time remote sensing data (e.g., Sentinel data) appear to be perfectly complementary to large-scale vector data (e.g., BDTopo IGN, CORINE Land Cover, digital cadastral plan) of infrastructure, construction, vegetation cover, and their evolution. Moreover, the vector data are usually annual; satellite observation data, in contrast, are able to provide data at a higher frequency (nearly daily).

21

However, although remote sensing has become the principal source of LULC information, the European Copernicus Programme and national facilities (e.g., Equipex GEOSUD, THEIA) provide access to a particularly large amount of satellite data for local communities, scientist communities, and industries. LULC information in coastal Pays de Brest is insufficient, and very few previous studies have focused on vegetation cover.

Therefore, local governments and communities seek an efficient way to gain an understanding of LULC in the region and follow the changes that occurred over time to manage urban development and the evolution of LULC, also protecting the natural landscape. In this context, local public sectors are interested in the potential contribution of the new technologies to environmental data processing. In particular, multi-source remote sensing data are available and access is free.

In this thesis, three specific LULC studies were conducted in three different studies areas located within Pays de Brest according to the immediate need of local communities (especially ADEUPa Brest, Parc Naturel Régional d'Armorique, Brest Métropole).

Problematic

The general problematic of the thesis is how remote sensing technologies contribute to fundamental local issues such as coastal LULCC in the context of population growth, urban sprawl and climate change, as well as how remote sensing technologies-especially the latest generation of satellite imagery and advanced classification-bring important breakthroughs to LULC studies of Pays de Brest.

Moreover, some detailed problematics must also be found out:

1) How can the application of the latest generation of high spatial and spectral resolution satellite imagery be strengthened to better respond to environmental management and urban planning issues?

2) What are the significant past and current changes in the coastal Pays de Brest that

have not yet been observed by ground research and vector data? In addition, what are their driving factors, as well as short or long-term environmental and socio-economic influences?

3) What are the most suitable tools and methodologies for better detecting, identifying, and monitoring LULCC over time at different spatial scales? 4) Is it possible to generate change models in order to develop forward-looking scenarios?

Apart from the general problematic, local communities are also interested in three specific LULC questions:

1) How can machine learning (ML) classification methods contribute to vegetation cover mapping in Brest?

2) What were the major LULCCs on the Crozon Peninsula (Pays de Brest) from 2007 to 2018, and what are the most commonly adopted classification methods for LULCC detection?

3) How to map winter crops in Pays de Brest and monitor their phenology using openaccess, high spectral and spatial resolution Sentinel-1 and -2 data.

Objective

In order to address this issue, this thesis focuses on the different ways to explore the potential of European satellite data (e.g., Vénμs, Pleiades, SPOT, and Sentinel) to propose a robust ML methodology for LULCC analysis and mapping. This methodology has been partially transferred to some local authorities of the Pays de Brest to use them in their territorial governance mission. The main objective can be decomposed into several steps. First, LULC classification was performed to provide accurate thematic maps using different ML and deep learning (DL) algorithms. The observation and monitoring of LULCC were then carried out, mainly using post-classification processing methods.

Moreover, we sought to identify and understand the socio-economic factors behind these changes, eventually modeling the changes and impacts, and forecasting the potential changes in a context of global change.

The following specific objectives were also formulated for local government and communities:

1) To propose specific and reproductive image-processing methodology to enable better analysis, modeling and definition of LULC, as well as improved monitoring of LULCC in Pays de Brest. The methodology proposed should effectively improve the accuracy of LULC and vegetation cover maps.

2) Evaluate and validate the classification results and compare different methodologies to select the most suitable methodology for coastal LULC classification in Pays de Brest.

3) To transfer high-resolution remote sensing data to managers for operational purposes, and to propose a three-dimensional (i.e., natural areas, agricultural areas, and builtup areas) LULC model at different scales (e.g., city, municipality, or region).

Moreover, three specific aims have been formulated according to the needs of local communities:

1) To detect, identify, and classify urban green land in three years (2016)(2017)(2018) in the city of Brest and eventually to study the changes.

2) To map different LULCs and then monitor LULCC from 2007 to 2018, eventually look for abandoned agricultural land on the Crozon Peninsula.

3) To study the feasibility of mapping winter crops with Sentinel-2 data in a fragmented area dominated by small-size fields.

4) To study the correlation between winter crop phenology and Sentinel-1 C-band Synthetic-Aperture RaDAR (SAR) backscattering time-series data.

Outline of thesis

This thesis consists of three parts comprising seven chapters. The first part presents the context of the work; part two discusses the general methodologies, including data and image-processing methodologies. The third part presents the three specific case studies (and publications) carried out during the thesis. The seven chapters are summarized below:

1) Chapter 1 presents the definition of LULC, LULCC and its impacts, as well as the importance of LULC monitoring and management. This is followed by an extensive literature review of previous studies of LULC classification and LULCC detection using remote sensing technologies, especially LULC studies in coastal areas.

2) Chapter 2 analyzes geographical characteristic of the study area essential for our study, such as the localization and geographical condition. In addition, the major LULC in Pays de Brest, including salient issues and conflicts of interest, are presented. This chapter concludes with the detailed research objectives of each case study.

3) Chapter 3 deals with the data employed in our works, including various optical images and SAR time series, auxiliary data, and field studies. The thesis concludes with a synthesis of the results, a discussion of the success and limitations of the works, and, ultimately, the proposition for further research. Land cover and Land use have often been confused in daily practice [START_REF] Giri | Brief Overview of Remote Sensing of Land Cover[END_REF] and are often used interchangeably. However, these are two distinct concepts.

PART I CONTEXT OF THE STUDY

Land cover and land use

Land cover has been defined as the observed biotic and abiotic assemblage of the Earth's surface and immediate subsurface [START_REF] Meyer | Changes in Land Use and Land Cover: A Global Perspective[END_REF], which refers to the physical ground cover information of the land. Land cover refers to the quantity and type of certain surfaces on the ground in a geographical area, such as vegetation cover, croplands, urban areas, water bodies, and glaciers [START_REF] Meyer | Changes in Land Use and Land Cover: A Global Perspective[END_REF].

As the most important descriptor of the Earth's terrestrial surface, land cover allows for studying the operational relationship between terrain, climate, and soil, the characteristics of anthropogenic activity, and biogeographical and eco-climatic diversity [START_REF] Loveland | Development of a global land cover characteristics database and IGBP DISCover from 1 km AVHRR data[END_REF][START_REF] Turner | The emergence of land change science for global environmental change and sustainability[END_REF][START_REF] Wulder | Land cover 2.0[END_REF]. Therefore, land cover information monitoring is critical to foreground a range of natural resource management decisions at local, national, and global levels [START_REF] Wulder | Land cover 2.0[END_REF].

Land use, on the other hand, refers to the purpose the land serves and describes the social, economic, and cultural utility of the land occupied by humans [START_REF] Turner | The Sustainability Principle in Global Agendas: Implications for Understanding Land-Use/Cover Change[END_REF].

Therefore, compared to land cover (which is most often pertinent to the natural sciences), land use is more likely related to social sciences such as economy, sociology, anthropology, and social geography [START_REF] Meyer | Changes in Land Use and Land Cover: A Global Perspective[END_REF].

The same land use can relate to several different lands covers reciprocally. For example, a vegetated area can be used as grassland, forest, or public green space, and an artificial surface can be a commercial area, residence for inhabitants, or industrial zone.

By contrast, urban parks and residential areas generally comprise built-up area and vegetation cover. However, most land uses nowadays-mainly built-up areas-have various important impacts on ecosystem service, including biodiversity degradation, greenhouse effect, and deforestation. For example, agriculture-the most critical land use worldwide, which occupies half of the Earth's habitable land-is threatening biodiversity due to the ceaseless expansion of croplands and intensive agricultural activities.

Thus, knowledge of land use is crucial for understanding the conflict between different land uses and developing strategies to balance the conflict for sustainable development.

LULC changes and the importance of management

Land cover change and Land use change can also be interpreted separately. Land cover change refers to the complete modification from one class of land cover to another or to the anthropologic or natural change within the same land cover class [START_REF] Meyer | Changes in Land Use and Land Cover: A Global Perspective[END_REF]. One example would be conversion from cropland to urban area or forest degradation due to natural disaster (e.g., forest fire and storm damage). Nonetheless, the principal land cover change nowadays is converting the natural environment for human use or changing practices on human-occupied lands [START_REF] Foley | Global consequences of land use[END_REF].

Land use change, in comparison, refers either to a shift toward a different employment of land occupied by humans or to an intensification of the current land use.

In addition, land use change is human-induced; it can be conducted without land cover change. For example, a primary forest can be occupied by humans and used to produce wood, without ceasing to be forest. Overall, land use and land cover change (LULCC) reflects the pattern of human land use in a region (Y. [START_REF] Chang | Review of Land Use and Land Cover Change research progress[END_REF] and significantly affects the function of the Earth's system [START_REF] Lambin | The causes of land-use and land-cover change: Moving beyond the myths[END_REF].

Although various factors are driving LULCC, including biophysical, economic, social, cultural, political, or institutional factors [START_REF] Verburg | Modeling Land-Use and Land-Cover Change[END_REF], human-induced LULCCs are far more common than natural changes in recent decades [START_REF] Giri | Brief Overview of Remote Sensing of Land Cover[END_REF]).

Humanity's growing impact on the environment is responsible for most of the recent changes in the biosphere and modification of the Earth [START_REF] Meyer | Changes in Land Use and Land Cover: A Global Perspective[END_REF]. As shown [START_REF] Defries | Ecosystems and Land Use Change[END_REF][START_REF] Foley | Global consequences of land use[END_REF]. Moreover, LULCC is usually related to population growth and poverty and mostly drove by people's immediate needs for natural resources [START_REF] Foley | Global consequences of land use[END_REF] as well as their responses to economic opportunities [START_REF] Lambin | The causes of land-use and land-cover change: Moving beyond the myths[END_REF]. Consequently, rapid LULCC is mainly due to economic globalization, which can directly determine the LULC of a region or indirectly affect LULC through information and technological development [START_REF] Lambin | The causes of land-use and land-cover change: Moving beyond the myths[END_REF].

Furthermore, LULCC is not unidirectional; the environmental changes induced by human activities or natural changes generally impact LULC (Y. [START_REF] Chang | Review of Land Use and Land Cover Change research progress[END_REF]. [START_REF] Lambin | The causes of land-use and land-cover change: Moving beyond the myths[END_REF] have summarized that the most essential current LULCC representation on

Earth is tropical deforestation (forest shifting to cultivation), rangeland modification and degradation, agricultural intensification, and urbanization in the form of built-up or paved-over areas. In addition, LULCC has direct effects on biodiversity [START_REF] Sala | Global biodiversity scenarios for the year 2100[END_REF][START_REF] Falcucci | Changes in land-use/land-cover patterns in Italy and their implications for biodiversity conservation[END_REF][START_REF] Cousins | Regional-scale land-cover change during the 20th century and its consequences for biodiversity[END_REF], global carbon cycle change [START_REF] Houghton | The U.S. Carbon Budget: Contributions from Land-Use Change[END_REF][START_REF] Olofsson | Effects of human land-use on the global carbon cycle during the last 6,000 years[END_REF], and the global climate warming effect (Houghton et al., Part I Context of the study Chapter 1. Detection of land use/land cover in coastal area with remote sensing 30 1999; [START_REF] Fearnside | Global Warming and Tropical Land-Use Change: Greenhouse Gas Emissions from Biomass Burning, Decomposition and Soils in Forest Conversion, Shifting Cultivation and Secondary Vegetation[END_REF]J. Li et al., 2021), as well as regional or local climate change such as the urban island heat effect (X.-L. [START_REF] Chen | Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes[END_REF][START_REF] Deng | Systematic Modeling of Impacts of Land Use and Land Cover Changes on Regional Climate: A Review[END_REF], soil degradation (X. [START_REF] Wang | Assessment of soil erosion change and its relationships with land use/cover change in China from the end of the 1980s to 2010[END_REF][START_REF] Borrelli | An assessment of the global impact of 21st century land use change on soil erosion[END_REF], degradation of sustainable natural resources-especially forest resources [START_REF] Vitousek | Human Domination of Earth's Ecosystems[END_REF][START_REF] Lira | Land-use and land-cover change in Atlantic Forest landscapes[END_REF]-water and air pollution (S. T. Y. [START_REF] Tong | Modeling the relationship between land use and surface water quality[END_REF]Ren et al., 2003;[START_REF] Wu | Impacts of changes in land use and land cover on atmospheric chemistry and air quality over the 21st century[END_REF], and increases in infectious disease due to the increased proximity of people and livestock brought on by urbanization (M. X. [START_REF] Tong | Infectious Diseases, Urbanization and Climate Change: Challenges in Future China[END_REF][START_REF] Hassell | Urbanization and Disease Emergence: Dynamics at the Wildlife-Livestock-Human Interface[END_REF].

Even though LULCC has some positive effects on human society-for instance, the conversion from forests to croplands allows it to feed the rapidly growing population worldwide-it also entails a variety of adverse effects that lead to the significant degradation of the Earth's ecosystem [START_REF] Giri | Brief Overview of Remote Sensing of Land Cover[END_REF]. Therefore, monitoring and understanding the distribution and dynamics of LULC [START_REF] Giri | Brief Overview of Remote Sensing of Land Cover[END_REF] as well as modeling, predicting, and managing LULCC at the local or/and global level [START_REF] Meyer | Changes in Land Use and Land Cover: A Global Perspective[END_REF] have become crucial and have led to the demand for maintaining a balance between immediate human needs and the sustainable capacity of the ecosystem, in addition to ensuring resources and services in the future [START_REF] Foley | Global consequences of land use[END_REF]. Moreover, as a critical element in current strategies for natural resource management and environmental changes following up [START_REF] Turner | Land-Use and Land-Cover Change: Science/research plan[END_REF][START_REF] Attri | Remote Sensing & GIS based Approaches for LULC Change Detection -A Review[END_REF], LULC monitoring can provide valuable information-first of all, to governments (from national to local) for implanting sustainable development policies-as well as to nongovernmental organizations and scientists for studying the Earth's ecosystem and human societies [START_REF] Giri | Brief Overview of Remote Sensing of Land Cover[END_REF].

Considering the indispensability of, and increasing need for, understanding LULC and LULCC mechanisms [START_REF] Petit | Quantifying processes of land-cover change by remote sensing: Resettlement and rapid land-cover changes in south-eastern Zambia[END_REF][START_REF] Foley | Global consequences of land use[END_REF], the link between LULC and human activities has been intensively studied in recent decades. The connection has been analyzed on the American and African continents since the 1930s; however, the studies were highly focused on planning and managing land use (Y. [START_REF] Chang | Review of Land Use and Land Cover Change research progress[END_REF]. Sixty years on, LULC researches were beginning to attract considerable attention and interact with other disciplines; the LULCC mechanism has since been studied at a regional or global scale (Y. [START_REF] Chang | Review of Land Use and Land Cover Change research progress[END_REF]. Also in the 1990s, remote sensing technologies were introduced and developed in the LULC research field for mapping LULC and simulating the major socio-economic and biophysical driving factors and consequences of LULCC [START_REF] Verburg | Modeling Land-Use and Land-Cover Change[END_REF]. Afterward, remote sensing technologies soon improved to meet research needs, leading to a massive breakthrough in LULC and LULCC studies.

LULC mapping and detecting changes with remote sensing techniques

Remote sensing technologies can directly detect land cover, and infer land uses, generally with the help of prior knowledge and auxiliary data. As a revolutionary breakthrough, remote sensing provides near-real-time, spatially continuous, and highly consistent information at a variety of spatial and temporal scales in order to accurately detect LULC on diverse scales and help to identify the different characteristics of LULCC that occur over time and cannot be observed by ground research [START_REF] Foody | +%28University+of+Salford%2C+Salford%2C+UK.%29&publication_year=1996[END_REF][START_REF] Verburg | Modeling Land-Use and Land-Cover Change[END_REF][START_REF] Weih | Object-based classification vs. Pixel-based classification: Comparitive importance of multi-resolution imagery[END_REF], including the types, spatial distribution, and quantity of changes.

Overall, remote sensing technologies facilitate dynamic monitoring and quantitative analysis (Y. [START_REF] Chang | Review of Land Use and Land Cover Change research progress[END_REF] and have become the major sources for LULC mapping and LULCC detection for their rich spectral information of ground objects, a wide selection of spatial and spectral resolutions, high temporal frequency, and low computational complexity [START_REF] Lunetta | Impacts of imagery temporal frequency on land-cover change detection monitoring[END_REF][START_REF] Coops | Identifying and Describing Forest Disturbance and Spatial Pattern: Data Selection Issues and Methodological Implications[END_REF][START_REF] Chen | Object-based change detection[END_REF].

Meanwhile, LULC research has reciprocally always been in a key position and has made significant contributions in the remote sensing field, especially in environmental science [START_REF] Kalnay | Impact of urbanization and land-use change on climate[END_REF][START_REF] Aplin | Remote sensing: Land cover[END_REF][START_REF] Chang | Review of Land Use and Land Cover Change research progress[END_REF]. In the beginning, LULC information was collected using visual photo interpretation; however, this method was hindered by increasing labor costs, long processing times for outputs, and human subjectivity (because photo interpretation is highly dependent on a priori knowledge) [START_REF] Wulder | Land cover 2.0[END_REF]. In the same year, [START_REF] Gordon | Utilizing LANDSAT imagery to monitor land-use change: A case study in ohio[END_REF] applied two Landsat images to monitor land use change in Franklin County, Ohio, USA, induced by an extensive regional project. The results showed that some major errors remained in the image processing and classification (such as misalignment and misclassification) especially in an urban environment. In contrast to the complex and heterogeneous landscape of urban

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In the 1980s, the spatial resolution of Landsat images was improved from 69 m to 30 m; in addition, three more spectral bands were appended [START_REF] Loveland | History of land cover mapping[END_REF], thus facilitating better classification of LULC.

Several papers in the 1980s and 1990s are well-recognized for their contribution to LULC classification with medium spatial resolution satellite data. [START_REF] Toll | Effect of Landsat thematic mapper sensor parameters on land cover classification[END_REF] and [START_REF] Haack | An assessment of landsat MSS and TM data for urban and near-urban land-cover digital classification[END_REF] both assessed Landsat Thematic mapper (TM) and MSS through the classification performance of urban/suburban land cover by using the maximumlikelihood classifier (MLC) and transformed divergence (TD) calculations, respectively. Some research are well-established and presentative for LULC mapping in the urban area, (Gong & Howarth, 1992) developed a cover-frequency method for extracting land use in a rural-urban fringe environment in the town of Markham, Toronto, Canada, which was a typical rural-urban with much agricultural and natural land converted into an urban area for commercial, industrial, and residential uses. A classification with 12 land cover categories was first performed on a single SPOT HRV multispectral data point using MLC, and then cover frequencies were extracted from the classified map for another classification of 14 land use classes using a supervised minimum-city-block classifier. The cover-frequency method proved more efficient than the traditional MLC (with a higher In another study of rural-urban fringe LULC mapping in Toronto, [START_REF] Treitz | Application of satellite and GIS technologies for landcover and land-use mapping at the rural-urban fringe-A case study[END_REF] employed both SPOT HRV multispectral and panchromatic data for LULC classification at the rural-urban fringe. First, multispectral and panchromatic data were combined, resampled, and corrected to preserve the spectral and spatial resolution.

Second, the LULC classification was performed by using the convolutional supervised MLC.

The classification of eight rural-urban fringe LULC classes achieved a high kappa coefficient of 82.2% for training data and 70.3% for test data.

Later on, [START_REF] Alves | Characterizing land cover dynamics using Multitemporal imagery[END_REF] analyzed land cover dynamics in the western Brazilian Amazon from 1986 to 1992, using a time series composed of five SPOT HRV images and MLC, to delimit the total deforested area and characterize areas of secondary vegetation growth. The results indicated that land cover change classification accuracy was significantly increased by using SPOT HRV Multitemporal satellite data.

Since the 1990s, large-area land cover mapping has become possible with the emergence of National Oceanic and Atmospheric Administration (NOAA) Advanced Very High Resolution Radiometer (AVHRR) sensors. The new series of sensors provided global remote sensing data [START_REF] Loveland | History of land cover mapping[END_REF], which are widely used for LULC mapping at regional, national, or even global scales.

Some earliest presentative works have successfully demonstrated the excellent performance of this large-scale sensor using different processing methods for different purposes. The paper by [START_REF] Ehrlich | Applications of NOAA-AVHRR 1 km data for environmental monitoring[END_REF]) is one of the first studies that discussed the general applications of NOAA-AVHRR 1 km data for environmental monitoring and LULC characterizing. [START_REF] Cihlar | Land cover classification with AVHRR multichannel composites in northern environments[END_REF] demonstrated the efficiency of multispectral and Multitemporal AVHRR data for providing land cover information in the northern environment. [START_REF] Defries | Global discrimination of land cover types from metrics derived from AVHRR pathfinder data[END_REF] improved discrimination between 12 land cover types on a global scale from metrics derived from AVHRR data. [START_REF] Lambin | Land-cover changes in sub-saharan Africa (1982-1991): Application of a change index based on remotely sensed surface temperature and vegetation indices at a continental scale[END_REF] mapped land cover changes at broad spatial scales in sub-Saharan Africa between 1982 and 1991, identifying seasonal patterns of change using AVHRR data and vegetation index. [START_REF] Loveland | History of land cover mapping[END_REF]. Furthermore, LULC classification accuracy was highly improved during the 1990s. Certain errors (e.g., sub-pixel mixing) were also corrected due to the progress of classification methods, such as the first generation of Artificial NN (ANN) [START_REF] Kanellopoulos | Land-cover discrimination in SPOT HRV imagery using an artificial NN-A 20-class experiment[END_REF][START_REF] Foody | Relating the land-cover composition of mixed pixels to artificial NN classification output[END_REF]Atkinson et al., 1997), regression and decision trees [START_REF] Moody | The influence of scale and the spatial characteristics of landscapes on land-cover mapping using remote sensing[END_REF][START_REF] Hansen | Classification trees: An alternative to traditional land cover classifiers[END_REF][START_REF] Friedl | Decision tree classification of land cover from remotely sensed data[END_REF][START_REF] De Fries | Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers[END_REF].

However, some limitations persisted, mainly induced by the narrow availability and lack of accessibility of data, limited types of sensors, processing complexity, and relatively poor accuracy of classification results. Therefore, some studies began to discuss the possibility of use of multitemporal data or SAR sensors (ERS-1) to break through the limitation of meteor impacts, derive phenological variation, and improve land cover discrimination [START_REF] Townshend | Land cover[END_REF]. However, the Radio Detection And Ranging (RaDAR) data remained complex to interpret in that time.

Since the development of remote sensing sensors in the late 1990s, LULC maps have been increasingly generated from fine-resolution data because higher spatial resolution can significantly improve discrimination among different types of land cover as well as cloud detection, reduce misclassification errors, and facilitate short-term change detection. In addition, more and more studies were carried out to answer defined user needs and solve related social and environmental issues instead of simply classifying the study area.

Among the most-cited works, [START_REF] Wardlow | Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains[END_REF] improved and updated LULC datasets from regional to global scales in the U.S. Central Great Plains by using time-series MODIS 250 m vegetation index datasets for performing large-area crop monitoring in an 2) Development of advanced automated processing algorithm, great computational capacity, classification approach, and classifiers. For example, the introduction of Object-Based Classification (OBC) improves land cover types extraction and reduces misclassification errors efficiently. Meanwhile, various reliable non-parametric supervised classification methods, such as ML algorithms [START_REF] Talukdar | Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations-A Review[END_REF][START_REF] Pandey | Land use/land cover in view of earth observation: Data sources, input dimensions, and classifiers-a review of the state of the art[END_REF] and the emergence of DL classifier (L. Ma et al., 2019;[START_REF] Vali | Deep Learning for Land Use and Land Cover Classification Based on Hyperspectral and Multispectral Earth Observation Data: A Review[END_REF], have opened a new era of LULC classification.

Although parametric methods (e.g., MLC) were widely used in the 1970s and 1980s, they had a severely limited capacity for dealing with heterogeneous complex land covers; moreover, land cover distributions cannot be defined based on data distributions due to a great deal of uncertainty (D. [START_REF] Lu | A survey of image classification methods and techniques for improving classification performance[END_REF][START_REF] Phiri | Developments in Landsat Land Cover Classification Methods: A Review[END_REF]. In contrast, non-parametric algorithms, including SVM (C. [START_REF] Huang | An assessment of support vector machines for land cover classification[END_REF][START_REF] Pal | Ensemble of support vector machines for land cover classification[END_REF][START_REF] Singh | Appraisal of land use/land cover of mangrove forest ecosystem using support vector machine[END_REF], Decision Tree (DT) [START_REF] Friedl | Decision tree classification of land cover from remotely sensed data[END_REF][START_REF] De Fries | Global land cover classifications at 8 km spatial resolution: The use of training data derived from Landsat imagery in decision tree classifiers[END_REF][START_REF] Pal | An assessment of the effectiveness of decision tree methods for land cover classification[END_REF], a variety of DL classifiers (e.g., ANN, Convolutional NN (CNN), and Recurrent NN (RNN) [START_REF] Foody | Land cover classification by an artificial NN with ancillary information[END_REF][START_REF] Srivastava | Selection of classification techniques for land use/land cover change investigation[END_REF][START_REF] Ienco | Land Cover Classification via Multitemporal Spatial Data by Deep Recurrent NNs[END_REF]C. Zhang et al., 2019a;[START_REF] Tong | Land-cover classification with high-resolution remote sensing images using transferable deep models[END_REF], make no assumption before classification and are more efficient and accurate [START_REF] Wulder | Land cover 2.0[END_REF][START_REF] Talukdar | Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations-A Review[END_REF]. [START_REF] Liu | Integration of classification methods for improvement of land-cover map accuracy[END_REF][START_REF] Lu | Comparison of Land-Cover Classification Methods in the Brazilian Amazon Basin[END_REF][START_REF] Otukei | Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms[END_REF][START_REF] Szuster | A comparison of classification techniques to support land cover and land use analysis in tropical coastal zones[END_REF][START_REF] Talukdar | Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations-A Review[END_REF].

3) Advancement of cloud detection algorithms [START_REF] Goodman | Cloud detection and analysis: A review of recent progress[END_REF][START_REF] Foga | Cloud detection algorithm comparison and validation for operational Landsat data products[END_REF][START_REF] Jeppesen | A cloud detection algorithm for satellite imagery based on deep learning[END_REF] and the emergence of well-functioning cloud masks [START_REF] Baetens | Validation of Copernicus Sentinel-2 Cloud Masks Obtained from MAJA, Sen2Cor, and FMask Processors Using Reference Cloud Masks Generated with a Supervised Active Learning Procedure[END_REF], such as MAJA [START_REF] Lonjou | MACCS-ATCOR joint algorithm (MAJA)[END_REF]), Sen2Cor (Main-Knorn et al., 2017) and FMask (Z. [START_REF] Zhu | Object-based cloud and cloud shadow detection in Landsat imagery[END_REF][START_REF] Zhu | Improvement and expansion of the Fmask algorithm: Cloud, cloud shadow, and snow detection for Landsats 4-7, 8, and Sentinel 2 images[END_REF].

4) For real-value, continuous, or discrete series of data, time-series analysis was widely utilized as a powerful tool to reveal land surface dynamics and to analyze the magnitude of these dynamics (e.g., socio-economic transformation, urbanization, deforestation) [START_REF] Lasaponara | Satellite time-series analysis[END_REF]Kuenzer et al., 2015). Additionally, the time-series employed in LULC classification, especially in LULCC detection, are usually annual and may be composed of different satellite sensors.

Among the works which made a significant contribution of time-series analysis in remote sensing, [START_REF] Yang | Using a time series of satellite imagery to detect land use and land cover changes in the Atlanta, Georgia metropolitan area[END_REF] extracted LULCC of Atlanta, Georgia, USA, over 25 years for modeling the impacts on temperature and air quality in Atlanta by using a time-series of Landsat MSS and TM images. Among the most-recognized works, [START_REF] Bruzzone | A neural-statistical approach to multitemporal and multisource remote-sensing image classification[END_REF] proposed using a data fusion approach to classify multi-source remote sensing data, based on applying the Bayes rule of minimum error to the "compound classification" of multi-source and Multitemporal image pairs. The proposed technique was performed on Landsat TM and ERS-1 SAR data and was shown to outperform classification using a single sensor. [START_REF] Phiri | Effects of pre-processing methods on Landsat OLI-8 land cover classification using OBIA and random forests classifier[END_REF] conducted land cover classification in a heterogeneous landscape in Zambia using Landsat-8 data. Meanwhile, the importance of preprocessing methods (e.g., pan-sharpening) was also exanimated in the study; the results indicated that classification accuracy was improved to 93% when using pansharpened data, compared to 86% with standard data. [START_REF] Hall | An introduction to multisensor data fusion[END_REF][START_REF] Solberg | Multisource classification of remotely sensed data: Fusion of Landsat TM and SAR images[END_REF] and is still commonly used today. Furthermore, satellite images can also fuse with other suitable remote sensing data, including aerial images [START_REF] Gianinetto | Fusion of aerial and satellite imagery over the city of Venezia[END_REF][START_REF] Persson | Fusion of aerial images and sensor data from a ground vehicle for improved semantic mapping[END_REF], Geographic

Information System (GIS) data [START_REF] Weis | A framework for GIS and imagery data fusion in support of cartographic updating[END_REF][START_REF] Aydöner | The role of the integration of remote sensing and GIS in land use/land cover analysis after an earthquake[END_REF], and Light Detection And Ranging (LiDAR) data [START_REF] Zabuawala | Fusion of LIDAR and aerial imagery for accurate building footprint extraction[END_REF][START_REF] Huang | Information fusion of aerial images and LIDAR data in urban areas: Vector-stacking, re-classification and post-processing approaches[END_REF][START_REF] Singh | LiDAR-Landsat data fusion for large-area assessment of urban land cover: Balancing spatial resolution, data volume and mapping accuracy[END_REF] in order to achieve greater LULC classification accuracy.

6) Ancillary data from distant sources other than remote sensing data are typically utilized to assist image processing and analysis, and the most commonly used ancillary data are DEM [START_REF] Sanyal | Application of Remote Sensing in Flood Management with Special Reference to Monsoon Asia: A Review[END_REF][START_REF] Dar | Remote sensing technology and geographic information system modeling: An integrated approach towards the mapping of groundwater potential zones in Hardrock terrain, Mamundiyar basin[END_REF], maps and prior knowledge [START_REF] Combal | Improving canopy variables estimation from remote sensing data by exploiting ancillary information. Case study on sugar beet canopies[END_REF][START_REF] Wright | Improved wetland remote sensing in Yellowstone National Park using classification trees to combine TM imagery and ancillary environmental data[END_REF], geospatial data [START_REF] Maselli | Combining remote sensing and ancillary data to monitor the gross productivity of water-limited forest ecosystems[END_REF][START_REF] Stevens | Disaggregating Census Data for Population Mapping Using Random Forests with Remotely-Sensed and Ancillary Data[END_REF]. on the composition of the main LULC of the study area [START_REF] Manandhar | Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement[END_REF][START_REF] Qi | A novel algorithm for land use and land cover classification using RADARSAT-2 polarimetric SAR data[END_REF][START_REF] Ghosh | Random forest classification of urban landscape using Landsat archive and ancillary data: Combining seasonal maps with decision level fusion[END_REF][START_REF] Rwanga | Accuracy Assessment of Land Use/Land Cover Classification Using Remote Sensing and GIS[END_REF], the development and the impacts of urbanization [START_REF] Georganos | Very High Resolution Object-Based Land Use-Land Cover Urban Classification Using Extreme Gradient Boosting[END_REF][START_REF] Cai | Detailed Urban Land Use Land Cover Classification at the Metropolitan Scale Using a Three-Layer Classification Scheme[END_REF][START_REF] Jozdani | Comparing Deep NNs, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification[END_REF], agricultural land-and crop-type mapping [START_REF] Wardlow | Analysis of time-series MODIS 250 m vegetation index data for crop classification in the U.S. Central Great Plains[END_REF][START_REF] Hütt | Best Accuracy Land Use/Land Cover (LULC) Classification to Derive Crop Types Using Multitemporal, Multisensor, and Multi-Polarization SAR Satellite Images[END_REF][START_REF] Gibril | Fusion of RADARSAT-2 and multispectral optical remote sensing data for LULC extraction in a tropical agricultural area[END_REF], and spatial distribution of natural area and vegetated cover (e.g., forest) [START_REF] Moskal | Monitoring Urban Tree Cover Using Object-Based Image Analysis and Public Domain Remotely Sensed Data[END_REF][START_REF] Sinha | Improved Land-use/Land-cover classification of semi-arid deciduous forest landscape using thermal remote sensing[END_REF][START_REF] Hurskainen | Auxiliary datasets improve accuracy of object-based land use/land cover classification in heterogeneous savanna landscapes[END_REF].

Second, with rapid population growth and urbanization, LULCC has attracted significant attention in the remote sensing field. "LULCC detection" refers to the comparison of more than one remote-sensing dataset acquired at different times in the [START_REF] Aplin | Remote sensing: Land cover[END_REF]. Due to its multidisciplinary character, LULCC detection plays a crucial role in environment and resource management; it is thus generally employed to provide valuable information on social and environmental issues, such as changes in the composition of LULC in the area of interest [START_REF] Alphan | Land-use change and urbanization of Adana, Turkey[END_REF][START_REF] Fan | Land Use and Land Cover Change in Guangzhou, China, from 1998 to 2003, Based on Landsat TM /ETM+ Imagery[END_REF][START_REF] Srivastava | Selection of classification techniques for land use/land cover change investigation[END_REF][START_REF] Kindu | Land Use/Land Cover Change Analysis Using Object-Based Classification Approach in Munessa-Shashemene Landscape of the Ethiopian Highlands[END_REF], urban expansion and the monitoring of its impact [START_REF] Lo | Land-Use and Land-Cover Change, Urban Heat Island Phenomenon, and Health Implications[END_REF][START_REF] Pauleit | Modeling the environmental impacts of urban land use and land cover change-A study in Merseyside, UK[END_REF][START_REF] Araya | Analysis and Modeling of Urban Land Cover Change in Setúbal and Sesimbra, Portugal[END_REF], human-or nature-induced environment degradation (e.g., deforestation) [START_REF] Eva | Fires and land-cover change in the tropics:a remote sensing analysis at the landscape scale[END_REF][START_REF] Walker | Theorizing Land-Cover and Land-Use Change: The Case of Tropical Deforestation[END_REF][START_REF] Symeonakis | Land Use Change and Land Degradation in Southeastern Mediterranean Spain[END_REF], or LULC conversion [START_REF] Roy | Multitemporal Land Use Land Cover (LULC) change analysis of a dry semi-arid river basin in western India following a robust multisensor satellite image calibration strategy[END_REF][START_REF] Mardian | Evaluating the temporal accuracy of grassland to cropland change detection using multitemporal image analysis[END_REF][START_REF] Moisa | Impacts of land use/land cover dynamics on land surface temperature using geospatial techniques in Anger River Sub-basin, Western Ethiopia[END_REF].

In addition, LULCC detection usually involves changed areas and their change rate, as well as the spatial distribution, changed types and trajectories, and the precision of the change detection [START_REF] Attri | Remote Sensing & GIS based Approaches for LULC Change Detection -A Review[END_REF].

As early as the 2000s, [START_REF] Lepers | A Synthesis of Information on Rapid Land-cover Change for the Period 1981-2000[END_REF] summarized the most important rapid changes in LULC at the regional to global scale from 1981 to 2000 based on existing datasets, revealing the global geographic patterns of land cover changes. One such finding was that the most significant rapid land cover changes were concentrated in Asiaespecially dryland degradation and rapid cropland increase in Southeast Asia. Other findings included significant forest degradation in the Amazon basin and Siberia, as well as rapid cropland decrease in the southeastern USA and eastern China. [START_REF] Killeen | Thirty Years of Land-cover Change in Bolivia[END_REF] documented LULCCs in Bolivia over 30 years .

The landscapes of eastern lowland Bolivia, known for its rich ecosystem, were primarily composed of different tropical forest types (e.g., humid forest, inundated forest, seasonally dry forest, and cloud forest). The results showed that eastern Bolivia experienced significant deforestation; 9% of the original forest cover had been removed, and 17% of the total original land cover had been changed over the 30-year period of study. [START_REF] Kindu | Land Use/Land Cover Change Analysis Using Object-Based Classification Approach in Munessa-Shashemene Landscape of the Ethiopian Highlands[END_REF]

42

and grassland; and a significant increase in cropland. In addition, 60% of the land cover had been changed over the 39-year period of study.

Accurate LULC mapping and LULCC detection are widely applied in diverse study areas; in particular, the LULC of the coastal area is one of the current topics in the remote sensing LULC classification field. The coastal area, where the land and ocean are separated by the coastline (Figure 2), has be defined as "the dynamic interface zones where land, water, and atmosphere interact in a fragile balance that is constantly being altered by natural and human influence" [START_REF] Beatley | An Introduction to Coastal Zone Management: Second Edition[END_REF].

As the transition point between the sea and the land, offering rich and diverse resources [START_REF] Crossland | The Coastal Zone-A Domain of Global Interactions[END_REF], the coastal area is known as the most essential food production source, a center of transport and industrial development, a significant source of minerals and geological production, a repository of biodiversity and ecosystems that are critical for the Earth, and the most developed tourism location [START_REF] Crossland | The Coastal Zone-A Domain of Global Interactions[END_REF].

Approximately half of the world's population, as well as most of its socio-economic activities and global interactions, are concentrated in coastal areas. Naturally, the coastal zone is recognized as an important place of diverse natural systems and resources (Figure 3) [START_REF] Crossland | The Coastal Zone-A Domain of Global Interactions[END_REF]; however, coastlands also experience many natural hazards (primarily cyclones and powerful coastal storms), which have inflicted incalculable losses [START_REF] Beatley | An Introduction to Coastal Zone Management: Second Edition[END_REF]. and conflict over how the use of limited resources [START_REF] Beatley | An Introduction to Coastal Zone Management: Second Edition[END_REF][START_REF] Hadley | Land use and the coastal zone[END_REF].

These main factors are often manifested as pollution from different sources (R. W. [START_REF] Howarth | Coastal nitrogen pollution: A review of sources and trends globally and regionally[END_REF][START_REF] Tiquio | Management frameworks for coastal and marine pollution in the European and South East Asian regions[END_REF], such as industrialization (S.-L. [START_REF] Wang | Heavy metal pollution in coastal areas of South China: A review[END_REF][START_REF] Patnaik | Impact of Industrialization on Environment and Sustainable Solutions -Reflections from a South Indian Region[END_REF], artificialization [START_REF] Ferreira | The Coastal Artificialization Process. Impacts and Challenges for the Sustainable Management of the Coastal Cities of Santa Catarina (Brazil)[END_REF][START_REF] Freire | Recent urbanization and land use/land cover change in Portugal-The influence of coastline and coastal urban centers[END_REF], intense agricultural production (e.g., aquaculture) [START_REF] Primavera | Overcoming the impacts of aquaculture on the coastal zone[END_REF][START_REF] Moreno-González | Influence of an intensive agricultural drainage basin on the seasonal distribution of organic pollutants in seawater from a Mediterranean coastal lagoon (Mar Menor, SE Spain)[END_REF], overfishing [START_REF] Jackson | Historical Overfishing and the Recent Collapse of Coastal Ecosystems[END_REF][START_REF] Scheffer | Cascading effects of overfishing marine systems[END_REF], sand mining [START_REF] Mensah | Causes and Effects of Coastal Sand Mining in Ghana[END_REF][START_REF] Masalu | Coastal Erosion and Its Social and Environmental Aspects in Tanzania: A Case Study in Illegal Sand Mining[END_REF], and waste disposal [START_REF] Bascom | The effects of waste disposal on the coastal waters of Southern California[END_REF][START_REF] Masalu | Coastal Erosion and Its Social and Environmental Aspects in Tanzania: A Case Study in Illegal Sand Mining[END_REF][START_REF] Clark | Coastal Zone Management Handbook[END_REF]. Thus, most coastland development patterns are not sustainable. It is therefore crucial and urgent to change human activity patterns in coastal zones, find a balance between the immediate needs of humans and the needs of future generations, and develop a solid understanding of coastal geography. To achieve these goals, accurate LULC mapping is needed for complex and dynamic coastal zone management [START_REF] Beatley | An Introduction to Coastal Zone Management: Second Edition[END_REF].

Many studies on LULC classification in coastal areas have been carried out in recent years due to the advancement of classification methods and the arrival of HR and VHR remote sensing data. First, coastal land cover mapping is applied in physical geography studies, such as shoreline extraction [START_REF] Dellepiane | Coastline extraction from SAR images and a method for the evaluation of the coastline precision[END_REF][START_REF] Maglione | Coastline extraction using high resolution WorldView-2 satellite imagery[END_REF][START_REF] Bengoufa | Machine learning and shoreline monitoring using optical satellite images: Case study of the Mostaganem shoreline, Algeria[END_REF][START_REF] Boussetta | Spatio-temporal analysis of shoreline changes and erosion risk assessment along Jerba island (Tunisia) based on remote-sensing data and geospatial tools[END_REF], dune extraction [START_REF] Breed | Morphology and distribution of dunes in sand seas observed by remote sensing[END_REF][START_REF] Hugenholtz | Remote sensing and spatial analysis of aeolian sand dunes: A review and outlook[END_REF]; wetland mapping [START_REF] Ozesmi | Satellite remote sensing of wetlands[END_REF][START_REF] Rebelo | Remote sensing and GIS for wetland inventory, mapping and change analysis[END_REF],

Part I Context of the study Chapter 1. Detection of land use/land cover in coastal area with remote sensing 44 climate and morphological processes analysis [START_REF] Seker | Investigation of coastal morphological changes due to river basin characteristics by means of remote sensing and GIS techniques[END_REF][START_REF] Mason | Remote sensing of intertidal morphological change in Morecambe Bay, U.K., between 1991 and 2007[END_REF], and coastal ecosystem research [START_REF] Klemas | Sensors and Techniques for Observing Coastal Ecosystems[END_REF][START_REF] Klemas | Remote Sensing Techniques for Studying Coastal Ecosystems: An Overview[END_REF].

Moreover, remote sensing scientist communities are most interested in LULC mapping and LULCC detection for multidisciplinary studies-for example, reducing coast degradation, improving management for population growth and, ultimately, preparing for the coming impacts of sea-level rise caused by climate change [START_REF] Hadley | Land use and the coastal zone[END_REF].

Since Our study area, Pays de Brest (Figure 4), located on the west coast of France, is an urban cluster in the northwestern part of the Finistère department and the region of Brittany. Pays de Brest was created in 2002 and covers a land area of 2,101 km² that extends between latitudes 48°05′39″ N and 48°40′39″ N, longitudes 3°52′34″ W and 4°47′42″ W. Pays de Brest also occupies 599 linear km of coastline. The area's population of 420,000 yields a population density of approximately 200 inhabitants per km².

Besides the vital concentration of population and its flourishing local socioeconomic system, Pays de Brest has a wealth of natural and environmental resources.

Despite highly developed economic activities, the city of Brest has preserved a large surface of natural area (urban forest, watercourse, valleys). Meanwhile, many urban green spaces have been constructed for citizens' physical and mental health, biodiversity, Part I Context of the study Chapter 2. Geographic localization and major land use/land cover in Pays de Brest 47 and environmental protection. Outside the city, the region is occupied by an essential part of the natural landscape with high geographical research value, such as coastal wetlands, coastlines with dunes and cliffs, mountain ranges, and the regional natural park of Armorique. The natural landscape will be discussed in detail in section 2.2, "Geographical condition of the study area." The agglomeration is composed of 103 communes; more than half of them are coastal, representing a quarter of the coastline in the French territory. The 103 communes are grouped into seven intercommunalités for a joint project and different forms of cooperation between them (Figure 5). Most of the urban areas and socio-economic activities are clustered together in the coastal zone and near to Brest Métropole, which is the most crucial agglomeration with the largest population and economy in the department of Finistère. In this chapter, intercommunalité will be used as the smallest administrative region in our study area.

