Polina Lemenkova
email: pauline.lemenkova@gmail.com

Conservation Area Designation in the Andes

Introduction

Páramo National Park in Ecuador is known for its unique natural resources in high altitude grasslands. The ecosystems of Páramo consist mostly of rare species and are the key protected area for exceptionally high endemism. ENVI software enables to make an analysis of the area and to produce a map based on 2 criteria: vegetation amount and altitude. We need to show vegetation growing on different heights and to create 3D-visualization of the analysis.

Image: Display

Creating Altitude Layer

Creating Altitude Zones by "Intersect Regions" for each pair of ROIs. Final altitude zones are: Lowland Vegetation (1-2500m), Subparamo Vegetation (2501-3500), Paramo Vegetation (3501-4100) and Superparamo Vegetation (4101 -5000). These zones are shown on the map in different colors (yellow, beige, two greens)

Mapping and Design

Vegetation Map with grid lines. We finally added Geographic coordinate system representing a Grid Line on the Map. Map saved as Geo-TIFF.

3D-Mapping (Final

  a): True-colour composite of the ETM+ image, bands 3,2,1 (RGB). b): Image enhancement was done, since the default contrast is bad. stretched ETM+image In bands 3,2,1 (RGB). Method: Enhance-Gaussian. a): 'Forestry' composite of ETM+ Image in 4,5,3 bands. b): ETM+ Image in 7-4-2 bands (RGB) with bright vegetation usually used for general public. c): The most common false composite 4-3-2 (RGB).

  SRTM-image (Shuttle Radar Topography Mission) displayed in ENVI. b): (SRTM_resampl285) It has only 1 Band, So that we display it In grey colour. SRTM is necessary to derive elevation model. It has .hgt format and Contains the height of terrain in meters. is necessary for classification of different vegetation communities ("Transform -Tasseled Cap" ENVI) Each of the TC Bands is represented in grey scale. ETM_TC file. Right: Wetness and 4th Band. Brightness & Greenness. Bands of Vegetation Indexes. TC Greenness Index gives us a value of zero greenness: no vegetation. We use it for creating ROI. 6. Creation Vegetation Layer ROI ROI (Region of Interest). Creation of ROI In vegetation using "Tools-Region of Interest-ROI". Input Band -Greenness. Lower limit = 1.0 Max Value = upper limit In TC Image. Result: all Vegetation is selected (coloured yellow). Creation of ROI layer (vegetation). SRTM as a background image. Computation of Statistics (vegetation).

  Map + DEM) Displaying final map in 3D view by SRTM DEM image. 3D of the map was draped as classified final image of Paramo upon the SRTM file of elevation (heights). The colors were changed by ENVI (TIFF-conversion) Bibliography Author's publications on Geography, Remote Sensing and GIS: 1 S. Gauger, G. Kuhn, K. Gohl, T. Feigl, P. Lemenkova, and C. Hillenbrand, "Swath-bathymetric mapping", The expedition ANTARKTIS-XXIII/4 of the Research Vessel 'Polarstern' in 2006 557, 38-45 (2007). 2 K. Gohl et al., Crustal and Sedimentary Structures and Geodynamic Evolution of the West Antarctic Continental Margin and Pine Island Bay (Bremerhaven, Germany, 2006).

P. Lemenkova, "Using ArcGIS in Teaching Geosciences", Russian, B.Sc. Thesis (Lomonosov Moscow State University, Faculty of Educational Studies, Moscow, Russia, June 5, 2007), 58 pp.

P. Lemenkova, "Geoecological Mapping of the Barents and Pechora Seas", Russian, B.Sc. Thesis (Lomonosov Moscow State University, Faculty of Geography, Department of Cartography and Geoinformatics, Moscow, Russia, May 18, 2004), 78 pp.

