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DEDICATION

To my parents Bernard and Véronique SAGANG I hope that this achievement will complete the dream that you had for me all those many years when you chose to give me the best education you could. with little effect of the satellite sensor used. However, these results also confirm that, whatever the spectral indices used and attention paid to sensor quality and pre-processing, the signal is xvi not sufficient to warrant accurate pixel wise predictions, because of large relative RMSPE, especially above (200-250 Mg.ha -1 ). The design-based approach, for which average AGB density values were attributed to mapped land cover classes, proved to be a simple and reliable alternative (for landscape to region level estimations), when trained with dense ALS samples.

LIST OF FIGURES

AGB and species diversity measured within 74 field inventory plots (distributed along a savanna to forest successional gradient) were higher for the vegetation located in the MDNP compared to their pairs in the Nachtigal area. les pixels de savane avant la transition vers la forêt n'ont enregistré aucun évènement de feu.

Des assemblages distincts d'espèces spectrales (β-diversity) sont apparents en forêt et sont corrélés avec l'âge de la transition. L'accumulation de l'AGBALS avec l'âge de la forêt est de of 4,3 Mg.ha -1 an -1 pour les jeunes forêts (< 20 ans) contre of 3,2 Mg.ha -1 an -

I.1.1 Context and justification

A major controversy emerged after a recent study suggested large scale afforestation as the most effective solution for climate change mitigation [START_REF] Bastin | The global tree restoration potential[END_REF]. Among the issues raised, the economic cost of such a strategy is not the biggest. The actual impacts in terms of carbon storage, but also biodiversity reduction, as other ecosystems are forcibly transformed are discussed [START_REF] Thomas | Reconciling biodiversity and carbon conservation[END_REF][START_REF] Abreu | The biodiversity cost of carbon sequestration in tropical savanna[END_REF]. Moreover, afforestation often implies interfering with natural succession, for clearing the land prior to planting (sometimes exotic)

trees. It makes sense to improve our knowledge of the dynamics taking place naturally in absence of direct human impacts, prior to launching vast, costly, possibly damaging and ultimately uncertain national afforestation programs. In Africa, forest and savanna are the dominant biomes covering respectively 11% and 34% of the land surface of the continent [START_REF] Nangendo | Composition of woody species in a dynamic forestwoodland -savannah mosaic in Uganda : implications for conservation and management[END_REF][START_REF] Parr | Tropical grassy biomes: misunderstood, neglected, and under threat[END_REF] and accounting for more than 60% of terrestrial productivity [START_REF] Beer | Terrestrial gross carbon dioxide uptake: Global distribution and covariation with climate[END_REF]. Several studies, scattered from Guinea to the Central African Republic [START_REF] Achoundong | Formation et évolution des recrûs sur savanes[END_REF][START_REF] Youta-Happi | Bilan de la dynamique du contact forêt-savane en quarante ans ( 1950-1990 ) Dans la région du confluent du Mbam et du Kim, Centre-Cameroun. Peuplements anciens et actuels des forêts tropicales[END_REF][START_REF] Mitchard | Measuring biomass changes due to woody encroachment and deforestation / degradation in a forestsavanna boundary region of central Africa using multi-temporal L-band radar backscatter[END_REF][START_REF] Mitchard | Woody encroachment and forest degradation in sub-Saharan Africa ' s woodlands and savannas 1982 -2006[END_REF] and central Gabon [START_REF] Jeffery | Fire management in a changing landscape: A case study from Lopé national park, Gabon[END_REF][START_REF] Cardoso | A distinct ecotonal tree community exists at central African forest-savanna transitions[END_REF] have illustrated a widespread forest encroachment over savannas, while referring to time windows of variable lengths within the last six decades. While forest encroachment seems appealing for climate change mitigation policies, like the Reducing Emissions from Deforestation and Degradation (REDD+) initiative, it is at the expense of reduced species diversity especially for species-rich savannas [START_REF] Abreu | The biodiversity cost of carbon sequestration in tropical savanna[END_REF]. Therefore promoting forest cover (Bonn Challenge) for carbon sequestration may not always be reconcilable with a biotope conservation/restoration agenda, despite claims to the contrary [START_REF] Thomas | Reconciling biodiversity and carbon conservation[END_REF][START_REF] Abreu | The biodiversity cost of carbon sequestration in tropical savanna[END_REF][START_REF] Dinerstein | A "global safety net" to reverse biodiversity loss and stabilize earth's climate[END_REF].