Historically, the region's population has always preferred to settle near the ocean; the inland, by contrast, is sparsely populated and less developed [START_REF] Ollivro | Bretagne. 150 ans d'évolution démographique[END_REF]. First, the Thus, a significant imbalance in terms of population has emerged between the coastal area, which is also close to the metropolis, and the other relatively isolated communes.

Main activities and protected natural areas

Even though agriculture has always been the region's primary activity, it is clear that for several decades, many other socio-economic activities have been increasing

sharply and taking over more and more space in Pays de Brest, accompanied by urbanization trends. The two dominant economic activities in Pays de Brest are commerce and industry, followed by the "tertiary," or service sector (which includes transportation, restaurants/hospitality, and sales), or some combination thereof. Reflecting the concentration of artificial areas and population, construction is generally located in the urban areas, mainly on the seaside and in the Brest Métropole. In consequence, the majority of economic activities are concentrated near the cluster of construction-in particular, commercial activities, tertiary sector activities, and marine activities on the northern coast. In addition, some listed activities can also be found near the beaches due to the tourism sector's current rapid development, which-as mentioned previously-is one of the principal factors driving the urbanization and artificialization of coastal areas.

Whereas the tertiary sector and commercial activities can be found only near the principal agglomerations, various other activities are located near the small cities and countryside or along the coasts. Most industrial activities tend to avoid the big cities, where space to build and expand is scarce and more expensive.

Natural area preservation is also a key to development in Pays de Brest; some 99 communes in the department of Finistère cover at least one sensitive natural area and pre-emption zones. Pays de Brest has always been a population and economic center in the Finistère department; 44% of the department's population are concentrated within 25% of its surface. In particular, the metropolis of Brest provides essential socio-economic activities and employment opportunities; 44% of department employment is found in the Brest Métropole [START_REF] Sparfel | Etude des changements d'occupation des sols dans la zone côtière à partir de données hétérogènes: Application au pays de Brest[END_REF]. Even though the foundation of Pays de Brest aimed to strengthen intercommunalités and develop joint strategies and policies, the communes of Brest Métropole-which occupy less than 8% of the departmental surface area- [START_REF] Adeupa | La lettre des observatoires de l'ADEUPa-Octobre[END_REF].

Furthermore, artificialization is more significant on the coastline; coastal communes experienced approximately 46% of urban sprawl, despite representing less than 29% of the departmental surface [START_REF] Adeupa | La lettre des observatoires de l'ADEUPa-Octobre[END_REF]. Therefore, the coastal area and the Brest Métropole have a high artificial area rate ranging from 10% to 76%. Otherwise, the surface is highly occupied by construction in the surrounding area of Brest Métropole. By contrast, the Crozon Peninsula and some areas far from the coastlines, located in the center of the region, experienced a more minor change in the artificial surface rate ranging from 0.4% to 10%, because of their isolation and remoteness from socio-economic activities relative to the coastal areas.

The fundamental cause of this significant artificialization is the rising population in Pays de Brest due to immigrants who can be placed into one of three categories: (1) young workers attracted to the jobs created by rapid economic development in the region;

(2) vacationers or seasonal residents attracted to the well-protected environment and diverse landscape of the region, which has seen a boom in the construction of second residences or vacation homes, especially on the coast; and (3) retirees-many of them relatively young-are choosing to settle in the region, mainly in the coastal area, owing to the reasons enumerated in item (2). According to Figure 13, the population has, on average, increased in all intercommunalités between 1990 and 2019 at a rate between 2% and 35%. In the

57

went through urbanization very early, most of the population and socio-economic activities are highly concentrated in these intercommunalités. However, the extension of the metropolis is limited by the presence of the sea in the south. Thus, there is a tendency to be overloaded for several decades, and the weak population evolution rate can be explained.

On the other hand, as the essential part of the regional natural park of Armorique, considering the construction restrictions for environmental protection and biodiversity Therefore, they usually look for an isolated house with a private garden far from the city center but still close to the metropolis [START_REF] Rieu | Le renouveau des campagnes bretonnes (Octant Analyse n°13)-Octant Analyse[END_REF].

Moreover, less population evolution can be seen on the coastline; Lesneven-Côte des Légendes has even experienced population decrease on the coast. Besides the population evolution near Brest Métropole, the imbalance between inland communes and the coast is another significant development issue. In maps, mostly the coastal communes had a significant change in the number of inhabitants, which indicates the significant inflow of population and a positive annual growth compared with the inland communes, which lost population. Even though the population flow was still very weak in inland communes, the phenomenon of urbanization was slowly emerging. In 2011-2016, except for a few coastal communes (Brest Métropole, Pays d'Iroise, and Pays de Landerneau-Daoulas), which kept gaining inhabitants, the great majority of communes encountered negative population growth of around -1.6%, which means an important number of inhabitants has left the inner communes for the more significant agglomerations. The process of urbanization and suburbanization between these years created-and then aggravated-the socio-economic imbalance between Brest Métropole with its periphery, the coast, and the inland communes in Pays de Brest.

The regional disparity brings a series of consequences. For example, the cities

(

61

principally traffic congestion (particularly during weekday rush periods), lack of housing in the city and its periphery despite urban sprawl, and tension in public facilities (such as public transport, schools, hospitals, libraries, and sports facilities). At the same times, the high concentration of population and socio-economic activities cause the problems of pollution, sanitation, environmental degradation, etc. In contrast to the populated coastal and urban areas, the small inland communes far from the coast encounter a serious population decrease due to the aging problem. These small cities or villages are slowly but steadily emptying due to the loss of young laborers and the lack of immigrants. The gaps between the coastal urban area and the isolated inner communes are gradually expanding, and the development imbalance is carried to the extreme.

Imbalance between different land uses

The previous section about LULC explained that the cultivated areas are always the leading land use in Pays de Brest, followed by the vegetated and urban areas. However, urbanization and suburbanization not only increase regional inequalities but also cause competition for land uses. Urbanization is a transitory process to decrease the importance of agriculture with artificialization and industrialization. First, the process switches the sectoral composition; the secondary and tertiary sectors outpace agriculture to become the economic lifeblood of the region. Second, the urbanization process turns agricultural land into an artificial surface. Third, the traditional rural agricultural population leaves for the urban area, seeking more employment opportunities. [START_REF] Moomaw | Urbanization and Economic Development: A Bias toward Large Cities?[END_REF][START_REF] Henderson | Urbanization and Economic Development[END_REF]. In most places, urbanization is usually accompanied by rapid economic growth; thus, it is generally considered an indicator of the economic development level (S.

Li [START_REF] Li | Urbanization, Economic Development and Environmental Change[END_REF]. In consequence, the urban area is most often the privileged land use.

Nevertheless, urbanization has created conflicts between different land uses, especially the artificial surfaces, which are slowly occupying not only available unused land, but also many agricultural lands or even natural areas. de Brest and its coastal area between 1990and 2018(Source: CORINE Land Cover 1990, 2018) The figure 15 illustrates the evolution of three main types of land use (natural, agricultural, and artificial areas) in Pays de Brest between 1990 and 2018. It can be seen that the artificial area was the only land use that had a significant growth during 28 years, with more than 40 km² increase in surface area. In contrast, agricultural land lost 25 km² in surface area, and the natural area surface declined precipitously as well. It is also worth noting that the land use changes were usually found in the coastal area; 80% of the decrease in agricultural land and 73% of the artificial area growth were on the coast, although the degradation of the natural area was more evenly distributed in Pays de Brest, with 48% of the decrease on the coast. The evolution indicates the process of urbanization, coastalization, and competition for land uses; the result is natural landscape degradation.

Research has long focused on the delicate balance between urbanization and environmental protection [START_REF] Ahmed | Moving towards a sustainable environment: The dynamic linkage between natural resources, human capital, urbanization, economic growth, and ecological footprint in China[END_REF][START_REF] Seto | The New Geography of Contemporary Urbanization and the Environment[END_REF]; some studies show that urbanization rate and changes in regional environmental quality are closely related (S. [START_REF] Li | Urbanization, Economic Development and Environmental Change[END_REF][START_REF] Liang | The effect of urbanization on environmental pollution in rapidly developing urban agglomerations[END_REF]). The regional environment is significantly impacted by economic development. Typically, the environment is improved when the degree of economic development rises. Nonetheless, rapid and extensive economic growth can negatively impact the environment and increase social tension [START_REF] Datar | Construire ensemble un développement équilibré du littoral[END_REF][START_REF] Li | Urbanization, Economic Development and Environmental Change[END_REF]. The negative impacts include not only the problem of pollution and over- Several local studies have mentioned that the preservation of agricultural and natural areas is essential, not just for landscape harmony and the well-being of the population, but also to prevent excessive urbanization and over-development of tourism that contribute can ultimately lead to the formation of a mono-economic activity in the region [START_REF] Lebahy | Le littoral agressé Pour une politique volontariste de l'aménagement en Bretagne[END_REF]. Even though the coast is an essential reservoir for marine and coastal biodiversity and many natural protected areas were also created in order to preserve the environment. However, many natural areas have been eroded due to urban sprawl, in particular, the undeniable detriments of the coastline due to urbanization and artificialization were observed (Région Bretagne, 2013). For example, 65% of coastal wetlands have been destroyed by reclamation, diking, or filling in the region, and the increasing pollution threatens many natural habitats and species.

Research objectives and study sites

As analyzed in the section above, Pays de Brest has experienced significant LULCCs over several decades, especially urbanization and artificialization, which have provoked various environmental and social issues, such as the conflict between different land uses and natural environment degradation due to urbanization and uneven development within communes. For this reason, it is crucial to study LULCCs and their consequences in Pays de Brest, particularly for decision-makers in the community and local government, which has long been aware of the population overload and the natural environmental crisis. However, even though the local government is seeking sustainable development, no effective solution has been found, and very few recent studies have investigated LULC or LULCC monitoring in Pays de Brest.

In response to that knowledge gap, this thesis focuses on LULC in Pays de Brest. Its objective is to detect, monitor, and ultimately follow LULCCs, especially the transition between three principal land uses-agricultural lands, vegetated areas, and urban areas-as well as the consequences of that transition. This thesis has chosen to discuss three essential subjects, using three different study sites in Pays de Brest: As the largest agglomeration in Pays de Brest, the city of Brest is the most impacted by urbanization and artificialization. Nonetheless, by the will of local government and citizens, a remarkable number of urban green spaces have been preserved (Figure 16); such preservation is considered to be an essential pre-requisite for the economic development and population growth (Région Bretagne, 2013). Therefore, it is essential to study the urban green spaces changes in the city of Brest and prevent their environmental degradation (G. [START_REF] Xie | Machine learning methods and classification of vegetation in Brest, France[END_REF]. A highly fragmented coastal area protected by the regional natural park of Armorique since 1969, the Crozon Peninsula is a unique but rarely studied area (Figure 17). Due to the restriction of construction and the relatively isolated location, the peninsula is sparsely populated. However, with the sharply developed tourism sector in recent years, the problem of artificialization and coastalization of this protected area has become severe. Thus, it is essential to learn about the LULCCs and the driving factors of the changes (G. [START_REF] Xie | Mapping and Monitoring of Land Cover/Land Use (LCLU) Changes in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional NN) and by Post-classification Comparison (PCC)[END_REF]. In the context of rising urbanization and artificialization in Pays de Brest, more and more agricultural lands are being turned into artificial surfaces for secondary or tertiary sector use, especially in northern Finistère, which is near Brest Métropole and thus strongly impacted by the process of suburbanization (Figure 18). Artificialization and urbanization negatively impact biodiversity and the environment, thereby diminishing agricultural production and food security. Therefore, to ensure agricultural supply and facilitate crop management in the region, this study has mapped the croplands, identified the main crop types, studied their spatial distribution in Pays de Brest, and ultimately monitored their growth pattern(G. [START_REF] Xie | Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France[END_REF].

PART II GENERAL METHODOLOGY

Part II General methodology Chapter 3. Data structure and field research 68 Chapter 3. Data structure and field research

This chapter discusses the data structure used in the three case studies of the thesis.

All data applied will be detailed below.

To detect LULCCs and study the trend of LULC evolution and its consequences, it is necessary to have real-time, ultra-high-precision, and easily accessible data. However, traditional ground data are usually quite rare, and involve high labor and time costs. In Apart from the optical and SAR satellite data, some auxiliary data were applied, such as RPG in the agricultural field and Google Earth. Several field studies were performed to validate the results, including a survey with local farmers (see questionnaire in Appendix1).

Part II General methodology Chapter 3. Data structure and field research 69

Satellite imagery

Pays de Brest is known for its picturesque, highly fragmented landscape. The area has been vigorously attacked by artificialization and coastalization in recent years, and its small-scale fields have been most severely impacted. However, it is difficult to observe these "micro-changes" with coarse-resolution data [START_REF] Sparfel | Etude des changements d'occupation des sols dans la zone côtière à partir de données hétérogènes: Application au pays de Brest[END_REF]. Thus, a new generation of high-precision satellite images was acquired in the studies, from the highspatial-resolution (HR) image used in crop spatial distribution studying and LULCCs detecting to very-high-spatial-resolution (VHR) image for detecting small-scale vegetation in the city of Brest.

Due to the oceanic climate in Pays de Brest, high-quality, cloud-free operational satellite images are difficult to obtain due to the high-intensity annual rainfall and substantial attendant cloud cover. Despite these limitations, a few optical images were acquired from SPOT, Sentinel, and Pléiades platforms during the growing season of vegetation or crops.

Otherwise, compared to a single optical image, the time series is much more difficult to access due to frequent rainfall in the area. Thus, the previous study applied the SAR backscatter time series to follow the phenology of winter crops during their growing season.

Optical image

Since the 1972 launch of Landsat-the first EO satellite-began an era of remote sensing, optical images have become the most widely used type of satellite data. The passive imaging system depends entirely on solar light as the only illumination source.

The optical sensors produce images of the Earth's surface by detecting the solar radiation reflected by the target on the ground [START_REF] Kumar | Panchromatic and Multispectral Remote Sensing Image Fusion Using Machine Learning for Classifying Bucolic and Farming Region[END_REF] (Figure 19). The principle of this system is that each object has a unique spectral signature that measures the different ratios between the radiation reflected, absorbed and transmitted at different wavelengths.

A sufficient spectral resolution of the sensing system allows the identification of an object from an image with its spectral reflectance profile. An optical image is composed of pixels, the most fundamental element and the smallest individual unit of the image, and the criterion to define the quality of an image [START_REF] Fisher | The pixel: A snare and a delusion[END_REF]. A pixel is generally square and depicts a certain region on an image; the number of pixels utilized in the construction of the image determines the spatial resolution, which refers to the size of the finest possible detail in the object being imaged

Part II General methodology Chapter 3. Data structure and field research 71 that a system is capable of detecting. For example, 10 m of spatial resolution indicates that each pixel on the image represents a 10 m x 10 m area on the ground.

Furthermore, depending on the number of spectral bands employed for the imaging process, optical remote sensing can be categorized into various groups, among which panchromatic imaging and multispectral (MS) imaging systems are the most known and used. In the panchromatic scheme, the sensor is a single-channel detector sensitive to radiation over a wide wavelength range. The resulting image is known as a "panchromatic image" when the wavelength range matches the visual range. In contrast, the MS scheme is a multi-channel detector with a limited number of spectral bands, and each channel is sensitive to radiation within a narrow wavelength band [START_REF] Kumar | Panchromatic and Multispectral Remote Sensing Image Fusion Using Machine Learning for Classifying Bucolic and Farming Region[END_REF]. However, each system has its advantages and disadvantages. Panchromatic imaging can produce higher spatial resolution images, but it loses spectral information and produces black-and-white images. On the other hand, the MS imaging system generally has numerous spectral bands (e.g., up to 13 bands for Sentinel-2) but a lower spatial resolution. These two imaging systems are usually merged in image processing (pan-sharpening) to acquire a high-quality image with multiple spectral bands.

In recent years, two key problems have received significant attention in the optical remote sensing area: object detection and change detection. First, object detection refers to identifying and locating an object of interest in images. Object detection has always been a fundamental challenge in optical remote sensing, giving rise to numerous studies methods [START_REF] Cheng | A survey on object detection in optical remote sensing images[END_REF]. In addition, since the beginning of the application of remote sensing data, satellite images have been digitally processed by comparing pixel intensities for detecting changes in a variety of applications, including urban sprawl and LULCCs, vegetation monitoring [START_REF] Maktav | Remote sensing of urban areas[END_REF][START_REF] Jones | Remote Sensing of Vegetation: Principles, Techniques, and Applications[END_REF][START_REF] El-Kawy | Land use and land cover change detection in the western Nile delta of Egypt using remote sensing data[END_REF][START_REF] Tewkesbury | A critical synthesis of remotely sensed optical image change detection techniques[END_REF]. In this thesis, object detection was applied to detect urban green spaces in Brest and locate winter crops in northern Finistère with optical imaging.

In recent years, numerous VHR or HR sensors have been developed with different spatial, spectral, radiometric, and temporal resolutions. Optical satellite images from different platforms were applied in all three case studies featured in this thesis. VHR

Part II General methodology Chapter 3. Data structure and field research 72 images of Pléiades-1 were applied to monitor the vegetation changes in the city of Brest (i.e., urban green spaces on a small scale). LULCCs on the Crozon Peninsula were detected using SPOT-5 VHR images and Sentinel-2 HR images due to the limited open-source data and climate conditions. Finally, a single cloud-free Sentinel-2 image acquired during the growing season of winter crops was employed for winter crop mapping.

Very high spatial resolution image (Pléiades-1, SPOT-5)

 SPOT-5 As the continuity of the SPOT missions, SPOT-5 provided a better quality image with a higher spatial resolution other than the previous SPOT missions by significantly improving the spatial resolution. SPOT-5 operated in both panchromatic and multispectral modes; the spatial resolution was 2.5 m in panchromatic mode and 10 m in multispectral mode (Table 1). Furthermore, the stereo pair acquisition capabilities were enhanced through a dedicated instrument, the High-Resolution Stereoscopic (HRS) operated in panchromatic mode. In addition, SPOT-5 was equipped with a wide swath of 60 km in both modes and a high frequent revisit capacity of 2 or 3 days (depending on latitude).

The SPOT-5 image of the study was acquired from ESA (earth.eas.int). The successor of the SPOT program before the arrival of SPOT-6 and SPOT-7, Pléiades-1 (Figure 22) is composed of two Earth-observation spacecraft, Pléiades-1A and Pléiades-1B, launched by CNES in December 2011 and 2012, respectively. These two identical satellites are placed at 180° on the same sun-synchronous phased obit, which is the same orbital plane as the SPOT-6 and SPOT-7. A constellation with the aim of EO is therefore forming with these four satellites operated on a single interface. According to Table 2, the nominal repeat cycle is 26 days (with a viewing angle of 5°). Nonetheless, the revisiting time of Pléiades-1 for global access is as long as 5 days with one single satellite, whereas the two satellites of the system can achieve that in 4 days when the viewing angle is 30°. Earth in a variety of modes of operation (eoPortal, 2012c).

Pléiades-1 is operated in both panchromatic or MS modes; for each image, the panchromatic and MS modes are acquired simultaneously. As a VHR image, the resolution of Pléiades reaches 70 cm in the panchromatic band, while the four MS bands-blue, green, red, and NIR (Table 3)-have a spatial resolution of 2.8 m. Later, the images are resampled to the ground at 50 cm in panchromatic mode and 2 m in MS to obtain a robust product for further image processing. Eventually, by combining information from the panchromatic and MS products, MS data sampled at 50 cm can be acquired (IGN, n.d.-a).

The Pléiades-1 images are usually applied in studies of such fields as vegetation cover, landscape topography, LULC, and ice sheet topography (eoPortal, 2012c). The main objectives are to ensure continuity of high-resolution, free-accessed satellite images for multiple purposes (e.g., monitoring inland and coastal areas, managing crops and forests), complete the current Landsat and SPOT projects, and to provide full and systematic coverage of Earth's land surface with a wide swath of 290 km and a significant revisit capability of 5 days with two operational satellites (eoPortal, 2012b).

Sentinel-2 data are also used to support the production and update of high-level geographical products, such as LULCC detection maps, geophysical maps, and CLC maps. The Sentinel-2 satellite provides 13 HR spectral bands (Table 4) Moreover, THEIA platform (theia.cnes.fr) provides Sentinel Level-3A images, an utterly cloud-free product created during a synthesis period of 45 days. For each pixel in each band of a Sentinel Level-3A image, the average cloud-and shadow-free surface reflectance collected during 45 days is calculated to avoid cloudy or shadowed pixels.

Thus, the Sentinel Level-3A image is a mosaic of the pixels acquired on different dates in different view angles. However, to achieve this result, the cloud-free image sacrifices its temporal resolution; it is rarely used to detect the evolution of the surface in a short time.

Thus, it can be applied only in studies that do not require high data accuracy (CESBIO, 2018)

3.1.2. Synthetic-aperture RaDAR image (Sentinel-1) Four observation modes of the Sentinel-1 SAR system for different purposes are displayed in the Figure 27 and Table 6, each with a different swath and spatial resolution: Part II General methodology Chapter 3. Data structure and field research 82

Auxiliary data

Auxiliary data, also known as reference data structure external to the survey data collected, provide supplementary information to the study. In this thesis, apart from optical and SAR HR images collected. An auxiliary structure composed principally of Google Earth and RPG is used as additional information to produce training data and test data for image classification.

Graphic parcel register

Field research

Field research, which is essential for most scientific studies, refers to a method of collecting qualitative data by observing the study area's geographical conditions and interacting with groups of people who live in the area to be deeply involved in the research subject. In remote sensing, field research generally refers to direct observation of the ground by visiting the area and collecting data with recording devices (e.g., Global

Nativation Satellite System (GNSS) device and camera) or questioning the people on site. Brest. During the image processing, we visited several areas that were not clearly visible from the satellite image to determine the precise vegetation type and structure. After the classification, we also visited some areas where vegetation changes were found to verify the type of change (i.e., permanent or temporary). The Crozon Peninsula is marked by a highly fragmented landscape on a small scale.

Observation and validation of vegetation in the city of Brest

Observation and validation of LULC on the Crozon Peninsula

Thus, direct observation and the validation of the LULCCs detected were indispensable for this study. Before image processing, a field study was performed with a GNSS device to compare the actual landscape and that seen in the satellite image and collect data to be used in the creation of the training dataset (Figure 31). Field research was performed before the image processing step to observe and understand the study area, especially the croplands (Figure 32). However, due to the crop rotation system, planted crops change frequently in the study area, making it impossible to confirm the information on RPG 19 (our reference data in the study) and thereby collect ground truth. For this reason, we conducted additional fieldwork to interact with local farmers using the same questionnaire as that found in Appendix 1.

Part II General methodology Chapter 4. Analysis and image processing 87

Chapter 4. Analysis and image processing

In this chapter, different methodologies used in the different topics will be discussed in detail.

Remote sensing refers to the gathering of information in digital or analog form using space-based equipment or sensors without direct contact. Remote sensing data with various spectral, spatial, radiometric, and temporal resolutions are collected from spaceborne or airborne platforms such as satellites, aircraft and drones. Hence, most products must be processed before being applied in studies of different thematic [START_REF] Khorram | Digital Image Acquisition: Preprocessing and Data Reduction[END_REF]. imaging acquisition [START_REF] Khorram | Digital Image Acquisition: Preprocessing and Data Reduction[END_REF]. Geometric distortions, including systematic distortions, result from image motion caused by a forward movement of the spacecraft, variations in the mirror scanning rate, panoramic distortions, variations in platform velocity, and distortions due to the curvature of the Earth, as well as nonsystematic distortions due to variations in satellite altitude and attitude acquisition [START_REF] Khorram | Digital Image Acquisition: Preprocessing and Data Reduction[END_REF] and a variety of radiometric distortions.

Methodology of image pre-classification

Image pre-classification processing was performed after the study area selection and satellite image acquisition.

Satellite data used in the studies were already preprocessed through atmospheric, geometric, and radiometric corrections already applied by the distribution data platform.

Otherwise, a series of preprocessing procedures have been performed in our studies-for instance, area of interest extraction by subsetting the raw images with the boundary, data merging, and the two most important techniques: vegetation indices calculation and image segmentation. The performance of the two last techniques will be discussed in the following subchapter.

Calculation of vegetation indices

Definition of the vegetation index

As an essential part of remote sensing data interpretation-especially in image pre-classification processing-vegetation indices (VIs) are widely discussed and implanted in the remote sensing field.

The VI is the qualitative and quantitative evolution of the vigor of vegetation as assessed by spectral measurements [START_REF] Campbell | Introduction to Remote Sensing[END_REF][START_REF] Bannari | A review of vegetation indices[END_REF], which can be Part II General methodology Chapter 4. Analysis and image processing 90 defined as the combination of certain bands to obtain differences and ratios between spectral bands by division, addition, subtraction, or multiplication. The indices yielded by the calculation can effectively lead to enhanced information for vegetation detection [START_REF] Khorram | Digital Image Acquisition: Preprocessing and Data Reduction[END_REF]. These relationships between radiometric response and vegetative covers have been a highly active research area. The first research studies of VIs revealed that the red and NIR spectral bands were well suited for vegetation detection. Moreover, as the spectral response of vegetated areas, these indices can be affected by many factors.

Various recent studies have sought to maximize the radiant response of vegetative covers-including the vegetation mixture and composition-and to minimize environmental factors such as soil characteristics (e.g., color, brightness, and humidity), and the spatial-temporal atmospheric variations [START_REF] Bannari | A review of vegetation indices[END_REF].

As mentioned in Chapter 3, each vegetation type has its specific spectral signature, which suggest that each vegetation specy has its unique behavior concerning radiant energy (Figure 34). Hence, calculations between different spectral bands reveal different results, and more than 100 VIs have been developed over two decades to enhance detection of vegetation cover (type and composition). ). Thus, it is easy to distinguish green vegetation from other elements on the surface by using the combination of red and NIR channels [START_REF] Major | A ratio vegetation index adjusted for soil brightness[END_REF][START_REF] Bannari | A review of vegetation indices[END_REF].

The first generation of VIs (i.e., the Ratio Vegetation Index (RVI) and the Vegetation Index Number (VIN)), was developed by [START_REF] Pearson | Remote mapping of standing crop biomass for estimation of the productivity of the shortgrass prairie, Pawnee National Grasslands, Colorado[END_REF] by simply calculating the ratio of red and NIR bands. After that, the RVI and VIN indices were enhanced by various studies; the best-known version is the Normalized Difference Vegetation Index (NDVI), which was first proposed by [START_REF] Rouse | Monitoring Vegetation Systems in the Great Plains with ERTS[END_REF]. This VI, which is still widely used in the remote sensing field, can not only capture the presence of green vegetation but can also monitor crops and precipitation [START_REF] Sellers | Canopy reflectance, photosynthesis and transpiration[END_REF][START_REF] Nicholson | A comparison of the vegetation response to rainfall in the Sahel and East Africa, using normalized difference vegetation index from NOAA AVHRR[END_REF][START_REF] Bullock | Crop rotation[END_REF].

Another well-known index, the Non-Linear Vegetation Index (NLI), also uses NIR and red channels, with straightforward modification by adding the square value of the NIR band;

this slight optimization develops a significant sensitivity for detecting Leaf Area Index (LAI) changes by reducing the reflection contrast and saturation [START_REF] Goel | Influences of canopy architecture on relationships between various vegetation indices and LAI and Fpar: A computer simulation[END_REF][START_REF] Feng | An optimized non-linear vegetation index for estimating leaf area index in winter wheat[END_REF].

In addition to chlorophyll and leaf cellular structure-the essential elements of For instance, the Normalized Different Greenness Index (NDGI) has been shown to help map various active vegetation formations in flooded areas [START_REF] Chamard | Utilisation des bandes spectrales du vert et du rouge pour une meilleure évaluation des formations végétales actives[END_REF]. In the Normalized Difference Water Index (NDWI), [START_REF] Gao | NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space[END_REF] applies the ratio between NIR and SWIR to explore water content at the leaf level. The Green Red Vegetation Index (GRVI), which replaces the NIR channel with the green channel, can determine the vegetation cover and recognize phenological crop changes, detect heterogeneity in field irrigation, and track plant photosynthesis [START_REF] Tucker | Red and photographic infrared linear combinations for monitoring vegetation[END_REF]A. Chen et al., 2019;[START_REF] Yin | A Broadband Green-Red Vegetation Index for Monitoring Gross Primary Production Phenology[END_REF]. The Enhanced Normalized Difference Vegetation Index (ENDVI), developed with additional visible blue and green channels compared to traditional NDVI, has been shown

Part II General methodology Chapter 4. Analysis and image processing 92 to be more efficient for isolating plant health indicators and detecting plant disease. Thus it can be applied as a reliable marker of plant health (PrecisionHawk, n.d.). The Visual Atmospheric Resistance Index (VARI) [START_REF] Gitelson | Vegetation and soil lines in visible spectral space: A concept and technique for remote estimation of vegetation fraction[END_REF] is an index designed to minimize atmospheric effects by using blue channels to better detect Earth's surface and provide more accurate vegetation detection.

VIs are widely used in the remote sensing area, most commonly in vegetation cover mapping; various VIs have been developed and applied for this purpose [START_REF] Bannari | Transformed difference vegetation index (TDVI) for vegetation cover mapping[END_REF][START_REF] Xie | Remote sensing imagery in vegetation mapping: A review[END_REF][START_REF] Sadeghi | Mapping boreal forest biomass from a SRTM and TanDEM-X based on canopy height model and Landsat spectral indices[END_REF]. For example, [START_REF] Xavier | Mapping leaf area index through spectral vegetation indices in a subtropical watershed[END_REF] conducted a study in the subtropical rural watershed of Piracicaba in the State of Sao Paulo, Brazil, using various spectral ratio as Simple Ratio (SR), NDVI, and Soil Adjusted Vegetation Index (SAVI) calculated from Landsat-7 data to generate a LAI map for the watershed, which is closely associated with vegetation productivity. NDVI achieved the best fit among all three VIs, with a correlation of 0.72.

Change detection is also a widespread application of VIs, which have been shown

to have a remarkable ability to detect changes in both forest and urban environments (P.

J. [START_REF] Howarth | Landsat digital enhancements for change detection in urban environments[END_REF][START_REF] Mouat | Remote sensing techniques in the analysis of change detection[END_REF][START_REF] Nordberg | Vegetation index differencing and linear regression for change detection in a Swedish mountain range using Landsat TM® and ETM+® imagery[END_REF] Various VIs are widely applied to detect and evaluate plant disease [START_REF] Shafri | Hyperspectral Signal Analysis for Detecting Disease Infection in Oil Palms[END_REF][START_REF] Devadas | Evaluating ten spectral vegetation indices for identifying rust infection in individual wheat leaves[END_REF][START_REF] Feng | Improved remote sensing detection of wheat powdery mildew using dual-green vegetation indices[END_REF][START_REF] Isip | Exploring vegetation indices adequate in detecting twister disease of onion using Sentinel-2 imagery[END_REF]. [START_REF] Ashourloo | Evaluating the Effect of Different Wheat Rust Disease Symptoms on Vegetation Indices Using Hyperspectral Measurements[END_REF] successfully evaluated the effect of various plant diseases and detected wheat leaf rust disease. This prevalent disease had different symptoms, using various spectral vegetation ratios on hyperspectral images. The authors found that all VIs consistently increased with

Part II General methodology Chapter 4. Analysis and image processing 93 disease severity; in particular, the narrow-band normalized difference VI (NBNDVI), NDVI, Photochemical Reflectance Index (PRI), Greenness Index (GI), and Red-edge Vegetation Stress Index (RVSI) showed excellent capacity for plant disease detection.

VIs provide valuable and helpful information for crop mapping, forecasting [START_REF] Wiegand | Modern approaches in deep learning for SAR ATR[END_REF][START_REF] Thenkabail | Hyperspectral Vegetation Indices and Their Relationships with Agricultural Crop Characteristics[END_REF][START_REF] Panda | Application of Vegetation Indices for Agricultural Crop Yield Prediction Using NN Techniques[END_REF][START_REF] Sharifi | Remotely sensed vegetation indices for crop nutrition mapping[END_REF] VIs can be an indicator of drought as well; the strong connection between VIs and drought was revealed by previous studies. In particular, NDVI and Vegetation Condition Index (VCI) are widely applied in the drought detection field [START_REF] Peters | Drought monitoring with NDVI-based Standardized Vegetation Index[END_REF][START_REF] Rahimzadeh Bajgiran | Using AVHRRbased vegetation indices for drought monitoring in the Northwest of Iran[END_REF][START_REF] Bushra | The relationship between the Normalized Difference Vegetation Index and drought indices in the South Central United States[END_REF][START_REF] Rousta | Impacts of Drought on Vegetation Assessed by Vegetation Indices and Meteorological Factors in Afghanistan[END_REF]. NDVI was first used to evaluate vegetation conditions in a study by [START_REF] Kogan | The thermal zones of the Earth according to the duration of hot, moderate and cold periods and to the impact of heat on the organic world[END_REF]. Furthermore, the AVHRR-based VCI built on NDVI has successfully detected large-and small-scale droughts in the USA and provided accurate drought information, such as intensity, duration, and impact on vegetation. In addition, the Temperature Condition Index (TCI), which provides

𝑁𝐷𝑉𝐼 = 𝑁𝐼𝑅-𝑅𝑒𝑑 𝑁𝐼𝑅+𝑅𝑒𝑑 (1)
Even though some limitations remain in this, one of the earliest developed VIs-such as sensitivity to the impacts of soil characteristics and some atmospheric factors [START_REF] Xue | Significant Remote Sensing Vegetation Indices: A Review of Developments and Applications[END_REF]-it is nevertheless the most commonly employed VI in regional or global vegetation monitoring and agriculture-related fields. Thus, NDVI was applied in the studies of LULCCs detection and winter crop mapping.

 NDWI, proposed by Gao in 1996 for remote sensing of vegetation liquid water from space. The index is well known for its ability to measure liquid water molecules in vegetation canopies with weak atmospheric aerosol scattering compared to NDVI.

However, NDWI does not eliminate the background soil reflectance effects. The equation is as follows (B. [START_REF] Gao | NDWI-A normalized difference water index for remote sensing of vegetation liquid water from space[END_REF]:

𝑁𝐷𝑊𝐼 = 𝑁𝐼𝑅-𝑆𝑊𝐼𝑅 𝑁𝐼𝑅+𝑆𝑊𝐼𝑅 (2)
NDWI was used for winter crops mapping in the thesis because several studies demonstrated its excellent ability for irrigation and drought detection, as well as for

Part II General methodology Chapter 4. Analysis and image processing 95 mapping specific crop types, especially winter crops [START_REF] Mulianga | Mapping Cropping Practices of a Sugarcane-Based Cropping System in Kenya Using Remote Sensing[END_REF][START_REF] Hao | Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA[END_REF][START_REF] Zhang | Retrieving Leaf and Canopy Water Content of Winter Wheat Using Vegetation Water Indices[END_REF][START_REF] Valero | Synergy of Sentinel-1 and Sentinel-2 Imagery for Early Seasonal Agricultural Crop Mapping[END_REF].

 Green Normalized Difference Vegetation Index (GNDVI), proposed by [START_REF] Gitelson | Use of a green channel in remote sensing of global vegetation from EOS-MODIS[END_REF], is documented to be very similar to NDVI. The index was developed to evaluate the photosynthetic activity of vegetation. However, GNDVI uses visible green light that resists atmospheric effects instead of visible red light. In addition, the new index has a wider dynamic range and is much more sensitive than NDVI to chlorophylla concentration. The expression of GNDVI is displayed below [START_REF] Gitelson | Use of a green channel in remote sensing of global vegetation from EOS-MODIS[END_REF]:

𝐺𝑁𝐷𝑉𝐼 = 𝑁𝐼𝑅-𝐺𝑟𝑒𝑒𝑛 𝑁𝐼𝑅+𝐺𝑟𝑒𝑒𝑛 (3)
For its numerous advantages and outstanding performance, GNDVI is widely applied in vegetation and agriculture research, especially in vegetation cover, agricultural estimation, and crop type classification [START_REF] Shanahan | Use of Remote-Sensing Imagery to Estimate Corn Grain Yield[END_REF][START_REF] Barati | Comparison the accuracies of different spectral indices for estimation of vegetation cover fraction in sparse vegetated areas[END_REF][START_REF] Ustuner | Crop Type Classification Using Vegetation Indices of RapidEye Imagery[END_REF][START_REF] Rahman | A Novel Approach for Sugarcane Yield Prediction Using Landsat Time Series Imagery: A Case Study on Bundaberg Region[END_REF][START_REF] Marcial-Pablo | Estimation of vegetation fraction using RGB and multispectral images from UAV[END_REF].

Therefore, GNDVI was implemented in LULCCs and winter crop mapping studies.

 Enhanced Vegetation Index (EVI), proposed by the MODIS Land Discipline Group, sought to augment the vegetation signal and correct the imprecision of NDVI with improved sensitivity in high-biomass areas by adding several extra spectral bands.

The expression is displayed as follows (A. [START_REF] Huete | Overview of the radiometric and biophysical performance of the MODIS vegetation indices[END_REF][START_REF] Matsushita | Sensitivity of the Enhanced Vegetation Index (EVI) and Normalized Difference Vegetation Index (NDVI) to Topographic Effects: A Case Study in High-Density Cypress Forest[END_REF]:

𝐸𝑉𝐼 = 𝐺 * (𝑁𝐼𝑅-𝑅𝑒𝑑) (𝑁𝐼𝑅+𝐶1 * 𝑅𝑒𝑑-𝐶2 * 𝐵𝑙𝑢𝑒+𝐿) (4)
(L value can be adjusted for canopy background)

EVI was initially derived from MODIS satellite data; nevertheless, it has been shown to be effective and compatible with other satellite data. Due to its sensitivity in discriminating vegetation differences and its improved linearity with vegetation in highly vegetated areas while minimizing soil and atmosphere influences (A. [START_REF] Huete | Overview of the radiometric and biophysical performance of the MODIS vegetation indices[END_REF][START_REF] Wang | From AVHRR-NDVI to MODIS-EVI: Advances in vegetation index researc[END_REF][START_REF] Jiang | Development of a two-band enhanced vegetation index without a blue band[END_REF], the index is usually applied for crop type mapping and crop phenology detecting (Y. [START_REF] Pan | Winter wheat area estimation from MODIS-EVI time series data using the Crop Proportion Phenology Index[END_REF][START_REF] Li | Mapping Crop Cycles in China Using MODIS-EVI Time Series[END_REF][START_REF] Cao | An improved logistic method for detecting spring vegetation phenology in grasslands from MODIS EVI time-series data[END_REF]. Although EVI is highly sensitive to vegetation variations in high-biomass regions, however, the index was designed with a blue band, which means that it cannot be used in a sensor system without a blue channel, such as SPOT-5, AVHRR, and the ASTER instruments. Thus, EVI2 was invented to break through the limit of the sensor system, with three optimal parameters (L, β, and G) appending in the equation of EVI2 to reduce noise and reach the best similarity between EVI and EVI2 (Z. [START_REF] Jiang | Development of a two-band enhanced vegetation index without a blue band[END_REF]:

𝐸𝑉𝐼2 = 2.5 * 𝑁𝐼𝑅-𝑅𝑒𝑑 𝑁𝐼𝑅+2.4𝑅𝑒𝑑+1 (5)
The similarity between EVI and EVI2 was validated; EVI2 can produce the same index as EVI without a blue band. It is able to retain the soil-noise adjustment function and maintain improved sensitivity and linearity in high-biomass regions (Z. [START_REF] Jiang | Development of a two-band enhanced vegetation index without a blue band[END_REF]. EVI2 has been extensively applied in a variety of research since its invention (J. Liu et al., 2012;[START_REF] Qiu | Mapping paddy rice areas based on vegetation phenology and surface moisture conditions[END_REF][START_REF] Zou | Sensitivity of Common Vegetation Indices to the Canopy Structure of Field Crops[END_REF]. In this thesis, EVI2 was used in LULCCs, knowing that the study was performed with an image of SPOT-5, which was limited by the lack of a blue band (Z. [START_REF] Jiang | Development of a two-band enhanced vegetation index without a blue band[END_REF].

 SAVI was developed by Huete in 1988. Although various VIs were developed with the aim of vegetation detection, soil backgrounds continue to be a considerable influence factor on VIs, especially soil brightness. Therefore, SAVI can be considered a transformation of NDVI to minimize soil brightness influences from VIs calculated with visible red and NIR channels by refining or calibrating spectral indices to normalize soil substrate variations. With an adjustment factor (L from 0.5 to 1) that varies depending on vegetation density, the index was found to be effective in eliminating most of the soil-induced variation in vegetation indices (A. R. [START_REF] Huete | A soil-adjusted vegetation index (SAVI)[END_REF]:

𝑆𝐴𝑉𝐼 = (𝑁𝐼𝑅-𝑅𝑒𝑑) (𝑁𝐼𝑅+𝑅𝑒𝑑+𝐿) (1 + 𝐿) (6)
As an important step to establish a model that is capable of describing dynamic soil-vegetation systems from remote sensing data (A. R. [START_REF] Huete | A soil-adjusted vegetation index (SAVI)[END_REF], SAVI was commonly used in the agricultural field for specific crop types monitoring, crop growth monitoring, and crop yield forecasting [START_REF] Gontia | Yield Estimation Model and Water Productivity of Wheat Crop (Triticum aestivum) in an Irrigation Command Using Remote Sensing and GIS[END_REF][START_REF] Li | Remote sensing indicators for crop growth monitoring at different scales[END_REF][START_REF] Messina | A Comparison of UAV and Satellites Multispectral Imagery in Monitoring Onion Crop. An Application in the 'Cipolla Rossa di Tropea' (Italy)[END_REF][START_REF] Nagy | Wheat Yield Forecasting for the Tisza River Catchment Using Landsat 8 NDVI and SAVI Time Series and Reported Crop Statistics[END_REF]. SAVI was also found helpful in

With the lower sensitivity to soil background and temporal variations, MSAVI can be used in the agricultural fields (H. [START_REF] Wang | Variability of vegetation growth season in different latitudinal zones of North China: A monitoring by NOAA NDVI and MSAVI[END_REF][START_REF] Samasse | A High-Resolution Cropland Map for the West African Sahel Based on High-Density Training Data, Google Earth Engine, and Locally Optimized Machine Learning[END_REF][START_REF] Wyawahare | Agricultural Field Analysis Using Satellite Surface Reflectance Data and Machine Learning Technique[END_REF] or even more effectively for winter wheat mapping (Z. [START_REF] Li | Remote sensing indicators for crop growth monitoring at different scales[END_REF]. MSAVI was therefore applied in the study of winter crop mapping. Consequently, PBC methods produce relatively fragmented feature classes [START_REF] Weih | Object-based classification vs. Pixel-based classification: Comparitive importance of multi-resolution imagery[END_REF]. Furthermore, the speckle effect, also known as "salt-and-pepper noise" (Figure 35), which refers to a common form of image degradation caused by high local spatial heterogeneity between neighboring pixels, may be derived when PBC is performed , 2004;[START_REF] Weih | Object-based classification vs. Pixel-based classification: Comparitive importance of multi-resolution imagery[END_REF][START_REF] Kelly | Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis[END_REF]. For these reasons, PBC is often less accurate for most thematic studies.