5 P. Lemenkova, Ecological and Geographical Mapping of the Baltic Sea Region in the Gulf of Finland, Russian, Moscow, Russia: Lomonosov Moscow State University, Mar. 30, 2002, Term Paper

.6 H. W. Schenke and P. Lemenkova, "Zur Frage der Meeresboden-Kartographie: Die Nutzung von AutoTrace Digitizer für die Vektorisierung der Bathymetrischen Daten in der Petschora-See", German, Hydrographische Nachrichten 25, 16-21 (

2008).7 I. Suetova, L. Ushakova, and P. Lemenkova, "Geoinformation mapping of the Barents and Pechora Seas", Geography and Natural Resources 4, 138-142 (2005).

Erasmus Mundus MSc Scholarship GEM-L0022/2009


STUDY AREA

Study area: Victoria Lake and 3 countries: Tanzania, Kenya and Uganda. Lake Victoria is the 2 nd largest fresh water body on the Earth. It has unique environment and gives origin to Nile. Focus: natural landscapes. Sustainable development: prerequisite for successful environmental monitoring in the Victoria Lake. Economic development model: 1) resource-saving technologies; 2) planting and protecting forests areas; 3) environmental policy and conservation.

RESEARCH PROBLEM

Victoria Lake: unique environment, important role in the economy of countries supporting 25 M people through fish catchment reaching up to 90-270$ per capita per annum. Kenya, Tanzania and Uganda control 6%, 49% and 45% of the lake surface.

Lake catchment provides livelihood of 1/3 of the population of 3 countries with agricultural economy supported by fishing and agriculture (tea and coffee plantations). Quality of the environment is a fundamental factor in maintaining and increasing living standards of the population.

Main problem is sustainable development, well-balanced between using natural resources and human economic progress. Essential factors in this problem are population and natural resources management.

RESEARCH OBJECTIVES

Research focuses on the investigation of the environmental, biological, social and economic characteristics of the region. Aim: regional monitoring and sustainable management. Research objective: create a geodatabase (.mbd) and mapping in ArcGIS 9.3 for sustainable development and environmental monitoring. GIS project for spatial analysis: integration of raster & vector data, metadata, descriptions of layers (land cover map legend). Data organizing & management: ArcGIS 9.

DATA AND MATERIALS

• ArcGIS format .shp files of administrative boundaries fof Uganda, Tanzania, Kenya. Each .shp file has .shx and .prj: info on prj and metadata. UNEP grids.

• Precipitation climatic data in text format, digital meteorological data: precipitation values. Computing how much cropland is needed by 2030. Areas of cropland were estimated using statistics of the raster layer Land cover. Amount of crop per capita was computed by dividing total cropland in each country and total population. Estimation of cropland needed in 2030: multiplying crop per capita needed for the total population by 2030. What % of the total forest area has to be converted to cropland to expand cropland by 2030? Total forest area per country was calculated using raster statistics. Amount of cropland needed in 2030 was estimated by distraction cropland in 2030 from current cropland. Amount of forest areas was distracted. Negative values were received: there is more need for croplands than available forest areas. The % from the actual areas was calculated.

How much forest can be converted to cropland from protected areas ? Clipped vector layer of the protected areas was converted to raster using Spatial Analyst / Vector to Raster. New raster was re-classified to one class. Land cover raster layer was multiplied to the Protected areas layer. After calculation, raster of Land Cover for the territory of protected area zones was received. Areas for each country were calculated. Outside and inside located areas were estimated by subtraction those areas from the total for each country: Tanzania, Kenya and Uganda.

  for a GIS Project. Course: 'GISA 02 GIS: Geographical Information Systems -Advanced Course 0701' MSc in Geo-Information Science and Earth Observation for Environmental Modelling and Management, 09/04/2010

  Lake Victoria and surrounding countries. Graphic: Eric Daigh/Circle. b): Relief 3D image. Source: Wikipedia.