Methodology of segmentation

Figure 36 Comparison of the original image and salt-and-pepper-noised image [START_REF] Kelly | Terrestrial Remotely Sensed Imagery in Support of Public Health: New Avenues of Research Using Object-Based Image Analysis[END_REF] Consequently, OBC, another classification approach, was introduced in the 1970s as an alternative to the traditional PBC, especially for the classification of medium-to high-spatial resolution data (Y. [START_REF] Gao | A comparison of the performance of pixel based and object based classifications over images with various spatial resolutions[END_REF][START_REF] Whiteside | Comparing object-based and pixel-based classifications for mapping savannas[END_REF]. After that, OBC became highly developed starting in the 1990s, when HR remote sensing data began to rapidly increase, and the existing PBC methods were limited owing to the "salt-andpepper noise" effect.

Roughly speaking, the OBC approach was developed with the aim of overcoming the limitations of PBC. In OBC methods, the individual to be classified is no longer pixel but an object, which refers to a group of pixels. Pixels are regrouped into homogeneous, continuous, and contiguous objects based on some criterion of homogeneity (spectral and contextual) (Y. [START_REF] Gao | A comparison of the performance of pixel based and object based classifications over images with various spatial resolutions[END_REF][START_REF] Whiteside | Comparing object-based and pixel-based classifications for mapping savannas[END_REF]. In contrast with PBC, OBC considers not only spectral characteristic features but also spatial and contextual characteristic features (e.g., shape, texture, topology, and color), which are the important factors for an accurate classification because the contextual features allow for distinguishing objects of different forms of land cover that share similar spectral information. For example, the crop reaches its mature stage in the cropland, and the shrubs nearby are much easier to be distinguished by their shape feature than by their

Part II General methodology Chapter 4. Analysis and image processing 100 spectral properties [START_REF] Aggarwal | Comparative Analysis of Pixel-Based and Object-Based Classification of High Resolution Remote Sensing Images -A Review[END_REF]. Thus, OBC was first applied in agriculture to identify croplands with clear predefined boundaries.

Compared to PBC-which performs classification directly on the individual pixelto perform OBC, the image must be divided into regions of similar pixels before classification. This procedure is known as "image segmentation." Once the regions (also known as "segments") are created by adjacent grouping pixels with similar spectral and contextual properties, they are ready to be assigned to a predefined class by the chosen classifier (Van de [START_REF] Van De Voorde | Extraction of land use / land cover-Related information from very high resolution data in urban and suburban areas[END_REF].

Previous publications confirm a variety of advantages of OBC; for instance, OBC is able to produce classification with more uniform and meaningful LULC objects (Dingle

Robertson & King, 2011), produce more accurate results [START_REF] Oruc | COMPARISON OF PIXEL-BASED AND OBJECT-ORIENTED CLASSIFICATION APPROACHES USING LANDSAT-7 ETM SPECTRAL BANDS[END_REF][START_REF] Whiteside | Comparing object-based and pixel-based classifications for mapping savannas[END_REF], detect LULCCs with HR satellite images [START_REF] Niemeyer | A Deep Learning Hybrid CNN Framework Approach for Vegetation Cover Mapping Using Deep Features[END_REF], facilitate meaningful statistic and texture calculation [START_REF] Benz | Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information[END_REF], and derive better comprehension of a landscape [START_REF] Thenkabail | Remotely Sensed Data Characterization, Classification, and Accuracies[END_REF].

OBC and PBC were carried out in our studies for different purposes, and the results of these two approaches were also used for comparison. In addition, considering that OBC was used extensively in our studies, image segmentation and the two most commonly used segmentation algorithms must be introduced and presented in the following section.

Definition of image segmentation

After calculating VIs, segmentation can be performed in the case of applying OBC methods. Segmentation is one of the essential steps of remote sensing image processing.

According to [START_REF] Schiewe | Segmentation of high-resolution remotely sensed data-concepts, applications and problems[END_REF], segmentation can be defined as the process of completely partitioning a scene into non-overlapping regions or segments in scene space.

The process of image segmentation has been present in remote sensing for at least two decades [START_REF] Benz | Multi-resolution, object-oriented fuzzy analysis of remote sensing data for GIS-ready information[END_REF] following the launch of the first EO satellite, Landsat-1.

One of the first applications of image segmentation in the remote sensing field was the Extraction and Classification of Homogeneous Objects (ECHO), successfully operated by Kettig and Landgrebe in 1976 [START_REF] Dey | A review on image segmentation techniques with remote sensing perspective[END_REF]. Afterward, the first success was rapidly

Part II General methodology Chapter 4. Analysis and image processing 101 followed by several extensive fundamental studies of image segmentation applied in remote sensing-for example, [START_REF] Haralick | Image segmentation techniques[END_REF], N. R. Pal &[START_REF] Pal | Classification of Multitemporal spectral indices for crop type mapping: A case study in Coalville, UK[END_REF][START_REF] Spirkovska | A summary of image segmentation techniques[END_REF]. In addition, recognition is one of the first generations of operational software production in the 2000s and the most used OBC software today.

In the remote sensing field, segmentation can be considered the preliminary step in OBC, which plays a crucial and fundamental role in the classification results [START_REF] Schiewe | Segmentation of high-resolution remotely sensed data-concepts, applications and problems[END_REF][START_REF] Dey | A review on image segmentation techniques with remote sensing perspective[END_REF][START_REF] Kotaridis | Remote sensing image segmentation advances: A metaanalysis[END_REF]. Even though a large variety of image segmentation techniques have been developed over two decades, the objectives and principal processes of all segmentation techniques are relatively similar. The objective of segmentation is to produce homogeneous objects and effectively enhance the accuracy of classification. The segmentation process generally starts with objects that are one pixel in size. Then the adjacent pixels are merged in order to form bigger objects with respect to spatial continuity, certain similarities based on spectral and contextual information, and the scale parameter, which can be defined by the user (Y. [START_REF] Gao | Optimal region growing segmentation and its effect on classification accuracy[END_REF]. The principle of the process is to minimize the heterogeneities inside the object while maximizing the homogeneities among objects with a clear, spatially accurate, and non-ragged boundary [START_REF] Haralick | Image segmentation techniques[END_REF]. After all of the pixels in the image have been clustered into regions of a number predefined, the regions or segments are ready to be labeled and transformed into a meaningful objects by classification algorithms [START_REF] Kotaridis | Remote sensing image segmentation advances: A metaanalysis[END_REF].  Threshold: This method may require prior knowledge of the study area to select the threshold before processing. In this method, the image is directly divided into regions based on intensity value relative to the threshold given by the user. [START_REF] Schiewe | Segmentation of high-resolution remotely sensed data-concepts, applications and problems[END_REF]. MRS is the most common region-growing-based segmentation method.

 Edge-based: This segmentation approach generates the segments through edge detection, which operates with contrast, texture, color, and saturation variations. The process is then pursued by a contour-generating algorithm that is able to produce outlines of segments. Eventually, contour-filling methods produce the segments by transitioning from the outlines to the interior regions [START_REF] Schiewe | Segmentation of high-resolution remotely sensed data-concepts, applications and problems[END_REF].

 Cluster: As with the region-based method, in this method the image is divided into a number of clusters with common characteristics (e.g., color, intensity). Each pixel can find more similarities with other pixels in the same segment than those in distinct segments. The most widely used cluster segmentation method is K-means.

Broadly speaking, segmentation techniques are powerful tools in image processing, and these techniques considerably improve image classification accuracy. Nevertheless, several defects persist in the segmentation process. The main reasons for inadequate performance are over-or under-segmentation, which refer to too many (small) regions and too few (large) segments, respectively. The two problems can occur in the same image at the same time. Over-segmentation can be solved by the following classification step, although it will cause insignificant boundaries and unnecessary computing time costs. On the other hand, under-segmentation might cause the mixed-pixel problem in classification, which means that elements belonging to more than one class are found in the same segment and will be assigned to the same class in the following processing step.

Eventually, misclassification, or even a less accurate classification result, may be induced by the under-segmentation problem. "Multiresolution" means that the segments can appear simultaneously on different scales in an image to produce highly homogeneous multi-scale image objects [START_REF] Baatz | Multiresolution Segmentation: An optimization approach for high quality multi-scale image segmentation[END_REF]. In parallel, MRS (Figure 36) is a relatively complex and userdependent algorithm; it was proposed by Baatz & Schäpe in 2000 with the purpose of creating universal high-quality object primitives as the first step of the OBC process in the software eCognition for further processing procedures [START_REF] Baatz | Multiresolution Segmentation: An optimization approach for high quality multi-scale image segmentation[END_REF]. MRS is also the most commonly used algorithm of region-growing-based segmentation and one of the most efficient segmentation algorithms in the OBC framework [START_REF] Witharana | Optimizing multi-resolution segmentation scale using empirical methods: Exploring the sensitivity of the supervised discrepancy measure Euclidean distance 2 (ED2)[END_REF].

Being a classical region growing-based segmentation algorithm, the process of MRS starts with an individual pixel; each pixel is considered an individual segment (socalled "seeds"). After that, each segment seeks to merge their adjacent segments with those possessing similar properties, and pairs of image objects with the smallest increase in the predefined local homogeneity criterion are merged to become more extensive segments [START_REF] Darwish | Image segmentation for the purpose of objectbased classification[END_REF]. Moreover, the merge decision is based on attributes of homogeneous structures of the image objects recently merged in previous steps [START_REF] Baatz | Multiresolution Segmentation: An optimization approach for high quality multi-scale image segmentation[END_REF]. Decision heuristics are applied to determine the image objects that will merge at each step. Eventually, the MRS process is complete when the smallest increase of homogeneity exceeds a threshold defined by the user [START_REF] Darwish | Image segmentation for the purpose of objectbased classification[END_REF].

Part II General methodology Chapter 4. Analysis and image processing 104

As a user-dependent algorithm, adjusting parameters is crucial for the MRS process. The merging decision is determined by three principal parameters: scale, shape, and compactness, which are entirely predefined by the user. Additionally, a trial-anderror process usually takes place to reach the optimal parameter. The three main parameters of MRS techniques are:

 Scale: This parameter is set to specify the maximum standard deviation of the heterogeneity to manage the amount of spectral fluctuation within objects and the size of their results (G. [START_REF] Xie | Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France[END_REF]. It is usually considered the most critical factor among the three parameters because it is able to control the segment size. A higher-scale parameter value authorizes more merging, thus creating larger segments [START_REF] Darwish | Image segmentation for the purpose of objectbased classification[END_REF]. Additionally, the scale parameter directly affects the following classification step [START_REF] Smith | Image segmentation scale parameter optimization and land cover classification using the Random Forest algorithm[END_REF][START_REF] Witharana | Optimizing multi-resolution segmentation scale using empirical methods: Exploring the sensitivity of the supervised discrepancy measure Euclidean distance 2 (ED2)[END_REF].

 Shape: As one of the homogeneity criteria, the shape parameter allows us to define the weight between the object's shape and its spectral color. A higher value gives more weight to the shape and decreases the influence of color homogeneity on object generation (Landmap, n.d.;[START_REF] Redowan | Multi-resolution segmentation for object-based classification and accuracy assessment of land use/land cover classification using remotely sensed data[END_REF].

 Compactness: Another component of the homogeneity criteria defines the weight for representing the compactness of the objects during the segmentation (Landmap, n.d.).

Due to the fact that the parameters are entirely user-defined, a trial-and-error process is usually applied with different combinations of the parameter values so that the user is able to create a hierarchical network of image objects (Rejaur [START_REF] Redowan | Multi-resolution segmentation for object-based classification and accuracy assessment of land use/land cover classification using remotely sensed data[END_REF]. However, the traditional trial-and-error method can be relatively time-consuming and tedious. Thus, as one of the most used methods of image segmentation, a variety of studies and applications were performed to find the optimal parameters, especially the scale parameter [START_REF] Drǎguţ | ESP: A tool to estimate scale parameter for multiresolution image segmentation of remotely sensed data[END_REF][START_REF] Nikfar | Optimization of multiresolution segmentation by using a genetic algorithm[END_REF][START_REF] Cánovas-García | A local approach to optimize the scale parameter in multiresolution segmentation for multispectral imagery[END_REF][START_REF] Aguilar | Assessment of Multiresolution Segmentation for Extracting Greenhouses from WORLDVIEW-2 Imagery[END_REF][START_REF] Saba | Optimization of Multiresolution Segmentation for Object-Oriented Road Detection from High-Resolution Images[END_REF], and better performance in LULC classification [START_REF] Aksoy | Multi-resolution segmentation and shape analysis for remote sensing image classification[END_REF][START_REF] Redowan | Multi-resolution segmentation for object-based classification and accuracy assessment of land use/land cover classification using remotely sensed data[END_REF].

Part II General methodology Chapter 4. Analysis and image processing 105

Cánovas-García & Alonso-Sarría (2015) developed a method to optimize the scale parameter in MRS in order to perform land cover mapping in a large and heterogeneous agricultural area, in which intra-object and inter-object measurements are used to evaluate segmentation. The authors discovered that the spectral contrast between objects is larger with the local approach by using uniform spatial units than with the global approach. In another study, [START_REF] Nikfar | Optimization of multiresolution segmentation by using a genetic algorithm[END_REF] also found optimal parameters of the MRS approach by using an efficient genetic algorithm (GA) to replace the traditional trial-anderror methods, which can be very time-consuming.

Rejaur [START_REF] Redowan | Multi-resolution segmentation for object-based classification and accuracy assessment of land use/land cover classification using remotely sensed data[END_REF] demonstrated the advantage of MRS in LULC classification and compared the results with those of traditional PBC. The results indicated that the OBC with MRS achieved 92% OA (6% higher than PBC) and 90% Kappa (7% higher than PBC). Therefore, MRS appeared to be a suitable image segmentation method for LULC classification.

Kavzoglu & Tonbul (2018) evaluated three segmentation approaches (MRS, SLIC, K-means) by using them on four LULC classes, including artificial surface and vegetation.

Overall, MRS achieves the best accuracy not only in segmentation quality but also in classification accuracy.

Because MRS was shown to be well adapted for LULC studies, it was used in the study of LULCC detection on the Crozon Peninsula. The image segmentation was generated on eCognition 9.0 and 10.0. The concept of the superpixel was first introduced by [START_REF] Ren | Learning a classification model for segmentation[END_REF].

Superpixels (Figure 37) are the results of clustering pixels carrying common characteristics, which are defined by classical Gestalt theory, including proximity, similarity (e.g., contour, texture, and brightness), and good continuation [START_REF] Nixon | 8-Region-based analysis[END_REF][START_REF] Ren | Learning a classification model for segmentation[END_REF].

Superpixel algorithms refer to segmentation algorithms that can produce the desired number of regular, compact, and meaningful superpixels with low computational overhead [START_REF] Achanta | SLIC superpixels[END_REF]. For its numerous advantages-such as possessing the perceptual meaning, excellent ability to carry more information than a simple pixel, and In SLIC segmentation, local clustering of the pixel is performed in a five-dimension (5D) color and image plane space. Among them, L, a, and b are the pixel color vector generated with the CIELAB color space, which gives a suitable similarity measure for color perception [START_REF] Nixon | 8-Region-based analysis[END_REF], and x and y are the pixel coordinates [START_REF] Achanta | SLIC superpixels[END_REF]. Compactness and regularity in the superpixel shapes are enforced using an original distance metric. Moreover, the algorithm can also be flawlessly operated in grayscale images [START_REF] Achanta | SLIC superpixels[END_REF].

The process of the SLIC segmentation algorithm starts with a single user-defined parameter that indicates the desired number of superpixels. Then the initial cluster centers are evenly sampled on a regular grid to produce nearly equally sized superpixels.

Once the primitive superpixels are formed, the cluster centers are moved to locations corresponding to the lowest gradient position in a 3 x 3 neighborhood to prevent the cluster center from being located on the edge. After that, each pixel is assigned by the nearest cluster center whose search region overlaps its location, which is limited.

Afterward, the cluster centers are adjusted to be the mean vector of all pixels in the 5D space, and a residual error between the new and previous cluster center locations is calculated. The last two steps can be repeated iteratively until the error converges [START_REF] Achanta | SLIC Superpixels Compared to State-of-the-Art Superpixel Methods[END_REF].

SLIC algorithm is effective in image segmentation processing, simple and practical to manipulate, capable of adhering to image boundaries and of producing superpixels with lower computational cost and memory efficiency, as well as possessing only a single parameter to be determine the desired number of superpixels, SLIC has therefore quickly surpassed the existing superpixel methods [START_REF] Achanta | SLIC superpixels[END_REF][START_REF] Achanta | SLIC Superpixels Compared to State-of-the-Art Superpixel Methods[END_REF]. For all the above reasons, SLIC was selected to detect vegetation in the city of Brest.

The SLIC algorithm was operated in the python language with the scikit library.

Methodology of image classification

This section presents two crucial image classification approaches-the shallow machine learning classification methods and the recently developed deep learning classification methods-along with their most popular and representative algorithms.

Definition of image classification

Pre-classification processing is the fundamental function of image processing. The classification process is the most critical step in the process and the most complex process that can be affected by various factors (D. [START_REF] Lu | A survey of image classification methods and techniques for improving classification performance[END_REF]. [START_REF] Cormack | A Review of Classification[END_REF]. The interpretation of the first generation of remote sensing data was performed in printed images, and most classification methods were initially developed in the 1970s and 1980s.

However, specific classifiers and algorithms have made significant advancements and led to wider applications in more recent decades [START_REF] Phiri | Developments in Landsat Land Cover Classification Methods: A Review[END_REF], owing to the development of computer software and hardware [START_REF] Steiner | Automation in photo interpretation[END_REF]. In addition, land cover classification has been revolutionized with the introduction of numerical-based pattern recognition algorithms [START_REF] Steiner | Automation in photo interpretation[END_REF][START_REF] Thompson | Automation in photogrammetry: Recent developments and applications (1972-1976)[END_REF].

Despite making important advances over the decades, accurate classification has always been a challenge in the computer science field because the classification result is easily affected by the complexity of the landscape, the similarity between different categories, the scale of the object to be categorized, and the selection of remote sensing data, training sample and classification system; each decision could be crucial for the result. Thus, studying the user's needs before classification processing is essential. The complete classification process-which involves several steps, including identifying a suitable classification system-selecting training samples, choosing an appropriate classification method, and evaluating accuracy (D. [START_REF] Lu | A survey of image classification methods and techniques for improving classification performance[END_REF], is briefly presented below.

As mentioned in the previous chapter, "remote sensing" refers to gathering [START_REF] Trotter | Remotely-sensed data as an information source for geographical information systems in natural resource management a review[END_REF][START_REF] Richards | Sources and Characteristics of Remote Sensing Image Data[END_REF][START_REF] Barnsley | Digital remotely-sensed data and their characteristics[END_REF][START_REF] Estes | Characteristics, sources, and management of remotely-sensed data[END_REF].

information
Selecting a suitable classification system for the user's purpose determines the classification structure; a classification system includes remote sensing data, a classification algorithm, as well as preprocessing and postprocessing methods (D. [START_REF] Lu | A survey of image classification methods and techniques for improving classification performance[END_REF]. & Weng, 2007;[START_REF] Phiri | Developments in Landsat Land Cover Classification Methods: A Review[END_REF]. The most commonly applied classification methods can be categorized as follows:

Training
 PBC: PBC, which was discussed in the previous section, refers to a direct assignment of a suitable class to each pixel in the image (D. [START_REF] Liu | Assessing object-based classification: Advantages and limitations[END_REF]. The most commonly used algorithms in PBC are NNs (NN), SVM, and RF.

 OBC: Pixels are first regrouped into homogenous image objects using a segmentation algorithm, after which each image object is assigned to one of the predefined classes (D. [START_REF] Liu | Assessing object-based classification: Advantages and limitations[END_REF].

 Sub-pixel classification: This method evaluates each pixel spectrum as a linear combination of a set of endmember spectra, after which it outputs fraction images with one image for each endmember spectrum (D. [START_REF] Lu | A survey of image classification methods and techniques for improving classification performance[END_REF]. The most known sub-pixel classification algorithm is the Fuzzy Classifier.

 Contextual-based classification: This method exploits spatial information among neighboring pixels in order to improve the quality of classification results.

 Knowledge-based classification: In this method, a priori knowledge and ancillary data are necessary and incorporated into the classification procedure.

 Hybrid classification: Also known as the Multiple Classifier System (MCS), hybrid classification refers to a merging of the classification results from more than one classifier to provide the complementary information of the pattern classifiers [START_REF] Du | Multiple Classifier System for Remote Sensing Image Classification: A Review[END_REF].

However, there is usually more than one characterization of the classification methods. Classifiers can also be categorized as "supervised" and "unsupervised" methods:

Supervised classification refers to the classification process with a priori knowledge. The Furthermore, numerous studies have shown several classification algorithms from different approaches to be efficient in LULC classification. These include MLC [START_REF] Hung | A SUBPIXEL CLASSIFIER FOR URBAN LAND-COVER MAPPING BASED ON A MAXIMUM-LIKELIHOOD APPROACH AND EXPERT SYSTEM RULES[END_REF][START_REF] Dean | An evaluation of per-parcel land cover mapping using maximum likelihood class probabilities[END_REF][START_REF] Shivakumar | Investigation on Land Cover Mapping Capability of Maximum Likelihood Classifier: A Case Study on North Canara, India[END_REF], RF [START_REF] Gislason | Random Forests for land cover classification[END_REF]Rodriguez-Galiano, Chica-Olmo, et al., 2012;[START_REF] Kulkarni | Random Forest Algorithm for Land Cover Classification[END_REF], SVM (C. [START_REF] Huang | An assessment of support vector machines for land cover classification[END_REF][START_REF] Kavzoglu | A kernel functions analysis for support vector machines for land cover classification[END_REF], and ANN [START_REF] Kavzoglu | The use of backpropagating artificial NNs in land cover classification[END_REF][START_REF] Kadavi | Land cover classification analysis of volcanic island in Aleutian Arc using an artificial NN (ANN) and a support vector machine (SVM) from Landsat imagery[END_REF]. Each algorithm has its limitations and advantages in accordance with the study area or objective. Thus, the choice of classifier is critical for classification results, and diverse classifiers are usually performed simultaneously in a study to make a comparison (M. [START_REF] Pal | An assessment of the effectiveness of decision tree methods for land cover classification[END_REF][START_REF] Otukei | Land cover change assessment using decision trees, support vector machines and maximum likelihood classification algorithms[END_REF][START_REF] Talukdar | Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations-A Review[END_REF].

The final part of the classification process is post-classification processing, which is able to effectively reduce the noise in the map being classified. For example, the speckle (also called the "salt-and-pepper-effect") induced in PBC methods eliminates spectral confusion, modifies and completes the classification results with auxiliary data, and evaluates the classification results (Section 4.4: Methodology of post-classification).

Shallow machine learning methods

This section will introduce the concept of ML with various related works. Then, two ML methods used in our studies-SVM and RF-will be presented in detail.

Part II General methodology Chapter 4. Analysis and image processing 113

Definition of machine learning

ML is a subdivision of artificial intelligence and computer science based on the biological learning process; it uses data and algorithms to imitate how humans learn. It optimizes their performance iteratively over time by learning from data [START_REF] Waske | Machine learning techniques in remote sensing data analysis[END_REF][START_REF] Lary | Machine learning in geosciences and remote sensing[END_REF]IBM, 2022).

For nearly two decades, ML has undergone extensive development as new EO satellites are launched with increasing spatial and temporal resolution and revisiting frequency. Today, ML has become an important topic and one of the hottest research areas in remote sensing data analysis [START_REF] Waske | Machine learning techniques in remote sensing data analysis[END_REF][START_REF] Maxwell | Implementation of machine-learning classification in remote sensing: An applied review[END_REF]. Even though ML has an excellent capability to deal with multivariate, non-linear, non-parametric regression or classification problems [START_REF] Lary | Machine learning in geosciences and remote sensing[END_REF], ML techniques are used mainly in image classification tasks to predict a specific phenomenon, create a thematic map of an area of interest, and monitor nature-or human-induced environmental changes and their impacts. Moreover, ML techniques are applied as a supervised classification method.

Thus, a training dataset should be generated, and the ML algorithms can make a prediction and improve classification accuracy by learning from training data.

In comparison with traditional classification methods, ML methods provide more effective and efficient classification. The strengths of ML can be attributed to its capability to generate a variety of data with a high degree of dimension and predict highly complex techniques and their principal algorithms, as well as their recent attempt to improve classification accuracy [START_REF] Kotsiantis | Machine learning: A review of classification and combining techniques[END_REF][START_REF] Akinsola | Supervised Machine Learning Algorithms: Classification and Comparison[END_REF][START_REF] Soofi | Classification Techniques in Machine Learning: Applications and Issues[END_REF][START_REF] Sen | Supervised Classification Algorithms in Machine Learning: A Survey and Review[END_REF].

ML techniques have been widely used in remote sensing image analysis in recent decades; main applications and related works are summarized below:

Vegetation study is one of the most important application fields of ML classification techniques; ML methods provide highly accurate classification and valuable information for vegetation cover detection, vegetation health monitoring, and mapping of vegetation types or species [START_REF] Hengl | Global mapping of potential natural vegetation: An assessment of machine learning algorithms for estimating land potential[END_REF][START_REF] Tichý | GRIMP: A machine-learning method for improving groups of discriminating species in expert systems for vegetation classification[END_REF][START_REF] Gao | Remote sensing algorithms for estimation of fractional vegetation cover using pure vegetation index values: A review[END_REF]X. Li et al., 2021).

Y. [START_REF] Xie | Remote sensing imagery in vegetation mapping: A review[END_REF] Agriculture is also one of the most important fields where ML techniques are applied; after all, food is a basic need of all human beings. Previous studies have shown that progress in the area of ML helped improve agricultural production by providing essential insights into crops [START_REF] Liakos | Machine Learning in Agriculture: A Review[END_REF][START_REF] Benos | Machine Learning in Agriculture: A Comprehensive Updated Review[END_REF][START_REF] Meshram | Machine learning in agriculture domain: A state-of-art survey[END_REF]. and natural disasters-LULC study has become essential to the effective planning and management of natural and man-made resources [START_REF] Karpatne | Monitoring Land-Cover Changes: A Machine-Learning Perspective[END_REF][START_REF] Camargo | A Comparative Assessment of Machine-Learning Techniques for Land Use and Land Cover Classification of the Brazilian Tropical Savanna Using ALOS-2/PALSAR-2 Polarimetric Images[END_REF][START_REF] Talukdar | Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations-A Review[END_REF]. Moreover, the increasing availability of remote sensing data and improving ML techniques allow real-time accurate LULC mapping and change detection worldwide [START_REF] Gómez | Optical remotely sensed time series data for land cover classification: A review[END_REF][START_REF] Karpatne | Monitoring Land-Cover Changes: A Machine-Learning Perspective[END_REF]. Therefore, many prior studies have summarized the progress and challenges of ML methods and examined their accuracy in land cover classification; the difficulties encountered have attracted particular attention.

LULC classification for change detection and verification remains challenging for several reasons, such as the heterogeneity in the characteristics of LULCCs. Each classification model must be built and adjusted according to the study area and research object. In addition, LULCCs are often negligible and brief. Otherwise, there are natural phenomenon that take a long time to detect. For detection purposes, the ML model must be trained with available data acquired before and after changes, and must be adjusted in case of dealing with multi-source data when the availability of data is limited (J. S. [START_REF] Deng | PCA-based land-use change detection and analysis using multitemporal and multisensor satellite data[END_REF][START_REF] Karpatne | Monitoring Land-Cover Changes: A Machine-Learning Perspective[END_REF]. Furthermore, previous studies show that the accuracy of the classification can be affected spectrally and spatially by coarse-resolution data [START_REF] Manandhar | Improving the Accuracy of Land Use and Land Cover Classification of Landsat Data Using Post-Classification Enhancement[END_REF][START_REF] Yang | Improving Land Use/Land Cover Classification by Integrating Pixel Unmixing and Decision Tree Methods[END_REF]. The training data dataset may be challenging to generate due to the difficulty of accessing the ground truth before changes occur or due to the lack of auxiliary data [START_REF] Karpatne | Monitoring Land-Cover Changes: A Machine-Learning Perspective[END_REF].

Nevertheless, ML is well known for its capability to produce satisfactory classification results even on complex problems. As a result of the emergence and development of ML techniques, and in combination with HR remote sensing datasets, today it is easier to address the computational difficulties associated with LULC classification on a global scale [START_REF] Gómez | Optical remotely sensed time series data for land cover classification: A review[END_REF][START_REF] Talukdar | Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations-A Review[END_REF]. Additionally, the great capability of ML techniques-such as multi-task dealing capacity, multi-instance,

Part II General methodology Chapter 4. Analysis and image processing 117 and multi-view learning-provides accurate, cost-effective, and time-saving classification [START_REF] Karpatne | Monitoring Land-Cover Changes: A Machine-Learning Perspective[END_REF].

Therefore, despite all the limitations of ML application in LULC classification, a number of LULC studies demonstrate the efficiency and effectiveness of ML methods. For Even though numerous ML algorithms have been rapidly developed for different purposes in recent years, some well-known, well-developed, and frequently used ML algorithms are still worth noting, such as MLC, SVM, RF, ANN, k-NN, and DT. Among these, SVM, RF were applied in our studies; thus, these ML classifications methods will be presented in the following section. Moreover, the DL networks and their related algorithms will be presented in section 4.2.3. Thus, the algorithm aims to generate the optimal hyperplanes that will separate the classes with the fewest possible errors, thereby significantly minimizing the confusion between classes and maximizing the margin between support vectors of two classes from the samples closest to the hyperplane (C. [START_REF] Huang | An assessment of support vector machines for land cover classification[END_REF]. However, the theoretical margin can only be applied in the separable dataset, and the soft margin is usually required for non-separable samples, which means relaxing the requirement that all training vectors must lie on the same side of the optimal hyperplane in a given class [START_REF] Gualtieri | Support vector machines for hyperspectral remote sensing classification[END_REF]. Moreover, a user-defined c-parameter, the penalty value of misclassification errors, can highly impact the selection of a support vector in the case of a soft margin applied. In general, a low c value allows more misclassification. In contrast, higher c values mean more complex decision boundary and less generalization, and an overweight c value might cause overfitting problems and generalization errors [START_REF] Cortes | Support-vector networks[END_REF][START_REF] Foody | A relative evaluation of multiclass image classification by support vector machines[END_REF][START_REF] Huang | An assessment of support vector machines for land cover classification[END_REF][START_REF] Maxwell | Implementation of machine-learning classification in remote sensing: An applied review[END_REF].

Support vector machine classification

Part II General methodology Chapter 4. Analysis and image processing 120

In addition, another critical parameter in the SVM algorithm is γ, which is used to determine the extent to which the influence of a single training example reaches. Hence, a low value of γ will consider samples at great distances for finding the optimal hyperplane.

Therefore, more samples will be considered.

Figure 40 SVM non-linear model

The traditional SVM model was originally developed to solve the linear problem.

Thus, in the theoretical model, samples belonging to the same class usually lie on the same sides of the hyperplane [START_REF] Gualtieri | Support vector machines for hyperspectral remote sensing classification[END_REF]. However, the ideal case occurs rarely, the samples are frequently non-separable, and a misclassification problem might result.

Consequently, a non-linear surface is built for solving a non-linear problem, assuming that a higher dimension might exist. As shown in Figure 39, the input is first projected into higher dimensional feature space, and then an optimal hyperplane is generated in the same space afterward; this projection to the higher dimensionality is known as kernel function K. The choice of the kernel has a critical impact on the applicability of SVMs [START_REF] Mountrakis | Support vector machines in remote sensing: A review[END_REF]. Many user-defined kernels are developed since, for example, polynomial and radial kernel functions are tested for land cover classification in [START_REF] Kavzoglu | A kernel functions analysis for support vector machines for land cover classification[END_REF].

Part II General methodology Chapter 4. Analysis and image processing 121  RBF: The kernel can be applied when no prior knowledge provides; alternatively, in addition, the radial basis function is developed to improve the kernel.

 Linear: This kernel is only considered when the data is linearly separable, which is helpful for dealing with a large number of features.

 Polynomial: Fundamentally, this kernel represents the linear kernel in a more generalized way.

Part II General methodology Chapter 4. Analysis and image processing 122

The SVM algorithm was initially developed as a binary classifier; in other words, it was designed only to separate two groups, but not to handle multi-class classification.

However, because multiclass problems frequently appear in real-case applications, [START_REF] Gualtieri | Support vector machines for hyperspectral remote sensing classification[END_REF] proposed two strategies for converting a binary classifier into N classes classifiers:

 One-against-all: N binary classifiers are created, and each classifier is trained to identify the samples of a class from the rest. The pixel is categorized as the class for which the largest decision value was determined [START_REF] Foody | A relative evaluation of multiclass image classification by support vector machines[END_REF].

 One-against-one: The complex multiclass classification problem is broken down into the binary problem: N (N-1)/2 binary classifiers are applied to each pair of classes, and each pixel is labeled as the most commonly computed class [START_REF] Foody | A relative evaluation of multiclass image classification by support vector machines[END_REF].

However, previous studies have also indicated that multi-class classification may be computationally exhaustive, and that the processing time increases with the number of classes [START_REF] Cortes | Support-vector networks[END_REF].

Various studies report that the SVM classifier has great stability and can usually

Random forest classification

RF was first proposed by Ho in 1995 to overcome the DT classifier limitation, which is the loss of classification accuracy when processing complex data. Afterward, the idea of RF was extended by [START_REF] Breiman | Random Forests[END_REF], who combined it with bootstrap aggregating (bagging) theory.

RF is a non-parametric classification method that is one of the most widely known and used classifiers in the remote sensing field over the past two decades due to its efficiency, effectiveness, high processing speed, and ability to handle data of high dimensionality and multi-collinearity. In addition, it can generate accurate classifications or regression results and survival analyses in both supervised and unsupervised learning (Rodriguez-Galiano, Ghimire et al., 2012, p. 201;[START_REF] Belgiu | Random forest in remote sensing: A review of applications and future directions[END_REF].

Although previous simple classifiers (e.g., SVM, MLC, and CART) are capable of generating excellent results, they reach their limits when they encounter the complex interplay between factors such as the complexity of study area, scale, and aggregation [START_REF] Marceau | Remote sensing and the measurement of geographical entities in a forested environment. 2. The optimal spatial resolution[END_REF]. Thus, ensemble learning algorithms based on a number of individual identical classifiers-or different classifiers trained with bagging or boosting techniques-have been developed to enhance classification accuracy [START_REF] Gislason | Random Forests for land cover classification[END_REF][START_REF] Belgiu | Random forest in remote sensing: A review of applications and future directions[END_REF]. subset of the complete training datasets. Thus, some of the training datasets can be selected and used several times (so-called "in-bag" samples), whereas some others have never been used ("out-of-bag" samples). The out-of-bag data can be used as test data for evaluating the bagging model [START_REF] Gislason | Random Forests for land cover classification[END_REF]. As shown in Figure 42, the RF algorithm produces an ensemble of n tree-like predictors with high variance and low bias to generate a single final prediction [START_REF] Breiman | Random Forests[END_REF]. By using a randomly selected subset of the training dataset and variables, for each instance or sample in the subset, each tree in the forest casts a vote independently for the most popular class. The final prediction is then determined by the majority of the vote of all decision trees in the forest [START_REF] Breiman | Random Forests[END_REF][START_REF] Belgiu | Random forest in remote sensing: A review of applications and future directions[END_REF]. This technique can effectively protect the classifiers from variants by minimizing the correlation between the classifiers in an ensemble [START_REF] Gislason | Random Forests for land cover classification[END_REF].

In addition, two user-defined parameters are set to train the model: the number of decision trees in the forest (Ntree) and the number of variables to be randomly selected at each split when growing the tree (Mtry) (Díaz-Uriarte & Alvarez de Andrés, 2006; [START_REF] Genuer | Variable selection using Random Forests[END_REF][START_REF] Stumpf | Object-oriented mapping of landslides using Random Forests[END_REF][START_REF] Millard | On the Importance of Training Data Sample Selection in Random Forest Image Classification: A Case Study in Peatland Ecosystem Mapping[END_REF]. The Ntree value can often be extensive, considering that RF algorithms do not have an overfitting problem.

However, some studies suggest that the tree's number N has no influence on classification results [START_REF] Du | Random Forest and Rotation Forest for fully polarized SAR image classification using polarimetric and spatial features[END_REF]. On the other hand, Mtry is generally set to the square root of the number of input samples [START_REF] Gislason | Random Forests for land cover classification[END_REF]. [START_REF] Gislason | Random Forests for land cover classification[END_REF]. Therefore, RF, which is considered an essential classifier for multi-source classification, has been shown to make significant progress in land cover classification by significantly improving prediction accuracy [START_REF] Cutler | Random Forests[END_REF][START_REF] Ghimire | An Evaluation of Bagging, Boosting, and Random Forests for Land-Cover Classification in Cape Cod, Massachusetts, USA[END_REF][START_REF] Rodriguez-Galiano | An assessment of the effectiveness of a random forest classifier for land-cover classification[END_REF][START_REF] Kulkarni | Random Forest Algorithm for Land Cover Classification[END_REF].

In the early years, RF was usually applied in classification as a PBC method [START_REF] Evans | Gradient modeling of conifer species using random forests[END_REF][START_REF] Ghimire | Contextual land-cover classification: Incorporating spatial dependence in land-cover classification models using random forests and the Getis statistic[END_REF][START_REF] Senf | Using MODIS time series and random forests classification for mapping land use in South-East Asia[END_REF][START_REF] Grinand | Estimating deforestation in tropical humid and dry forests in Madagascar from 2000 to 2010 using multi-date Landsat satellite images and the random forests classifier[END_REF][START_REF] Vanselow | Predictive Mapping of Dwarf Shrub Vegetation in an Arid High Mountain Ecosystem Using Remote Sensing and Random Forests[END_REF]. However, its applications in OBC have become more and more common, and the technique was demonstrated to be effective and useful for improving accuracy by combining contextual factors [START_REF] Schroff | Object Class Segmentation using Random Forests[END_REF][START_REF] Duro | A comparison of pixel-based and object-based image analysis with selected machine learning algorithms for the classification of agricultural landscapes using SPOT-5 HRG imagery[END_REF][START_REF] Stefanski | Optimization of Object-Based Image Analysis With Random Forests for Land Cover Mapping[END_REF][START_REF] Vogels | Agricultural cropland mapping using black-and-white aerial photography, Object-Based Image Analysis and Random Forests[END_REF][START_REF] Dornik | Classification of Soil Types Using Geographic Object-Based Image Analysis and Random Forests[END_REF].

Consequently segmentation algorithm. To find the best parameter combination of SPC, various combinations were tested on land cover classification using multitemporal RapidEye and SPOT-5 data. Eventually, the best parameter was found based on the cross-validationlike out-of-bag error provided by RF.

Deep learning methods

Definition of deep learning

As part of a broader family of ML, deep structured learning, known as "deep learning" (DL) or "hierarchical learning," first emerged in 2006 with the study of NNs by [START_REF] Hinton | Reducing the Dimensionality of Data with NNs[END_REF]. Thereafter, between 2006 and 2012, the first generation of simple DL models was developed, including AutoEncoder (AE) [START_REF] Hinton | Reducing the Dimensionality of Data with NNs[END_REF], deep belief networks (DBNs) [START_REF] Hinton | Deep belief networks[END_REF], and Stacked Autoencoder (SAE) [START_REF] Vincent | Stacked Denoising Autoencoders: Learning Useful Representations in a Deep Network with a Local Denoising Criterion[END_REF]. Until recent years, DL has been developed at a surprising speed, and has already become a powerful tool in many application fields, the birth of a more advanced technique that is able to exploit complex, non-linear compositional functions (L. [START_REF] Deng | Deep Learning: Methods and Applications[END_REF].

Consequently, DL was born in the ANN research, which refers to the use of deep architecture, a multiple-layer network for signal and information processing (Figure 43).

Inspired by neuron systems and functions of the human brain, the deep structure is composed of numerous artificial neurons, each of which can receive and process information, and then send it to the next neuron-just as a biological neuron deals with electrical impulse.

Although DL has many definitions, roughly speaking, it can be defined as "a class of machine learning techniques that exploit multiple layers (usually but not necessarily with Consequently, various DL algorithms are designed and/or employed to overcome these difficulties and improve the processing procedure and/or result. The algorithms can be categorized according to their applications. For example, two main categories are widely applied for visual data processing:

 AE models (e.g., AE [START_REF] Cheng | Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities[END_REF], sparse autoencoder [START_REF] Ng | Sparse autoencoder[END_REF], restricted Boltzmann machine [START_REF] Fischer | An Introduction to Restricted Boltzmann Machines[END_REF] and DBNs [START_REF] Hinton | Deep belief networks[END_REF]) constitute a type of feedforward ANN for unsupervised learning. These models were specifically

MLP deep architecture

The first concept of perceptron was developed by Rosenblatt in the late 1950s, becoming the first model of a NN. The original simple perceptron consists of a superficial layer and a simple node that receives weighted input and therefore addressed binary linear separable data [START_REF] Atkinson | Introduction NNs in remote sensing[END_REF]. Due to the multiple layers, MLP is a more sophisticated version of the perceptron that can resolve the non-linear problem of regression and supervised classification with inseparable datasets [START_REF] Murtagh | Multilayer perceptrons for classification and regression[END_REF].

Figure 45 Example of an MLP model with two hidden layers

A MLP model is characterized by its architecture, such as the number of layers and the number of neurons in each layer, as well as the activation methods used at various neurons [START_REF] Murtagh | Multilayer perceptrons for classification and regression[END_REF]. The most common network is a feedforward network with 3

Part II General methodology Chapter 4. Analysis and image processing 134 (or >3) layers, as shown in Figure 44. The first layer is always the input layer, which receives training data in the form of a vector; it is followed by at least one hidden layer.

Ultimately, the model is completed with an output layer that presents the output data [START_REF] Atkinson | Introduction NNs in remote sensing[END_REF]. As a fully connected algorithm, all nodes at one level have links to all nodes at another level (called "interconnected nodes"), and each node is designed to process the weight it receives from other nodes of the previous layer [START_REF] Atkinson | Introduction NNs in remote sensing[END_REF]. In addition, the arrangement of the number of hidden layers and the number of nodes (also called "units") can vary according to the user's needs. Although more nodes generally mean better sensitivity too many can also cause an overfitting problem [START_REF] Murtagh | Multilayer perceptrons for classification and regression[END_REF].

Since the late 1980s, many studies have attested to the performance of MLP [START_REF] Hecht-Nielsen | Kolmogorov's Mapping NN Existence Theorem[END_REF][START_REF] Lippmann | An introduction to computing with neural nets[END_REF][START_REF] Hornik | Multilayer feedforward networks are universal approximators[END_REF][START_REF] Lapedes | How Neural Nets Work[END_REF][START_REF] Chester | Why two hidden layers are better than one[END_REF], although its performance relies highly on training method, suitable representative training dataset, and optimal network parameter adjustment to reduce overall error.

The training method plays a crucial role in MLPs' deep structure, as MLPs have the ability to learn through training [START_REF] Gardner | Artificial NNs (the multilayer perceptron)-A review of applications in the atmospheric sciences[END_REF], which is the process of determining the value of individual weights in order to accurately resolve the relationship that the network is modeling [START_REF] Gardner | Artificial NNs (the multilayer perceptron)-A review of applications in the atmospheric sciences[END_REF]. The most commonly used training method is backpropagation.

The backpropagation algorithm, introduced in 1986 by Rumelhart et al. is considered the most computationally straightforward algorithm for training the MLP [START_REF] Gardner | Artificial NNs (the multilayer perceptron)-A review of applications in the atmospheric sciences[END_REF]. The backpropagation algorithm is about to use the gradient descent technique to locate the minimum error. Initially, the weights in the network are set to random values, and the first sample (in the form of a vector) from the training dataset is fed to the network as input to obtain the output. Afterward, an error signal is calculated based on the deviation between the desired result and the actual output; this is then propagated back through the network to adjust weights to minimize overall errors.

Finally, each training vector is fed to the network individually and repeats all the steps of error calculation until the overall error is acceptable [START_REF] Bishop | NNs for Pattern Recognition[END_REF][START_REF] Gardner | Artificial NNs (the multilayer perceptron)-A review of applications in the atmospheric sciences[END_REF]. The backpropagation algorithm can be adjusted by two user-defined parameters-

Part II General methodology Chapter 4. Analysis and image processing 135 learning rate and momentum term-the former to control the step size while updating the weights, and the latter to help accelerate gradient descent [START_REF] Gardner | Artificial NNs (the multilayer perceptron)-A review of applications in the atmospheric sciences[END_REF].

Optimizing the MLP structure has always been challenging, and many studies have sought to establish optimal parameters. For example, in response to the poor convergence times of the backpropagation algorithm, [START_REF] Swanston | Simple adaptive momentum: New algorithm for training multilayer perceptrons[END_REF] presented a simple adaptive momentum training algorithm that was much more efficient in convergence and significantly improved the computational cost compared to the classical backpropagation. [START_REF] Thimm | High-order and multilayer perceptron initialization[END_REF] Beginning in the late 1980s, various NNs have come into public view [START_REF] Lippmann | An introduction to computing with neural nets[END_REF]; for many years, the networks most widely used for remote sensing image processing were MLPs-until the emergence of CNNs [START_REF] Paola | A review and analysis of backpropagation NNs for classification of remotely-sensed multispectral imagery[END_REF][START_REF] Atkinson | Introduction NNs in remote sensing[END_REF][START_REF] Huang | An assessment of support vector machines for land cover classification[END_REF]. MLP is well regarded for its ability to solve regression and classification problems. It is, therefore, one of the typical supervised DL methods used for image recognition.

In order to learn the weights in the learning phase, the MLP model is trained by a training dataset composed of positive and negative samples. Afterward, the input image should be reshaped into a vector for image classification. A final feature representation generated by the fully connected layers is sent to the classification layer to label the input image. The number of nodes in the output layer should match the number of classes in the classification [START_REF] Atkinson | Introduction NNs in remote sensing[END_REF]L. Zhang et al., 2016). Although MLP is no longer the most commonly used DL method, it remains a powerful tool for image classification due to its remarkable ability to achieve high accuracy with minimum error. Thus, MLP is widely applied for many applications, such as  Non-linearity: In this layer, a pointwise non-linearity function is applied to each component of a feature map (L. Zhang et al., 2016).

Convolutional NNs deep architecture

 Max-pooling: The max-pooling layer aims to merge semantically similar features into a single feature; decrease redundant information in images; and reduce the spatial size of the feature maps, the number of parameters, and network computational cost. This layer is applied after the convolutional function for generating the pooling operation.

In other words, the maximum value of the current view is selected with a predefined window in the max-pooling layer [START_REF] Lecun | Deep learning[END_REF][START_REF] Li | Deep Learning for Hyperspectral Image Classification: An Overview[END_REF].

 Flatten: This layer refers to the transition function from the feature extraction stage to the classification of fully connected layers; it consists of converting the multidimensional-pooled feature map into a one-dimensional array.

 Fully connected: The fully connected layer is the fundamental component of the classification stage, based on the flattened input created by the previous flatten layer.

As a traditional fully connected model, each input in this layer is connected to all neurons; each neuron is then connected to every neuron in the next layer until reaching the end of CNN architectures. In order to generate the probability of each class for the classification task, the last fully connected layer is generally followed by a softmax classifier as a normalization function [START_REF] Cheng | Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities[END_REF].

Part II General methodology Chapter 4. Analysis and image processing 140 Finally, the output results that include one unit for each class predicted can be acquired at the end of the classification stage. Furthermore, the backpropagation gradients are used for the learning procedure. Initially, the process data are in the form of multiple arrays [START_REF] Lecun | Deep learning[END_REF][START_REF] Ma | Deep learning in remote sensing applications: A meta-analysis and review[END_REF]; thus, they are well adapted for processing multispectral remotely sensed data in which the pixels are generally arranged (L. Ma et al., 2019). Depending on the data modalities, CNNs may be constructed differently. In primitive 1-D CNNs [START_REF] Kiranyaz | 1D convolutional NNs and applications: A survey[END_REF], the kernel moves in one dimension in the form of signals and sequences used for speech recognition and document reading. In 2-D CNNs, the kernel moves in two dimensions (x, y), the model recognizes spatial features of image for images or audio spectrograms processing. When the kernel moves in three dimensions, the 3-D CNNs are usually performed in spatial and spectral dimension, for video or volumetric image processing [START_REF] Lecun | Deep learning[END_REF][START_REF] Li | Deep Learning for Hyperspectral Image Classification: An Overview[END_REF].

Advanced deep CNNs began their ascendance when AlexNet [START_REF] Krizhevsky | ImageNet Classification with Deep Convolutional NNs[END_REF] emerged victorious from the Large-Scale Visual Recognition Challenge (LSVRC) in 2012; since then, they have steadily become the leading DL model (X. X. [START_REF] Zhu | Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources[END_REF][START_REF] Cheng | Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities[END_REF]. CNNs are principally designed for image classification with the advantage of end-to-end feature learning; they have been shown to be effective for solving complex classification problems by many studies (T. [START_REF] Fu | Using convolutional NN to identify irregular segmentation objects from very high-resolution remote sensing imagery[END_REF][START_REF] Timilsina | MAPPING URBAN TREES WITHIN CADASTRAL PARCELS USING AN OBJECT-BASED CONVOLUTIONAL NN[END_REF][START_REF] Cheng | Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities[END_REF]. Moreover, considering various characteristics of CNNs-such as high effectiveness in large-scale recognition, efficiency in object detection, and semantic segmentation areas-the deep architectures are well recognized for their achievement in advanced classification accuracy as well as mid-and high-level feature abstraction (X. X. [START_REF] Zhu | Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources[END_REF]. In other words, the CNN deep structures can learn highly abstract feature detectors and map the input features into representations that significantly impact the performance of subsequent classifiers (L. Zhang et al., 2016).

The applications of CNN models can be categorized into different types according to the deep networks utilized (X. X. [START_REF] Zhu | Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources[END_REF].

Initially, the pre-trained networks were often used as feature extractors on an image dataset (X. X. [START_REF] Zhu | Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources[END_REF] when CNNs were newly introduced into remote sensing scene classification scenarios [START_REF] Penatti | Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?[END_REF]. [START_REF] Penatti | Do deep features generalize from everyday objects to remote sensing and aerial scenes domains?[END_REF] CNNs is confirmed by previous litteratures. However, CNNs generally need to be trained with massive training data, but the training dataset size is generally limited. Thus, it is difficult to fine-tune a CNN model (X. X. [START_REF] Zhu | Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources[END_REF][START_REF] Cheng | Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities[END_REF].

Consequently, more recent studies have chosen to use newly trained networks, although it is more challenging than previous applications. When the existing CNNs are

Part II General methodology Chapter 4. Analysis and image processing 142 not suitable for the characteristics of target datasets or the study's aims-or when it is impossible to modify the pre-trained models-researchers prefer to build a smaller structure and train it with selected satellite image datasets.

In [START_REF] Luus | Multiview Deep Learning for Land-Use Classification[END_REF], a multiscale input strategy for multiview DL is introduced for supervised LULC classification problems. F. Zhang, Du, et al. (2016) 2020) presented another method for remote sensing scene classification: the skip-connected covariance (SCCov) network, which could skip connection and covariance pooling so that the multi-resolution CNN feature maps were merged, and the second-order information in some feature maps could be intensely exploited. The most significant shortcomings of the newly trained CNN models are their decreased generalization ability and the overfitting problem (X. X. [START_REF] Zhu | Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources[END_REF].

As the leading DL algorithm today, CNNs are widely applied in different research areas in the remote sensing field, such as vegetation cover [START_REF] Niemeyer | A Deep Learning Hybrid CNN Framework Approach for Vegetation Cover Mapping Using Deep Features[END_REF][START_REF] Langford | Arctic Vegetation Mapping Using Unsupervised Training Datasets and Convolutional NNs[END_REF][START_REF] Ayhan | Vegetation Detection Using Deep Learning and Conventional Methods[END_REF]Collin et al., 2021;[START_REF] Kattenborn | Review on Convolutional NNs (CNN) in vegetation remote sensing[END_REF], agriculture [START_REF] Kamilaris | A review of the use of convolutional NNs in agriculture[END_REF][START_REF] Milioto | Real-Time Semantic Segmentation of Crop and Weed for Precision Agriculture Robots Leveraging Background Knowledge in CNNs[END_REF][START_REF] Zhu | Deep learning for smart agriculture: Concepts, tools, applications, and opportunities[END_REF][START_REF] Lu | Review on Convolutional NN (CNN) Applied to Plant Leaf Disease Classification[END_REF], urban area mapping [START_REF] Yoo | Comparison between convolutional NNs and random forest for local climate zone classification in mega urban areas using Landsat images[END_REF][START_REF] Zhang | Detecting Large-Scale Urban Land Cover Changes from Very High Resolution Remote Sensing Images Using CNN-Based Classification[END_REF]W. Zhou et al., 2020), and noise (e.g., cloud and shadow) detection [START_REF] Shendryk | Deep learning for multi-modal classification of cloud, shadow and land cover scenes in PlanetScope and Sentinel-2 imagery[END_REF][START_REF] Yang | CDnet: CNN-Based Cloud Detection for Remote Sensing Imagery[END_REF]Y. Chen et al., 2020;[START_REF] Guo | CDnetV2: CNN-Based Cloud Detection for Remote Sensing Imagery With Cloud-Snow Coexistence[END_REF]. Thus, as one of the most studied areas in remote sensing, CNNs occupy an essential position in LULC classification [START_REF] Bhosle | Evaluation of Deep Learning CNN Model for Land Use Land Cover Classification and Crop Identification Using Hyperspectral Remote Sensing Images[END_REF][START_REF] Carranza-García | A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional NNs[END_REF]S. Zhang et al., 2020;[START_REF] Bui | Gradient Boosting Machine and Object-Based CNN for Land Cover Classification[END_REF].

SAR backscattering coefficient processing on Google Earth Engine (GEE)

This section presents GEE, an image-processing platform, following a brief introduction of the SAR time series used in the studies. Generally, the raw SAR image needs to be pre-processed to extract the backscatter information, which can be used for further analysis. In order to quantify and compare the backscattering behavior, the backscattering coefficient is usually converted to decibels (dB) by five pre-processing techniques displayed in the Figure 47; the details of each step are listed below (GEE, 2022).

Figure 48 SAR satellite imagery preprocessing procedure [START_REF] Xie | Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France[END_REF]  Apply orbit file: The first step is to update the orbit metadata, which contains the satellite's precise orbit state vectors (if the precise orbit is available to download). The orbit file allows the acquisition of accurate satellite positions and velocity information [START_REF] Filipponi | Sentinel-1 GRD Preprocessing Workflow[END_REF]GEE, 2022).

 GRD border noise removal: This algorithm aims to remove radiometric artifacts caused by the azimuth and range compression on the image edges. The artifacts represent low-intensity noise and invalid data [START_REF] Filipponi | Sentinel-1 GRD Preprocessing Workflow[END_REF].

 Thermal noise removal: The thermal noise removal operator aims mainly to reduce noise effects in the sub-swath and normalize the backscatter signal within the entire image. For scenes in multi-swath acquisition mode, it can also reduce discontinuities between sub-swaths [START_REF] Filipponi | Sentinel-1 GRD Preprocessing Workflow[END_REF]GEE, 2022).

 Radiometric calibration: Calibration is one of the principal pre-processing procedures to convert digital pixel values to radiometrically calibrated SAR backscatter. In this step, image intensity values are changed into sigma-naught values while keeping the nominally horizontal plane [START_REF] Filipponi | Sentinel-1 GRD Preprocessing Workflow[END_REF].

Part II General methodology Chapter 4. Analysis and image processing 146  Range Doppler terrain correction: Also known as "orthorectification," this is a correction procedure that reduces geometric distortions caused by topography and takes into account real terrain information by using a DEM, since most SAR images are captured with some distortions related to side-looking geometry [START_REF] Filipponi | Sentinel-1 GRD Preprocessing Workflow[END_REF]GEE, 2022).

 Conversion to dB: In this final pre-processing procedure, a logarithmic transformation is used to convert non-dimensional backscatter coefficients to dB [START_REF] Filipponi | Sentinel-1 GRD Preprocessing Workflow[END_REF].

The backscattering coefficient is widely used in diverse disciplines. For example, vegetation cover is one of the earliest application areas of the SAR backscattering coefficient. [START_REF] Mougin | Multifrequency and multipolarization RaDAR backscattering from mangrove forests[END_REF] In ocean and water resource management, [START_REF] Solberg | Remote Sensing of Ocean Oil-Spill Pollution[END_REF] detected oil-spill pollution on the ocean surface using the backscattering coefficient of several SAR sensors (e.g., ERS, ENVISAT ASAR, RADARSAT-1 and -2, TerraSAR-X and COSMO SkyMed). [START_REF] Kim | Monitoring Everglades freshwater marsh water level using L-band synthetic aperture RaDAR backscatter[END_REF] measured the water level with L-band SAR backscatter coefficients in the freshwater marsh.

In our study of winter crop phenology monitoring in Northern Finistère, Sentinel-1 SAR data were generated on the GEE(GEE) platform.

Google Earth Engine platform

Since the rapid development of multiple free-access remote sensing archives in recent years (e.g., Landsat, Sentinel, and NOAA), various tools have also been developed

for large-scale processing of geospatial data [START_REF] Gorelick | Google Earth Engine: Planetary-scale geospatial analysis for everyone[END_REF], such as TerraLib [START_REF] Câmara | TerraLib: Technology in Support of GIS Innovation[END_REF], Hadoop [START_REF] Whitman | Spatial indexing and analytics on Hadoop[END_REF], and GeoSpark (J. [START_REF] Yu | GeoSpark: A cluster computing framework for processing largescale spatial data[END_REF].

Yet, the lack of a powerful, easy, free-access, highly popular tool remains an issue for satellite image processing, which is still limited by fundamental information technology problems, data acquisition and storage, computer configuration (GPUs, CPUs), and network [START_REF] Gorelick | Google Earth Engine: Planetary-scale geospatial analysis for everyone[END_REF].

Under such circumstances, GEE arrived in 2010 as an open-source, planetary-scale cloud-computing platform for a single or a collection of analysis-ready image processing based on Google's massive computational capabilities [START_REF] Gorelick | Google Earth Engine: Planetary-scale geospatial analysis for everyone[END_REF]. GEE is a platform that provides international collaboration, data-intensive analysis, substantial

Part II General methodology Chapter 4. Analysis and image processing 148 computing resources, and high-end visualization [START_REF] Goodchild | Next-generation Digital Earth[END_REF][START_REF] Clinton | What is Google Earth Engine? Google Docs[END_REF].

Moreover, unlike the majority of coding platforms, the GEE platform is in an interactive mode that allows good visualization of data, analysis results preview, overlapping different data for comparison or detecting the evolution over time.

As displayed in the Figure 48, the platform is composed mainly of ( 1) an application programming interface (API) for performing processing, analysis, and visualization of the data in JavaScript language;

(2) an extensive data catalog ( 50Petabytes) that allows access to a variety of Earth observation data, such as optical and SAR satellite imagery (e.g., Sentinel, Landsat, MODIS) with only one or two days of interval between image acquisition and when it is added in the GEE data catalog, geospatial vectors, terrain, and atmosphere data; and (3) a visualization window with an exportation option [START_REF] Clinton | What is Google Earth Engine? Google Docs[END_REF].

Figure 49 Example of GEE processing interface

The satellite images in the data catalog are generally preprocessed for access and calculation. In addition, various geospatial-processing functions are integrated into the platform, which can perform raster and vector data processing, ML algorithms, kernel application, projection transformation, dataset combination, bandmath calculation, etc. C. [START_REF] Hansen | High-Resolution Global Maps of 21st-Century Forest Cover Change[END_REF]B. Chen et al., 2017;[START_REF] Oliphant | Mapping cropland extent of Southeast and Northeast Asia using multi-year time-series Landsat 30m data using a random forest classifier on the Google Earth Engine Cloud[END_REF], disaster, including drought [START_REF] Sazib | Leveraging the Google Earth Engine for Drought Assessment Using Global Soil Moisture Data[END_REF][START_REF] Zhao | Drought Monitoring over Yellow River Basin from 2003-2019 Using Reconstructed MODIS Land Surface Temperature in Google Earth Engine[END_REF], flood [START_REF] Uddin | Operational Flood Mapping Using Multitemporal Sentinel-1 SAR Images: A Case Study from Bangladesh[END_REF], wildfire (T. [START_REF] Long | 30 m Resolution Global Annual Burned Area Mapping Based on Landsat Images and Google Earth Engine[END_REF][START_REF] Sarricolea | Recent wildfires in Central Chile: Detecting links between burned areas and population exposure in the wildland urban interface[END_REF], food security or agriculture [START_REF] Jin | Smallholder maize area and yield mapping at national scales with Google Earth Engine[END_REF][START_REF] Rembold | ASAP: A new global early warning system to detect anomaly hot spots of agricultural production for food security analysis[END_REF], land cover [START_REF] Ge | Mapping annual land use changes in China's poverty-stricken areas from 2013 to 2018[END_REF]X. Wang et al., 2020), water management (C. Wang et al., 2018;Y. Wang et al., 2020), atmosphere and climate monitoring (H. Zeng et al., 2019;[START_REF] Fuentes | Big geospatial data analysis for Canada's Air Pollutant Emissions Inventory (APEI): Using google earth engine to estimate particulate matter from exposed mine disturbance areas[END_REF], and urban sprawl [START_REF] Gong | Annual maps of global artificial impervious area (GAIA) between 1985 and 2018[END_REF]D. Liu et al., 2020).

Post-classification methodology

Post-classification processing is considered the final objective of the project and the showcase of the data process, which allows the results to be presented to the general public in a comprehensible way.

In general, post-classification processing is generated in the form of statistical analysis and map, which allows for showing classification accuracy, evaluating the efficiency and effectiveness of the proposed method, and facilitating the comparative analysis of independently produced classifications from different methods (D. T. Bui et al., 2020, p. 202;[START_REF] Delancey | Comparing Deep Learning and Shallow Learning for Large-Scale Wetland Classification in Alberta, Canada[END_REF][START_REF] Xie | Mapping and Monitoring of Land Cover/Land Use (LCLU) Changes in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional NN) and by Post-classification Comparison (PCC)[END_REF], and/or from different dates, so as to visualize specific natural or societal issues or evolutions, such as urban expansion [START_REF] Weng | A remote sensing?GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China[END_REF]X. Liu et al., 2020), deforestation [START_REF] Saatchi | Mapping deforestation and land use in amazon rainforest by using SIR-C imagery[END_REF][START_REF] Margono | Mapping and monitoring deforestation and forest degradation in Sumatra (Indonesia) using Landsat time series data sets from 1990 to 2010[END_REF] and LULCCs (Abd El-Kawy et al., 2011;[START_REF] Dingle Robertson | Comparison of pixel-and object-based classification in land cover change mapping[END_REF].

This section will present two main post-classification processes: accuracy assessment and PCC. Accuracy assessment is the application of a series of statistical analyses of the classification results to validate the results and compare two or more classifiers performed on the same or similar data. Accuracy assessment can also be used to evaluate remote sensing data from different sensors. Otherwise, PCC refers to the comparative analysis of independently produced classifications from different dates in

Part II General methodology Chapter 4. Analysis and image processing 151 the form of a map [START_REF] Serra | Post-classification change detection with data from different sensors: Some accuracy considerations[END_REF]. Thus, PCC techniques are also known as a "map-tomap comparison."

Accuracy assessment

Accuracy assessment is the quality evaluation system for the measure of classification accuracy, determined by the degree of "correctness" of a map or classification [START_REF] Foody | +%28University+of+Salford%2C+Salford%2C+UK.%29&publication_year=1996[END_REF]; in other words, accuracy assessment refers to the level of agreement between the ground truth and the classification results.

As one of the key components of remote sensing studies, the accuracy assessment refers to the overall measure of the quality of a classified map. The principle objective is to not only verify and validate the reliability and accuracy of the output results in order to make effective and proper decisions, but also refine the classification and identify the errors, especially for interclass confusion. Consequently, the sophisticated statistical analysis of error matrices-the most widely used procedure of accuracy assessment-are developed with the aims of acquiring unbiased classification results and, therefore, of producing reliable thematic maps, especially for LULC mapping [START_REF] Van Genderen | TESTING LAND-USE MAP ACCURACY[END_REF]. Furthermore, the most critical part of the accuracy assessment is to choose suitable validation samples in the forms of polygons or pixels. Sample design can use random, stratified random, systematic, double, and cluster sampling (D. [START_REF] Lu | A survey of image classification methods and techniques for improving classification performance[END_REF]; stratified random techniques have been accepted as the most suitable sampling method for LULC studies [START_REF] Van Genderen | TESTING LAND-USE MAP ACCURACY[END_REF].

Error matrix (also called "confusion matrix") is considered to be the core of accuracy assessment in addition to being the most widely used and recommended method of accuracy assessment [START_REF] Foody | +%28University+of+Salford%2C+Salford%2C+UK.%29&publication_year=1996[END_REF][START_REF] Lu | A survey of image classification methods and techniques for improving classification performance[END_REF]. The matrix describes the pattern of the class allocation made relative to the reference data [START_REF] Foody | +%28University+of+Salford%2C+Salford%2C+UK.%29&publication_year=1996[END_REF] by building a table contrasting ground truth and predicted results, one on either side. In addition, the table comprises four categories of values: true positive, false positive, false negative, and true negative. A non-bias matrix is generated by comparing all the data in classification results and the reference data issue of the field survey [START_REF] Story | Accuracy Assessment: A User's Perspective[END_REF]. Once the error matrix is generated, other detailed statistical statements of accuracy assessment can be easily derived to express classification accuracy [START_REF] Foody | +%28University+of+Salford%2C+Salford%2C+UK.%29&publication_year=1996[END_REF][START_REF] Lu | A survey of image classification methods and techniques for improving classification performance[END_REF]; some accuracy indices used in our studies are displayed below (Figure 50):  Kappa coefficient, a powerful method for classification results evaluation, refers to a measure of the overall statistical agreement of an error matrix by considering all elements in the errors table (D. [START_REF] Lu | A survey of image classification methods and techniques for improving classification performance[END_REF]. It can accommodate the deficiencies of OA, such as some elements being correctly classified only by chance.

Additionally, Kappa is a better measurement of interclass agreement than OA.

 Local accuracy indices:

 Producer's accuracy (PA), also called "recall" or "positive predictive value," is a measure of errors of omission, which refer to a pixel abnormally excluded from the class to which it belongs-in other words, when samples have been omitted from

Part II General methodology Chapter 4. Analysis and image processing 154 the correct class [START_REF] Story | Accuracy Assessment: A User's Perspective[END_REF]. PA is usually applied to measure the probability that an area of interest can be correctly mapped for the producer.

 User's accuracy (UA), also called "precision" or "sensibility," is a measure of errors of commission, which refer to pixels abnormally included in a class when they should belong to another class. In other words, it indicates the probability that an element assigned to a class by the classifier actually represents that class in reality and how accurate the maps are for users [START_REF] Story | Accuracy Assessment: A User's Perspective[END_REF].

 F-score, generated from the precision and recall values, is able to provide harmonic mean of the two values.

As a major topic of debates and research in remote sensing for several decades, accuracy assessment remains a highly active research area, especially as it pertains to uncertainty and confidence analysis developed in recent years. For example, an algorithm based on research by P. [START_REF] Olofsson | Good practices for estimating area and assessing accuracy of land change[END_REF] designed for evaluating the accuracy of a change map and area estimation is essential to quantify uncertainty by reporting confidence intervals for accuracy and estimated area changes. In accuracy assessment, each indicator is represented by an uncertainty rate. Higher uncertainty refers to a greater accuracy interval, and lower uncertainty indicates greater accuracy. The objective of the confidence analysis is to provide continuous improvements and reduce uncertainties (P.

Olofsson et al., 2014).

There is no standard method of accuracy assessment; an increasing number of recent studies provide more than one measure of classification accuracy.

Post-classification comparison

Change detection is the comparative analysis of the individual classification of the rectified images from two different dates in order to identify changes that occurred and to provide valuable information about area changes and change rate, the spatial distribution of changed types, change trajectories between different LULC types, and

Part II General methodology Chapter 4. Analysis and image processing 155 accuracy assessment of change detection (D. Lu et al., 2004;[START_REF] El-Hattab | Applying post classification change detection technique to monitor an Egyptian coastal zone (Abu Qir Bay)[END_REF]. Additionally, change detection can be used to estimate and project the potential changed area. In change detection, multi-temporal data are involved in the evolution of a phenomenon over time.

Various methods are employed for change detection, such as visual analysis, image differencing, principal component analysis, change-vector analysis, and PCC (D. Lu et al., 2004). Of these, PCC change detection is the most commonly used method due to its robustness and superb capacity to detect land cover transition [START_REF] Castellana | A composed supervised/unsupervised approach to improve change detection from remote sensing[END_REF]. In addition, compared to visual analysis, PCC compares two classified maps from two data that are processed and classified independently. Thus, this change detection technique requires rectifying and classifying each remote sensing image. Moreover, considered the second step of post-classification processing, the PCC process strongly relies on accurate individual classification results that have undergone the evaluation of the accuracy assessment techniques described in the previous section. The classification should be the most accurate possible.

Based on the complete classification of each data, a change map is created that contains X² categories (assume that the classification results contain X classes). The analysis is performed at the pixel level, providing detailed "from-to" information; each change from one category to another is highlighted in the change map. Users can also extract the desired changes between two specific categories exclusively. The comparison is performed on a "pixel-by-pixel" basis, as demonstrated in Figure 51.

156 Sources of errors in PCC analysis can be various. For instance, the temporal, spatial, radiometric, and spectral characteristics of sensors may considerably affect the change detection results. In particular, each error in the input classification map will result in an error on the PCC change detection map. Thus, the comparison between images of two seasons is usually challenging. It is, therefore, preferable to choose images of the same season with similar states, especially for vegetation change detection (D. Lu et al., 2004).

Differences in pixel size can also affect the comparison results; usually, the finer spatial resolution provides more details in classification and change detection. Furthermore, the thresholds used to differentiate change from no change are also crucial for comparison [START_REF] Serra | Post-classification change detection with data from different sensors: Some accuracy considerations[END_REF].

Accordingly, avoiding using images from different sensors is essential for an accurate PCC analysis, especially images with different spatial resolutions and grid origins that might produce extra problems due to geometric corrections and different pixel sizes [START_REF] Serra | Post-classification change detection with data from different sensors: Some accuracy considerations[END_REF]. However, using multi-source data in applications is often unavoidable due to a lack of available data. For example, some sensors may not have existed prior to a certain time, or others may have suspended service for change detection over several decades [START_REF] Serra | Post-classification change detection with data from different sensors: Some accuracy considerations[END_REF]. For instance, G. [START_REF] Xie | Mapping and Monitoring of Land Cover/Land Use (LCLU) Changes in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional NN) and by Post-classification Comparison (PCC)[END_REF] , 2011;[START_REF] Huiping | Landscape and Water Quality Change Detection in Urban Wetland: A Post-classification Comparison Method with IKONOS Data[END_REF][START_REF] Thonfeld | Long-Term Land Use/Land Cover Change Assessment of the Kilombero Catchment in Tanzania Using Random Forest Classification and Robust Change Vector Analysis[END_REF]; human-or nature-induced change in cover condition (e.g., clearcutting, drought) [START_REF] Virk | Comparison of Techniques for Forest Change Mapping Using Landsat Data in Karnataka, India[END_REF][START_REF] Wang | Comparison of remote sensing change detection techniques for assessing hurricane damage to forests[END_REF][START_REF] Fadhil | Drought mapping using Geoinformation technology for some sites in the Iraqi Kurdistan region[END_REF]; cover transition (e.g., vegetation succession, soil erosion) (W. Q. [START_REF] Guo | Vegetation cover changes and their relationship to climate variation in the source region of the Yellow River, China, 1990-2000[END_REF][START_REF] Phinzi | Land use/land cover dynamics and soil erosion in the Umzintlava catchment (T32E), Eastern Cape, South Africa[END_REF]; and change in cover properties (e.g., biomass accumulation) [START_REF] Coppin | Operational monitoring of green biomass change for forest management[END_REF][START_REF] Klemas | Remote Sensing of Coastal Wetland Biomass: An Overview[END_REF].

Introduction

The urban area is the most important land use in the world nowadays. According to the studies, 70% of the world's population will reside in urban areas by 2050 due to rapidly increasing urbanization worldwide (Q. [START_REF] Chang | MSPA-Based Urban Green Infrastructure Planning and Management Approach for Urban Sustainability: Case Study of Longgang in China[END_REF]. With such an urbanization rate, urban green space (UGS) has become a vital component of the urban environment [START_REF] Neyns | Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review[END_REF]. UGS has various definitions; it can be defined as all kinds of predominantly unsealed, permeable and soft surfaces such as soil, grass, shrubs, trees, and water [START_REF] James | Towards an integrated understanding of green space in the European built environment[END_REF] in different forms, or all-natural, semi-natural, and artificial systems within, around and between urban areas of all spatial scales (Q. [START_REF] Chang | MSPA-Based Urban Green Infrastructure Planning and Management Approach for Urban Sustainability: Case Study of Longgang in China[END_REF].

Therefore, UGSs play a critical role in people's daily lives in different aspects; they not only address environmental issues-such as reducing pollution, noise, and climate perturbations (e.g., urban heat island effect), maintaining microclimate, preserving biodiversity, improving city water and air quality, and carbon sequestration-but also provide physical and psychological wellbeing to urban dwellers [START_REF] Ossola | Measuring urban tree loss dynamics across residential landscapes[END_REF].

Moreover, UGSs are crucial for urban geography and planning [START_REF] Shahtahmassebi | Remote sensing of urban green spaces: A review[END_REF]. However, UGSs are threatened by the currently rising population and increasing urbanization. Thus, adequate management and planning with the detection and monitoring of UGS have become crucial for urban development.

Part III LULC changes in Pays de Brest: Case studies Chapter 5. Machine learning methods and classification of vegetation in Brest, France 160 Consequently, various approaches have been developed and employed for collecting information about UGS. First, an extensive field survey examined the largest cities. After that, visual interpretation or manual digitizing from maps or aerial photographs became the primary source of USG mapping from the 1970s to the 1990s [START_REF] Nowak | Measuring and analyzing urban tree cover[END_REF][START_REF] Morgan | Automated analysis of aerial photographs and potential for historic forest mapping[END_REF][START_REF] Shojanoori | Review on the Use of Remote Sensing for Urban Forest Monitoring[END_REF][START_REF] Shahtahmassebi | Remote sensing of urban green spaces: A review[END_REF]. However, these two methods are costly, labor-intensive, subjective, and difficult to replicate. In addition, they is usually limited to the major cities, and only public green areas can be mapped.

Subsequently, remote sensing was introduced in UGS studies at the beginning of the 21st century, quickly becoming the most common and significant technique due to its efficiency and effectiveness. Remote sensing techniques have been proven able to provide valuable information on complete coverage of UGS in highly heterogeneous and complex urban areas in different spatial scales (large or medium scale such as parks or urban forests, and small scales such as private gardens and street trees) and for different seasons compared to the previous approaches [START_REF] Pu | A comparative analysis of high spatial resolution IKONOS and WorldView-2 imagery for mapping urban tree species[END_REF]. Furthermore, they can effectively generate different characteristics of UGSs, such as spatial distribution and species compositions [START_REF] Shahtahmassebi | Remote sensing of urban green spaces: A review[END_REF]. For example, [START_REF] Mathieu | Object-Based Classification of Ikonos Imagery for Mapping Large-Scale Vegetation Communities in Urban Areas[END_REF] developed a methodology to produce vegetation community maps in Dunedin, New Zealand, by combining orthorectified HR multispectral IKONOS images and a hierarchical OBC approach. The authors extracted all vegetated units in the satellite data and then Even though the accuracy and efficiency of UGS mapping were considerably increased due to remote sensing techniques, UGSs remain challenging to monitor for several reasons: (1) urban environments are commonly characterized by the complex and spatially and spectrally heterogeneous landscape; (2) UGSs are generally on a smaller scale than forests (from small patches of vegetation to scattered trees), which frequently change due to urbanization policies and increasing artificialization; and (3) urban land cover classification is highly affected by the problem of shadow resulting from the complex dimensional structure of urban areas [START_REF] Adeline | Shadow detection in very high spatial resolution aerial images: A comparative study[END_REF].

Moreover, the traditional classification methods and medium-or high-spatialresolution remote sensing data allow only brief mapping of the spatial distribution of UGSs and identifying the main categories of vegetation (trees, shrubs, or grassland).

Therefore, UGS classification remains a significant challenge that continues to attract interest from remote sensing communities and decision-makers.

In recent years, the already significant number of UGS studies has rapidly increased even further. Fine-scale UGS mapping has become possible due to the arrival of the new generation of free-access high-quality optical and SAR satellite data with very high temporal spectral and spatial resolution, the emergence of available LIDAR and hyperspectral data [START_REF] Voss | Seasonal Effect on Tree Species Classification in an Urban Environment Using Hyperspectral Data, LiDAR, and an Object-Oriented Approach[END_REF][START_REF] Alonzo | Urban tree species mapping using hyperspectral and LiDAR data fusion[END_REF][START_REF] Tong | A Two-Phase Classification of Urban Vegetation Using Airborne LiDAR Data and Aerial Photography[END_REF][START_REF] Liu | Mapping urban tree species using integrated airborne hyperspectral and LiDAR remote sensing data[END_REF], advance classification algorithms, and the increase in available computational power [START_REF] Neyns | Mapping of Urban Vegetation with High-Resolution Remote Sensing: A Review[END_REF].

Part III LULC changes in Pays de Brest: Case studies Chapter 5. Machine learning methods and classification of vegetation in Brest, France 162

In particular, VHR imagery (< 1 m of spatial resolution) and advanced ML algorithms are widely applied in a variety of thematic areas of applications of remote sensing in USGs studies, including change detection [START_REF] Rafiee | Assessment of changes in urban green spaces of Mashad city using satellite data[END_REF][START_REF] Zhou | Spatial-temporal dynamics of urban green space in response to rapid urbanization and greening policies[END_REF][START_REF] Qian | Understanding the dynamic of greenspace in the urbanized area of Beijing based on high resolution satellite images[END_REF], biomass and carbon estimation (C. [START_REF] Liu | Carbon storage and sequestration by urban forests in Shenyang, China[END_REF][START_REF] Raciti | Mapping carbon storage in urban trees with multisource remote sensing data: Relationships between biomass, land use, and demographics in Boston neighborhoods[END_REF], and urban vegetation mapping and characterizing [START_REF] Höfle | Urban vegetation detection using radiometrically calibrated small-footprint full-waveform airborne LiDAR data[END_REF][START_REF] Liu | Mapping vegetation in an urban area with stratified classification and multiple endmember spectral mixture analysis[END_REF][START_REF] Feng | UAV Remote Sensing for Urban Vegetation Mapping Using Random Forest and Texture Analysis[END_REF], especially urban garden [START_REF] Baker | A Combined Approach to Classifying Land Surface Cover of Urban Domestic Gardens Using Citizen Science Data and High Resolution Image Analysis[END_REF][START_REF] Haase | Front and back yard green analysis with subpixel vegetation fractions from earth observation data in a city[END_REF] and street tree mapping [START_REF] Puissant | Object-oriented mapping of urban trees using Random Forest classifiers[END_REF][START_REF] Parmehr | Estimation of urban tree canopy cover using random point sampling and remote sensing methods[END_REF]. [START_REF] Ardila | Markov-random-field-based superresolution mapping for identification of urban trees in VHR images[END_REF] proposed a method for the detection of tree crowns in complex urban areas by using a Markov random field (MRF)-based Super-Resolution Mapping (SRM) approach, which took account of the spatial smoothness prior and the conditional probability of multispectral and panchromatic bands of VRH QuickBird satellite images (spatial resolution ranged from 450-900 nm). The method was found operational and effectively improved tree crown detection over other ML methods, such as MLC and SVM. The study area, Brest (Figure 52), is a coastal city and the most important urban area of the department of Finistère (48° 23′ 27″, 4° 29′ 08"), which is located on the west coast of the region of Brittany, France. The city of Brest covers a land surface of 49.51 km², and the population was 319,947 in 2016.

Study area and materials

Study area

Brest is mainly occupied by artificial areas, including various types of construction and sealed surfaces located mainly in the city's center; military areas and harbors can be found in the south near the ocean. Moreover, a variety of UGSs are present, which are enumerated below (Figure 53):

Data

In this study, three VHR Pléiades satellite images used are displayed as follows (Table 7): In our research, three ready-to-use pan-shaped multispectral images were downloaded from the DINAMIS platform (https://dinamis.teledetection.fr/home) through l'EQUIPEX GEOSUD. Furthermore, different scenes were merged to create a whole image covering our entire area of interest. Then the images were clipped by the contour of the city of Brest to extract the study area and remove the undesired area.

Eventually, a new band of NDVI was calculated with the visible red and NIR spectral channels and added to the original spectral bands to improve vegetation classification accuracy.

Methodology

Figure 55 Scheme of the proposed methodology in the study

In this research, the OBC approach was performed on the preprocessed VHR Pléiades satellite data using the SLIC superpixel segmentation algorithm and an MLP DL classification model, detailed in Chapter 4 ("Analysis and image processing") (Figure 54).

At first, the SLIC superpixel algorithm was used for segmentation before classification, after which training samples were selected manually from the segmentation results to train the MLP model, and a trial-and-error method was used to find the optimal parameters. Finally, the well-trained MLP model was applied to the entire scene for image classification.

SLIC Segmentation

As described previously, superpixel algorithms have recently become widely used in computer vision; they can effectively cluster pixels into regions to reduce the size of the image and the complexity of image processing tasks, thereby providing better performance and results. SLIC, recently developed, is a fast and memory-efficient and has a better performance on segmentation by grouping pixels into N regions based on their color similarity and spatial proximity [START_REF] Achanta | SLIC Superpixels Compared to State-of-the-Art Superpixel Methods[END_REF].

The SLIC algorithm was applied to both images using scikit-image, an imageprocessing library of python. In this function, three parameters were generated by the user:

 N_segments, which is the approximate number of clusters in the segmented output image (value = 1,600,000)

 Compactness the balance between color proximity and space proximity (value = 1)

 Sigma, the width of the Gaussian smoothing kernel for pre-processing for each dimension of the image (value = 2)

Training samples of each class were selected manually from the image segmented;

80% of the samples selected were assigned to training data, and 20% were assigned to validation data. Once samples were selected, tables containing the mean value of each spectral band of all pixels belonging to the same segments were created for MLP model training.

MLP model training and classification

MLP is one of the most well-known supervised ANN classification methods, which refers to a multilayer feedforward NN that includes at least three layers: an input layer to receive signals, an output layer to provide the prediction of result, and at least one hidden layer constituted by neurons with a non-linear activation function. The number of hidden layers and the number of neurons above can be varied according to the user's need. In general, more neurons gave greater sensitivity to the problem, but this also may lead to an overfitting problem [START_REF] Murtagh | Multilayer perceptrons for classification and regression[END_REF].

Results and discussion

Classification accuracy of MLP algorithm

The processing chain presented in the previous section was used on three VHR satellite images acquired in the same scene: the city of Brest. Various accuracy indicators of the MLP model are presented in Table 8 and9 As shown in Table 8, the MLP algorithm generally achieved very high accuracy in vegetation classification, especially in low-and non-vegetation categories. Several errors occurred in the tree and water categories, although the result was still acceptable.

According to Table 9, the primary error was the misclassification between different vegetation heights. For example, many trees were misclassified as shrubs or grasses.

Meanwhile, the misclassification between water and non-vegetation may be attributable to the similarity of their spectral behaviors. Considering that most of the non-vegetation was found in the urban area, artificial surfaces and construction could cause mirror reflection as the water surface. In addition, the global kappa was also very high, which indicates high agreement between the classification and ground truth. To sum up, the DL MLP algorithm extracted vegetation in a highly heterogenous and complex area with high construction density. VHR remote sensing data was well adapted for urban area classification due to the small scale of objects.

Additionally, three classified maps are demonstrated in Figure 57. Some changes between high and low vegetation were also common in the results.

For example, some high vegetation in 2016 turned to low vegetation in 2018 in the urban forest near the Penfeld River in the center, and in southwestern Brest, these changes were likely due to pruning, or to misclassification errors. However, most of the vegetation in

Comparison with RF classification results

Furthermore, one of the most widely used and efficient ML classifiers, RF, was also used in this study to compare the two classification methods (MLP and RF). This method applied supervised classification to the same VHR Pléiades image with manually selected training samples.

The accuracy assessment of the RF classification is demonstrated in the table 10, and the three classified maps are displayed in the figure 60. Additionally, the OA was globally higher than kappa, which may indicate that the global classification accuracy was better than interclass accuracy. Nevertheless, some differences could still be noticed between the classification results of the two classifiers. Regardless of the classification method, the boundary between vegetation and non-vegetation was clear, but the disagreement between high and low vegetation classes was more critical. For instance, according to Figure 62, although the two algorithms were used on the same satellite image, RF detected more trees or high vegetation. At the same time, MLP found more shrubs and grass or low vegetation, even in some croplands. Thus, the RF algorithm resulted in greater confusion between high and low vegetation in this case, which led to the misclassification of low vegetation.

Overall, MLP and RF obtained identical and satisfactory results (close to 1) of simple vegetation extraction in a complex and heterogonous urban environment by using the new generation of VHR remote sensing data. The vegetated area was especially well extracted, and the changes between vegetation and artificial surface were also successfully detected by both classifiers. However, RF may misclassify some low vegetation as high vegetation, which leads to lower accuracy. Furthermore, the evaluation

Conclusion

In this study, MLP, a DL method, was applied to Pléiades VHR resolution satellite images from three years (2016)(2017)(2018) in the city of Brest to study the spatial distribution and changes of UGSs in Brest, a highly heterogeneous urban area. Moreover, the performance of MLP was evaluated and compared to RF.

In Brest, the UGSs are not only segmented and on a small scale (e.g., street trees); they are also highly varied, including urban forests, public parks, private gardens, and croplands.

It is, therefore, challenging to obtain accurate classifications.

In response to the challenge, this study proposed an OBC-based DL classification method.

The processing chain began with SLIC superpixel segmentation to create objects. The training dataset was then selected manually based on segmentation results with the aim of MLP deep architecture training. Finally, three satellite images were fed into the welltrained model to generate the classification result.

According to the accuracy assessment, the classification results of the MLP deep structure model were highly accurate (0.99% OA and 0.98 kappa coefficient), and both classes were also well classified, despite slight confusion between trees and shrubs and grass.

Moreover, the changes in vegetation and the transition between vegetation and nonvegetation were also adequately detected. Therefore, combining the SLIC segmentation algorithm, MLP, and VHR remote sensing data was suitable for UGS mapping and change detection in the urban area.

Furthermore, the RF classification method showed highly similar results to those of MLP, although they were less accurate and involved more misclassification between high and low vegetation.