••

  DEMs: elevations (USGS). 2 tiles of the USGS DEM map format (file set of ASCII-encoded text with coordinates and elevations). Reference: WGS84, prj: Lambert Azimuthal Equal Area. Metadata of geo-referencing info: .prj files. 2 data of GTopo30 E020N40.DEM cover Kenya and Uganda; E020S10.DEM: SE Tanzania. DEM files: horizontal grid spacing of 30 arc sec (1 km). • Land cover data (raster), .bsq format: IGBP.bsq, a part of global land cover database. Pixel values: class numbers for land cover classification scheme legend.c Population data: UNEP. Raster data for Africa for 1990 and 2000. Each dataset had 2 different files: population density (popd00 and popd90) and population total. Additional miscellaneous datasets: Landsat ETM+ images, MrSid format (S-36-00_2000.sid). • Left: Landuse clipped layer. Center: Clipped DEM raster; Right: Highlighted water areas: joined 2 DEM tiles. =⇒ MAPPING AND CALCULATIONS Success Failure Statistics: Population for the country and provincial level calculated by Zonal Statistics, ArcGIS Spatial Analyst. The area statistics was calculated by Dissolve Manager, ArcToolbox. Population data for provinces: the area of each country and population raster clipped were used. Area: Add Field / Calculate Geometry / Square Km. Info about the size of each province and country: from the attribute table of each country layer. Rain Distribution: Precipitation level is higher in areas with middle precipitation (450-500mm): layers' overlay made by ArcMap and transparency. Correlation between rain distribution and topography was examined by overlay: DEM relief raster layer + Precipitation GRID ESRI layer. Notable is high precipitation in the valleys and above the lake. Low levels: highlands. Middle val-ues: plateaus. Croplands: uplands. Slicing DEM into 5 classes => Statistics on distribution of elevation types in pixels: 56440 total, 11231 of class 1: 0-999 m (19.8%); 13478 of class 2: 1000-1999 m (23,8%); 26899 of class 3: 1999-3000 m (47,6%), 4811 of class 4: 3000,1 -4000 m (8.5%) and 21 of class 5: > 4000,1 m (0.03%).

DATA PROCESSING

RESULTS

Areas of cropland needed in 2030: Kenya -248,778 km 2 ; Tanzania -991,469 km 2 ; Uganda -153,992 km 2 .


Polina Lemenkova

Bringing Geospatial Analysis to the Social Studies: an Assessment of the City Sprawl in China

Introduction

Research area: Taipei, Taiwan. Located on the north of the island, Taipei is Taiwan's core urban, political and economic center; population >2.6 M continuing to expand affecting urban landscapes. Research aim: spatiotemporal analysis of urban dynamics in study area during 15 years (1990-2005) Research objective: application of GIS methodology and remote sensing data to spatial analysis for a case study of Taipei. Data: Landsat TM images taken from the USGS. Software: ENVI GIS.

Research Problem

Recently, urbanization is notable in Taipei. Expansion of urban landscapes in Taipei has unique features. Taiwan's exceptional economic and industrial growth since 1980s: "industrial wonder of Taiwan". Country transformed from agriculture-based traditional economy into a highly industrialized hightech oriented economy.

Changes: modified social and natural landscapes. Taiwan is now a highly urbanized country with 80% of population living in cities. Intensive urbanization => affect biodiversity and ecosystems sustainability => transformation of natural landscapes. Natural land cover type => human-affected artificial surfaces; city enlarges. Human pressure increases rapidly due to the city expansion. Rapid urbanization affects complex interrelations of natural and urban ecosystems, changes structure, size and shape of landscapes. Taiwan has unique landscapes with high environmental value, outstanding natural beauty, rare and extinct species. Environmental monitoring is effective for city management and nature conservation.

K-mean Classification and Spatial Statistics:

Change detection statistics: process, in 7 steps: 1). Change detection statistics (selection the tool from the menu )2). Setting options of process 3). Choice of 'Initial Stage' image. 4). Choice of 'Final Stage' image 5). Defining equivalent classes 6). Defining pixel size for area statistics 7) Statistics output

Methods

Workflow includes following steps: 1) Preliminary processing 2) Creation color composites 3) Classification using K-means algorithm 4) Mapping using classification results 5) Accuracy assessment. The preliminary data processing includes image contrast stretching, which is useful as by default, ENVI displays images with a 2% linear contrast stretch. For better contrast the histogram equalization contrast stretch was applied to the image in order to enhance the visual quality.