Introduction:

Coastal zones are the shores of seas or oceans. Today, nearly half of the world's population lives in coastal regions where multiple activities are developed [START_REF] Crowell | How Many People Live in Coastal Areas[END_REF]. Over the last century, coastal zones throughout the world have undergone major changes related to a significant influx of the population. Coveted, densely populated, and exploited by human societies, coastal zones are therefore subject to significant pressures that generate territorial dynamics and changes in LULC. LULC is always influenced by human actions and environmental features and processes, and it mediates the interactions of these two factors. This means that land use changes are primarily due to human actions, which are associated with economic development, techneology, environmental change, and especially, population growth, which usually has parallel rates to land use change [START_REF] Waldhoff | Multi-Data Approach for remote sensing-based regional crop rotation mapping: A case study for the Rur catchment, Germany[END_REF][START_REF] Briassoulis | Analysis of Land Use Change: Theoretical and Modeling Approaches[END_REF]. However, traditional methods require direct observations in the field; usually, they are not only ineffective, expensive, time-consuming, and labor intensive but are also limited on the local scale. [START_REF] Giri | Next generation of global land cover characterization, mapping, and monitoring[END_REF]. Since high-resolution satellite images are now available, land cover change mapping and monitoring at the landscape or local scale have been developed at a high rate of speed (Y. [START_REF] Xie | Remote sensing imagery in vegetation mapping: A review[END_REF][START_REF] Ma | Deep learning in remote sensing applications: A meta-analysis and review[END_REF][START_REF] Chughtai | A review on change detection method and accuracy assessment for land use land cover[END_REF].

Several national and international organizations have produced regional land change maps that represent a location on a single date (e.g., CLC 2000 in Europe), with

Landsat observations acquired in a target year interval (e.g., ±1-3 years). Some programs repeat land cover mapping periodically (e.g., NLCD 2001NLCD /2006NLCD /2011 in the USA and CESBIO in France) to allow the observation of the changes. The local accuracy of these global or national land cover maps generated from coarse spatial resolution data is low, especially in regions with fragmented land covers (T. Zeng et al., 2019).

At the same time, for studies at larger scales, satellite data have been used to monitor LULC changes worldwide in various fields of research, such as mapping cropland conversions (L. [START_REF] Zhu | Long-Term Monitoring of Cropland Change near Dongting Lake, China, Using the LandTrendr Algorithm with Landsat Imagery[END_REF], monitoring urbanization and its impacts (H. [START_REF] Xu | A remote sensing and GIS integrated study on urbanization with its impact on arable lands: Fuqing City, Fujian Province, China[END_REF][START_REF] Dewan | Land use and land cover change in Greater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization[END_REF][START_REF] Xiong | The Impacts of Rapid Urbanization on the Thermal Environment: A Remote Sensing Study of Guangzhou, South China[END_REF], monitoring deforestation [START_REF] Frohn | Using satellite remote sensing analysis to evaluate a socio-economic and ecological model of deforestation in Rondônia, Brazil[END_REF][START_REF] Gao | Determination of land degradation causes in Tongyu County, Northeast China via land cover change detection[END_REF][START_REF] Brink | Anthropogenic pressure in East Africa-Monitoring 20 years of land cover changes by means of medium resolution satellite data[END_REF][START_REF] Weng | Land-use scene classification based on a CNN using a constrained extreme learning machine[END_REF][START_REF] De Bem | Change Detection of Deforestation in the Brazilian Amazon Using Landsat Data and Convolutional NNs[END_REF] ,evaluating the environment [START_REF] Wilson | Evaluating environmental influences of zoning in urban ecosystems with remote sensing[END_REF][START_REF] He | Environmental degradation in the urban areas of China: Evidence from multi-source remote sensing data[END_REF]J. Li et al., 2020) and biodiversity losses, and examining the influence of LULC on climate change [START_REF] Mahmood | Impacts of Land Use/Land Cover Change on Climate and Future Research Priorities[END_REF]. Nonetheless, all types of land use might lead to detrimental impacts and effects in many fields: for example, the abandonment of agricultural land without restoration is linked to a specific set of problems, including landscape degradation and an increased risk of erosion [START_REF] Giri | Next generation of global land cover characterization, mapping, and monitoring[END_REF]. These irreversible impacts of LULC change have significantly increased in recent decades, and so the mapping and and RF are well-known for their optimal classification accuracies in land cover classification applications [START_REF] Shi | Support Vector Machines for Land Cover Mapping from Remote Sensor Imagery[END_REF][START_REF] Zafari | Evaluating the Performance of a Random Forest Kernel for Land Cover Classification[END_REF][START_REF] Sheykhmousa | Support Vector Machine Versus Random Forest for Remote Sensing Image Classification: A Meta-Analysis and Systematic Review[END_REF].

These algorithms have significant advantages and similar abilities in classifying multitemporal and multisensor data, including high-dimensional datasets and improved overall accuracy (M. [START_REF] Pal | Support vector machine-based feature selection for land cover classification: A case study with DAIS hyperspectral data[END_REF][START_REF] Song | Comparison of artificial NNs and support vector machine classifiers for land cover classification in Northern China using a SPOT-5 HRG image[END_REF]. The accurate and timely detection of changes is the most important aspect of this process. Moreover, CNN, a more recently developed but well-represented deep learning method, allows the rapid and effective analysis and classification of LULCs and has proven a suitable and reliable method for accurate CD in complex scenes. Although it is more recent, many studies have made use

Study area and materials

Study area

The study area, the Crozon Peninsula canton south of the Landerneau-Daoulas canton, is located on the west coast of France in the Pays de Brest, Department of Finistère and the region of Brittany (Figure 63). Nevertheless, the land cover was actually in sharp transition in our study area between 2008 and 2018, with the peninsula especially marked by an increasing service and commerce sectors. Therefore, the study area was chosen as a typical ideal case to study land cover changes.

Data

Operable high-quality cloud-free satellite images in this area are extremely rare due to the annual high-intensity rainfall and, hence, heavy cloud cover. Despite these limitations, three cloud-free images from two dates in 2007 and 2018 with the same scene area were acquired from either the SPOT or Sentinel platforms to study land cover changes in the study area during the summer, which is the growing season for crops (Table 11). Second, two level 2A atmospheric effect-corrected Sentinel-2 images of the same date in the middle of the summer in 2018 were acquired from Theia (catalog.theia-land.fr); a mosaic was then created by combining two images to cover the whole study area, and four spectral bands at a 10 m resolution (red, green, blue, and near-infrared) were extracted for further use. For the purpose of land cover identification at the sample selection step, we also used Google Earth and RPG maps and a French database with agricultural parcel identification as the reference data, complemented by observation and survey in the field when necessary.

Methodology

The methodology of this paper is detailed in three main parts as follows: preprocessing, image processing, and postprocessing. Three satellite images of two dates were processed in QGIS (SAGA, Grass, OrfeoToolbox7.3.0), eCognition 9.5 and 10.0. A flow chart of the proposed global methodology is displayed below (Figure 64). Thereafter, three vegetation indices were calculated and utilized in our study:

• NDVI, a normalized ratio between the red and the NIR spectral bands.

• GNDVI, which considers the green spectral band instead of red.

• EVI2 developed as an adaptation of the EVI without the blue band to break through the limit of sensor systems.

After calculating the three vegetation indices, we created an image stack with the original spectral bands and all of the indices for image processing.

Image processing

In this study, supervised object-based classification was performed on two image stacks of two different years. Segmentation was applied first, followed by two nonparametric machine learning algorithms. SVM and RF were trained and applied in this step.

MRS was performed as the first processing step of object-oriented image analysis.

In this study, the scale, compactness, and shape parameters used were assigned as follows: 10, 0.1, and 0.3, respectively. The selection of the parameters was completed on a trialand-error basis.

Afterwards, since Supervised methods were performed in our research, all of the samples presented in Table 12 were selected manually with Google Earth, an RPG map, and ground truth as the reference data, and the ground truth values were taken during a field survey with a GNSS device in August. The samples were then used to train two classifiers in the next step. with 64 feature maps in the first hidden layer, and the second hidden layer had a kernel size and feature map of 4×4 and 64, respectively. For the 2018 images, the batch size was set to 10, and both hidden layers were assigned 3×3 and 32 for the kernel size and number of feature maps, respectively. Both hidden layers of the two models contained a max-pooling stage using a 2×2 filter. Then the two models were trained based on the trialand-error method, with a learning rate of 0.001. After obtaining a satisfactory CNN accuracy, the two models were validated and used to produce the classification of two satellite images, from 2007 and 2018 separately. an overlap of these images to cross the data at the pixel level and differentiating the land cover changes according to the differences between the two maps. As the output, a change layer is created, and there is also a table that shows how the pixels move between the classes. In this class, the fact that the PA is considerably greater than the UA reveals the error of omission, except for the classification of SVM in 2007 and both CNN classifications, which suggests an error of commission instead. In all cases, the CNN was always the most stable and reliable method.

Results and discussion

Comparison of classifiers

Overall, three different algorithms were applied to two high spatial resolution satellite images from 2007 and 2018, and all of them achieved a good accuracy level, with the OA ranging from 70% to 90% despite the complex landscape and small field size. The RF and SVM models both performed well for the LULC classification; nonetheless, the CNN obviously is better suited to performing classification in our study area since it generally performs best regardless of the type of dataset and accuracy index.

Therefore, the CNN has proven to be a feasible, reliable method with remarkable performance for precisely mapping LULC and analyzing the changes. However, some important considerations regarding its effectiveness are worth discussing. Previous applications of CNN models have tended to emphasize the complexity of these models compared to RF models and SVMs. In this case, parameter tuning and optimization are often performed by cross-validation for CNN algorithms. However, in some cases, CNN models can have millions of weights to optimize at each iteration [START_REF] Lecun | Deep learning[END_REF]. In such situations, training these models can be tedious. Manual tuning or rules of thumb for cross-validation should be implemented in this case. This manual manipulation could have repercussions on the accuracy of the model. A well-known solution is transfer learning (J. Ma et al., 2019). In this case, instead of a model being trained from scratch, pretrained models are retrained on the user's classes of interest. Pretrained models allow for better accuracy [START_REF] Hendrycks | Using Pre-Training Can Improve Model Robustness and Uncertainty[END_REF]. In our study, the deep model was very useful for generalization.

Furthermore, according to the accuracy assessment, the lower accuracy occurred for 2018, and we deduced that the spatial resolution of the image is a crucial part of classification that can explain the differences between the SVM and RF's overall accuracy in the different years. The RF performed better on the 2007 data with a 2 m spatial resolution SPOT 5 image; in contrast, SVM achieved a better accuracy in 2018 with a 10 m spatial resolution Sentinel 2 image. Among all of the classes, except for the water areas, which have a very different spectral signature than the other classes, vegetation was the best-detected class, most likely because it occurred on the greatest part of the study area; therefore, it also had the largest sample dataset, since all of the samples were randomly and evenly selected in the images. Non-vegetation areas that are mostly urban land, rocks, and sand were relatively simple to discriminate. Cropland with bail soil was betterclassified than planted cropland. Misclassification largely occurred between the vegetation and crops due to their spectral signature similarities, especially during the The results also indicated that the choice of a suitable classifier was critical, and the OBC approach clearly influenced the final classification at the same time. Beside the confusion between growing crops and vegetation, our previous results can be confirmed by Artificialization is the main change that has affected the coastal zone of the peninsula, with preferential locations around the major urban centers and on certain coastal sectors.

Despite the regulatory protection established by the Littoral Law, the changes are also important in the 100 m band nearest to the sea and then decrease as one moves away from it. In 1986, the Littoral Law provided an initial regulatory response to the need to control the anarchic development of construction on the coast. One of the most significant consequences of development has been the drastic reduction of the vegetation surface.

Vegetation has been removed for two main reasons: increasing agricultural activities and urban land growth. Therefore, economic development can have negative social and economic implications on the peninsula; in addition, environmental conservation and protection are required.

Conclusion

CD methods involve analyzing the state of a specific geographic area to identify variations from images taken at different times. With satellite remote sensing, high spatial and spectral resolution images are recorded and used to analyze the scales of changes. In this study, in order to detect multiannual change classes between the series of multitemporal images using a pixel-by-pixel PCC technique, three different well-known and frequently used algorithms, including two machine learning algorithms (i.e., SVM and RF) and one deep learning algorithm (i.e., CNN), were tested on two high spatial resolution satellite images. RF and SVM were applied with an object-based approach, which requires a segmentation step to create subpixel-level objects to avoid the error of mixed pixels since the study area was mainly covered by small fields. The inclusion of the CNN significantly improved the classification performance (5-10% increase in the overall accuracy) compared to the SVM and RF classifiers applied in our study.

Our results showed that the use of remote sensing for complex multiannual smallscale LULC change studies was completely reliable. The results may also lead to the conclusion that economic development is rapidly occurring in the peninsula, manifested as urban land and tourism growth, increasing the agricultural activities and grossly decreasing the vegetative areas. Hence, environmental protection measures are demanded for the future. In this context of change, the coastal zones of the peninsula tend to specialize socially and economically, and the maintenance of the agricultural areas, as well as the preservation of the natural areas, are both more sensitive and more complex. Moreover, it appears that the change in land use must be understood in the context of climate change, which is a factor in the aggravation of risks (e.g., flooding and, coastal risks), especially in the sectors that are most subjected to urbanization pressures.

Although we observed relatively high classification accuracies, several uncertainties and limitations persisted. The first is the misclassification between vegetation and planted croplands: the very similar spectral characteristics that they share and their geographical localization lead to this confusion. Second, the two classifications were based on two images with different spatial resolutions; thus, some errors of the land use change analysis could have been induced. Third, useful cloud-free satellite images of the growing season were not easy to obtain in our study area; therefore, a series of annual mappings with more precision was not performed in the study. Hence, some recommendations can be made for further studies, such as applying more vegetation indices or using hyperspectral images to differentiate between vegetation and planted croplands or exploring the potential of synthetic-aperture RaDAR images as a supplement to the traditional optical images on cloudy days. [START_REF] Schlund | Sentinel-1 time series data for monitoring the phenology of winter wheat[END_REF]. This makes reliable information about crop spatial distribution and growing patterns crucial for studying regional agriculture production and supply, making political decisions, and facilitating crop management (X.-P. [START_REF] Song | National-scale soybean mapping and area estimation in the United States using medium resolution satellite imagery and field survey[END_REF][START_REF] Yin | Mapping agricultural land abandonment from spatial and temporal segmentation of Landsat time series[END_REF].

The classification of crop spatial distributions are valuable for agricultural monitoring and for the implementation and evaluation of crop management strategies [START_REF] Birrell | Comparison of sensors and techniques for crop yield mapping[END_REF][START_REF] Sun | Using of Multi-Source and Multitemporal Remote Sensing Data Improves Crop-Type Mapping in the Subtropical Agriculture Region[END_REF] In recent years, most studies in the agricultural field have explored the performance of different classification algorithms. RF is one of the most well-known and widely used algorithms in the field for its optimal classification accuracy, effectiveness on large data bases, and its capability of estimating the importance of the variables in the classification [START_REF] Ok | Evaluation of random forest method for agricultural crop classification[END_REF][START_REF] Rodriguez-Galiano | An assessment of the effectiveness of a random forest classifier for land-cover classification[END_REF][START_REF] Forkuor | Integration of Optical and Synthetic Aperture RaDAR Imagery for Improving Crop Mapping in Northwestern Benin, West Africa[END_REF][START_REF] Tatsumi | Crop classification of upland fields using Random forest of time-series Landsat 7 ETM+ data[END_REF][START_REF] Son | Assessment of Sentinel-1A data for rice crop classification using random forests and support vector machines[END_REF]. The RF classification algorithm is traditionally run as a PBC, which has proven efficient and accurate in agriculture fields by many studies [START_REF] Ok | Evaluation of random forest method for agricultural crop classification[END_REF][START_REF] Hao | Feature Selection of Time Series MODIS Data for Early Crop Classification Using Random Forest: A Case Study in Kansas, USA[END_REF][START_REF] Saini | CROP CLASSIFICATION ON SINGLE DATE SENTINEL-2 IMAGERY USING RANDOM FOREST AND SUPPOR VECTOR MACHINE[END_REF]H. Li et al., 2020). On the other hand, the advantage of OBC is well documented and many recent studies have the conclusion that OBC usually outperforms PBC for its higher classification accuracy, better potential for extracting land cover information in a heterogeneous area with small size field, and the capacity to produce a more homogenous class [START_REF] Weih | Object-based classification vs. Pixel-based classification: Comparitive importance of multi-resolution imagery[END_REF] Aside from mapping and analyzing the crop spatial distribution, understanding agricultural growing patterns is also a key element for crop management. Crop phenology monitoring and the identification of the main phenological stages are highly necessary for agricultural production predicting, efficient interventions of farmers and decision-makers during the phenological phases such as fertilization, pesticide application, and irrigation [START_REF] Nasrallah | Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping[END_REF]. In particular, germination is the most critical phase to be understood, and it is the starting point of the growing season. Based on the germination information, the farmer and decision-makers are able to make a future projection of the season, estimate the whole seasonal phenology for crop growth, and predict its production [START_REF] Nasrallah | Sentinel-1 Data for Winter Wheat Phenology Monitoring and Mapping[END_REF]. Furthermore, phenology is highly related to the seasonal dynamics of a growth environment, therefore, in the context of global warming, the phenology of many plants, especially crops, may have changed (Y. Song & Wang, 2019).

Crop phenology is usually monitored with optical satellite images using vegetation Compared to the optical data, SAR data is less used in agricultural areas.

Nevertheless, lately, with the emergence of a new generation of high-resolution SAR data, in particular since the Copernicus program Sentinel-1 C-band high spatial-temporal resolution images became available, SAR data has begun to draw interest, especially for its advantage of having its own source of energy, making it nearly independent of weather conditions [START_REF] Forkuor | Integration of Optical and Synthetic Aperture RaDAR Imagery for Improving Crop Mapping in Northwestern Benin, West Africa[END_REF]. Thus, SAR backscattering coefficient time series data is now more frequently used for crop phenology monitoring. While optical data strongly depends on the chlorophyll content in the plants, SAR data can reveal the main changes in the canopy structure, identify significant phenological stages, and determine the main The main objectives of this study are listed as follows:

(1) Study the feasibility of mapping winter crops with Sentinel-2 10 m spatial resolution data in a fragmented area that is dominated by small-size fields;

(2) Perform hierarchical classification and classical direct extraction and evaluate the performance of both classification approaches;

(3) Perform PBC and OBC and compare the performance in each level of the hierarchical classification structure;

(4) Study the correlation between crop phenology and Sentinel-1 C-band SAR backscatter time series data and identify three phenological stages and the main growth period of the winter crops.

Study area and materials

Study area

The study area is located on the west coast of France in the north of the Finistère department and the region of Brittany (Figure 70). The study area covers a land surface of 1, 034.41 km², and extends between the latitudes of 48°19′39″N and 48°40′41″N, and the longitudes of 4°12′50″W and 4°47′13″W.

According to IGN, the northern part of Finistère is mostly dominated by plains, and the elevation of the area ranges between 0 m and 100 m (Géoportail, n.d.). The study area is mostly occupied by cropland, temporal or permanent grasslands, small area of forests and shrubs, urban agglomeration in the south, and a wetland area in the north [START_REF] Rouault | Observer l'occupation des sols pour guider les politiques d'aménagement (MOS) (L'Ouest breton, Le Finistère, Le Pays de Brest et ses EPCI)[END_REF].

On average, the northern region of Finistère receives 941 mm of total precipitation per year, with the annual average temperature being 12.1 °C (7.7 °C and 16.8 °C are the monthly average temperatures for the coldest and warmest months, respectively), and therefore the warm temperate climate with frequent rainfall provides very favorable conditions for agriculture activities.

With such climate and topography conditions, agriculture is an important economic sector in the study area, and a considerable number of locals work in an agricultural or related sector in the department. There are 384, 408 ha of useful agricultural area in the department, so 57% of the department's surface is devoted to agricultural use (ADEUPa Brest, n.d.). One of main agricultural productions are crops, including corn, winter wheat, and winter barley, and vegetables (Chambres d'Agriculture de Bretagne, n.d.).

208

Hence, it is important to develop a methodology to map one or several specific crop types and monitor their growth stages by using free access, high quality satellite images for crop production management. The north of the Finistère department was chosen as our first study area because of its favorable natural conditions, highly active agricultural activities, and its proximity, which facilitate the field research and interaction with farmers.

Data

The study was executed in the Finistère department in France during 2019, using open-access high-quality satellite data from the Sentinel platform. It is worth noting that the latest version of the graphic parcel register was published in 2019 by IGN, and this information was relevant to our study.

Due to the annual high-intensity precipitation there is frequent heavy cloud cover in the region, therefore, operable optical satellite images are very rare in the study area. 18). Ten spectral bands (Table 19) were extracted for further processing and analysis. Moreover, in order to create a cloud-free time series of the study, the phenological phrases of winter crops from the SAR data were applied to the phenology monitoring process. In this study, interferometric Wide (IW) Swath mode level-1 Ground Range Detected (GRD) Sentinel-1 data with an incidence angle ranging from 30 to 46 were acquired to create the time series of the growing period of the winter crops (winter wheat and winter barley) in 2019, from 1 October 2018 to 1 September 2019. Both polarizations (VV + VH) were used, but only the descending orbit was retained for the processing. In total, 109 Sentinel-1 C-band SAR images with descending orbit were acquired for this study.

In addition, RPG was applied as the ground truth data in our study, used for creating training data and test data. RPG 2019 is the latest version of the very precise, georeferenced agricultural land database that covers the entire France territory (except Mayotte) that was published by IGN. The databases show the precise crop types (e.g., wheat, corn, vegetables, sunflower) or temporary and permanent grasslands in that are in the recorded agricultural lands in each year ( RPG, n.d.).

Methodology

The methodology of this paper is detailed in two parts, which relate to the two research subjects: mapping winter crop types using Sentinel-2 data, and monitoring crop Secondly, supervised image processing using different approaches was performed in order to make comparisons and attempt to reach the most adapted classification in this study (Figure 72). Finally, the results of the two classification approaches were evaluated with accuracy indices, in order to distinguish which one had better agreement with the ground truth data.

212

In addition, inside the hierarchical classification structure, except for during the first step, separating vegetation and non-vegetation exclusively used the pixel-based RF algorithm and this reached a very close agreement with ground truth data. Each step has been performed using the two methods, pixel-based and object-based RF classification, in order to determine the result with better accuracies for further processing and analysis.

In OBC methods, MRS algorithm was applied as the first step, and several combinations of parameters were used, and the optimal ones were found on a trial-anderror basis. The scale, compactness, and shape parameters were assigned as follows: 15, 0.5, and 0.3, respectively, for cropland extraction, and 20, 0.5, and 0.1, respectively, for winter crops extraction.

Moreover, the training data and test data in RF classification were selected manually using the RPG 2019 map. For the purpose of comparing different methods of classification, training data that were selected for PBC and OBC were as similar as possible, such as using the same area and very approximate surfaces, in order to improve the global comparability of the two methods. The training and classification of the RF module were applied using the Orfeo toolbox with two user-defined parameters that were set on a trialand-error basis: the number of decision trees grown in the forest and the maximum tree depth, which is the length of each tree in the forest. The two parameters that were used in this study were defined as 100 and 25, respectively. Thirdly, the accuracy assessment was performed with test data after the classification in order to evaluate the classification's degree of agreement with the reality and therefore assess the reliability of the classified results. In this study, in order to evaluate the classification quality and compare it amongst the different classification methods, five well-known and highly promoted accuracy indices were calculated for each classification method and each class. Among them, OA and kappa were employed for the global accuracy assessment, otherwise, precision, recall, and F-score were computed to assess the classification results of each class.

Moreover, in the hierarchical classification process, test data that were used for evaluating the classification of each step were generated as random points from the image that were used to perform classification, which is the result of the previous steps.

213

Afterwards, the random points were labelled manually with the Graphic Parcel Register map as ground truth. However, with the aim of evaluating the performance of the proposed hierarchical classification approach by comparing with traditional direct extraction, a completely new test dataset was produced from the original Sentinel-2 image that was not classified.

Crops phenology monitoring methods

This second part of the study was performed, based on the mapped winter crops from the previous step. Due to limited climate conditions in the study area, winter crop phenology monitoring was performed with Sentinel-1 C-band SAR data using the GEE platform.

In this study, the backscatter coefficient (σ°) in dB of both polarizations (VV and VH) and their ratios of a Sentinel-1 image time series during a complete growing period of winter crops (from October to September) on a few chosen croplands was automatically generated in a line chart on the GEE platform. In order to study the scattering behavior of our target croplands, each image was preprocessed, and the backscatter coefficient was converted to dB by GEE using the Sentinel-1 Toolbox. A flow chart of the Sentinel-1 image time series process in GEE is displayed as follow (Figure 73): Pursuant to Table 21 andTable 22, even though the global accuracy indices of the results of OBC are slightly better than PBC with a difference of 0.024 in kappa and 0.004 in OA, the indices of the two results are still comparable. The tables below show that a large proportion of pixels are correctly predicted in general and that the level of agreement with the ground truth data is somewhat lower but still acceptable. Furthermore, for the interclass accuracy evaluation, cropland generally has the highest precision, recall, and F-score, which are all around 0.90. The models were well trained to make a good prediction of the cropland class, especially for the OBC model, and most of the individual pixels belonging to the cropland class were correctly detected. This can be explained by the OBC taking into account the geometry, form, and texture, which are the key elements used to distinguish the croplands from other vegetation. The classification of the vegetation has slightly lower accuracy of approximately 0.2 in comparison with croplands because of the mix of different kinds of vegetation and the uncertain form of the vegetated area, though OBC remains more precise compared to PBC. Finally, the classes of the other pixels in our study area, which are mainly some isolated pixels left from the previous step due to some errors, were better classified with PBC since the non- With the lack of possibility of visual comparison of the two methods, they were evaluated and compared by using accuracy assessments (Table 23 and24). In regards to the global accuracy indices, all classes were accurate using the two methods, which signifies a good performance of both methods with a high accuracy and a strong level of agreement for the classification. Beyond that, it is worth noticing that PBC shows a better potential with about 0.03 higher in OA and 0.04 in kappa, moreover, PBC basically achieves a better accuracy indicator of three classes in comparison with OBC. The results illustrate that the difference in spectral behavior was exploited to distinguish winter crops from other crops, since all the croplands share similar geometry, form, and texture characteristics. Nonetheless, among different crop types presented in our area of study, winter wheat has the most distinctive spectral signature, thus it was found to be the class with the best accuracy indices in both results, with very strong reliability in terms of prediction and a high rate of precisely identifying winter wheat. In contrast, the classification of winter barley and other crops are somewhat less accurate with approximately 0.1-0.5, and the advantage of PBC is more significant, with higher accuracy indicators around 0.04, which might be caused by the confusion of winter barley and other crops due to the similarity of their spectral behavior. In addition, the difference between these two classes were better detected by PBC with spectral information. Generally, winter wheat and winter barley were well detected and extracted from the Sentinel-2 image as shown in Figure 76; the results of two classification approaches are globally identical, particularly the homogeneous distribution of the winter crops over the area of interest. Nevertheless, the classical direct extraction approach identified more winter croplands, especially winter barley, and the winter croplands detected are much more fragmented; many small pixels were classified as croplands. This could be explained by the fact that winter crops are directly extracted from the preprocessed image; in addition, there might be some confusion between winter barley, grasslands, and some different crops considering the resemblance of their spectral behavior. To make a better comparison, the accuracy assessments of the two approaches are displayed in Table 25 and26. According to the tables, both classification results are very satisfactory as mostly all of the accuracy indicators range from 0.8 to 1, specifically with the hierarchical classification almost all indices are superior to 0.9. This suggests a good performance and training of the models and also a strong agreement with ground truth of all classification approaches in the study. Still, it is worth noticing that the hierarchical classification shows a better potential for specific crop types mapping as compared to classical direct extraction (approximately 0.1 higher in kappa and 0.07 in OA).

Additionally, nearly every class achieves a higher accuracy in hierarchical classification, which indicates that the model is solid and able to make a good prediction. Among three classes, winter wheat is the most correctly classified class in both classification approaches, the indicators range from 0.90 to 0.99 with the F-score highly similar.

Hierarchical classification reaches a better precision index, that means the model is more exact, yet classical direction extraction achieved a finer recall, which means the models returned more relevant results, it can correctly and efficiently identify winter wheat.

Besides, winter barley and the other classes were evaluated and less accurately classified, especially with the classical direct extraction approach. According to Table 8, the winter barley class obtained a high recall (0.960) and a relatively lower precision (0.683), which suggests a high false positive rate, many individuals predicted as winter barley that the model returns were found misclassified when compared to the test data. On the contrary, the others class received a high precision index (0.955) and a comparatively low recall (0.797), the indicators demonstrate that the pixels were correctly detected and labelled despite less results returned by the model. The comparably low accuracy of the two classes and the imbalance between precision and recall indices might be explained by (1) the similarity of the spectral behavior between winter barley and other crops and even To conclude, both classification approaches achieved a good accuracy level despite the complex occupation and small cropland size in the region. In addition, the accuracy assessment indicates that the hierarchical classification has proven to be reliable and more accurate with outstanding performance in classification of both winter crops classes, particularly the winter wheat by turning a complex multi-class classification problem into series of smaller classifications. Additionally, PBC and OBC were implemented in two steps of the classification process within the hierarchical classification structure (croplands extraction from all vegetated area, and winter croplands extraction from all croplands). Although numerous studies in the remote sensing field demonstrate that OBC usually achieves a better classification with different data and in different landscapes over PBC by bringing complementary information other than the spectral signal and turning classification units from pixels to image objects (D. [START_REF] Liu | Assessing object-based classification: Advantages and limitations[END_REF][START_REF] Estoque | Pixel-based and object-based classifications using high-and medium-spatial-resolution imageries in the urban and suburban landscapes[END_REF][START_REF] Fu | Comparison of objectbased and pixel-based Random Forest algorithm for wetland vegetation mapping using high spatial resolution GF-1 and SAR data[END_REF]. However, the results illustrate that each method has its advantage in the classification process in this work. OBC slightly outperformed PBC in cropland extraction as the complementary texture, geometry, and shape information are helpful for cropland detecting. On the other hand, PBC reaches a higher accuracy in winter crops extraction, since all croplands have a similar shape but winter crops can be easily distinguished from other crops with direct spectral information. Additionally, the statistical difference between the results of PBC and OBC is not particularly significant.

Therefore, small differences induced by several factors between two methods can be noticed, yet both methods are equally useful for our classification. As shown in Figure 78, besides the profiles of the water area, which fluctuate significantly due to the weather conditions, the temporal profiles of vegetation, urban, and bare soil are much more stable than the profiles of winter crops, which have a significant fluctuation according to their different growth stages. Especially in the σvh profile, the vegetation, urban, and bare soil profiles are generally close to their mean value regardless of the season. Nonetheless, the variation of the backscattering coefficients of the two winter crops are clearly evident, for example a peak is seen in early December, followed by a minimum value in early summer and a maximum value in midsummer. Thus, the results indicate that it is feasible to distinguish winter crops from other types of land cover, particularly vegetation and other crops, and furthermore we are able to identify and study the main phenological stages from germination to ripening (harvesting) by using Sentinel-1 temporal profiles.

Based on prior knowledge and field research with local farmers, winter wheat and winter barley are both cereal crop types that are planted from October to November. The best polarization for each phenological stage (Germination, Heading, Ripening (Harvesting)) are detailed in Table 27 and28. The phenology monitoring of winter wheat highly relies on VV polarization, while the VH/VV ratio is also very helpful in identifying the germination and heading stages. Otherwise, VH polarization was used to detect the ripening stage and the harvesting event.

Meanwhile, phenology monitoring of the winter barley depends more on VV and VH polarizations, which are able to easily identify the three phenological statuses. In addition, VH/VV polarization is also effective for detecting the ripening and the harvesting.

Overall, this study proved that Sentinel-1 C-band SAR polarized backscatter time series has great potential to monitor winter crop phenology in a coastal area marked by frequent precipitation, and some important considerations of the behavior of different polarizations in regards to different phenological stages are worth discussing.

Firstly, despite the σ° of both polarizations and the ratio being relatively similar, the curves of the VH and VV polarizations are sharper compared to those of the ratio, due to the fact that the ratio is less sensitive to varying conditions like moisture and incidence angle variations. This can be explained by such effects having certain impacts in both polarizations, where the impacts would be reduced in the ratio [START_REF] Schlund | Sentinel-1 time series data for monitoring the phenology of winter wheat[END_REF].

As seen in Figure 78, the curves of the ratio VH/VV of winter wheat and winter barley are Secondly, the timing of phenological stages or growing periods of the crops based on the field knowledge are in agreement with the observations of the results. Based on prior knowledge, the sowing takes place between October and November, winter barley is usually planted earlier than winter wheat, and the germination occurs 3-4 weeks after sowing. This period can be confirmed by noting the large variations of the curves in the beginning induced by the interaction between bare soil and vegetation caused by stemground double scattering (Y. Song & Wang, 2019), afterwards the germination is represented by the first peak of the curves, especially well demonstrated in the polarization ratio for winter wheat and in the single polarizations for winter barley. After the overwintering period, the stem elongation, which begins in spring, can be recognized on the curves as a decreasing period caused by the attenuation of the signal when the vegetation cover occurs. Thereafter, the heading stage, where the crops attain their maximum height, occurs in early summer. This stage was confirmed with a minimum value on the curves around 1 May, which can be well observed in the polarization ratio for winter wheat and in single polarization for winter barley. After heading, the volume backscattering increased due to the increase of plant biomass ratio [START_REF] Schlund | Sentinel-1 time series data for monitoring the phenology of winter wheat[END_REF], and the winter barley is harvested in early summer and winter wheat in mid summer. This is illustrated by the curves in all polarizations decreasing as expected with large variations post-harvesting depending on the in soil conditions.

This leads to the conclusion that it is feasible to map crop phenology with high accuracy by using SAR data, which is highly sensitive to the phenology of agriculture crops.

In addition, unlike many methods which exclusively use the single polarization or the ratio [START_REF] Mcnairn | A Review of Multitemporal Synthetic Aperture RaDAR (SAR) for Crop Monitoring[END_REF][START_REF] Mandal | Dual polarimetric RaDAR vegetation index for crop growth monitoring using sentinel-1 SAR data[END_REF][START_REF] Son | A phenological object-based approach for rice crop classification using time-series Sentinel-1 Synthetic Aperture RaDAR (SAR) data in Taiwan[END_REF], our study shows that the combination of both is able to provide a better observation of agriculture phenology.

Further studies can investigate the feasibility and performance of combining SAR and optical data for crop phenology monitoring.

Conclusion and perspectives

Three issues surrounding winter crops have been studied and discussed in this paper. Firstly, two types of winter crops (winter wheat, winter barley) were mapped by To respond to the objectives of the study and as the contribution of this paper, our results showed that winter crops in a fragmented landscape with heterogeneous land cover were successfully detected with high accuracy by using a Sentinel-2 image and the classification approaches proposed. In particular, the hierarchical classification framework significantly improved the classification accuracy (0.1 and 0.06 increase in the kappa and OA, respectively, against classical direct extraction), moreover the classification of winter barley is also enhanced by reducing confusion between winter barley and grassland with hierarchical classification framework (0.094 increase in Fscore). Within the hierarchical classification, each classification method has its advantage, OBC slightly outperformed PBC in cropland extraction, yet PBC achieved higher accuracy in winter crops mapping. Although some small differences can be noticed, however there is no significant statistical divergence between the two classification methods.

The results also lead to the conclusion that Sentinel-1 C-band SAR polarized

backscatter time series has great potential to monitor winter agriculture phenology in a coastal area with frequent rainfalls. Three phenological stages and main growing periods 227 could be easily identified from the time series in single polarization or the ratio, and furthermore the timing of the stages and growing periods of the crops observed on the results highly conform to the field knowledge.

Although very satisfactory results were acquired in this study, some limitations were revealed during the process of result analyzing. Despite the fact that the hierarchical classification approach acquired a better accuracy (0.099 in kappa and 0.066 in OA), this classification approach required more complicated processing steps and was more costly when one is comparing it to the direct extraction, for a slight enhancement in the results.

Moreover, the confusion between winter barley and grassland was no negligible. For increasing classification accuracy, extra data such as SAR or Sentinel-2 time series data can be applied for crop mapping. Additionally, even though the three main phenological statuses were successfully extracted from Sentinel-1 backscatter time series, more field research and expert knowledge, moreover, the combination of SAR and optical data could be required for identifying some others important phenological stages (e.g., tillering, flowering, soft dough and hard dough) and growth periods from the time series.

General conclusion

Synthesis

To conclude, this work has adequately achieved the initial objective, which is to propose robust, specific, and reproductive ML methodologies adapted to coastal areas for the cost-effective mapping LULC and the assessment of changes in Pays de Brest using the latest European satellite data (Pléiades, SPOT, and Sentinel). Specifically, LULC mapping was first accurately performed using diverse ML and DL classification algorithms. Then the major LULCC were successfully detected with post-classification methods based on the LULC classification results, the socio-economic driving factors of the changes were also well identified and analyzed, and eventually, the changes and their impacts were modeled for future studies. Therefore, the study contributes to the current LULC studies of Pays de Brest, a highly fragmented coastal area with small-scale fields, and provides valuable LULC information on Pays de Brest for local government and communities.

In this work, the latest generation of high spatial, spectral, and temporal resolution satellite imagery is shown to be well suited for LULC classification in fragmented coastal areas. In addition, the complex LULCC pattern was successfully revealed in various spatial and temporal dimensions. The satellite data, therefore, are able to provide helpful information to respond to environmental and urban management issues and to solve specific conflicts of interest.

In addition, remote sensing technologies were also shown to be a powerful tool for The accurate mapping and extraction of specific crop types are feasible with the HR Sentinel-2 image, and hierarchical classification was well adapted in this case.

Limitations of this study

The objectives and adequate results are achieved through the three successfully performed study cases. The results of this work can be helpful for the local government, communities, and urban planners to formulate policies for sustainable development in Pays de Brest. Nevertheless, some uncertainties and limitations persisted:

1) Due to the meteorological conditions and annual high-intensity precipitations, it is very rare to obtain cloud-free optical images of the study area. For example, two different sources and spatial resolution images were used in the study of LULC classification due to the lack of processable optical data.

2) Misclassification of different types of vegetation and planted crops (e.g., confusion between grassland and winter barley) is caused by very similar spectral characteristics.

3) It is difficult to validate cropland classification results in the past years owing to the crop rotation system.

Further research

Some suggestions can be proposed for completing the unfinished task of the study, improving the LULC studies to achieve better results, and further meaningful research on LULC in Pays de Brest.

1) To achieve the final unmet objective of generating LULCC models to develop forwardlooking scenarios with classification results.

2) To use hyperspectral data for LULC classification, especially for better discrimination between different types of vegetation and croplands.

3) To explore more potential contributions of SAR images, because the meteorological conditions limits the optical sensors, and eventually LiDAR morphometry.

4) To conduct the three study cases throughout the entire territory of Pays de Brest.

5)

To study the natural landscapes in Pays de Brest (e.g., wetland and intertidal areas), which provide essential resource but are increasingly degraded by human activities.

  FIGURE 1 LAND USE CHANGES OF THE WORLD FROM 1600 TO 2016 (SOURCE: HISTORY DATABASE OF THE GLOBAL ENVIRONMENT (HYDE))......................................................................................................................................................... FIGURE 2 TOTAL COASTLINES OF THE WORLD (SOURCE: WORLD BANK, 2020) ...................................................................... FIGURE 3 MARINE ECOREGIONS OF THE WORLD (DATA SOURCE: SPALDING ET AL., 2007 AND THE NATURE CONSERVANCY) ........... FIGURE 4 LOCALIZATION OF STUDY AREAS. A: LOCALIZATION OF PAYS DE BREST IN FRANCE; B: LOCALIZATION OF STUDY AREAS, (1) SENTINEL-2 SATELLITE IMAGE OF NORTHERN FINISTÈRE IN 2019, (2) PLÉIADES IMAGE OF CITY OF BREST IN 2018, (3) SENTINEL-2 SATELLITE IMAGE OF THE CROZON PENINSULA IN 2018 ........................................................................... FIGURE 5 PAYS DE BREST WITH THE SEVEN INTERCOMMUNALITES: PAYS D'IROISE, PAYS DES ABERS, LESNEVEN-COTE DES LEGENDES, BREST METROPOLE, PAYS DE LANDERNEAU-DAOULAS, PRESQU'ILE DE CROZON-AULNE MARITIME AND PLEYBEN-CHATEAULIN-PORZAY (SOURCE: BREST METROPOLE) ................................................................................................................. FIGURE 6 DISTRIBUTION OF THE POPULATION BY COMMUNITY IN PAYS DE BREST .................................................................... FIGURE 7 UMBROTHERMAL DIAGRAM OF THE MONTHLY MEAN TEMPERATURES AND MONTHLY MEAN TOTAL PRECIPITATIONS FROM 1991 TO 2021 OF BREST .................................................................................................................................. FIGURE 8 TOPOGRAPHY MAPS OF PAYS DE BREST WITH PRINCIPAL LANDFORM AND SUMMIT .................................................... FIGURE 9 CLC 2018 IN PAYS DE BREST .......................................................................................................................... FIGURE 10 DISTRIBUTION OF MAIN ECONOMIC ACTIVITIES IN PAYS DE BREST ......................................................................... FIGURE 11 PROTECTED NATURAL AREAS IN PAYS DE BREST ................................................................................................. FIGURE 12 ARTIFICIAL AREA RATE IN THE DEPARTMENT OF FINISTÈRE IN 2005, AND ITS EVOLUTION BETWEEN 1984 AND 2005 ..... FIGURE 13 EVOLUTION OF THE POPULATION (A) AND DENSITY OF POPULATION (B) IN PAYS DE BREST BETWEEN 1990 AND 2019 ... FIGURE 14 EVOLUTION OF POPULATION BETWEEN (A) 1999 AND 2008, (B) 2011 AND 2016 ................................................. FIGURE 15 EVOLUTION OF LAND USE BETWEEN NATURAL AREA, AGRICULTURAL LAND AND ARTIFICIAL AREA IN PAYS DE BREST AND ITS COASTAL AREA BETWEEN 1990 AND 2018 (SOURCE: CORINE LAND COVER 1990, 2018) ........................................... FIGURE 16 CITY OF BREST WITH URBAN GREEN SPACES ...................................................................................................... FIGURE 17 LOCATION OF THE CROZON PENINSULA AND TWO BORDERING REGIONS, LOCATED IN PAYS DE BREST, FINISTÈRE, BRITTANY, FRANCE, WITH THE RGB BAND COMBINATION FOR SENTINEL 2 (2018) ...................................................................... FIGURE 18 LOCATION OF THE STUDY AREA IN NORTHERN FINISTÈRE, AS PER THE RGB BAND COMBINATION OF A SENTINEL-2 SATELLITE IMAGE ON APRIL 20, 2019 AND THE DISTRIBUTION OF AGRICULTURAL LAND IN 2019. .................................................. FIGURE 19 ILLUSTRATION OF OPTICAL REMOTE SENSING (SOURCE: KUMAR ET AL., 2018) ........................................................ FIGURE 20 THE ELECTROMAGNETIC SPECTRUM BY WAVELENGTH (SOURCE: TOPAZ, 2018) ...................................................... FIGURE 21 SPOT-5 IMAGE OF THE CROZON PENINSULA ................................................................................................... FIGURE 22 PLÉIADES IMAGE OF CITY OF BREST ................................................................................................................. FIGURE 23 SENTINEL-2 IMAGE OF THE CROZON PENINSULA ............................................................................................... FIGURE 24 SENTINEL-2 IMAGE OF NORTHERN FINISTÈRE ....................................................................................................

FIGURE 25

 25 FIGURE 25 SAR SYSTEM (SOURCE: LAUKNES, 2011) ........................................................................................................ FIGURE 26 SAR BANDS IN ELECTROMAGNETIC SPECTRUM .................................................................................................. FIGURE 27 FOUR OBSERVATION MODES OF SENTINEL-1 ....................................................................................................

FIGURE 28

 28 FIGURE 28 RPG 2019 (SOURCE: GEOPORTAIL.GOUV.FR) ................................................................................................. FIGURE 29 PAYS DE BREST ON GOOGLE EARTH ................................................................................................................ FIGURE 30 VEGETATION IN SMALL SCALE ON GOOGLE EARTH ............................................................................................. FIGURE 31 SENTINEL-2 IMAGE OF THE CROZON PENINSULA WITH GNSS POINTS FOR GROUND TRUTH FIELD RESEARCH ................. FIGURE 32 CROPLANDS IN NORTHERN FINISTÈRE .............................................................................................................. FIGURE 33 GENERAL FLOW PATH OF IMAGE PROCESSING ................................................................................................... FIGURE 34 SPECTRUM VIEW OF VEGETATION, CROP, BARE SOIL AND ARTIFICIAL SURFACE ......................................................... FIGURE 35 COMPARISON OF THE ORIGINAL IMAGE AND SALT-AND-PEPPER-NOISED IMAGE (KELLY ET AL., 2011) .......................... FIGURE 36 EXAMPLE OF MRS APPLIED ON SENTINEL-2 IMAGE ......................................................................................... FIGURE 37 EXAMPLE OF SLIC APPLIED ON PLÉIADES IMAGE .............................................................................................. FIGURE 38 THEORETICAL SVM MODEL ........................................................................................................................ FIGURE 39 SVM NON-LINEAR MODEL .......................................................................................................................... FIGURE 40 FOUR MOST FREQUENTLY USED KERNEL FUNCTIONS OF SVM ............................................................................ FIGURE 41 BAGGING THEORY MODEL ........................................................................................................................... FIGURE 42 RF MODEL ............................................................................................................................................... FIGURE 43 MACHINE LEARNING AND DEEP LEARNING IN VISUAL DATA ANALYSIS ................................................................... FIGURE 44 EXAMPLE OF AN MLP MODEL WITH TWO HIDDEN LAYERS ................................................................................. FIGURE 45 EXAMPLE OF CNN MODEL .......................................................................................................................... FIGURE 46 BACKSCATTERED INTENSITY DEPENDS ON DIFFERENT TARGETS (CITY, VEGETATION, AND CALM WATER)....................... FIGURE 47 SAR SATELLITE IMAGERY PREPROCESSING PROCEDURE (XIE AND NICULESCU, 2022) .............................................. FIGURE 48 EXAMPLE OF GEE PROCESSING INTERFACE ..................................................................................................... FIGURE 49 CONFUSION MATRIX .................................................................................................................................. FIGURE 50 EXAMPLE OF SEVERAL ACCURACY INDEX CALCULATIONS (OA, KAPPA COEFFICIENT, USER'S ACCURACY (ALSO CALLED "PRECISION), PRODUCER'S ACCURACY (ALSO CALLED "RECALL), AND F-SCORE) ........................................................... FIGURE 51 EXAMPLE OF CHANGE DETECTION TECHNIQUE ................................................................................................. FIGURE 52 LOCALIZATION OF THE CITY OF BREST ............................................................................................................ FIGURE 53 VEGETATED AREAS IN THE CITY OF BREST: (A) URBAN FOREST (B) MEADOW (C) PUBLIC PARK IN THE CITY CENTER (D) CROPLANDS (E) STREET TREES (F) PRIVATE GARDENS IN THE RESIDENTIAL AREA ............................................................ FIGURE 54 SCHEME OF THE PROPOSED METHODOLOGY IN THE STUDY................................................................................. FIGURE 55 MLP MODEL USED FOR CLASSIFICATION IN THE STUDY ...................................................................................... FIGURE 56 CLASSIFICATION RESULTS OF VEGETATION IN THE CITY OF BREST ON 2016, 2017, AND 2018 BY USING MLP DEEP STRUCTURE .................................................................................................................................................... FIGURE 57 EXAMPLE OF A TRANSITION FROM VEGETATED AREA TO NON-VEGETATED AREA IN BREST BETWEEN 2016 AND 2018 ...

FIGURE 58

 58 FIGURE 58 EXAMPLE OF TRANSITION FROM VEGETATION TO ARTIFICIAL SURFACE .................................................................. FIGURE 59 CLASSIFICATION RESULTS OF VEGETATION IN THE CITY OF BREST IN 2016, 2017, AND 2018 USING RF MACHINE LEARNING CLASSIFIER ..................................................................................................................................................... FIGURE 60 COMPARISON OF MLP AND RF CLASSIFIERS ................................................................................................... FIGURE 61 DIFFERENCES BETWEEN MLP AND RF CLASSIFICATION RESULTS .........................................................................

FIGURE 62

 62 FIGURE 62 LOCATION OF THE STUDY AREA, INCLUDING THE CROZON PENINSULA AND TWO BORDERING REGIONS, LOCATED IN PAYS DE BREST, FINISTÈRE, BRITTANY, FRANCE, WITH THE RGB BAND COMBINATION FOR SENTINEL 2 (2018) AND THE LOCATION OF THE GROUND TRUTH FIELD RESEARCH. ................................................................................................................ FIGURE 63 GLOBAL METHODOLOGY PROPOSED .............................................................................................................. FIGURE 64 DETAILED CNN METHODOLOGY .................................................................................................................. FIGURE 65 CNN MODEL PROPOSED AND USED FOR THE CLASSIFICATION OF IMAGES FROM 2018 IN THE STUDY ......................... FIGURE 66 CLASSIFICATION RESULTS WITH SVM, RF AND CNN ........................................................................................ FIGURE 67 LULC CHANGE MAPS WITH THE CLASSIFICATION OF SVM (A), RF (B), AND CNN (C). ............................................ FIGURE 68 DETAILS OF THE THREE MAIN LAND COVER CHANGE TYPES OF THE CNN CLASSIFICATION WITH COMPARISONS BETWEEN 2007 AND 2018............................................................................................................................................ FIGURE 69 LOCATION OF STUDY AREA, THE NORTH OF THE FINISTÈRE DEPARTMENT, BRITTANY, FRANCE, AS PER THE RGB BAND COMBINATION OF A SENTINEL-2 SATELLITE IMAGE ON 20 APRIL 2019 AND THE DISTRIBUTION OF AGRICULTURAL LAND IN 2019. .......................................................................................................................................................... FIGURE 70 HIERARCHICAL CLASSIFICATION METHODOLOGY USED IN THE STUDY FOR CROP MAPPING ......................................... FIGURE 71 PROPOSED DETAIL IMAGE PROCESSING METHODOLOGY CHART ........................................................................... FIGURE 72 SENTINEL-1 IMAGE PROCESS IN THE GEE PLATFORM ........................................................................................ FIGURE 73 LEVEL 1: PBC VEGETATION (INCLUDING CROPLAND) EXTRACTION RESULTS. .......................................................... FIGURE 74 LEVEL 2: PBC AND OBC CROPLANDS EXTRACTION RESULTS............................................................................... FIGURE 75 LEVEL 3: PBC AND OBC WINTER CROPS EXTRACTION RESULTS .......................................................................... FIGURE 76 CLASSIFICATION RESULTS WITH HIERARCHICAL CLASSIFICATION AND CLASSICAL DIRECT EXTRACTION ........................... FIGURE 77 SENTINEL-1 TEMPORAL BACKSCATTERING COEFFICIENT PROFILES OF DIFFERENT LAND COVERS (VEGETATION, WATER, URBAN AREA, BARE SOIL, WINTER WHEAT, AND WINTER BARLEY) IN THE STUDY AREA AT VV AND VH POLARIZATIONS FROM 1 OCTOBER 2018 TO 1 SEPTEMBER 2019. ......................................................................................................................... FIGURE 78 WINTER WHEAT AND WINTER BARLEY SENTINEL-1 TEMPORAL BACKSCATTERING COEFFICIENT PROFILES AT VV, VH, AND VH/VV POLARIZATIONS OF THE NORTH FINISTÈRE REGION FOR THE 2018-2019 GROWING SEASON, WITH THE DAILY PRECIPITATION DATA AND THREE MAIN PHENOLOGICAL STAGE .................................................................................

  .............................................................................................TABLE 3 SPATIAL RESOLUTION AND WAVELENGTH OF PLÉIADES PRODUCT .............................................................................. TABLE 4 SPATIAL RESOLUTION AND WAVELENGTH OF SENTINEL-2 OPERATED BANDS (SOURCE: XIE AND NICULESCU, 2022) ........... TABLE 5 THE MOST COMMON BANDS USED IN SAR IMAGING SYSTEMS ................................................................................. TABLE 6 CHARACTERISTICS OF FOUR ACQUISITION MODES OF SENTINEL-1 ............................................................................. TABLE 7 VHR PLÉIADES SATELLITE IMAGES USED IN THE STUDY ......................................................................................... TABLE 8 OA OF VEGETATION CLASSIFICATION ................................................................................................................

  ..................................................................................................................................... TABLE 14 VALIDATION OA (%) ..................................................................................................................................TABLE 15 TRAINING PRODUCER'S ACCURACY AND USER'S ACCURACY BY CLASS ..................................................................... TABLE 16 VALIDATION PRODUCER'S ACCURACY AND USER'S ACCURACY BY CLASS .................................................................. TABLE 17 LAND COVER CHANGE AREA AND PROPORTION OF THE CHANGE TYPE WITH THE THREE METHODS OF CLASSIFICATION. ..... TABLE 18 SENTINEL-2 IMAGE USED IN THE STUDY ........................................................................................................... TABLE 19 SENTINEL-2 SPECTRAL BANDS USED IN THE STUDY ............................................................................................. TABLE 20 ACCURACY ASSESSMENT OF PBC VEGETATION (INCLUDING CROPLAND) EXTRACTION. .............................................. TABLE 21 ACCURACY ASSESSMENT OF OBC CROPLANDS EXTRACTION ................................................................................. TABLE 22 ACCURACY ASSESSMENT OF PBC CROPLANDS EXTRACTION ................................................................................. TABLE 23 ACCURACY ASSESSMENT OF OBC WINTER CROPS EXTRACTION ............................................................................. TABLE 24 ACCURACY ASSESSMENT OF PBC WINTER CROPS EXTRACTION ............................................................................. TABLE 25 ACCURACY ASSESSMENT OF HIERARCHICAL CLASSIFICATION ................................................................................. TABLE 26 ACCURACY ASSESSMENT OF CLASSICAL DIRECT EXTRACTION .................................................................................

4)

  Chapter 4 outlines the methodologies of image processing applied in our case studies, namely image pre-classification processing, image classification by using different shallow machine learning or deep architectures, and methods of post-classification processing. 5) Chapter 5 presents the first case study, "Machine Learning Methods and Classification of Vegetation in Brest, France." 6) Chapter 6 presents the second case study, "Mapping and monitoring of land cover / land use (LULC) changes on the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning algorithms (Support Vector Machine, Random Forest, Convolutional NN) and by Post-classification comparison (PCC)." 7) Chapter 7 presents the third case study, "Mapping crop types using Sentinel-2 data Machine Learning and monitoring crop phenology with Sentinel-1 backscatter time series in Pays de Brest, Brittany, France."

Part I Context of the study Chapter 1 .Chapter 1 . 1 . 1 .

 1111 Detection of land use/land cover in coastal area with remote sensing 27 Detection of land use/land cover changes in coastal area with remote sensing Definition of land use/land cover

  Part I Context of the study Chapter 1. Detection of land use/land cover in coastal area with remote sensing 29 in Figure 1, human activities are changing the Earth's surface in pervasive and unprecedented ways, especially after the industrial revolution (1760s-1840s)(R. S.

Figure

  Figure 1Land use changes of the world from 1600 to 2016 (Source: History Database of the Global Environment (HYDE))

  The breakthrough occurred in 1972 when the first Earth's Observation (EO) satellite, Landsat (originally named Earth Resources Technology Satellite), was launched. This first-generation EO satellite was efficient in providing stable, global, and multispectral satellite data, which officially started the satellite era of LULC research. As one of the earliest representative works of LULC classification,[START_REF] Townshend | Land cover[END_REF] Part I Context of the study Chapter 1. Detection of land use/land cover in coastal area with remote sensing 32 summarized a wide range of applications of land cover information with the first generation of remote sensing data of coarse spatial resolution[START_REF] Loveland | History of land cover mapping[END_REF] and highlighted the necessity to focus on the activities of land cover monitoring over time, and eventually expand the study area by including land use.The first works were derived from coarse-resolution USA sensor systems, namely the four-spectral-band Landsat MultiSpectral Scanner (MSS), which collected in 79 m (resampled to 60 m in processing). For instance, among the most-cited work,[START_REF] Todd | Urban and regional land use change detected by using Landsat data[END_REF] explored the use of four-band Landsat MSS digital images to identify six diverse LULC categories and therefore detect LULCCs between 1972 and 1974 in the urban area of the city of Atlanta. In this study, two satellite images were digitally overlaid, after which LULCCs were extracted by directly comparing two images on a pixel-to-pixel basis. The final decision was made by the ratio of the intensity of reflected electromagnetic radiation (EMR) between two images; the ratio of an unchanged area was around 1.0, yet, whereas the ratio was significantly different where LULC had changed between 1972 and 1974.Afterward,[START_REF] Byrne | Monitoring land-cover change by principal component analysis of multitemporal landsat data[END_REF] conducted a study of land cover change monitoring by performing the principal component analysis (PCA) of an eight-dimensional data array composed of two four-band Landsat MSS images. PCA effectively identifie areas in which changes have occurred between two dates. Moreover, numerous features were detected in the study, including vegetation growth, deforestation activity, intensive management in forest areas, and urban development.

  [START_REF] Pouliot | Development and assessment of a 250m spatial resolution MODIS annual land cover time series (2000-2011) for the forest region of Canada derived from change-based updating[END_REF] studied annual land cover changes covering Canada from 2000 to 2011 using MODIS 250 m coarse-resolution data to identify dominant change transitions in the country. Fu & Weng (2016) used the Continuous Change Detection and Classification algorithm on 507 Landsat TM/ETM+ images acquired from 1984 to 2011 to examine human-induced LULCCs and their impacts on the urban thermal environment in Atlanta, Georgia, USA. In an investigation by Yan et al. (2019), MODIS time-series data were also applied to detect LULCC in Wuhan, China, from 2000 to 2018. The research achieved very high accuracy (over 90%) by using the Prophet algorithm to detect the change point, as well as the Dynamic Time Warping algorithm to improve the accuracy of time-series classification. Part I Context of the study Chapter 1. Detection of land use/land cover in coastal area with remote sensing 39 5) Data fusion approaches are also commonly performed in LULC classification to investigate the potential and characteristics of different satellite sensors. The data can be acquired within the same sensors (e.g., data fusion of panchromatic and multispectral bands) or from different sensors of the same types (e.g., by merging data from different SAR satellites or optical satellites).

  [START_REF] Clerici | Fusion of Sentinel-1A and Sentinel-2A data for land cover mapping: A case study in the lower Magdalena region, Colombia[END_REF] took advantage of the fusion of Sentinel-1 and Sentinel-2 imagery to increase LULC mapping of Colombia's Lower Magdalena region by combining the all-weather capability of the SAR sensor and the spectral information of the optical sensor. The combination of Sentinel-1 and the near-infrared (NIR) band in Sentinel-2 produced the most accurate classification. Part I Context of the study Chapter 1. Detection of land use/land cover in coastal area with remote sensing 40

  7) Ultimately, post-classification processing has become further developed and is more common in LULC classification. Various post-classification processes (e.g., postclassification comparison, accuracy assessment) are effective for error correction (e.g., salt-and-pepper effect reduction) to improve final classified maps. Methods of LULC classification may vary according to the user's need and the research objective. First, LULC classification at a given time, with either a single image or multiple images acquired in the short term, was widely carried out to understand the LULC and environment of the study area, generating thematic maps for further studies or policy-making. LULC classification is mostly multiclass classification and target class extraction. Moreover, the most important studies of LULC classification nowadays focus

Figure 2

 2 Figure 2 Total coastlines of the world (Source: World Bank, 2020)

Figure 3

 3 Figure 3 Marine ecoregions of the world (data source: Spalding et al. (2007) and The Nature Conservancy, n.d. ) Besides the environmental vulnerability, most coastal regions are under the pressure of human activities. The main driving factors of most of the environmental problems in the coastal zone are population growth, intense socio-economic activities,

Chapter 2 .Figure 4

 24 Figure 4 Localization of study areas. A: localization of Pays de Brest in France; B: Localization of study areas, (1) Sentinel-2 satellite image of northern Finistère in 2019, (2) Pléiades image of city of Brest in 2018, (3) Sentinel-2 satellite image of the Crozon Peninsula in 2018.

Figure 5

 5 Figure 5 Pays de Brest with the seven intercommunalités: Pays d'Iroise, Pays des Abers, Lesneven-Côte des Légendes, Brest Métropole, Pays de Landerneau-Daoulas, Presqu'île de Crozon-Aulne Maritime and Pleyben-Châteaulin-Porzay (source: Brest Métropole)

Figure 6

 6 Figure 6 Distribution of the population by community in Pays de Brest According to Figure 6, the population is highly concentrated in the northern coastal intercommunalités, among which Brest Métropole is the most inhabited area with 211,156 inhabitants in 2019, representing half of the population in Pays de Brest. In addition, the neighboring intercommunalités (Pays d'Iroise, Pays des Abers) act as satellite cities hosting much of the rest of the population. Therefore, a significant contrast can be observed between the populated intercommunalités and the intercommunalités not adjacent to, or more distant from, Brest Métropole (Pays de Landerneau-Daoulas, Pleyben-Châteaulin-Porzay and Lesneven-Côte des Légendes); the latter communes host fewer than 4,000 inhabitants on average, except at the center of each intercommunalité.

Figure 10

 10 Figure 10 Distribution of main economic activities in Pays de Brest

Figure 11

 11 Figure 11 Protected natural areas in Pays de BrestDue to its rich biodiversity reserve and diverse landscape, nature has always been highly valued in the region of Brittany and the Finistère department (Figure11). The first National Natural Reserves (NNRs) and Regional Natural Reserves (RNRs) were created in the region in 1913; these two types of reserves mainly cover the islands in the west of the continent and some areas located in the north of the Crozon Peninsula. Since 1979, Special Protection Zones (ZPS) have been created to conserve wild birds, and Special Conservation Zones (ZSC) were created in 1992 to ensure biodiversity by conserving natural habitats as well as wild fauna and flora. ZPSs and ZSCs mainly cover the coasts, a part of the coterminous sea area, and the principal vegetated area, such as the moorlands of the Crozon Peninsula and the forest in the center of the study area. In addition, natural areas with ecological, faunistic, and floristic interest were created in 1982. Sensitive natural areas, pre-emption zones, and department-protected natural sites cover both the coasts, the forests, and the drainage basin of the river of Aulne. Eventually, two essential natural parks were created: in 1969, the regional natural park of Armorique (located on the Crozon Peninsula, Bassin de Châteaulin, and extended to the center of Pays de Brest), and in 2007, the natural marine park of Iroise, which covers a vast area of water surface in the west. Parks play an essential role in the conservation of biodiversity and the environment.

Figure 12

 12 Figure 12 Artificial area rate in the department of Finistère in 2005, and its evolution between 1984 and 2005

Figure 13

 13 Figure 13 Evolution of the population (a) and density of population (b) in Pays de Brest between 1990 and 2019

Figure 14

 14 Figure 14 Evolution of population between (a) 1999 and 2008, (b) 2011 and 2016 (source: Insee)

Figure 15

 15 Figure 15 Evolution of land use between natural area, agricultural land and artificial area in Communes in Pays deBrest and its coastal area between 1990 and 2018 (Source: CORINE Land Cover 1990, 2018)

  Part I Context of the study Chapter 2. Geographic localization and major land use/land cover in Pays de Brest 63 exploitation of natural resources but also the transition from agricultural or natural land use to artificial areas.

  Figure 16 City of Brest with urban green spaces



  Part I Context of the study Chapter 2. Geographic localization and major land use/land cover in Pays de Brest 65 Mapping and Monitoring of Land use/land cover (LULC) Change in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional NN) and by Post-classification Comparison (PCC).

Figure 17

 17 Figure 17 Location of the Crozon Peninsula and two bordering regions, located in Pays de Brest, Finistère, Brittany, France, with the RGB band combination for Sentinel 2 (2018)



  Part I Context of the study Chapter 2. Geographic localization and major land use/land cover in Pays de Brest 66 Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France.

Figure 18

 18 Figure 18 Location of the study area in Northern Finistère, as per the RGB band combination of a Sentinel-2 satellite image on April 20, 2019 and the distribution of agricultural land in 2019.

  addition, by using coarse spatial resolution data, global or national land cover maps generally result in poor local accuracy, especially in a highly fragmented area like Pays de Brest. Therefore, high-quality, primarily open-access optical and SAR satellite images, which facilitate easy detection of LULCCs, were applied in our studies because such satellite data are widely used in the field of LULCC detection. Considering that this thesis is based on several projects of collaboration with local government and communities, their needs usually dictated the period chosen for each study. The first study following the recent changes in vegetation in the city of Brest was requested by the local government of Brest Métropole. Thus, the recent data of very high resolution were applied. The local engineering office then took an interest in LULCCs on the Crozon Peninsula between 2005 and 2020 (due to limited free accessible data and the frequent presence of heavy cloud cover in the region, two images from 2007 and 2018 were finally acquired in this study). At last, the study of winter crop mapping was performed with an image from 2019, which corresponds to our auxiliary data, the latest version of the graphic parcel register (RPG; French: Registre Parcellaire Graphique) published by the French National Institute of Geographic and Forest Information (IGN).

Part II General methodology Chapter 3 .Figure 19

 319 Figure 19 Illustration of optical remote sensing (Source: Kumar et al. (2018))The optical imaging system uses the light portion of the electromagnetic spectrum (Figure20), with the wavelength from the visible spectrum to the near-infrared (NIR), the middle infrared (MIR), or even thermal infrared (TIR) to gather the radiation reflected from the target surfaces. This system facilitates the frequent and wide-ranging observation of the Earth's surface at various electromagnetic radiation wavelengths (IREA, n.d.).

Figure 20

 20 Figure 20 The electromagnetic spectrum by wavelength (Source: Topac (2018))

Figure 21

 21 Figure 21 SPOT-5 image of the Crozon Peninsula SPOT-5 (Figure 21) (Earth Observation Satellite; French: Satellite pour l'Observation de la Terre) is a commercial Earth-imaging system from the French National Center for Space Studies (CNES; French: Centre national d'études spatiales). As the fifth generation of SPOT mission satellites, SPOT-5 was launched in May 2002 (ceased service in March 2015) to ensure the continuity of service of the SPOT system and improve the quality of data. Thus, the objective was to supply high-resolution, wide-area optical imagery to observe the Earth's surface, monitor human activities, help decision-making

Figure 22

 22 Figure 22 Pléiades image of city of Brest

  In this thesis, three Pléiades-1 Level-2 orthorectified MS images from 2018 to 2021 with 50 cm of spatial resolution used in the study of vegetation in the city of Brest were acquired on the DINAMIS platform (https://dinamis.teledetection.fr). The detail of the images will be discussed in Part 3, Chapter 5: Machine Learning Methods and Classification of Vegetation in Brest, France. Part II General methodology Chapter 3. Data structure and field research 76 3.1.1.2. High-spatial-resolution open-source image (Sentinel-2) Even though HR optical imaging systems have rapidly improved in recent years, several Sentinel-2 images acquired in different locations and at different times were applied in two studies: (1) Mapping and monitoring of LULCCs on the Crozon Peninsula (Figure 23) and (2) Mapping crop types in Pays de Brest (Figure 24).

Figure 23

 23 Figure 23 Sentinel-2 image of the Crozon Peninsula

  : three visible bands (blue, green, and red) and a NIR band with 10 m of spatial resolution for primary land-cover classification, three NIR vegetation red-edge bands (B5, B6, B7) with a resolution of 20 m for advanced land-cover classification and vegetation state detection, and finally six short-wave infrared (SWIR) bands with a resolution of 60 m dedicated mainly to atmospheric corrections and cirrus-cloud screening. All products can be obtained in several processing levels, of which only two-Levels 1C and 2A-are released to users. Level-1C results from radiometric and geometric Part II General methodology Chapter 3. Data structure and field research 78 corrections with orthorectification and spatial registration on a global reference system with sub-pixel accuracy (ESA, Sentinel-2); cloud and water masks are also generated at this level. On the other hand, Level-2A is generated with the Sen2Cor atmospheric correction processor, which is capable of detecting three different forms of clouds, snow, and cloud shadows, thereby creating a corresponding mask. It also outputs an orthoimage bottom-of-atmosphere (BOA) corrected reflectance product, which is resampled and generated with an equal spatial resolution for all bands (10 m, 20 m, 60 m) (eoPortal, 2012b).

Figure 25

 25 Figure 25 SAR system (Source: Lauknes (2011))

Figure 26

 26 Figure 26 SAR bands in electromagnetic spectrumTable5The most common bands used in SAR imaging systems

Figure 27

 27 Figure 27 Four observation modes of

  StripMap (SM), Interferometric Wide swath (IW), Extra Wide swath (EW), and WaVe (WV) mode. Among the acquisition modes, SM, IW, and EW equip dual polarization capacity, whereas WV acquires an image in single polarization (HH or VV). Following a different mode, the swath width is also different. EW has the largest swath of 400 km, so it is usually used for maritime, ice, and polar zone observation. IW is performed with a narrower swath of 250 km but higher spatial resolution, and is generally employed for land monitoring. By contrast, WV and SM were equipped with relatively narrow swaths (20 km and 80 km, respectively) and are suited for diverse specific uses, such as open ocean observation as well as European remote sensing (ERS) and Envisat missions supporting (eoPortal, 2012a).Like Sentinel-2, Sentinel-1 data are available and freely accessible to the public in different types at different processing levels. Level 1, the most common and widely used, has already been through pre-processing (raw data analysis, internal calibration), Doppler centroid estimation, and focusing. Eventually, output as Single Look Complex (SLC) and Ground Range Detected (GRD), two formats of data types are produced. SLC has been georeferenced, contains preserved phase information. GRD, on the other hand, preserves the amplitude information and multi-looks to minimize the effect of speckle(ESA, n.d.-d).

Figure 28 Figure 29

 2829 Figure28 RPG 2019 (Source: Géoportail.gouv.fr) RPG is the highly accurate georeferenced agricultural land database produced by IGN (Figure28). The database records the specific crop types (such as wheat, corn, barley, and vegetables) or grassland for the livestock sector each year throughout all French territory (except Mayotte). RPG 2019, the most recent edition of RPG (at the time of data collection for this thesis) was applied in the studies as the ground truth data to be used for creating training data and test data, especially for the winter crops monitoring study(IGN, n.d.-b).

Figure 31

 31 Figure 31 Urban green spaces in the city of Brest

Figure 32

 32 Figure 32 Sentinel-2 image of the Crozon Peninsula with GNSS points for ground truth field research

3 .

 3 Figure 33 Croplands in northern Finistère

Figure 34

 34 Figure 34 General flow path of image processingThe complete methodology of image processing consists of pre-classification, classification, and post-classification (Figure33); each step is indispensable in the study of remote sensing. The chain of satellite image processing transforms raw, unprocessed spectral data or radio signals into comprehensible, meaningful and georeferenced data that can be used for further analysis and application in different thematics.

Figure 35

 35 Figure 35 Spectrum view of vegetation, crop, bare soil and artificial surface The earliest VIs focused on calculating red and NIR channels because most light in red channels is absorbed by chlorophyll; those in NIR channels are strongly reflected by leaf cellular structures. Thus, vegetation in the histogram is usually indicated by the decreasing and increasing wavelengths in the red and NIR channels, respectively (Figure

  vegetation detection-other vegetative factors also have considerable impacts on VI calculation, such as water content, leaf form, age, and vegetation diseases. These factors are usually more sensitive to radiation in wavelengths outside of the red and NIR channels. Thus, many indices have been developed to detect different vegetative factors. Since 2000, radiation in other wavelengths, such as green bands or MIR bands, have been exploited for VI; such indices help distinguish vegetation from other elements and eliminate disturbance factors mentioned earlier.

  ;[START_REF] Asokan | Change detection techniques for remote sensing applications: A survey[END_REF].[START_REF] Lyon | A change detection experiment using vegetation indices[END_REF] showed that VIs were well adapted for monitoring temporal changes associated with vegetation. In their study, a variety of vegetation indices-Difference Vegetation Index (DVI), Perpendicular Vegetation Index (PVI), RVI, Soil Adjusted Ratio Vegetation Index (SARVI), NDVI, SAVI, and Transformed Soil Adjusted Vegetation Index (TSAVI)-were applied in order to detect vegetation and land cover change between 1975 and 1986 in the State of Chiapas, Mexico, a region plagued by deforestation. Even though each index was developed to better detect vegetation, and despite their ability to identify changes, most were affected (to varying degrees) by topographic factors. Eventually, NDVI was found to be the best vegetation change evaluator in this study.

  Part II General methodology Chapter 4. Analysis and image processing 99 on HR images (Van de Voorde et al.

  Various algorithms and methods have been developed to solve the image segmentation problem and improve segmentation performance. Those methods can be categorized into many different classes. The most commonly used methods are threshold, region-based, edge-based, cluster, and others such as Watershed, ANN, and Partial Differential Equation (PDE).

  [START_REF] Aguilar | Assessment of Multiresolution Segmentation for Extracting Greenhouses from WORLDVIEW-2 Imagery[END_REF] focused on finding the optimal parameters (particularly the scale parameter) of the MRS to extract plastic greenhouses from WV-2 multispectral orthoimages. The initial success of plastic greenhouse extraction was achieved with an optimum scale parameter calculated on the local variance of object heterogeneity within a scene. The parameter value was evaluated with Potential Segmentation Error (PSE), Number-of-Segments Ratio (NSR), and Euclidean Distance 2 (ED2).

  providing a convenient image representation-superpixel algorithms have attracted considerable public attention in the computer sciences area in recent years. The main task of superpixel segmentation methods is to reduce the number of pixels, thereby reducing image processing complexity, computing time cost, and capture redundancy. SLIC, developed by Achanta et al. in 2010, is the most popular superpixel method today[START_REF] Nixon | 8-Region-based analysis[END_REF]. SLIC was first introduced as an adaptation of the K-means segmentation technique to generate superpixels. However, in comparison with K-means, SLIC can (1) significantly optimize the distance of calculations by limiting the search space of each cluster center and (2) combine color and spatial proximity to control the size and compactness of the superpixels[START_REF] Achanta | SLIC Superpixels Compared to State-of-the-Art Superpixel Methods[END_REF]. It is well known for its capability to generate meaningful, nearly uniform sizes that are compact and adhere well to region Part II General methodology Chapter 4. Analysis and image processing 107 boundaries' superpixels by clustering pixels based on their homogeneities and spatial proximity in the image scene.

Furthermore, the superior

  performance of SLIC has been proved by various studies in different research fields. For example,[START_REF] Crommelinck | Slic Superpixels for Object Delineation from Uav Data[END_REF] investigated the applicability of SLIC to HR unmanned aerial vehicle (UAV) orthoimages; furthermore, its ability to delineate object outlines and showed that SLIC was the most accurate and complete method compared to other segmentation methods. In a study by J.Yin et al. (2022), the SLIC clustering function was modified to adapt the characteristics of Part II General methodology Chapter 4. Analysis and image processing 108 polarimetric statistical measures. The proposed method was validated and shown to significantly improve the segmentation results with the capability to produce better boundary adherence as well as compact, uniform superpixels. In addition, the SLIC segmentation technique is widely applied in vegetation cover classification (G.[START_REF] Xie | Machine learning methods and classification of vegetation in Brest, France[END_REF][START_REF] Zimudzi | Segmenting mangrove ecosystems drone images using SLIC superpixels[END_REF][START_REF] Correa Martins | Machine learning and SLIC for Tree Canopies segmentation in urban areas[END_REF]. For instance,[START_REF] Kawamura | Discriminating crops/weeds in an upland rice field from UAV images with the SLIC-RF algorithm[END_REF] demonstrated that SLIC was well suited and useful for different kinds of vegetation and forest segmentation. SLIC can demarcate similar image objects, such as vegetation species, even though they share nearly identical color and texture information. The vegetation classification achieved a very high accuracy by combining SLIC with machine learning (e.g., RF) and deep learning (e.g., CNN) classification algorithms, especially in the urban environment.

  Image classification is a computer science technique that aims to classify and label all elements (e.g., individual pixels, or segments in the case of OBC) within an image into a pre-defined number of classes. During the processing, the classifier chosen by the user automatically assigns a suitable class for each element and turns image objects or meaningless pixels into an interpretable thematic map. Studies of image classification Part II General methodology Chapter 4. Analysis and image processing 109 started in the late 1950s and early 1960s with aircraft data

  about Earth's surface objects by systems located some distance away; thus, selecting remote sensing systems and data is critical for classification. Selection of the remote sensing data (e.g., airborne vs. spaceborne, RaDAR vs. optical, active vs. passive, different spectral and spatial resolutions) is generally the fundamental and the first encountered difficulty in image classification. Over three decades, various studies have explored the relation of the nature and properties of digital remotely sensed data (e.g., their reaction to the physical, chemical, and biological properties of the Earth's surface) acquired from different instruments and platforms with a data processing algorithm in order to fundamentally enhance classification. Furthermore, the studies demonstrated the importance of the selection of remotely sensed data according to the user's needs, such as the research objectives and the characteristics of the study area, so that the data can be used to produce accurate classification results with maximum effectiveness and Part II General methodology Chapter 4. Analysis and image processing 110 minimum cost

  data selection is necessary for supervised classification methods. The training data is usually presented and collected in three different forms: individual pixels, seeds, and polygons. In addition, the training strategies are affected by the size of the training group, the resolution of the images, and the autocorrelation within each class (D. Chen & Stow, 2002). The selection of training data directly impacts the classification result, especially for HR data and in a highly heterogeneous area. Consequently, the training data should be representative of each class and have a reasonable ratio between different classes (D. Lu & Weng, 2007).The classification algorithm (also called "the classifier") is the most crucial part of the classification process. Many algorithms have been developed for different uses in recent decades, especially advanced classification algorithms for image classification. In order to choose the optimal classification method, the merits of each method must be considered.In particular, LULC classification is one of the earliest applications of image classification in the remote sensing field. Early on, a variety of studies have contributed towards developing efficient and effective classification methods for LULC: classification methods for either coarse or fine spatial resolution were developed and summarized for large-scale land cover mapping by[START_REF] Cihlar | Land cover mapping of large areas from satellites: Status and research priorities[END_REF].[START_REF] Franklin | Remote sensing methods in medium spatial resolution satellite data land cover classification of large areas[END_REF] reviewed and assessed large-area land cover classification with general medium spatial resolution and identified the outstanding issues. Phiri & Morgenroth (2017) reviewed the development of land cover classification methods since the launches of a new series of Landsat sensors and advancements in computer science.Overall, the first generation PBC methods for LULC mapping began to emerge in the 1970s, right after the launch of Landsat-1, with the development of diverse classifiers, such as MLC, K-means, and Iterative Self-Organizing Data Analysis Technique (ISODAT) classifiers[START_REF] Phiri | Developments in Landsat Land Cover Classification Methods: A Review[END_REF]. Afterward, many classification approaches were Part II General methodology Chapter 4. Analysis and image processing 111 proposed after the 1980s, such as the sub-pixel method, knowledge-based method, contextual-based method, OBC, and hybrid approaches in land cover classification (D. Lu

  must be trained in advance with a series of data selected and assigned by the user. Unsupervised classification, by contrast, is based solely on the information contained in the input data. Alternatively, as in parametric and nonparametric methods, the parametric classification model is built using a fixed number of parameters; however, the number of parameters is flexible in non-parametric methods.In addition, parametric methods are used to test the means of groups, whereas nonparametric methods test the medians. The most widely used parametric algorithms are MLC, basic linear SVM, and non-parametric algorithms (most notably including NN algorithms such as MLP, or ANN).

  classes. Numerous studies have shown ML to be very effective at classification in different domains. For instance,[START_REF] Waske | Machine learning techniques in remote sensing data analysis[END_REF] discussed the general concept of ML techniques, their critical role in remote sensing data analysis, and the use of their recent advanced developments in supervised classification techniques.[START_REF] Lary | Machine learning in geosciences and remote sensing[END_REF] highlighted the capacity of ML for solving problems in geosciences and remote sensing by presenting its ability in multivariate non-linear nonparametric regression solving as well as in multivariate non-linear non-parametric classification dealing.[START_REF] Maxwell | Implementation of machine-learning classification in remote sensing: An applied review[END_REF] reviewed six relatively mature ML methods-SVM, DTs, boosted DTs, RF, ANN, and k-NN-and illustrated their performances in image processing. Meanwhile, another study by[START_REF] Schulz | Machine learning methods for remote sensing applications: An overview[END_REF] aimed to explore the potential applications of ML techniques in the remote sensing area, their excellent capacity to solve complex classification problems, and their perspectives. Moreover, many papers have sought to describe ML classification Part II General methodology Chapter 4. Analysis and image processing 114

  presented an overview of the application of remote sensing imagery and different classification methods, especially the advanced classifiers such as ANN and DT, to perform vegetation cover classification. Mishra & Crews (2014) examined the suitability of integrating hierarchical Object-Based Image Analysis (OBIA) with RF-one of the most widely used ML methods-with a stack of Landsat TM imagery by mapping vegetation morphology types. Moreover, the results indicated a high overall classification accuracy and validated the effectiveness of the ML method applied in mapping vegetation types. Nay et al. (2018) have successfully produced short-term forecasts of vegetation health at high spatial resolution by using global data and an ML approach.

  [START_REF] Meshram | Machine learning in agriculture domain: A state-of-art survey[END_REF] demonstrated the efficiency of recent ML techniques by performing ML classification for monitoring agricultural activities in three main agricultural stages: pre-harvest, harvest, and post-harvest. Moreover, they demonstrated the remarkable capability of ML methods to handle non-linear tasks and process highly complex input data.[START_REF] Chlingaryan | Machine learning approaches for crop yield prediction and nitrogen status estimation in precision agriculture: A review[END_REF] indicated that the development of ML techniques application in accurate crop yield prediction and nitrogen status estimation could effectively improve crop forecast, environment state estimation, and decision-making.[START_REF] Savla | Survey of classification algorithms for formulating yield prediction accuracy in precision agriculture[END_REF] implemented several classification algorithms, including ML algorithms (SVM and NN), for studying precision agriculture, such as soybean crop yield.Part II General methodology Chapter 4. Analysis and image processing 115 Urban area classification is one of the most critical and challenging research areas of ML techniques, due to the complex and heterogeneous characteristics of urban areas and of the objects to be classified (e.g., construction, green space, street), which are generally relatively small in scale compared to cropland and forest. Consequently, powerful ML techniques and HR data are critical for urban environmental studies.Examples include urban area mapping, urban green spaces detection, gentrification, or artificialization monitoring[START_REF] Chan | DETECTING THE NATURE OF CHANGE IN AN URBAN ENVIRONMENT: A COMPARISON OF MACHINE LEARNING ALGORITHMS[END_REF] Donnay et al., 2014;[START_REF] Yang | Urban Remote Sensing: Monitoring, Synthesis and Modeling in the Urban Environment[END_REF]. Weng (2012) conducted a study of extracting and mapping impervious surfaces in urban areas by using and evaluating various remote sensing classification approaches, including ML algorithms such as ANN. The author showed that the ANN model outperformed other classification methods in urban area mapping and impervious surface estimation by using HR imagery.[START_REF] Soergel | Review of RaDAR Remote Sensing on Urban Areas[END_REF] revealed the excellent performance of ML algorithms (ANN, SVM, DT) in urban environment classification using SAR data.[START_REF] Puissant | Object-oriented mapping of urban trees using Random Forest classifiers[END_REF] proposed a methodology to map and monitor the changes in urban tree spaces using an object-based RF classifier with VHR optical image in Strasbourg, France. The authors found that the RF classifier achieved good accuracy and high robustness of classification results in terms of user's and producer's accuracy.Biomass estimation and monitoring are also among the typical application fields of ML classification methods. Mapping and quantifying vegetation biomass is crucial for optimizing ecosystem services and effective management. Biomass is usually studied in forest ecosystems-especially in tropical ecosystems[START_REF] Jachowski | Mangrove biomass estimation in Southwest Thailand using machine learning[END_REF][START_REF] Chen | Estimation of Forest Above-Ground Biomass by Geographically Weighted Regression and Machine Learning with Sentinel Imagery[END_REF][START_REF] Ghosh | Aboveground biomass estimation using multisensor data synergy and machine learning algorithms in a dense tropical forest[END_REF]. For example,[START_REF] Pham | Monitoring mangrove biomass change in Vietnam using SPOT images and an object-based approach combined with machine learning algorithms[END_REF] conducted research on biomass in a mangrove forest. The purpose of their study was to model, map, and analyze the biomass changes in the mangrove forests in the Cangio region of Vietnam between 2000 and 2011 by using SPOT4 and SPOT5 images with OBC ML methods, applying the RF algorithm for modeling and mapping biomass. Their results indicated that the ML classifiers had great potential in biomass modeling with a satisfactory classification accuracy (77.1% for 2000 and 82.9% for 2011). Dang et al. (2019) conducted a study to predict forest aboveground biomass (AGB) in Yok Don National Park in Vietnam using a combination of spectral and texture variables extracted from Sentinel-2 images and the RF algorithm. The authors reported that the RF-based regression algorithm had accurately predicted the forest AGB distribution. Part II General methodology Chapter 4. Analysis and image processing 116 In addition to these common application fields, the application of ML techniques in LULC classifications and change detection has also attracted considerable attention in recent years. Due to increasing social as well as natural pressures-such as rising population, accelerated industrialization and urbanization, climate change, deforestation,

  example, in order to monitor LULC in a complex mix-used boreal landscape in southcentral Sweden,[START_REF] Abdi | Land cover and land use classification performance of machine learning algorithms in a boreal landscape using Sentinel-2 data[END_REF] utilized four advanced ML non-parametric algorithms-SVM, RF, extreme gradient boosting (Xgboost), and DL-in combination with multitemporal multispectral HR Sentinel-2 data. The results showed that both ML algorithms achieved high accuracy, around 75%, in an extremely complex LULC classification. Due to the complex, heterogeneous mixed landscape, the highly accurate classification of urban land cover remains a challenge.[START_REF] Lu | Land Cover Classification in a Complex Urban-Rural Landscape with QuickBird Imagery[END_REF] explored the possibility of improving urban land-cover classification in a highly complex urban-rural landscape with a large surface of impervious area by using MLC algorithms combined with the OBC classification methods and QuickBird imagery. The authors found that such a combination significantly improved urban land cover classification performance. In another study, Rodriguez-Galiano et al. (2012) applied an ML RF classifier on multiseasonal textural features extracted from Landsat TM imagery to perform land cover classification over a complex Mediterranean landscape with numerous different types of LULC, obtaining highly reliable classification results.Overall, ML classification methods have definitively been shown to provide more accurate classification results particularly with complex multi-resource data in a heterogeneous landscape compared to traditional classification methods (e.g., MLC, distance measure, clustering, logistic regression) (M.[START_REF] Hansen | Classification trees: An alternative to traditional land cover classifiers[END_REF][START_REF] Carranza-García | A Framework for Evaluating Land Use and Land Cover Classification Using Convolutional NNs[END_REF][START_REF] Talukdar | Land-Use Land-Cover Classification by Machine Learning Classifiers for Satellite Observations-A Review[END_REF]. Some studies have compared different ML techniques in an effort to determine the best technique for LULC classification, considering that the most suitable classifier may vary depending on the research area and objectives (D.[START_REF] Lu | Change detection techniques[END_REF]. Thanh[START_REF] Noi | Comparison of Random Forest, k-Nearest Neighbor, and Support Vector Machine Classifiers for Land Cover Classification Using Sentinel-2 Imagery[END_REF] examined and evaluated the performance of three ML non-parametric classifiers-RF, k-NN and SVM-in LULC classification In Vietnam's Red River Delta using Sentinel-2 images. Their results showed that both classifiers achieved high overall accuracies ranging from 90% to 95%; however, SVM outperformed RF and k-NN because it was able to generate fine classification results Part II General methodology Chapter 4. Analysis and image processing 118 with a relatively small volume of training data. Jamali (2019) conducted a study to evaluate eight ML algorithms-RF, DT, MLP, Non-Nested Generalized Exemplars (NN ge), Simple Logistic, DTNB, J48, and Lazy IBK-in LULC classification on Iran's northern coast. The classification results obtained were evaluated based on several accuracy indicators; NN ge achieved the highest accuracy score in both accuracy indicators, followed by Lazy IBK, RF and MLP, which also showed excellent potential for LULC classification. Keshtkar et al. (2017) aimed to compare three ML algorithms-RF, DT and SVM-in land cover classification in Thuringia in Germany, a highly heterogeneous area. Afterward, land cover changes detected from 1990 to 2010 were generated using Landsat data. The study showed that the SVM OBC algorithm obtained the most accurate classification results with 93.54% of OA; the PBC RF and SVM classifiers had also similar accuracy indices (90%).