Masking ROI

Region of Interest (ROI) was selected, masked and cut off from the whole image. Subset data via ROI and masking process is shown on the print screens above. Then, the classification is performed. Classification of the land cover types in the city adopted and adjusted to the current studies includes following types: 1) Forests; 2) Urban areas -2 (roads); 3) Grasslands; 4) Open fields (little or no vegetation); 5) Water areas; 6) Urban vegetation -1 (bushes); 7) Cultivated lands 8) Agricultural facilities; 9) Urban vegetation -2 (parks and squares); 10) Urban areas -2 (built-up surfaces).

ENVI GIS 4.8: Image Processing

Results

The analysis of landscape changes was performed by geospatial analysis. 2 satellite images Landsat TM were processed and classified using ENVI GIS. Result of classification: areas occupied by different land cover types were calculated and analyzed. It has been detected that different parts of the city of Taipei were developing with different rate and intensity. 3 different residential types of the city were recognized and mapped. The results demonstrated following outcomes: 1) intensive urban development of the city of Taipei; 2) decline of green areas and natural spaces and, on the contrary, increase in anthropogenic urban spaces; 3) not parallel urban development in different districts of the city of Taipei during the 15-year period of 1990-2005

Spatial Analysis

Spatial analysis performed by ENVI GIS enabled to process satellite images for urban studies. Spatio-temporal analysis was applied to Landsat TM images taken at 1990 and 2005. Built-in functions of the mathematical K-means algorithm enabled to classify Landsat TM images and derive information on land cover types. Image classification was used to analyze landscapes in Taipei which includes built-up areas and natural green areas.

Conclusion

To resume a resulting table, following conclusion can be drawn. Comparing regions I, II and III, the conclusion is the following: Region I has changes in the land cover types since 1990s. It has much more changes comparing to the core, old city area (region II) Region II (the core city) is the most stable region. It has the least changes, because this area has been already industrialized for a long time. Region III, has notable changes caused by the intensive relocation of the population to Taipei after 1980s. This is the most dynamically developing region of Taipei. Results of image processing and spatial analysis show changes in structure, shape and configuration of ur-ban landscapes in Taipei since 1990. Areas occupied by human activities increased, while natural landscapes undergone modifications. Changes in urban landscapes of Taipei are caused by the increased relocation of population, urbanization and occupied lands for urban needs.

  Left and center: 2005 and 1990 Landsat TM scenes classification (fragment). Random color visualization. Right: Combining classes

Acknowledgements

Current work was funded by Taiwan Ministry of Education Short Term Research Award (STRA) for author's 2-month visiting research stay (April -May 2013) at National Taiwan University (NTU), Faculty of Geography.


Risks of cryogenic landslide hazards and their impact on ecosystems in cold environments Polina Lemenkova

Research focuses on monitoring landscapes downgrading in specific conditions of Arctic ecosystems with cold climate conditions (marshes, permafrost, high humidity and moisture). Specific case study: cryogenic landslides typical for cold environments with permafrost. Area: Yamal Peninsula. Aim: analysis of the environmental changes caused by cryogenic landslides in northern landscapes affecting sensitive Arctic ecosystems. Thaw of the permafrost layer causes destruction of the ground soil layer and activates cryogenic landslide processes. After disaster, vegetation coverage needs a long time to recover, due to the sensitivity of the specific northern environment, and land cover types change. ILWIS GIS was used to process 2 satellite images Landsat TM taken at 1988 and 2011, to assess spatiotemporal changes in the land cover types.

Research shown ILWIS GIS based spatial analysis for environmental mapping.

Research Area

Research area: Bovanenkovo region, Yamal Peninsula. Geomorphology: flat homogeneous lowland region with low-lying plains of heights <90m. Such geographic settings create specific local environmental conditions. Yamal has the largest high-latitude wetland system in the World: 900,000 km 2 of peatlands, wetlands, dense lakes and river network. Seasonal flooding, active erosion, permafrost and local landslides. Dominating tundra vegetation types: heath, grasses, moss, lichens, woody plants (shrubs and willows). Environmental problems: climate change and landslides, affecting landscapes and causing changes in land cover types.