  Figure 39 Theoretical SVM modelInitially, the SVM model was designed for solving the linear binary problem with two principal factors: support vector and hyperplane (Figure38). The technique is focused on the training dataset; support vectors are the closest samples to the optimal boundary (also called the "hyperplane"), which is a single boundary that separates the samples from two different classes of datasets in a multi-dimensional space (C.[START_REF] Huang | An assessment of support vector machines for land cover classification[END_REF].

Figure 41 Four

 41 Figure 41 Four most frequently used kernel functions of Support Vector Classification (SVC)The most used kernel functions are polynomial, sigmoid, linear, and Gaussian kernel Radial Basis Function (RBF) (Figure40)[START_REF] Maxwell | Implementation of machine-learning classification in remote sensing: An applied review[END_REF], which are described below:

Figure 42

 42 Figure 42 Bagging theory model Among the various ensemble learning algorithms, the RF classification algorithm employs the technique of bootstrap aggregating, also known as "bagging" (Figure 41). The principle of the bagging technique is to decrease the variance of the model without increasing bias. First, an ensemble of n classifiers (ordinarily identical to the number of features in the training dataset) is created. Then, each classifier is trained on a random

  Figure 43 RF model

  a highly active research area for science and industry (X. X.[START_REF] Zhu | Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources[END_REF].

Figure 44

 44 Figure 44 Machine learning and deep learning in visual data analysisML algorithms (Figure43), also known as "shallow-structured architectures," are generally composed one or two of non-linear feature transformation, such as SVM and RF presented in previous sections. ML methods are widely recognized for their capability and effectiveness of solving simple or well-constrained problems; however, they are usually limited by the modeling and representational power, which is their main barrier to accomplishing more difficult tasks (L.[START_REF] Deng | Deep Learning: Methods and Applications[END_REF]. Meanwhile, the development of technologies, including equipment such as Graphics Processing Unit (GPU), encourages

  generated LULC classification tasks by using two pretrained architectures, CaffeNet and GoogleNet, adapted by two distinct datasets.[START_REF] Kussul | Deep Learning Classification of Land Cover and Crop Types Using Remote Sensing Data[END_REF] first attempted to apply two different CNN architectures (1-D with spectral convolution and 2-D with spatial convolution) to the fusion data of multitemporal and multisensor satellites (Landsat-8 and Sentinel-1A) for large-scale classification of land cover and crop types. The ensemble of 1-D and 2-D CNNs outperformed the other classifiers used to compare with CNNs (e.g., RF, MLP) with better accuracy and better discrimination of summer crops. In the research of Carranza-García et al. (2019), a general 2-D CNN model was applied to LULC classification with a fixed architecture and standard parametrization trained by a patchbased approach and using multi-source satellite data (SAR and hyperspectral). The results showed that the CNN models considerably improved LULC classification in both accuracy indicators.Furthermore, CNN models have been improved and performed for specific conditions (C.[START_REF] Zhang | Detecting Large-Scale Urban Land Cover Changes from Very High Resolution Remote Sensing Images Using CNN-Based Classification[END_REF][START_REF] Zhou | CNN-Based Land Cover Classification Combining Stratified Segmentation and Fusion of Point Cloud and Very High-Spatial Resolution Remote Sensing Image Data[END_REF][START_REF] Fan | Semi-MCNN: A Semisupervised Multi-CNN Ensemble Learning Method for Urban Land Cover Classification Using Submeter HRRS Images[END_REF][START_REF] Seydi | A New End-to-End Multi-Dimensional CNN Framework for Land Cover/Land Use Change Detection in Multi-Source Remote Sensing Datasets[END_REF]. Part II General methodology Chapter 4. Analysis and image processing 132 For instance, Luus et al. (2015) proposed a multi-scale input strategy for multi-view DL applied in supervised multispectral land use classification. The results indicated that a single deep CNN, trained simultaneously with multi-scale views, could significantly outperform multiple single-scale views. Gaetano et al. (2018) introduced MultiResoLCC, a new two-branch CNN architecture, to jointly use panchromatic and multispectral imagery for a direct classification without any pre-processing procedure. This novel endto-end network extracted features from panchromatic and multispectral images at their original spatial resolution. The features from different sources are combined to perform direct land cover classification at the finest resolution. CNNs are also developed to overcome the usual limitations encountered in LULC classification, such as the large and complex volumes of training data required for DL model training. Thus, some enhancements are developed to accelerate the training dataset creation (Shin et al., 2016; Lyu et al., 2018) or to use small training datasets to achieve high classification accuracy (B. Pan et al., 2017). Overall, CNNs are the most commonly used DL algorithms in LULC classification, although other deep architectures are occasionally used as well. For example, Y. Chen et al. (2015) introduced a new feature extraction and image classification framework based on DBN for hyperspectral image analysis. Their proposed framework-which combines PCA, hierarchical learning-based feature extraction, and logistic regression-obtained satisfactory classification results, even when compared to other well-known methods. B. Liu et al. (2018) performed spectral-spatial classification of land cover with a novel RNN model using hyperspectral data. To correctly label each pixel, an RNN model with five layers was constructed and fed with the information of the center pixel and the neighborhood pixels. The authors demonstrated that RNN has effectively improved the accuracy of hyperspectral image classification by modeling the spatial neighborhood relation. Jozdani et al. (2019) compared different classifiers of shallow or deep architecture for urban LULC classification using GEOBIA approach with two VHR satellite images. The classification methods used in this work included five DL methods (i.e., MLP, regular AE, SAE, AE, CNN) and five classical shallow ML methods (i.e., RF, Bagging Trees (BT), GB, Extreme GB and SVM). The authors found that the deep structure MLP algorithm obtained the most accurate classification result. However, neither unsupervised Part II General methodology Chapter 4. Analysis and image processing 133 pretrained AE models nor the integration of CNN and GEOBIA produced any improvement in classification accuracy. Two standard DL models-MLP and CNNs-were applied in the studies on detecting vegetation in the city of Brest and mapping LULCCs in the Crozon Peninsula, respectively.

  aimed to determine the optimal variance for the random weight initialization for MLP using different weight distribution and activation functions. Nearly a decade later,[START_REF] Thomas | A New Multilayer Perceptron Pruning Algorithm for Classification and Regression Applications[END_REF] introduced a new pruning approach for finding the optimal parameters based on variance sensitivity analysis by pruning the different types of units sequentially. Soon afterward,[START_REF] Tang | Extreme Learning Machine for Multilayer Perceptron[END_REF] proposed the Extreme Learning Machine (ELM)-based hierarchical learning framework with a single hidden layer to randomly generate hidden nodes for MLP. Most recently,[START_REF] Heidari | Ant Lion Optimizer: Theory, Literature Review, and Application in Multilayer Perceptron NNs[END_REF] proposed an efficient hybrid training technique, Ant Lion Optimizer MLP (ALOMLP), by combining Ant Lion Optimizer-a swarm-based meta-heuristic-with MLP, in order to find the optimal weights and biases of MLP parameters for achieving a minimum error with the highest possible classification accuracy.

  vegetation cover detection, LULC classification and change detection, and mapping urban areas. For urban area studies,[START_REF] Fiset | Map-image matching using a multilayer perceptron: The case of the road network[END_REF] conducted a study of road network mapping using a MLP model trained by two template-machining methods to identify road segments and intersections in the SPOT-HRV panchromatic image and thereby match a rasterized road map to a georeferenced SPOT image. The authors showed that using a deep NN (MLP) considerably improved classification over the previous methods.Moreover, it was shown that reliable results could be obtained by combining several classification approaches.[START_REF] Ozturk | Urban Growth Simulation of Atakum (Samsun, Turkey) Using Cellular Automata-Markov Chain and Multilayer Perceptron-Markov Chain Models[END_REF] determined the urban growth dynamics of the Atakum District in Samsun, Turkey, with two hybrid models: Cellular Automata-Markov Chain (CA-MC) and Part II General methodology Chapter 4. Analysis and image processing 137 MLP-Markov Chain (MLP-MC). In order to examine and compare the ability of the two models to accurately determine future urban growth, the authors simulated the urban growth of 2013 based on two Landsat TM/ETM+/OLI images from 1989 and 2000. The simulation results were compared with the 2013 LULC data for validation. The study results indicated that the models could effectively predict future urban growth, especially the MLP-MC method devoted to the most accurate simulation. In a similar study conducted by Losiri et al. (2016), CA-MC and MLP-MC were also applied to model LULC and urban expansions in Thailand's Bangkok Metropolitan Region. The use of these models has indeed improved prediction accuracy (exceeding 90% Overall Accuracy (OA)), especially in the MLP-MC model. W. Jiang et al. (2018) attempted to extract surface water bodies in specific regions for natural resource management by performing the MLP algorithm on Landsat-8 OLI images. In addition, the results were compared with those of a water index and SVM. The results demonstrated the excellent performance of MLP for precisely extracting different types of surface water bodies and suppressing noise in comparison with the water index and SVM. Additionally, the accuracy assessment showed that MLP could provide the most accurate classification results. In a recent study, Redowan et al. (2022) analyzed the historical tendency and LULC transitions from 1995 until 2015 at a 10-year interval at Raghunandan Hills Reserve, which had suffered severe forest degradation. Afterward, the future trend of forest LULC transitions for 2025 and 2035 was predicted by using the MLP-MC model. The authors found that the model effectively predicted the trend of increasing forest cover due to afforestation activities by 2025 and 2035 as well as the possibilities of forest expansions, which was valuable and useful to local forest resource management and deforestation monitoring. Ultimately, as one of the most critical factors and indicators of environmental changes, LULC has always been a focus of studies. V. N. Mishra & Rai (2016) revealed LULCCs in India's Patna district between 1988 and 2013 with the ML MLC method, after which an integrated MLP-MC model was trained to predict LULCCs for 2013 and subsequently validated by the 2013 LULC data. Eventually, the future LULC predictions of 2038 and 2050 were performed with the well-trained and validated MLP-MC model. The results Part II General methodology Chapter 4. Analysis and image processing 138 provided accurate and reliable predictions about the amount and location of potential changes, revealing the trend of decreasing agricultural land and rapidly increasing artificial area. Silva et al. (2020) sought to model land degradation in Brazil's Taperoá River Basin. Three classifications-1990, 1999, and 2002-were performed on Landsat-5 and Landsat-7 medium spatial resolution images using MLP algorithms (the main land cover change was detected with images from 1990 and 2002). After obtaining very satisfactory results with the MLP model (around 90% OA and 0.61 kappa coefficient), a predicted scenario for 2035 showed a significant increase in the herbaceous class but a decrease in the treeshrub class.

Figure 46 

 46 Figure 46 Example of CNN model As shown in Figure 45, a typical CNN model inspired by a human visual system is a multilayer architecture composed of at least one feature extraction stage and a classification stage; the former stage refers to one or more fully connected layers and the latter to a final classifier. The model takes image patches as input and outputs a final prediction of each object in the image. The feature extraction stage can be decomposed into two principal layers (i.e., a convolutional layer and a pooling layer), and non-linearity, flatten and fully connected layers (L.Zhang et al., 2016). These layers are described below:

  evaluated the general performance of ConvNets' in-depth features by performing image Part II General methodology Chapter 4. Analysis and image processing 141 classification with aerial imagery and satellite datasets. Hu et al. (2015) investigated the use of features extracted from successful pretrained CNNs in HR remote sensing imagery scene classification. Marmanis et al. (2016) proposed a new method by extracting representations of the pre-trained ImageNet, then transferring the representations into a supervised CNN classifier in order to address the limited dataset problem of the traditional CNN model. E. Li et al. (2017) presented a fusion strategy based on integrating multilayer features in a pre-trained CNN model used as an in-depth feature extractor for scene classification. N. He et al. (2018) proposed a new scene classification method, Multilayer Stacked Covariance Pooling (MSCP), which combines multilayer feature maps acquired from pre-trained CNN models.Afterward, the fine-tuning pretrained CNNS was developed. The principle of this type of CNN is to make a pretrained model adaptive to the specific conditions of the user's needs, and the research aims to fine-tune a pretrained model on a target dataset or optimize the structures-for example, fine-tuning an existing CNN model on a public satellite image dataset to make it more adaptive to satellite images (X. X.[START_REF] Zhu | Deep Learning in Remote Sensing: A Comprehensive Review and List of Resources[END_REF][START_REF] Cheng | Remote Sensing Image Scene Classification Meets Deep Learning: Challenges, Methods, Benchmarks, and Opportunities[END_REF].[START_REF] Castelluccio | Land Use Classification in Remote Sensing Images by Convolutional NNs[END_REF] explored the potential of two well-trained CNN architectures, CaffeNet and GoogleNet, for the semantic classification of LULC by only fine-tuning the target data. Nogueira et al. (2017) evaluated existing CNN structures in three different scenarios: using CNNs as feature extractors, fine-tuning, and training a new NN. They found that fine-tuning was the best-performing strategy, especially when combined with linear SVM. Y. Liu et al. (2018) developed a multiscale CNN (MCNN) framework for solving the scale variation problem of scene classification by developing a new structure with two branches of a fixed-scale net (F-net) and a varied-scale net (V-net) for training multiscale images with the same network weights. The excellent capability of fine-tuning pre-trained

  proposed a Gradient Boosting Random Convolutional Network (GBRCN) framework, the first deep ensemble framework used for scene classification. Combining a number of deep NNs, GBRCN outperformed the traditional single-CNN approach in the evaluation. Volpi & Tuia (2017) developed a CNN-based system based on a downsample-then-upsample architecture. The system learned how to densely classify each pixel at the original image resolution to improve prediction accuracy. G. Chen et al. (2018) introduced a knowledge distillation training method for scene classification; their proposed framework focused on the effectiveness and efficiency of more minor and shallower network models by matching the output of a small and shallow "student" model with a larger and deeper "teacher" model. N. He et al. (

  [START_REF] Scott | Training Deep Convolutional NNs for Land-Cover Classification of High-Resolution Imagery[END_REF] trained a deep CNN (DCNN) for land cover classification in HR remote sensing images by applying two techniques: (1) use of fine-tuning in the feature extraction Part II General methodology Chapter 4. Analysis and image processing 143 stage for Transfer Learning (TL), which could bootstrap a DCNN while preserving deep visual feature extraction from a different domain of images; and (2) data augmentation customized for remote sensing data for expanding training image datasets. With these two techniques, DCNN was shown to be more robust for remote sensing imagery as well as a vital improvement of classification accuracies by evaluating three DCNNs derived from CaffeNet, GoogleNet, and ResNet50 (ranging from 79.1% to 98.5%). Mahdianpari et al. (2018) aimed to map the complex land cover of wetlands in Canada's Avalon Peninsula using fully-trained or fine-tuned existing DCNNs-such as DenseNet121, InceptionV3, VGG16, VGG19, Xception, ResNet50 and InceptionResNetV2 on multispectral RapidEye data-evaluating the performance of each one. The results indicated that the deep hierarchical frameworks were effective at improving the classification accuracy of the complex land cover of wetlands-especially the full-training InceptionResNetV2 model using five spectral bands, which achieved the best accuracy among all (about 96%). Therefore, the extra multispectral bands and integration of the Inception and ResNet architectures may be recommendable to improve complex land cover classification. Helber et al. (2019) challenged the LULC classification problem by applying different patch-based ML methods (SVM, Scale-invariant feature transform (SIFT)) and pre-trained DL methods (a two-layer CNN model, GoogleNet, and ResNet-50) on a new HR multispectral Sentinel-2 dataset containing ten different classes, 27,000 georeferenced samples, and covering 13 spectral bands. The results indicated that DL outperformed both ML methods (GoogleNet > ResNet-50 > two-layer CNN). Moreover, a large-scale, highquality training dataset enabled every method (particularly the DL methods) to achieve better accuracy (around 20% increase with a more extensive training dataset).

1 .

 1 SAR backscattering coefficient (σ°)Backscatter can be defined as the RaDAR signal received by the RaDAR antenna that has been reflected by the target on Earth's surface. It is a measure of the reflective strength of a RaDAR target on the ground(ESA, n.d.-a). Moreover, the measure of the signal reflected can be quantified and normalized as the SAR backscattering coefficient.Similar to the spectral signature, each object reacts differently with the energy emitted and then reflects a different quantity of signal toward the RaDAR antenna. The backscattered intensity of each target depends on various factors, such as types, sizes, textures, shapes, frequency, polarization of RaDAR pulse, and incidence angle. Therefore, the SAR backscattering coefficient is an essential index for identifying different targets on the ground, even different stages of development of the same targets (e.g., different stages of development of crops). For example, a flat and smooth target surface yields a low backscattering coefficient (represented by darker areas in SAR images) due to the specular reflection effect, whereas a rough surface yields a high backscattering coefficient (represented by brighter areas in SAR images) due to the diffused reflection effect. As shown in Figure46, calm water appears darker than other areas because most of the RaDAR pulses are specularly reflected by the smooth water surface; the city surface is generally the brightest area in a SAR image due to the corner reflector effect caused by the buildings; the vegetation surface is moderately bright in the image because vegetation surfaces tend to be moderately rough.

Figure 47

 47 Figure47Backscattered intensity depends on different targets(city, vegetation, and calm water)

  investigated the correlation between structural mangrove forest parameters (e.g., tree height, tree diameter at breast height, tree density, and basal area) and total AGB and AIRSAR P-, L-and C-band multitemporal backscattered RaDAR signal. More recently,[START_REF] Frison | Potential of Sentinel-1 Data for Monitoring Temperate Mixed Forest Phenology[END_REF] revealed the potential of dualpolarization (VV, VH) Sentinel-1 backscattering plot to monitor the phenological behavior of temperate forests and the relationship between RaDAR backscattering coefficient and environmental variables, such as precipitation and temperature.In biomass estimation,[START_REF] Ferrazzoli | Sensitivity of microwave measurements to vegetation biomass and soil moisture content: A case study[END_REF] evaluated the performance of active and passive microwave sensors in vegetation biomass estimation. They reported that backscattering coefficients retrieved from both sensors offered good possibilities for vegetation biomass retrieving.[START_REF] Pulliainen | Seasonal dynamics of C-band backscatter of boreal forests with applications to biomass and soil moisture estimation[END_REF] used C-band multitemporal ERS-1 backscattering coefficient to analyze the seasonal dynamics in the boreal forest by estimating forest stem volume (biomass). In soils characteristics analysis, especially soil moisture is one of the crucial variables in hydrological processes (L. Wang & Qu, 2009), Aly et al. (2007) analyzed the backscattering behavior of salt-affected soils on RaDARSAT-1 SAR images and eventually explored the potential of the backscattering coefficient for salt content monitoring. In addition, Rodríguez-Fernández et al. (2015) retrieved soil moisture from Soil Moisture and Ocean Salinity (SMOS) data by using L-band SMOS brightness temperatures Part II General methodology Chapter 4. Analysis and image processing 147 complemented with C-band Advanced SCATterometer (ASCAT) backscattering coefficient and MODIS NDVI. In agricultural land mapping, (D. Wang et al., 2010) used dual-polarizations (VV, HH), Multitemporal, multi-polarization ENVIronmental SATellite (ENVISAT) Advance-SAR (ASAR) backscattering data for mapping seven main types of agricultural lands in the Pearl River Delta in China. Lopez-Sanchez et al. (2012) proposed a simple approach for retrieving rice field phenology by employing backscattering coefficients and ratios of the dual-polarization (HH, VV) TerraSAR-X sensors.

  For instance,[START_REF] Dong | Mapping paddy rice planting area in northeastern Asia with Landsat 8 images, phenology-based algorithm and Google Earth Engine[END_REF] analyzed the spatial expansion information of paddy rice in northeastern Asia, characterized by less cloud coverage, long growing seasons, and more side lap observations of Landsat. For this purpose, the classification was performed on the GEE platform with around 3,290 scenes of 143 path/rows by using the improved phenology completed with pixel-based paddy rice mapping algorithm based on VIs (NDVI, EVI, and land surface water index (LSWI)[START_REF] Chandrasekar | Land Surface Water Index (LSWI) response to rainfall and NDVI using the MODIS Vegetation Index product[END_REF]). The results indicated that the collaboration of an efficient mapping algorithm, high-quality satellite image, and GEE platform with excellent computational capabilities could effectively provide highly accurate classification results and model the spatial distribution and landscape pattern of paddy rice in northeastern Asia for the first time. B. Chen et al. (2017) developed a new method for accurately monitoring mangrove ecosystem changes in China due to the rapid deforestation of mangrove forests. The new classification method was developed based on the greenness, canopy coverage, tidal inundation calculated with Landsat time series, and auxiliary data such as elevation, slope, and intersection with sea criterion of mangrove forest. The methodology proposed was run on the GEE computing platform. Additionally, the classification was performed on the available open-source data catalog of GEE, including 1,941 Landsat scenes of 25 path/rows and 586 Sentinel-1 images. The authors reported that China's distribution of mangrove forests was adequately identified with very high accuracy indices (User Accuracy (UA), Producer Accuracy (PA), and OA greater than 95%).Part II General methodology Chapter 4. Analysis and image processing 150 GEE is usually limited by the processing algorithms for classification and regression problems[START_REF] Amani | Google Earth Engine Cloud Computing Platform for Remote Sensing Big Data Applications: A Comprehensive Review[END_REF]. However, Y.Wang et al. (2020) took advantage of a large data catalog, the outstanding computing capabilities of GEE, and a powerful DL multi-scale CNN (MSCNN) algorithm to perform an "offline training and online prediction" (OTOP) technique for accurate and efficient extraction of urban water from Landsat images. OTOP recommended that the MSCNN model be trained entirely offline. Subsequently, the urban water process extraction was performed on the GEE platform with the trained parameters of MSCNN. The classification results outperformed the RF classifier, thereby showing the efficiency of combining GEE and DL algorithms and the OTOP trick.

Figure 50

 50 Figure 50 Confusion matrixIn Figure49, the central diagonal (true positive and true negative), which indicates the portion of correctly classified units, is divided by the total number of classified units of each class (category); higher values indicate higher accuracy. However, there is a diversity of sources of errors that explain the low value of accuracy, such as the nature of the classification problem (e.g., the complexity of the landscape and the number of classes), the generation and adequacy of the training samples, and the dimensionality of the data and the classification method applied[START_REF] Raudys | On dimensionality, sample size, classification error, and complexity of classification algorithm in pattern recognition[END_REF][START_REF] Pal | An assessment of the effectiveness of decision tree methods for land cover classification[END_REF].

Figure 51

 51 Figure 51 Example of several accuracy index calculations (OA, Kappa coefficient, User's accuracy (also called "precision), Producer's accuracy (also called "recall), and F-Score)  Global accuracy indicators:  OA, one of the traditional classification accuracy measures, indicates the probability that an individual unit (pixel) will be correctly classified by the trained classification model. The number of correctly classified pixels is divided by the total number of pixels in the images.

Figure 52

 52 Figure 52 Example of change detection technique

  classified fifteen vegetation classes by performing MRS and nearest-neighbor classification algorithms. Their results demonstrated that the methodology proposed in the paper was sufficiently effective to generate accurate and detailed vegetation communities maps (with 77% OA). Pu & Landry (2012) focused on mapping seven tree species in the urban forest of the city of Tampa, Florida, USA, by employing OBC in IKONOS imagery (4 m of spatial resolution) with four spectral bands and WV2 imagery (2 m of spatial resolution) with four traditional bands. Both images were segmented with the MRS algorithm, after which the different tree species were extracted using two advanced classifiers, Stepwise Discriminant Analysis (SDA) and CART. The results showed that trees were successfully extracted from both images. Furthermore, WV2 imagery (with both bands) significantly Part III LULC changes in Pays de Brest: Case studies Chapter 5. Machine learning methods and classification of vegetation in Brest, France 161 increased the accuracy of identifying most of the tree species compared to IKONOS imagery, likely due to the finer spatial resolution. Meanwhile, Tigges et al. (2013) discussed the benefits of multitemporal RapidEye satellite data (5 m of spatial resolution) in urban vegetation classification. The authors investigated the potential of different spectral and temporal band combinations of five RapidEye images and an SVM classifier to perform high-precision classification of eight frequently occurring tree genera in Berlin, Germany. The results confirmed the possibilities of classifying tree genera in a heterogeneous urban structure with a high density of trees with Multitemporal and multispectral HR RapidEye images.

  Hashim et al. (2019) evaluated the use of VHR remote sensing data (i.e., Pléiades) for urban vegetation mapping in Kuala Lumpur, Malaysia. The authors employed NDVI as the VI and supervised MLC as a classification algorithm for extracting the UGSs. The method proved effective and accurate with a relatively high accuracy indicator (70.74% OA). Furthermore, the arrival of DL classification methods has significantly improved the accuracy of UGSs mapping. Z. Xu et al. (2020) proposed a new DL classification method for performing real-time, accurate and refined monitoring of UGS status by using phenological feature constraints provided by VHR images (GaoFen-2). The study employed a high-resolution deep NN (HRNet) as a classification model and the Focal Tversky Loss function to minimize the difference between the predicted value and actual value of the model. The results showed that the combination of the DL method, VHR images, and loss function significantly improved the accuracy of urban vegetation classification (with 93.24% OA). In another study by Abbas et al. (2021), the authors performed another DL method, MLP, on terrestrial hyperspectral data provided by SPECIM-IQ sensor for identifying and characterizing 19 urban tree species in the city of Hong Kong, one of the most urbanized areas in the world. The MLP model used in the study contained three hidden layers, and Part III LULC changes in Pays de Brest: Case studies Chapter 5. Machine learning methods and classification of vegetation in Brest, France 163 the SoftMax function was applied at the output layer. The results showed that the MLP DL model achieved high accuracy (ranging from 85% to 96%) and proved its efficiency in an intensely urban environment.

Figure 53

 53 Figure 53 Localization of the city of Brest

Figure 54

 54 Figure 54 Vegetated areas in the city of Brest: (a) urban forest (b) meadow (c) public park in the city center (d) croplands (e) street trees (f) private gardens in the residential area Although the city of Brest occupies a large urban surface area, vegetation plays a significant role in city planning. The vegetated areas in Brest can be categorized as natural and semi-natural areas and artificial vegetation. The first category includes principally: (1) urban forest (dry and mesophilic or wet), mainly located in the west, east, and center near the Penfeld River; (2) thicket, a specific landscape dominated by a very dense stand of trees or tall shrubs concentrated in the south, close to the harbors; and (3) meadow and lawn, which are evenly distributed in the city, especially in the periphery. The artificial vegetated areas include: (1) croplands located in the western and northern Brest;(2) street trees and public parks in the city center, intended to provide physical and

  Pléiades is a twin optical satellite launched in 2011 (Pléiades 1A) and 2012 (Pléiades 1B) for Earth monitoring. Two satellites operated on the same orbit can provide a 50 cm spatial resolution panchromatic image and a 2 m spatial resolution multispectral band (red, blue, green, NIR) image with a 20 km swath width and daily revisit time.

  algorithm of superpixel developed by Achanta et al. (2010) It adapts the traditional kmeans segmentation methods to superpixel generation, adheres well to the boundaries, Part III LULC changes in Pays de Brest: Case studies Chapter 5. Machine learning methods and classification of vegetation in Brest, France 167

  Figure 56 MLP model used for classification in the study As displayed in the figure 55, the MLP model used for classification was composed of one input layer with five nodes referring to five spectral bands (green, blue, red, NIR, and NDVI), three hidden layers with tanh activation function, and one output layer with five nodes referring to five output classes (tree, shrub and grass, non-vegetation, water, and no data). A softmax activation function was applied, and the model was also compiled by RMSprop as an optimizer, categorical_crossentropy as a loss function. The batch size and the epoch value were set to 500 and 200, respectively.

  . The evaluation of classification accuracy was performed only on the image of 2016 due to the similarity of the three images. It is worth noting that minor errors may exist between classification results and terrain truth; Part III LULC changes in Pays de Brest: Case studies Chapter 5. Machine learning methods and classification of vegetation in Brest, France 169 precise spatial information is not available for some areas in Brest for reasons of military security.

  Part III LULC changes in Pays de Brest: Case studies Chapter 5. Machine learning methods and classification of vegetation in Brest, France 170

Figure 57

 57 Figure 57 Classification results of vegetation in the city of Brest on 2016, 2017, and 2018 by using MLP deep structure According to the classification results, the classification results for 2017 found more trees than for other years due to the lack of available images in the same period (the 2017 image was acquired in late August, whereas the 2016 and 2018 images were acquired in late May or early June), as well as and meteorological conditions. The vegetation may differ between early and late summer, especially because the crops generally achieved their maximum height just before harvesting in late August or September and are thus easily confusable with high vegetation.

  Part III LULC changes in Pays de Brest: Case studies Chapter 5. Machine learning methods and classification of vegetation in Brest, France 171 the city of Brest remained unchanged during these three years. The croplands were located mainly in the north and the west of the city, and vegetation in the form of grass and trees was evenly distributed in the city as street trees and public gardens. In addition, urban forests were located in the northeast, southeast (botanical garden), and in the Penfeld River near the city center.

Figure 58

 58 Figure58Example of a transition from vegetated area tonon-vegetated area in Brest between 2016 and 2018 Besides the transition between high and low vegetation, the changes between vegetation and non-vegetation were also well detected. For example, in Figure58, aside from the vegetation types of the site, three classified maps of 2016, 2017, and 2018 demonstrated a gradual depletion of vegetation in an industrial area in southeastern Brest.Therefore, this site was likely under construction, and the vegetation was removed for this purpose. The hypotheses were further confirmed by the satellite images of 2016 and 2018 (Figure59).

Figure 59

 59 Figure 59 Example of transition from vegetation to artificial surface Overall, the combination of the DL algorithm and VHR images can effectively detect the transition of different LULCs in the urban area, particularly the LULCCs between vegetation and non-vegetation, regardless of the types and scales of the changes.

  As shown in Table 10, the RF classifier accuracy indicators of all three years are generally high; the kappa coefficient ranged from 0.85 to 0.97, and the OA ranged from 0.90 to .098. The most accurate result was obtained from the 2016 image (0.98 of OA and 0.97 of kappa). However, the classification results obtained from the 2018 Sentinel-2 image were less accurate, with 0.85 in kappa and 0.90 in OA. The difference in kappa was Part III LULC changes in Pays de Brest: Case studies Chapter 5. Machine learning methods and classification of vegetation in Brest, France 173 more important than the OA difference between the most and the least accurate results.

Figure 60

 60 Figure 60 Classification results of vegetation in the city of Brest in 2016, 2017, and 2018 using RF machine learning classifier

Figure 62

 62 Figure 62 Differences between MLP and RF classification results

  Part III LULC changes in Pays de Brest: Case studies Chapter 6.Mapping and Monitoring of LULCC in the Crozon Peninsula from 2007 to 2018 by ML algorithms (SVM, RF, and CNN) and by PCC 179 monitoring LULC is very important as the first step in the study and management of this phenomenon.In recent years, given the importance of LULC changes and the increasing availability of open-access archived multi-temporal datasets, many methods for analyzing and mapping LULC changes have been developed. The diversity of algorithms for studying LULC changes was also determined by the diversity of remote sensing sensor types (e.g., multispectral, hyperspectral, and SAR). Among the most commonly used satellite images in change detection (CD) studies are multispectral images due to the diversity of the types of sensors used to collect the data and the high temporal resolution of datasets for this type of study. For example, Z.Wang et al. (2018) conducted a study in a coastal area of Dongguan City, China, using SPOT-5 images acquired in 2005 and 2010. In this study, a scale self-adapting segmentation (SSAS) approach based on the exponential sampling of a scale parameter and the location of the local maximum of a weighted local variance was proposed to determine the scale selection problem when segmenting images constrained by LULC for detecting changes. Tran et al. (2015) conducted a study in coastal areas of the Mekong Delta on changes in LULC between 1973 and 2011 from Landsat and SPOT images. The supervised maximum likelihood classification algorithm was demonstrated to provide the best results from remotely sensed data when each class had a Gaussian distribution. Guan et al. (2020) studied a CD and classification algorithm for urban expansion processes in Tianjin (a coastal city in China) based on a Landsat time series from 1985 to 2018. They applied the c-factor approach with the Ross Thick-LiSparse-R model to correct the bi-directional reflectance distribution function (BRDF) effect for each Landsat image and calculated a spatial line density feature for improving the CD and the classification. Dou & Chen (2017) proposed a study in Shenzhen, a coastal city in China, from Landsat images using C4.5-based AdaBoost, and a hierarchical classification method was developed to extract specific classes with high accuracy by combining a specific number of base-classifier decisions. According to this study, the landscape of Shenzhen city has been profoundly changed by prominent urban expansion. In addition, in recent decades, remote sensing techniques have progressed, and many methods, such as machine learning, have been developed for LULC change studies, such as SVMs, RFs, and CNNs. Nonparametric machine learning algorithms such as SVM Part III LULC changes in Pays de Brest: Case studies Chapter 6. Mapping and Monitoring of LULCC in the Crozon Peninsula from 2007 to 2018 by ML algorithms (SVM, RF, and CNN) and by PCC 180

  of this method. M.Wang et al. (2020) proposed a new coarse-to-fine deep learning-based land-use CD method. In this study, several models of CNN well-trained with a new scene classification dataset were able to provide ac-curate pixel-level range CD results with a high detection accuracy and reflect the changes in LULC in detail. In another study of[START_REF] Han | Object-Based Change Detection of Very High Resolution Images by Fusing Pixel-Based Change Detection Results Using Weighted Dempster-Shafer Theory[END_REF], a weighted Demptster-Shafer theory fusion method was proposed. This method achieved reliable CD results with high accuracy using only two very highresolution multitemporal images by generating object-based CD through combining multiple pixel-based CDs.At the same time, in the Pays de Brest, which the Crozon Peninsula is part of, a category of LULC has been studied through shallow machine learning algorithms.[START_REF] Niculescu | Random forest classification using Sentinel-1 and Sentinel-2 series for vegetation monitoring in the Pays de Brest (France)[END_REF] and[START_REF] Niculescu | Rotation Forests and Random Forest Classifiers for Monitoring of Vegetation in Pays de Brest (france)[END_REF]applied the algorithms of rotation forest, canonical correlation forests and RF with satisfactory results for the classification of the different categories of land cover (vegetation) of the peninsula, as well as the summer and winter crops from the synergy of optical and RaDAR data from the Sentinel satellite. LULC changes in coastal areas have been studied with machine learning algorithms in different environments. Muñoz et al. (2021) analyzed the coastal wetland dynamics associated with urbanization, the sea level rise and hurricane impacts in the Mobile Bay watershed since 1984. They developed a land cover classification model with CNNs and a data fusion (DF) framework. The classification model achieved the highest overall accuracy (0.93) and f1-scores in the woody (0.90) and emergent wetland classes (0.99) when those datasets were fused into the framework. Part III LULC changes in Pays de Brest: Case studies Chapter 6. Mapping and Monitoring of LULCC in the Crozon Peninsula from 2007 to 2018 by ML algorithms (SVM, RF, and CNN) and by PCC 181More methodological work on the application of CNNs for CD was conducted by[START_REF] Jing | Land Cover Change Detection With VHR Satellite Imagery Based on Multi-Scale SLIC-CNN and SCAE Features[END_REF]. In this study, a CD method was proposed that combines a multiscale simple linear iterative clustering-convolutional NN (SLIC-CNN) with stacked convolutional auto encoder (SCAE) features to improve the CD capabilities with HR satellite images. This method uses the self-learning SCAE architecture as the feature extractor to integrate multiscale, spectral, geometric, textural and deep structural features to enhance the characteristics of ground objects in images.Machine learning methods were combined with OBIA techniques by[START_REF] Jozdani | Comparing Deep NNs, Ensemble Classifiers, and Support Vector Machine Algorithms for Object-Based Urban Land Use/Land Cover Classification[END_REF] for urban LULC classification. The multilayer perceptron model led to the most accurate classification results in this study. However, it is also important to note that GB/XGB and SVM produced highly accurate classification results, demonstrating the versatility of these ML algorithms.In this work, we aimed to study multiannual changes of LULC in the Crozon Peninsula, an area that has mainly been marked by conversion between three types of LULC: cropland, urban, and vegetation, in recent years, especially from 2007 to 2018. The challenge of this research was to deal with multiannual changes of a coastal area with different shapes and patterns by combining ML methods with PCC. To improve the CD capabilities using high-resolution satellite images, we implemented three remote sensing machine learning algorithms: SVM, RF combined with GEOBIA techniques, and CNN with SPOT 5 and Sentinel 2 data from 2007 and 2018, all effective and valid data sources. An evaluation of these three advanced machine-learning algorithms for image classification in terms of the OA, PA, UA, and confidence interval was conducted to more precisely detect the type of multiannual change.

Figure 63

 63 Figure 63 Location of the study area, including the Crozon Peninsula and two bordering regions, located in Pays de Brest, Finistère, Brittany, France, with the RGB band combination for Sentinel 2 (2018) and the location of the ground truth field Research. It covers a land area of 365.4 km² that extends between latitudes 48° 10′04″ N and 48°21′28″ N, longitudes 4°02′44″ W and 4°38′37″ W. The Crozon Peninsula is a sedimentary site with contrasting topography and contours that separate the Bay of Brest and the Bay of Douarnenez. The region is a mosaic of cliffs, dunes, moors, peat bogs, and coastal wetlands. The peninsula thus presents phytocenetic, faunistic, and landscape interests. The population of the study area is 29, 893; this makes the population density approximately 81.6 per km². The topography of the Crozon Peninsula is mostly dominated by plains, except for hills in the east and northeast, and the elevation of the area ranges between 0 m and 300 m. The land cover is characterized by forest, shrubs, and grasslands, which are mostly in the west, urban areas, cropland (including mainly corn and wheat) and meadow.

First

  , a SPOT-5 satellite image was obtained from the early summer of 2007. The multispectral SPOT-5 image downloaded from the ESA was obtained by merging the 2.5 m panchromatic band and the 10 m multispectral band, resulting in the spatial information of the image being identical to the information observed with the panchromatic sensor (earth.eas.int).

Figure 64

 64 Figure 64 Global methodology proposed

Figure 65

 65 Figure 65 Detailed CNN methodology Firstly, a well-trained CNN model requires a large number of samples to achieve high accuracy level. All of the training data in this study were thus prepared manually to obtain better accuracy. The samples in the form of points were automatically and evenly selected in QGIS by random selection for the purpose of avoiding bias. We then categorized all of the points manually into five distinct classes and created sample patches by including all of the pixels that surround each point for the model training. The algorithm then shuffled the labeled sample patches and created a random sample order for training.

Figure 66

 66 Figure 66 CNN model proposed and used for the classification of images from 2018 in the study For the processing of the 2007 images, the batch size, the number of training examples utilized in one iteration, was set to 32, the kernel size was assigned to be 3×3

6. 4 .

 4 Image post-processing 6.4.1. Accuracy assessment The accuracy assessment statistics of the classifiers (SVM, RF and CNN), based on confidence intervals (P. Olofsson et al., 2014), were calculated for each method and each class to check the model training and classification quality by comparing the classification with the reference values. The accuracy assessment used in this study included three indices: OA, PA, and UA -which are among the best-known and most highly promoted quantitative accuracy assessment metrics for the evaluation of classification quality or for comparisons among different classifications.Moreover, the indices of the accuracy assessment were generated with an algorithm from Olofsson based on the confidence interval. Therefore, all indicators presented in the tables are followed by an uncertainty rate. A higher uncertainty signifies that a larger accuracy rate can vary; in contrast, a small uncertainty represents a relationship with a certain accuracy.6.4.2. Post-Classification Comparison (PCC)To analyze the land cover changes between 2007 and 2018, a PCC was performed with the semi-automatic classification plugin on QGIS. The open-source plugin allows two classified images to be taken as the input (a new map and a reference map), then creating Part III LULC changes in Pays de Brest: Case studies Chapter 6. Mapping and Monitoring of LULCC in the Crozon Peninsula from 2007 to 2018 by ML algorithms (SVM, RF, and CNN) and by PCC 189

Figure 67

 67 Figure 67 Classification results with SVM, RF and CNNThe classification results of the three methods for the two years are presented in Figure67. The five classes detected in the classification process were cropland; cropland with bare soil; water; vegetated area; and non-vegetation, including urban area, sand, and rocks. Although some differences might exist, generally, the vegetation, non-vegetation, and cropland could be well-identified from different maps, which are globally identical.The vegetation is located in the south and east, with some vegetation near the coastlines,

6. 5 . 2 .

 52 Figure 68 LULC change maps with the classification of SVM (a), RF (b), and CNN (c).

Figure 68 Figure 69

 6869 Figure 68 revealed the status of each pixel, which stayed in the same class, meaning no change, or changes to another class, or another LULC. Some changes can be seen among the three land cover change maps with different classifications; for example, many croplands were transformed into vegetation, and vegetation was changed to cropland according to the maps of RF. The SVM maps indicate numerous transformations from vegetation to cropland and vegetation to non-vegetation. However, generally, the cropland surface has slightly increased in the peninsula, and many vegetation areas have turn into cropland, according to the LULC change map. At the same time, many cropland areas have become vegetation areas. However, there may be confusion between vegetation and crops due to the different acquired dates of the two images. Therefore, many of the new vegetation areas are most likely growing crops. The third most important land cover change is vegetation to non-vegetation, which mainly took place near urban areas on the coast, especially in the south, where tourism is the most developed. Some details of the three main land cover change types based on the classification results of the CNN, which is the most stable of the three methods, are shown in Figure 69, with comparisons between 2007 and 2018.

  indices. For example, Z. Pan et al. in 2015 analyzed the phenology of winter wheat and summer corn in the Guanzhong Plain in the Shanxi Province, China by using NDVI time series data and extracted seasonality information from the NDVI time series for measuring phenology parameters. The potential of another less-known index, the NDPI, is exploited by Gan et al. in 2020 in order to detect winter wheat green-up dates. During the evaluation with three other indices (NDVI, EVI, and EVI2), the results indicate that NDPI outperforms the other indices with the highest consistency with the ground truth.

  the hierarchical classification framework. With the classification results of the winter crops, we are able to monitor their phenology with Sentinel-1 C-band SAR backscatter time series and precipitation data in order to understand their temporal behavior from sowing to harvesting, identify the three main phenological stages (germination, heading, and ripening, including harvesting), and study how crop phenology responds to weather conditions.

Figure 70

 70 Figure 70 Location of study area, the north of the Finistère department, Brittany, France, as per the RGB band combination of a Sentinel-2 satellite image on 20 April 2019 and the distribution of agricultural land in 2019.

  Nevertheless, one cloud-free level 2A atmospheric effect-corrected Sentinel-2 image from 20 April 2019 was acquired from the Theia platform (catalog.theia-land.fr) (ESA, n.d.-e) (Table

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France 210 phenology with Sentinel-1 backscatter time series. The data were processed in QGIS with Orfeo Toolbox, eCognition 10.0, and GEE.7.3.1. Winter crops types mapping methodsA flow chart of the proposed global methodology is displayed below (Figure71).

Figure 71

 71 Figure 71 Hierarchical classification methodology used in the study for crop mapping Firstly, after the study area selection and satellite image acquisition, the boundary of the northern region of Finistère area was applied in order to extract our area of interest by sub setting the raw images for the purpose of reducing the image size and shortening the processing time.

Figure 72

 72 Figure 72 Proposed detail image processing methodology chart Compared to the direct extraction of winter crops with pixel-based RF algorithms, hierarchical classification methods are effectuated in three progressive levels, each with different objectives. The objectives from the first level of the hierarchy to the last one are extracting vegetation (including croplands) from raw images, extracting croplands from vegetated areas (trees, shrubs, and grassland), and finally, obtaining exclusively winter wheat and winter barley from all crop types detected in previous stages, respectively.

Figure 73

 73 Figure 73 Sentinel-1 image process in the GEE platform

Figure 74

 74 Figure 74 Level 1: PBC vegetation (including cropland) extraction results.Table 20 Accuracy assessment of PBC vegetation (including cropland) extraction.

Figure 75

 75 Figure 75 Level 2: PBC and OBC croplands extraction results

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France 216 vegetated area has highly different spectral behavior as compared to vegetation.Considering the better accuracy assessments of OBC, its classification result was preserved to perform the next step of classification.

In level 3 ,

 3 two winter crop types were extracted based on the results of the previous step, the classification result of the cropland extraction by using OBC. The results of the two classification methods (Figure76) are very close to identical in this level, differences between the two maps can hardly be noticed.

Figure 76

 76 Figure 76 Level 3: PBC and OBC winter crops extraction results

218

  In the second step, the final results of the hierarchical classification and classical direct extraction are displayed and compared with accuracy assessments as well, the two classified maps are presented in Figure77.

Figure 77

 77 Figure 77 Classification results with hierarchical classification and classical direct extraction

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France 220 grassland in the case of the classical direct extraction approach. (2) Since two winter crops are extracted directly from the Sentinel-2 image, the class others included not only nonvegetated urban areas, but also vegetation and other croplands which occupies a large area of our study site. Therefore an imbalance between classes was caused, thus, more training datasets of the class others were acquired in consideration of its weak intraclass correlation.

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France 221 7.4.2. Crops phenology monitoring The Sentinel-1 temporal backscattering coefficient profiles of diverse land cover types at VV and VH dual-polarizations from the study area during the growing season of the winter crops (from 1 October 2018 to 1 September 2019) are shown in Figure 78. The temporal profiles of mean σvv and σvh values of urban, vegetation (including other crops), water, bare soil, winter wheat, and winter barley land cover are displayed.

Figure 78

 78 Figure 78 Sentinel-1 temporal backscattering coefficient profiles of different land covers (Vegetation, water, urban area, bare soil, winter wheat, and winter barley) in the study area at VV and VH polarizations from 1 October 2018 to 1 September 2019.

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France222Generally, winter barley is sowed earlier than winter wheat in the Finistère department.Germination, which is the first growth stage of the crops, takes place three to four weeks after sowing, hence early December for winter wheat and mid-November for winter barley. The crops remain in their vegetative stage during winter, and stem elongation begins in spring and lasts until the plants reach their maximum height, usually in early summer. Lastly, ripening, the final growth stage, and harvesting occur in summer (early summer for winter barley and mid-summer for winter wheat).

Figure 79

 79 Figure 79 Winter wheat and winter barley Sentinel-1 temporal backscattering coefficient profiles at VV, VH, and VH/VV polarizations of the north Finistère region for the 2018-2019 growing season, with the daily precipitation data and three main phenological stage In Figure 79, both the raw signal and smoothed trend line of temporal backscattering coefficient profiles of VV, VH, and the VH/VV ratio for the 2018-2019growing season are displayed. Looking at the charts, it is shown that large variations occur before the germination due to the interaction between bare soil and vegetation caused by stem-ground double scattering[START_REF] Picard | Understanding c-band RaDAR backscatter from wheat canopy using a multiple-scattering coherent model[END_REF] Y. Song & Wang, 2019), while previous researches suggest that the fluctuation in the backscattering profiles are mostly induced

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France 225 smoother in comparison with the single polarization and less impacted by continuous rainfalls or drought due to absence of precipitation.

  using a Sentinel-2 high-resolution image, and two different classification approaches were performed. Both the hierarchical classification, which turns a complex classification problem into a series of smaller classifications and the classical direct extraction, which extracts the winter crops directly from the original satellite image, were carried out. The hierarchical classification was composed of three smaller classifications: vegetation extraction from the original image, cropland extraction from vegetation, and finallywinter crop extraction from other crops. Additionally, PBC and OBC were both performed in the last two steps and evaluated in order to keep the most accurate classification for further processing and analysis. Subsequently, crop phenology monitoring was performed based on the results of the previous step by using Sentinel-1 C-band SAR time series data, and the three important phenological stages (germination, heading, and ripening (harvesting)) and main growing periods were identified as well.

  mapping coastal Pays de Brest, particularly with the contribution of the latest generation of European satellite systems and advanced classification methods. However, the most suitable methodologies depend greatly on other factors, such as the geographical characteristic of the study area, the spatial and spectral resolution of the satellite data, and-most of all-the specific objective of the study. Generally speaking, both DL algorithms (MLP, CNN) achieved the highest accuracy, although ML classifiers (RF, SVM) also obtained adequate results. Moreover, the winter crop classification revealed that hierarchical classification is more appropriate for the extraction of specific crops or vegetation types. However, OBC and PBC have their advantages in crop-type classification.In addition, the SAR time-series backscattering coefficient was shown to have great potential to monitor winter crop phenology in coastal areas with frequent rainfalls.Urbanization remains the most significant change in coastal Pays de Brest; the conversion from vegetated areas to urban areas is particularly significant on the coastland.Accordingly, the vegetation cover was significantly decreased. However, most of the croplands remained surprisingly unchanged. As in the most coastal areas, the main driving factors of LULCC in Pays de Brest are urban expansion, artificialization, and coastal tourism development, which are especially important near the coastlines. Overall, economic development is rapidly occurring in Pays de Brest, manifested as urban sprawl, tourism growth, increasing agricultural activities, and environmental degradation.Furthermore, three study cases with specific objectives were performed in different test areas within Pays de Brest, in which adequate results were obtained as well:1) Vegetation cover in the city of Brest, a highly heterogeneous urban area, was accurately extracted by using a combination of OBC methods, VHR satellite data and deep structure MLP algorithms. RF algorithms showed similar results; however, misclassification between high and low vegetation could be observed. 2) Compared to RF and SVM classification methods, DL CNN was found to be the most suitable classifier for LULC classification in a highly fragmented coastal area with a small-scale field. Additionally, the coastline of Pays de Brest has encroached on economic development since the 2000s. 3)

TABLE 1 SPATIAL

 1 RESOLUTION AND WAVELENGTH OF SPOT-5 PRODUCT ...............................................................................

TABLE 2 REVISITING

 2 TIME OF PLÉIADES (SOURCE: EOPORTAL)

TABLE 9 CONFUSION

 9 MATRIX AND GLOBAL KAPPA COEFFICIENT OF VEGETATION CLASSIFICATION ..............................................TABLE 10 KAPPA COEFFICIENTS AND OA INDICATORS OF RF CLASSIFICATION RESULTS FROM 2016 TO 2018 ............................. TABLE 11 SATELLITE IMAGES USED IN THE STUDY ............................................................................................................ TABLE 12 TRAINING SAMPLES SURFACE AREA FOR SVM AND RF MODEL TRAINING IN 2007(2A) AND 2018(2B). ......................

TABLE 13 TRAINING OA (%) .

TABLE 27 THE

 27 BEST POLARIZATION OBSERVED FOR EACH PHENOLOGICAL STAGE OF WINTER WHEAT IN THE STUDY ....................... TABLE 28 THE BEST POLARIZATION OBSERVED FOR EACH PHENOLOGICAL STAGE OF WINTER BARLEY IN THE STUDY .......................

	NDPI	Normalized Difference Phenology Index
	NDVI	Normalized Difference Vegetation Index
	NDWI	Normalized Difference Water Index
	NIR	Near-InfraRed
	NLI	Non-Linear Vegetation Index
	NN	Neural Network
	NN ge	Non-Nested Generalized Exemplars
	NNR	Nation Natural Reserves; French: Réserve naturelle
		nationale
	NOAA	National Oceanic and Atmospheric Administration
	NSR	Number-of-Segments Ratio
	OA	Overall Accuracy
	OBC	Object-Based Classification
	OBIA	Object-Based Image Analysis
	OTOP	Offline Training and Online Prediction
	PA	Producer's Accuracy
	PAI	Plant Area Index
	PBC	Pixel-Based Classification
	PCA	Principal Component Analysis
	PCC	Post-Classification Comparison
	PDE	Partial Differential Equation
	PRI	Photochemical Reflectance Index
	PSE	Potential Segmentation Error
	PT-DTW	Phenology-Time-Weighted Dynamic Time Warping
	PVI	Perpendicular Vegetation Index
	RF	Random Forest
	RNN	Recurrent NN
	RNR	Regional Natural Reserves; French: Réserve naturelle
		régionale
	RoF	Rotation Forests
	RPG	Graphic parcel register
	RPG	Graphic Parcel Register ; French: Registre parcellaire
		graphique

  Part I Context of the study Chapter 1. Detection of land use/land cover in coastal area with remote sensing 28 According to World Bank (World Bank Group, n.d.) statistics and FAO (FAO, n.d.), the most important land uses worldwide are livestock grazing land and cropland, forested area, built-up area, shrubland, barren land, glacier, and fresh air and water resources.

  categories, agriculture and open spaces were the most accurately classified categories.

	However, the author indicated that remote sensing techniques were not yet mature. The
	direct application of satellite images must await advancements in classification to
	improve accuracy.

Part I Context of the study Chapter 1. Detection of land use/land cover in coastal area with remote sensing

  Both authors indicated that TM data could significantly improve accuracy in the homogenous suburban land cover because the TM data possessed finer spatial and

spectral resolution in comparison to MSS. Especially in Toll's study, TM provided considerable improvements in accuracy (from 74.8% to 83.2%). However, both studies also mentioned that the TM data might cause a reduced classification accuracy in heterogeneous complex urban land covers.

[START_REF] Fuller | Land cover map of Great Britain. An automated classification of Landsat Thematic Mapper data[END_REF]

presented the land cover map of Great Britain that records 25 land cover types, including 18 semi-natural vegetation types. The map was produced using a supervised MLC in Landsat TM multitemporal data acquired in summer and winter. The proposed method effectively classified land cover in Great Britain with high accuracy (around 80% to 85%) and separated a wide range of vegetation cover types. At the same time, the European satellite system arose in the late 1980s with the first launch of the SPOT mission, which was equipped with high-resolution visible instrument (HRV) (20 m); with this advance, urban LULC mapping emerged.

  in order to overcome the spectral variation within the same LULC class, the spectral confusion among LULC classes, and shadow impacts,[START_REF] Lu | Land Cover Classification in a Complex Urban-Rural Landscape with QuickBird Imagery[END_REF] examined the uses of VHR QuickBird-2 data, segmentation-based classification approach and spatial information to improve urban LULC classification in Lucas do

	Part I Context of the study
	Chapter 1. Detection of land use/land cover in coastal area with remote sensing
	agriculturally intensive region. Lu et al. (2007) mapped and monitored LULC types in
	rural settlements under human-induced stresses in the western Brazilian Amazon to
	evaluate and map potential land degradation risks associated with deforestation and soil
	erosion. Feyisa et al. (2016) accurately quantified LULC mapping and landscape change
	patterns in the urban environment of Addis Ababa, Ethiopia, using eight Landsat images Rio Verde in Mato Grosso State, Brazil. The research showed that suitable
	acquired from 1985 to 2012 to understand the relationship among rapid urbanization, combinations of imagery, classification method, and information could significantly
	increased surface thermal intensity, and urban biophysical composition. improve LULC classification in complex urban areas.
	Overall, LULC research has rapidly developed, and LULC classification has been Jawak & Luis (2013) explored the potential of multi-angle HR WV-2 panchromatic
	significantly more efficient and accurate in recent decades for the reasons presented and multispectral data for urban LULC information extraction by generating the
	below. traditional pan-sharpening for data fusion and multiple spectral index ratios for
	spectral distortion reduction.
	Furthermore, LULC classification was frequently performed using very high-
	resolution (VHR) remote sensing data, such as WorldView-1 and -2 (WV-1 and WV-2),
	Pléiades-1, and QuickBird. Compared to HR images, VHR images are generally applied
	for LULC mapping in a highly heterogeneous complex landscape that requires finer
	spatial resolution, such as an urban area.
	36

1) The arrival of the new generation of near real-time, analysis-ready, high-quality (high spatial, spectral, radiometric and temporal resolution), standardized, easy-access EO satellite data improves spatial information extraction efficiency and results in highly accurate LULC classification. For instance, van der Sande et al. (2003) used IKONOS-2 to classify land cover categories in the southern Netherlands. Afterward, the highly accurate classified maps (74% overall accuracy (OA)) were utilized as input to simulate flood hazards, derive an inundated areas map, and eventually assess damage to residential properties, infrastructure, and agricultural area. A decade later, Forkuor et al. (2018) evaluated the use of Landsat-8 and Sentinel-2 data in LULC mapping in rural Burkina Faso; they examined the contribution of the red-edge band of Sentinel-2 in improving classification accuracy by using three ML algorithms: Random Forest (RF), Support Vector Machine (SVM), and stochastic gradient boosting. The results indicated that Sentinel-2 outperformed Landsat-8 in LULC mapping, and the red-edge band of Sentinel-2 can effectively provide added value by deriving a more accurate LULC classification than Landsat-8 and Sentinel-2. Part I Context of the study Chapter 1. Detection of land use/land cover in coastal area with remote sensing 37 For example, Hashim et al. (2019) used Pléiades-1A VHR remote sensing image for urban vegetation mapping in the city of Kuala Lumpur by employing the Normalized Difference Vegetation Index (NDVI) and MLC.

de Brest 49 2.2. Geographical condition Figure

  Part I Context of the study Chapter 2. Geographic localization and major land use/land cover in Pays

	Part I Context of the study
	Chapter 2. Geographic localization and major land use/land cover in Pays de Brest
	area ranges
	from 0 m to 385 m. The natural landscape is a mosaic of cliffs, dunes, moors, peat bogs,
	Figure 7, on average, the total annual precipitation of the area is 941 mm; the wetter and coastal wetlands. However, a few essential landforms are worth noting in Pays de
	season lasts five months, from October to February, and the highest monthly rainfall of Brest. A vast plateau, Plateau de Leon, occupies a large area in the north, and a local
	110 mm is received in December. Conversely, June and July are the driest months, with mountain range, Mont d'Arrée, with a series of the highest summits of around 385 m, can
	only 52 mm of rainfall. As a temperate area, the annual average temperature is 12.2 °C. In be found in the region's center.
	contrast to the wet season, the hottest season lasts from July to August, with an average Moreover, Montagnes Noires, another essential local mountain range, crosses over
	temperature of 16.8 °C; an average minimum temperature of 7.7 °C is observed in the south from Menez Hom, a summit on the Crozon Peninsula, to the central department.
	February. The region's mild weather and ample rainfall provide an ideal habitat for rich Besides the principal summits, Bassin de Châteaulin, a vast hollow with a mean altitude
	biodiversity with landscape heterogeneity as well as optimal conditions for numerous lower than 100 m, lies between two mountain ranges in the south of the study area. The
	agricultural activities, such as crop planting. basin, originally constituted by soft carboniferous shales, was created by the Aulne River
	and its affluent, which take their source from Mont d'Arrée and Montagnes Noires.
	Because its landform is relatively plains, especially in the coastal area-and with a
	basin in the center of the region that secures water resources from the Aulne River and
	its affluent-Pays de Brest is naturally endowed with excellent geographical features to
	develop all kinds of socio-economic activities.

7 Umbrothermal diagram of the monthly mean temperatures and monthly mean total precipitations from 1991 to 2021 of Brest Climatically, according to the Köppen-Geiger climate classification, Pays de Brest is classified as type Cfb (temperate oceanic climate) (Köppen, 2011). As demonstrated in Part I Context of the study Chapter 2. Geographic localization and major land use/land cover in Pays de Brest 50 Figure 8 Topography maps of Pays de Brest with principal landform and summit Three types of resistant rock-sandstone, quartzite, and granite-gave rise to the prominent landform of Pays de Brest; thus, the topography of Pays de Brest consists mostly of plains, which means the low altitude (Figure 8). The elevation of the

51 2.3. Land use / land cover in Pays de Brest 2

  .3.1. CORINE Land Cover (CLC)

	Figure 9 CLC 2018

in Pays de Brest The CLC geographical database is a biophysics and land use inventory produced in the framework of Copernicus, the European Earth observation program (CORINE Land Cover). According to Figure 9, cultivated lands are the leading land use in Pays de Brest.

	Part I Context of the study
	Chapter 2. Geographic localization and major land use/land cover in Pays de Brest
	Peninsula and the north coast of the area; lakes on the Plateau du Léon comprise most of
	the water bodies.
	The location of artificial surfaces, known as "urban areas," is very similar to the
	population distribution described in the previous section. Both are highly concentrated in
	the Brest Métropole and its suburbanization trend, with much of the remainder in some
	small cities located in central intercommunalités. It is worth noting that although Pays de
	Brest was created with the principle of cohesive development between intercommunalités,
	the distribution of population and the urbanization level is highly imbalanced; more than
	half of the population of Pays de Brest is concentrated within the Brest Métropole. The
	presence of artificial areas is essential, even though agricultural land remains the most
	important land use in the region.
	They are uniformly distributed over the study area, especially in Plateau du Léon, except
	on the western Crozon Peninsula (Presqu'île de Crozon-Aulne Maritime), which is
	occupied by natural coastal moorlands. The presence of arborous, fruticose, and
	herbaceous vegetation is easily identified. Forest can usually be found near water areas,
	either around the coast (such as in the center of Pays de Brest and around the coast of the
	Crozon Peninsula) or near the inner water body-for example, the Bassin de Chateaulin.
	Moors and heathland are found mainly near the coast in the region's north and the west
	coast in the middle of the Crozon Peninsula. Other vegetation is dispersed throughout the
	region; wetlands are mainly present on the seaside and are easily identified on the Crozon
	52

  beginning, it can be easily noticed that the population increases are very poor in Presqu'île de Crozon-Aulne Maritime and Brest Métropole. Just as all metropolis, Brest Métropole,

	Part I Context of the study
	Chapter 2. Geographic localization and major land use/land cover in Pays de Brest

  especially Brest Métropole) are highly saturated in term of population, and rapid urbanization and suburbanization place different stresses on public city infrastructure,

	Part I Context of the study
	Chapter 2. Geographic localization and major land use/land cover in Pays de Brest

Table 1 Spatial

 1

resolution and wavelength of

Table 2 Revisiting

 2

time of Pléiades-1 (Source: eoPortal) Part II General methodology Chapter 3. Data structure and field research 75 Pléiades-1 satellites are well known for their distinctive characteristics, such as the broad swath of approximately 120 km x 110 km, their outstanding capacity to provide VHR MS imagery with highly accurate geo-location for dual civil/military use, and global coverage of Earth's surface with daily observation accessibility from every location on

Table 3

 3

Spatial resolution and wavelength of Pléiades product

Table 4

 4

Spatial resolution and wavelength of Sentinel-2 operated bands (Source : Satellite Imageing Corporation)

Table 6

 6 Characteristics of four acquisition modes of Sentinel-1

  EVI2 is a two-band VI developed by Jiang et al. in 2008 as a replacement for EVI.

	Part II General methodology
	Chapter 4. Analysis and image processing
	
	96

  The method is based on the similarities in the image. It usually begins by seeking similarity in each element in the area of interest. The segments are then created by grouping and merging adjacent elements (e.g., pixels) that share certain

	Part II General methodology
	Chapter 4. Analysis and image processing
	 Region-based: homogeneities. Region-based segmentation method has two approaches: region
	growing (starting with one individual) and region splitting (starting with the entire
	scene)
	102

  [START_REF] Song | Inversion of Rough Surface Parameters From SAR Images Using Simulation-Trained Convolutional NNs[END_REF][START_REF] Niu | Parameter Extraction Based on Deep NN for SAR Target Simulation[END_REF]. Ultimately, one of the most significant contributions of DL is to bring significantly enhanced LULC classification and object detection with optical imagery, especially with the new generation of VHR optical images.

	Part II General methodology Part II General methodology
	Chapter 4. Analysis and image processing Chapter 4. Analysis and image processing
	designed for representation learning and trained to ignore unnecessary noise to perform dimensionality reduction. 2017; T. Even though a great number of ML algorithms were already developed for this purpose,
	 Convolutional NNs (CNNs) (e.g., AlexNet (Krizhevsky et al., 2012), Visual Geometry and they have achieved excellent performance before the arrival of DL, some limitations
	Group (VGG) Networks (Sengupta et al., 2019), ReST-Net (B. Li et al., 2018), Fully yet remain. Therefore, it is increasingly common to use DL methods by combining them
	Convolutional Network (FCN) (J. Long et al., 2015)), constitute supervised deep NNs with HR images for LULC classification (L. Ma et al., 2019).
	with the filters involved to perform convolutions in the image domain. CNNs have
	demonstrated their ability to extract mid-and high-level abstract features from raw First, deep structures show great achievements in image representation,
	images by combining convolutional and pooling layers, and have shown efficiency in considering that hierarchical structures can provide high-level spatial information (H.
	large-scale recognition, object detection, and semantic segmentation (X. X. Zhu et al.,
	2017).
	The most familiar deep architectures used for hyperspectral image processing are
	SAEs (Zabalza et al., 2016), DBNs, supervised CNNs (Goodfellow et al., 2016; Lee & Kwon,
	2016), RNNs (Mou et al., 2017; Y. Liu et al., 2018), and Generative Adversarial Networks
	(GANs) (Goodfellow et al., 2020; Zhong et al., 2020). As presented in Chapter 3, SAR data
	has become increasingly popular, particularly after the launch of the Sentinel-1 C-band
	SAR satellite of the ESA's Copernicus Programme. Moreover, the advent of DL methods
	has revealed increased potential for SAR data application. Examples include Automatic
	Target Recognition (ATR) with mostly CNN architectures (S. Chen & Wang, 2014;
	Wilmanski et al., 2016; Pei et al., 2018), terrain surface classification using polarimetric
	130 131

With such advanced deep architectures, DL is widely applied in remote sensing with various remotely sensed data types. For instance, hyperspectral imagery-a specific type of optical imagery characterized by hundreds of narrow spectral bands-allows us to identify a better characterization and exploitation of the Earth's surface. However, the difficulty of processing is accordingly increased, and hyperspectral image analysis has always been an active research area in image processing and classification. As a practical feature extraction approach, DL techniques have brought revolutionary advancement in this field (S.

[START_REF] Li | Deep Learning for Hyperspectral Image Classification: An Overview[END_REF]

. SAR (PolSAR) with AE models (H.

[START_REF] Xie | Multilayer feature learning for polarimetric synthetic RaDAR data classification[END_REF][START_REF] Hou | Classification of Polarimetric SAR Images Using Multilayer Autoencoders and Superpixels[END_REF][START_REF] Zhang | Stacked Sparse Autoencoder in PolSAR Data Classification Using Local Spatial Information[END_REF]

, CNNs (Y.

[START_REF] Zhou | Polarimetric SAR Image Classification Using Deep Convolutional NNs[END_REF] Y. Wang et al., 2018; L. Wang et al., 2018)

, and parameter inversion and de-speckling with CNN models as well

[START_REF] Chierchia | SAR image despeckling through convolutional NNs[END_REF] P. Wang et al., Liang & Li, 2016; P. Li et al., 2018)

. Thus, the earliest studies of LULC classification revolve around simple feature representation rather than the final classification

[START_REF] Karalas | Deep learning for multi-label land cover classification[END_REF][START_REF] Romero | Unsupervised Deep Feature Extraction for Remote Sensing Image Classification[END_REF][START_REF] Ma | Deep learning in remote sensing applications: A meta-analysis and review[END_REF]

. Then, CNNs have attracted remarkable attention and have become the most commonly used DL algorithms in land cover classification. Furthermore, CNN models are most often used with HR images in order to achieve significant breakthroughs (L.

Ma et al., 2019)

. Classical CNNs were first widely applied

[START_REF] Al-Najjar | Land Cover Classification from fused DSM and UAV Images Using Convolutional NNs[END_REF] Y. Chen et al., 2019;[START_REF] Mazzia | Improvement in Land Cover and Crop Classification based on Temporal Features Learning from Sentinel-2 Data Using Recurrent-Convolutional NN (R-CNN)[END_REF][START_REF] Memon | Automatic land cover classification of multi-resolution dualpol data using convolutional NN (CNN)[END_REF]

. For instance,

[START_REF] Castelluccio | Land Use Classification in Remote Sensing Images by Convolutional NNs[END_REF]

have

  GEE has significantly contributed to traditional remote sensing data processing by combining an extensive catalog of different types of data, excellent calculation capabilities, and comprehensive manipulation. For that reason, many studies of crucial natural or social issues use the GEE platform. Examples include forest cover (M.

	Part II General methodology
	Chapter 4. Analysis and image processing
	Overall,
	149

  PCC was performed on images from two different sensors, because Sentinel-2 started collecting data only after 2013.PCC change detection is commonly applied in various areas, including LULC conversion due to human activities (e.g., forest to artificial land cover) (Abd El-Kawy et al.

	Part II General methodology
	Chapter 4. Analysis and image processing
	2 images;
	157

revealed LULCCs on the Crozon Peninsula, Brittany, France, from 2007 to 2018 by using SPOT-5 and Sentinel-

Table 7 VHR

 7 Pléiades satellite images used in the study

Table 8 OA

 8

of vegetation classification Table 9 Confusion matrix and global kappa coefficient of vegetation classification

Table 10

 10

Kappa coefficients and OA indicators of RF classification results from 2016 to 2018

  Part III LULC changes in Pays de Brest: Case studies Chapter 5. Machine learning methods and classification of vegetation in Brest, France 175 of two classification methods indicated that MLP outperformed RF, given that MLP successfully resulted in more accurate results in vegetation classification, although RF is recognized as a powerful machine learning classification tool as well.

  Part III LULC changes in Pays de Brest: Case studies Chapter 5. Machine learning methods and classification of vegetation in Brest, France 176 Therefore, vegetation extraction with simple categories (high and low vegetation) is easy to perform and achieves high accuracy. Various classes should be considered for further studies, such as different tree species and crop types. Xie, G.; Niculescu, S. Mapping and Monitoring of Land Cover/Land Use (LULC) Changes in the Crozon Peninsula (Brittany, France) from 2007 to 2018 by Machine Learning Algorithms (Support Vector Machine, Random Forest, and Convolutional NN) and by Post-classification Comparison (PCC). Remote Sens. 2021, 13, 3899.

	Part III LULC changes in Pays de Brest: Case studies
	Chapter 6. Mapping and Monitoring of LULCC in the Crozon Peninsula from 2007 to 2018 by ML algorithms
	(SVM, RF, and CNN) and by PCC
	Chapter 6. Mapping and Monitoring of Land
	use/Land cover Changes in the Crozon Peninsula
	(Brittany, France) from 2007 to 2018 by Machine
	Learning Algorithms (Support Vector Machine,
	Random Forest, and Convolutional Neural Network)
	and by Post-classification Comparison
	Journal article:
	177

https://doi.org/10.3390/rs13193899 Review: Remote sensing Published date: 29 September 2021

  Traditionally, the majority of local people practice agricultural or related activities in the Finistère Department, in which 57% of the department's surface is devoted to agricultural use. However, the French National Defense provides more than half of the employment in the Crozon Peninsula; hence, other activity sectors (e.g., agriculture, industry, construction and commerce) are proportionally less important.

	Part III LULC changes in Pays de Brest: Case studies
	Chapter 6. Mapping and Monitoring of LULCC in the Crozon Peninsula from 2007 to 2018 by ML algorithms
	(SVM, RF, and CNN) and by PCC
	183

Table 11

 11 Satellite images used in the study

  Part III LULC changes in Pays de Brest: Case studies Chapter 6. Mapping and Monitoring of LULCC in the Crozon Peninsula from 2007 to 2018 by ML algorithms (SVM, RF, and CNN) and by PCC 186Table 12 Training samples surface area forSVM and RF model training in 2007(2a) and 2018(2b).

Table 13 Training

 13 OA (%)

Table 14 Validation

 14 OA (%)According to Table13and 14, all of the accuracy indices range from 70% to 90%, and the two tables are very similar. Although the training accuracy is slightly higher and more certain (approximately 2-6%), it suggests a good performance and good training of all three models, a strong level of agreement, and a high level of reliability. Beyond that, it is worthwhile to note that the CNN demonstrated better potential (approximately 1-12% higher in accuracy) for the classification of land cover monitoring than RF and SVM in both years, and it is the most stable and certain method, given its low uncertainty of approximately 1.50 for training accuracy and 3.50 for validation accuracy in comparison with the other methods. In general, the 2007 images have better accuracy indicators and lower uncertainty rates than the 2018 images, and RF achieved better accuracy and lower uncertainty in the 2007 images than in the 2018 (e.g., 80.23 ± 03.87% and 70.51 ± 08.38% for 2007 and 2018, respectively, in the validation accuracy assessment), which is the Part III LULC changes in Pays de Brest: Case studies Chapter 6. Mapping and Monitoring of LULCC in the Crozon Peninsula from 2007 to 2018 by ML algorithms (SVM, RF, and CNN) and by PCC 191 opposite of SVM (e.g., 77.03 ± 04.36% and 78.14 ± 06.40% for 2007 and 2018,respectively, in the validation accuracy assessment). The most reasonable explanation is that the 2018 images have a rougher resolution than the 2007 images, and so fewer pixels are present in each segment, and since the SVM needs fewer samples and pixels to train the model, it achieved a better performance with the 2018 images.The PA and UA of each class with the three methods in both years are listed in Table15 and 16. Table 15 presents the satisfactory training accuracy of both models (approximately 70-90%, with a few acceptable exceptions, such as cropland, which has PA and UA values of approximately 40-50%), which indicates that the three models used in the classification: SVM, RF, and CNN, were generally well-trained. Even though the training accuracy and the validation accuracy are constantly approximate, as shown in Table 16 the training accuracy is very slightly more accurate and certain than the validation accuracy (approximately 1-10% higher). This suggests a slight overfitting problem in the models. Nevertheless, the presence of the overfitting problem is not an important obstacle in our study because the differences between the training and Part III LULC changes in Pays de Brest: Case studies Chapter 6. Mapping and Monitoring of LULCC in the Crozon Peninsula from 2007 to 2018 by ML algorithms (SVM, RF, and CNN) and by PCC

	validation accuracies are acceptable (between 1% and 10%). Additionally, the validation
	accuracy always has a higher uncertainty, which indicates that the real accuracy rate may
	vary to a large extent.

Table 15

 15 Training producer's accuracy and user's accuracy by classTable 16 Validation producer's accuracy and user's accuracy by class Part III LULC changes in Pays de Brest: Case studies Chapter 6. Mapping and Monitoring of LULCC in the Crozon Peninsula from 2007 to 2018 by ML algorithms(SVM, RF, and CNN) and by PCC 193 Among the three methods, the CNN remains the most stable and accurate method, and all of the values range between 70% and 100%. Among the classes, water areas were

	very well predicted but also very extensive (usually with an accuracy between 50% and
	70% with a large uncertainty and 100%) by means of their distinctive spectral signature.
	Cropland had training accuracy indicators between 50% and 70%, with CNN having the
	best performance in this class (approximately 70-80% in training accuracy and
	approximately 60-70% in validation accuracy). Of the other two methods, RF was mostly
	more accurate than SVM (approximately 5-20% higher). In addition, they performed
	better on the images from 2018 than those from 2007, with a stable uncertainty
	(approximately 10-20%); the validation accuracies were still approximate and slightly
	lower. Crops are easily confused with vegetation, which might explain the low accuracy of
	this classification. Croplands with bare soil are more correctly classified than croplands
	with plants (with a 20-50% higher rate), and all UA are significantly higher than PA, with
	a 50% to 70% difference, which suggests that fewer errors of commissions were made
	during the classification. Except for the CNN, both the UA and PA ranged from 82% to 92%,
	with less uncertainty at approximately 2.50 in the training accuracy and approximately 5
	in the validation accuracy. Even though vegetation has the potential to be confused with
	crops, it was still the best-predicted class besides water, and the accuracy indicators
	achieved approximately 80-95%, except for the PA of RF and SVM in 2018, which were
	approximately 65%. It can be assumed that some errors of omission were made during
	this classification. The non-vegetation class includes all types of urban land use, sand, and
	rock; hence, it is globally well-classified due to its particular spectral signature, especially
	with the 2018 images. The accuracies in general ranged from approximately 70% to 98%
	in the training accuracy and from approximately 63% to 97% in the validation accuracy.

Table 17 .

 17 The table ranges from the most important class with the greatest proportion of land cover change to the least changed class, with the two types of Part III LULC changes in Pays de Brest: Case studies Chapter 6. Mapping and Monitoring of LULCC in the Crozon Peninsula from 2007 to 2018 by ML algorithms (SVM, RF, and CNN) and by PCC 198 the CNN indicated that approximately 77% of the surface of the Crozon Peninsula did not change between the two years, which is more important. Cropland is clearly increasing:

	12.45% of the vegetated area has been converted into cropland, according to SVM
	classification; however, this transformation is less important according to the RF and CNN
	classifications, which show approximately 8% and 9%. In contrast, much cropland was
	identified as vegetation in 2018, more with the SVM and RF classifications (12-13%) than
	with the CNN (6.55%), which could confuse vegetation and crops due to the different
	acquired dates of the two images. Undoubtedly, non-vegetation, which includes urban
	areas, has certainly gained surface area from vegetated areas over the 11-year period by
	agreement of the three classifications, even though RF presents a lower land cover change
	value (1.41%) than SVM and CNN (5.66% and 3.40%, respectively). For the RF and SVM
	classifications, non-vegetation was developed from cropland as well (2.49% and 4.14% in
	To conclude, our classification results showed that it is possible to map land use
	with different algorithms and analyze land use changes between two years. First,
	increasing cropland surface indicates that agricultural activities remained an important
	economic sector in the peninsula, and there were essentially no signs of abandoned
	agricultural land during the study period. Second, non-vegetation areas have sharply
	grown in the 11-year period due to the dramatically increasing urbanization of the
	peninsula, especially some coastal cities that are highly frequented by tourists, since
	tourism is rapidly developed in the peninsula. The very dense population corresponds to
	a high level of artificialization of the territory, which is growing faster than the national
	average, fueled by a construction of housing and nonresidential premises. This human

cropland assembled into one class to facilitate the comparisons. The majority of the land in the peninsula retained the same LULC between 2007 and 2018, and at least 65% to 66% of the area remained unchanged according to the SVM and RF classifications. However, SVM and RF, respectively). A small part of the non-vegetated area was classified as cropland in 2018 in all three models (1.26% in SVM, 3.33% in RF, and 0.72% in CNN); however, it might have been confused with bare soil and non-vegetated areas such as concrete. Finally, the last two classes (non-vegetation to vegetation and all water-related areas) have very low proportions, approximately 0.30-1% in land cover change, which is likely due to the rising tides and increasing water storage in the mid-summer and to planting of small areas, such as in the city. concentration also implies the progression of urbanization toward the hinterlands, where the construction of housing and the arrival of new residents increased significantly.

  Part III LULC changes in Pays de Brest: Case studies Chapter 6.Mapping and Monitoring of LULCC in the Crozon Peninsula from 2007 to 2018 by ML algorithms (SVM, RF, and CNN) and by PCC 200 overall accuracy that ranged from 80 to 90%, making it the most suitable algorithm in our case, even though RF and SVM also achieved good accuracy levels.

The study resulted in two maps that showed five different land uses (cropland, cropland with bare soil, water, vegetation and non-vegetation) in 2007 and 2018 with high accuracy. In particular, the CNN had an

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France

	Chapter 7. Mapping Crop Types Using Sentinel-2
	Data Machine Learning and Monitoring Crop
	Phenology with Sentinel-1 Backscatter Time Series in
	Pays de Brest, Brittany, France
	Journal article: Xie, G.; Niculescu, S. Mapping Crop Types Using Sentinel-2 Data Machine
	Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays
	de	Brest,	Brittany,	France.	Remote	Sens.	2022,	14,	4437.
	https://doi.org/10.3390/rs14184437
	Review: Remote sensing
	Published date: 6 September 2022
	7.1. Introduction
		Crop supply is a global issue, particularly in the context of global climate change,
	rising population, and urbanization. With increasing food demand worldwide, agriculture
	production and food security should be guaranteed by ensuring biodiversity and limiting
	the environmental impacts
					201

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France 202 decades, the science of agriculture mapping and monitoring has developed quickly, with diverse types of high spatial and temporal resolution EO data. For example, Sun et al. in 2019 conducted a study of the crop types that were located at the lower reaches of the Arvor et al. (2011) in 2011provided a methodology for mapping the main crops and agricultural practices in the Mato Grosso state in Brazil; this study was performed by two successive, supervised classifications with the EVI time series from the MODIS sensor to create an agricultural mask and a crop classification of three main crops in the state. In another study by Forkuor et al. in 2014, they found that an integration of Multitemporal optical RapidEye and dual-polarized SAR TerraSAR-X data can efficiently improve the classification accuracy of crops and crop group mapping in West Africa, in spite of excessive cloud cover, small sized fields, and a heterogeneous landscape. Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France203most used classification approach that is used to extract single or multiple crop types directly from satellite images[START_REF] Del Frate | Wheat cycle monitoring using RaDAR data and a NN trained by a model[END_REF][START_REF] Wardlow | Large-area crop mapping using time-series MODIS 250 m NDVI data: An assessment for the U.S. Central Great Plains[END_REF] Y. Jiang et

	al., 2020). Moreover, we also propose the hierarchical classification approach for crops
	Yangzi River in China. They performed a classification of crop-type dynamics during the mapping in this study. Hierarchical classification is well known for its capacity to solve a
	growing season by using three advanced machine learning algorithms (SVM, ANN, and RF) complex classification problem by separating the problem into a set of smaller
	with a combination of three advanced sensors (Sentinel-1 backscatter, optical Sentinel-2, progressive classifications; it produces a series of thematic maps to progressively classify
	the image into detailed classes. Wardlow & Egbert (2008) investigated the applicability of time-series MODIS 250 m NDVI data for large-scale crop mapping in the Central Great Plains of the USA. The hierarchical classification scheme was applied in this study with high classification accuracy, and instead of directly solving a complex irrigated crop mapping problem, a four-level hierarchical classification framework was implemented to and Landsat-8). Furthermore, in the Finistère department, G. Xie & Niculescu (2021) evaluated the produce a series of crop-related thematic maps that progressively classified cropland
	multiannual change detections of different LULC regions, including agricultural land with
	accuracy indices between 70% and 90%, by using HR satellite imagery (SPOT-5 and
	Sentinel-2) and three algorithms that were implemented: RF, SVM, and CNN.
	More importantly, many studies of crop mapping focuses on winter crop mapping.
	Q. Dong et al. in 2020 proposed a method called phenology-time-weighted dynamic time
	warping (PT-DTW) for mapping winter wheat using Sentinel-2 time series data, and this
	new method may exploit phenological features in two periods, with a Normalized
	Difference Phenology Index (NDPI) providing more robust vegetation information and
	reducing the adverse impacts of soil and snow cover during the overwintering period. T.
	Zhou et al. in 2017 studied the feasibility of winter wheat mapping in an urban agricultural
	. Hence, crop type mapping is in high demand. Field region with a complex planting structure using three machine learning classification
	research and remote sensing have always been the most important sources for obtaining methods (SVM, RF, and NN), and the possibility of improving classification accuracy by
	agricultural information (Y. Song & Wang, 2019), and since the first launch of Earth combining SAR and optical data.

observation satellites in 1972, continuous agriculture mapping and monitoring over large areas became possible with EO data. Moreover, the new generation of EO data, nowadays, has increased the resolution of sensors for agriculture uses, therefore since the last few Besides the contributions of the new generation of EO data, the diversity of the classification approaches and methods have provided more resources for agriculture mapping and monitoring. The classical, direct extraction approach is the traditional and areas into detailed classes. Ibrahim et al. in 2021 have also employed the hierarchical classification scheme to map crop types and cropping systems in Nigeria, using the RF classifier and Sentinel-2 imagery. Firstly, they produced a land cover map with five classes in order to eliminate other land cover types, then the next classification was performed only on cropland, where the specific crop types and cropping systems were mapped. The results indicated that the crop types were well classified with high accuracy, despite the study area being heterogeneous and smallholder-dominated.

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France 204 2011). However, even though OBC is better developed and considered as more accurate than PBC, both classification methods are able to achieve a great degree of accuracy.

; Whiteside et al.,

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France 205 growing period with the signal that is received after interacting with the canopy of the plants. Therefore, studies of crop phenology monitoring using SAR data have increased considerably in recent years. Meroni et al. in 2020 conducted a study of retrieving the crop-specific land surface phenology (LSP) of eight major European crops from Sentinel-1 SAR and Sentinel-2 optical data, where crop phenology was detected on the temporal profiles of the ratio of the backscattering coefficient VH/VV from Sentinel-1 and NDVI from Sentinel-2. They revealed that the crop phenology that was detected by Sentinel-1 and 2 could be complementary. Wali et al. in 2020 introduced rice phenology monitoring in the Miyazaki prefecture of Japan by using Sentinel-1 dual polarization (VV and VH) time series data, and attempted to clarify the relationship between rice growth parameters and the backscattering coefficient using the combination of two linear-regression lines.Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France

	The feasibility and effectiveness of winter crop type mapping and phenology
	monitoring with optical or SAR satellite data has been proven by many studies in
	agricultural field, however, some limitations remain. For example, the potential of a
	vegetation index other than NDVI and EVI has rarely been explored, and the studies have
	never been performed in a coastal area with fragmented and small-scale fields. More
	importantly, almost all the research perform and evaluate a single classification approach
	or method, instead of comparing different approaches and methods for crop type
	mapping.
	In this study, we introduce a methodology to map two winter crop types (winter
	wheat and winter barley) with Sentinel-2 optical data that was acquired during the

Canisius et al. in 2018 exploited SAR polarimetric parameters that were derived from fully polarimetric RADARSAT-2 SAR time series data to predict the growth pattern and phenological stages of canola and spring wheat in the Nipissing agricultural district of Northern Ontario, Canada. Mandal et al. in 2020 proposed a dual-pol RaDAR vegetation index (DpRVI) from Sentinel-1 difference data (VV-VH) to characterize the vegetation growth of three crop types (canola, soybean, and wheat) from sowing to full canopy development, with the accumulation of the Plant Area Index (PAI) and biomass. growing season of the winter crops. Two different classification approaches (hierarchical classification and classical direct extraction) were performed using RF-supervised classification algorithms, and two classification methods (PBC and OBC) were operated

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France

Table 18

 18 Sentinel-2 image used in the study Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France

	209

Table 19

 19 Sentinel-2 spectral bands used in the study

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France

Table 21

 21 Accuracy assessment of OBC croplands extraction Table 22 Accuracy assessment of PBC croplands extraction

Table 23

 23 Accuracy assessment of OBC winter crops extractionTable 24 Accuracy assessment of PBC winter crops extraction Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France

Table 25

 25 Accuracy assessment of hierarchical classification Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France 219Table 26 Accuracy assessment of classical direct extraction

Table 27

 27 The best polarization observed for each phenological stage of winter wheat in the studyTable 28 The best polarization observed for each phenological stage of winter barley in the study

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France 226

  Part III LULC changes in Pays de Brest: Case studies Chapter 7. Mapping Crop Types Using Sentinel-2 Data Machine Learning and Monitoring Crop Phenology with Sentinel-1 Backscatter Time Series in Pays de Brest, Brittany, France