Land Cover Types

The defined classes include following landscapes types: shrub tundra, willows, tall willows, short shrub tundra, sparse short shrub tundra, dry grass heath, sedge grass tundra, dry short shrub tundra, dry short shrub sedge tundra, wet peatland, peatland (sphagnum). The pixels were associated with land cover classes using their digital numbers, similar to key samples.

Results

Willows

Geographic Settings

Cryogenic landslides have origin in thawing of underground permafrost layer, which has negative effects destructing upper soil layer and vegetation coverage. Area of the Kara Sea is shallow: almost 40% of the continental shelf is <50 m. The sea coasts are mostly flat, flooded during the high tide. Located in the area of permafrost distribution, soils are frozen for the most of the year, with the depth of the frozen soil up to 0.2 m in the north and 2 m in the south. The ecosystems of the region are adapted towards specific Arctic environment.

Conclusions

Results show overall increase of woody vegetation (willows and shrubs), not typical for local environment, and decrease of peatlands, grass and heath. Environmental factors: active cryogenic landsliding. Climatic factor: increase of annual average T => permafrost thawing and abnormal increase of woody plants. Gradual changes in plant species patterns and distribution affect landscapes in Yamal.

Triggering factors: complex climatic-environmental changes in Arctic and local cryogenic processes, e.g. successive change in vegetation recovering after cryogenic landslides.

Mapping

  GIS method consists inLandsat TM image classification, spatial analysis and thematic mapping, technically performed in ILIWIS GIS. Landsat scenes for land cover mapping: advantages of application in geosciences and cartography, ca 40 year history of the image record, and free availability. Landsat scenes are series of satellite imagery by NASA and the USGS with 30-m resolution. Workflow Workflow includes following steps: 1. Data: orthorectified Landsat TM files in GeoTIFF acquired over the area of Bovanenkovo, Yamal Peninsula. Images: 1988 and 2011, taken in growing season with visible vegetation. Data capture, import, converting .img file into ASCII raster format (GDAL). After converting, each image contained collection of 7 Landsat raster bands. 2. Data pre-processing: Enhancement of visual color and contrast. Geographic referencing of Landsat TM: setting UTM projection, E Zone 42, N Zone W, WGS 1984 datum (Georeference Corner Editor).3. Area Of Interest (AOI) was identified and cropped on raw images This area shows region in a large scale which represents tundra landscapes.

4 .

 4 Supervised Classification by Minimal Distance method. Method is based on spatial analysis of the spectral signatures of object variables.5. Sampling of classes: using Sample Set tool in ILWIS GIS. Training pixelsfor each land cover type with distinguishable contrast colors selected as representative samples and stored as classification key.

Acknowledgements Current work has been supported by the Finnish Center for International Mobility (CIMO) for author's research stay at the University of Lapland, Arctic Center (Grant No. TM-10-7124), during 01 July -31 December 2011.


Polina Lemenkova

Detection of vegetation coverage in urban agglomeration of Brussels by NDVI indicator using eCognition software and remote sensing measurements

The study focuses on the semi-automatic detection of the vegetation on the satellite panchromatic image covering area of Brussels, Belgium. Using functions of the Normalized Difference Vegetation Index (NDVI) and spectral reflectane parameters of the image, the vegetation was identified on the satellite scene. The research question was to assess, how NDVI measurements can be used for urban studies using remote sensing data. The aim is to distinguish and separate on the map built-up areas from the green spaces (parks, gardens, etc) within the urban landscape. The research is supported by the raster imageand the eCognition software for image analysis. The results show detected vegetation areas in eastern part of Brussels. The research demonstrated methodological applicability of eCognition software for GIS-based urban mapping and ecological assessment (areas and sizes of vegetation coverage).

Introduction

Study area encompasses selected regions of the Brussels municipality, Belgium. In the past years the city of Brussels is experiencing intensification of the density of building structures. Unlike some other European cities where the problem is urbanization and expansion of the city margins to the suburbia, urban structure of Brussels is intensification of the buildings density in the city centre and existing dwelling districts.

Research problem

City structure tends to become more intense and dense, due to the process of filling empty spaces in the urban patterns and high level of housing. Another example of urban processes in Brussels is reorganization of the industrial areas. At the same time, monitoring vegetation areas is essential for environmental sustainability of the capital. Lack of the green spaces may cause ecological instability and increase atmospheric pollution. Remote sensing data (raster image) were used together with NDVI function, in order to detect areas covered by city parks aimed to highlight specific problems of Brussels.

Data

Data include vector and raster types. Raster data consists o fVHR Pléiades satellite imagery covering Brussels city. The image IMG PHR1A P has been provided by the Astrium, EADS company, projected in UTM (Universe Transverse Mercator projection system), 31 • N, WGS84 reference ellipsoid. Vector data include ground topographic map in shp format (ArcGIS), a part of the municipal project of Brussels URBIS.

Research objective

Research objective: to evaluate, how to efficiently use object oriented image analysis for mapping land cover types using raster images. Detecting vegetation and built-up areas using remote sensing (RS) data enables to assess the percentage of the coverage of the city by newly created building. Vegetation areas were masked to highlight urban growth. This method is based on use of RS and cadastral data, processed by modelbased sub-pixel supervised classification and non-parametric ML method.

Software: eCognition eCognition enables object oriented multi-resolution processing of raster images. It is the first object-oriented image analysis commercial software on geospatial market. It provides appropriate link between RS imagery and GIS. Principle of eCognition: images processing by information contained in the image as not by single pixels but rather by objects and their topology. Using segmentation algorithm and extraction of image object primitives (parks, gardens, buildings), it classifies the whole segments of the homogeneous image, recognized as objects.

Methods

Step I. Panchromatic image was loaded and processed.

Step II. Image was segmented using principle of the Multiresolution Segmentation. This operation consists in splitting image into segments, to simplify the complexity of the initial image. It is done by the machine embedded logic based on mathematic algorithms and simplifying models. The general rule divides the area into regions according to the principle "neighbour pixels have similar parameters" (spectral reflectance value, texture, form, shape).

Step III. Vegetation coverage was detected and separated from other objects (impervious structures) using natural characteristics of its spectral reflectance. The arithmetic expression for Normalized Difference Vegetation Index (NDVI) was created in eCognition operators by formula for spectral reflectance in visible (VIS) and near infrared (NIR) bands. This enabled to detect pixels of vegetation: NDVI=(NIR-VIS)/(NIR+VIS). It was added to the conditions of objects processing thereafter.

Step IV. Vegetation extraction. Logical condition for vegetation detection: we assign all objects with NDVI values >0.3 as vegetation. This is based on the vegetation properties: dense tree canopy usually have positive values of NDVI (0.3 to 0.8). On the contrary, all other objects have low NDVI values. E.g.: water bodies have low reflectance in both spectral bands (band 3 and band 4), therefore, they have very low positive and sometimes slightly negative NDVI values (depending on the local hydro-chemical conditions, depth). Bare soils usually have small positive NDVI values (0.1 to 0.2), as their spectral reflectance in near-infrared bands is larger than in red ones. Based on this, the NDVI formula was applied and green areas within the city were distinguished. The objects with NDVI values >0.3 were assigned to the 'vegetation' class.

Results

Acknowledgement

Conclusions

Urban landscapes have complex environmental and socioeconomic function and serve as habitat and agricultural surface in the surroundings. Land cover studies supported by satellite image contribute to the development of urban management system. Using object-oriented approach together with GIS techniques applied to the RS data enables to perform geospatial analysis with special focus on urban landscapes and a case study of Brussels.

  Current work has been supported by Bourse d'excellence, Service de Bourse d'étude, Wallonie-Bruxelles International for author's 2-months research stay at l'Université libre de Bruxelles (01/09/2012 -30/10/2012). Grant Nr. SOR/2011/36604.

Proceedings of the International Cartographic Conference (2005).