In Central Africa, a significant challenge to predict the effect of global change on biome distributions is to bring out innovative approaches that offset the scarcity of spatially and temporally detailed landscape-scale information. Previous efforts have been either samplebased or employed coarse spatial resolution data [START_REF] Youta-Happi | Arbres contre graminées : la lente invasion de la savane par la forêt au Centre-Cameroun[END_REF][START_REF] Youta-Happi | Bilan de la dynamique du contact forêt-savane en quarante ans ( 1950-1990 ) Dans la région du confluent du Mbam et du Kim, Centre-Cameroun. Peuplements anciens et actuels des forêts tropicales[END_REF][START_REF] Cuni-Sanchez | African savanna-forest boundary dynamics: A 20-year study[END_REF][START_REF] Deklerck | Rate of forest recovery after fire exclusion on anthropogenic savannas in the Democratic Republic of Congo[END_REF] owing to the challenges of maintaining sampling over long periods combined with the inherent constraints of field data collection which do not consider the whole variability of the landscape. Remote sensing (RS) therefore has a great potential for use in mapping biodiversity [START_REF] Broadbent | Spatial partitioning of biomass and diversity in a lowland Bolivian forest: Linking field and remote sensing measurements[END_REF][START_REF] Hill | Vegetation index suites as indicators of vegetation state in grassland and savanna: An analysis with simulated SENTINEL 2 data for a North American transect[END_REF][START_REF] Féret | biodivMapR: An r package for αand β-diversity mapping using remotely sensed images[END_REF], biomass [START_REF] Bastin | Aboveground biomass mapping of African forest mosaics using canopy texture analysis : Toward a regional approach Aboveground biomass mapping of African forest mosaics using canopy texture analysis : toward a regional approach[END_REF][START_REF] Kumar | Review of the use of remote sensing for biomass estimation to support renewable energy generation[END_REF][START_REF] Pandit | Estimating Above-Ground Biomass in Sub-Tropical Buffer Zone Community Forests, Nepal, Using Sentinel 2 Data[END_REF][START_REF] Forkuor | Above Ground Biomass Mapping in West African Dryland Forest Using Sentinel-1 and 2 Datasets -A Case Study[END_REF] and periodical phenomena i.e. fires [START_REF] Nangendo | Composition of woody species in a dynamic forestwoodland -savannah mosaic in Uganda : implications for conservation and management[END_REF][START_REF] Miller | Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)[END_REF][START_REF] Escuin | Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images[END_REF][START_REF] Sunderman | Remote sensing approaches for reconstructing fire perimeters and burn severity mosaics in desert spring ecosystems[END_REF][START_REF] Chen | Mapping fire regimes in China using MODIS active fire and burned area data[END_REF] with continuous spatial coverage over large geographic areas. More recent satellite constellations carrying passive optical sensors (notably Sentinel 2; 10 m; Planet; 3.7 m) of improved temporal, spectral and spatial resolutions allow more detailed characterizations of compositional gradients in the vegetation, notably in terms of the abundance of broad functional/optical plant types. [START_REF] Féret | biodivMapR: An r package for αand β-diversity mapping using remotely sensed images[END_REF] proposed an unsupervised method for preliminary assessment of compositional gradients, which could be crudely referred to as spectral Beta diversity (βdiversity) gradients. However the performance of such spaceborne data is known to be poor in characterising some structural properties of the vegetation [START_REF] Avitabile | An assessment of forest biomass maps in Europe using harmonized national statistics and inventory plots[END_REF]. This is the case of aboveground biomass (AGB) where the sensitivity of currently available spaceborne data sources (prior to the launch of GEDI and Biomass sensors) is known to be poor to characterise high AGB densities (say, >200 Mg.ha-1; [START_REF] Avitabile | An assessment of forest biomass maps in Europe using harmonized national statistics and inventory plots[END_REF]Réjouméchain et al., 2019). In areas with a high degree of cloudiness like in western Central Africa [START_REF] King | Spatial and temporal distribution of tropospheric clouds observed by MODIS onboard the terra and aqua satellites[END_REF], atmospheric effects are responsible for spatial artefacts observed in the surface reflectance of spaceborne optical data [START_REF] Song | Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?[END_REF][START_REF] Morton | Amazon forests maintain consistent canopy structure and greenness during the dry season[END_REF] also some spaceborne sensors fail to distinguish the fine vegetation textures that are found in forest to grass transition areas. To overcome this problem, several authors [START_REF] Asner | Tropical forest carbon assessment: integrating satellite and airborne mapping approaches[END_REF][START_REF] Baccini | Improving pantropical forest carbon maps with airborne LiDAR sampling[END_REF][START_REF] Asner | Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric[END_REF]Zhang et al., 2017;[START_REF] Réjou-Méchain | Upscaling Forest Biomass from Field to Satellite Measurements : Sources of Errors and Ways to Reduce Them[END_REF] recommended the integration of airborne LiDAR scanning (a.k.a. ALS) data as an intermediate level in vegetation modelling. Due to its ability to accurately characterize the vegetation's threedimensional structure, ALS has indeed emerged as the reference technology for mapping vegetation structure at landscape scale [START_REF] Asner | Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric[END_REF][START_REF] Réjou-Méchain | Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest[END_REF][START_REF] Adhikari | Determinants of aboveground biomass across an afromontane landscape Mosaic in Kenya[END_REF][START_REF] Jha | Forest aboveground biomass stock and resilience in a tropical landscape of Thailand[END_REF] we focus on optical sensors) as well as common storage and computing facilities, such as the Google Earth Engine platform [START_REF] Gorelick | Google Earth Engine: Planetary-scale geospatial analysis for everyone[END_REF] enable the characterization of large areas and the generation of dynamic, transparent, systematic, repeatable, and spatially exhaustive information products. Helped with these game-changing tools, it is at long last possible to have the hindsight (45 years for Landsat) and coverage allowing the study of ecosystem dynamics at a meaningful spatiotemporal scale [START_REF] Estes | The spatial and temporal domains of modern ecology[END_REF][START_REF] Wulder | Land cover 2.0[END_REF]. It is also possible to focus on phenomena involving land covers of contrasted spectral signatures. The case of forest-savanna ecotone (FSE) is a perfect study case in this respect, because the two major ecosystem states do have distinct signatures. In such conditions, transition dates can be easily assessed (Zhu and Woodcock, 2014;[START_REF] Devries | Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series[END_REF][START_REF] Dutrieux | Monitoring forest cover loss using multiple data streams, a case study of a tropical dry forest in Bolivia[END_REF][START_REF] Hamunyela | Monitoring deforestation at sub-annual scales as extreme events in landsat data cubes[END_REF]. Besides, one of the main ecological drivers of the forest-savanna dynamics, fire [START_REF] Sankaran | Determinants of woody cover in African savannas[END_REF][START_REF] Bond | What limits trees in C4 grasslands and savannas? Annual review of ecology[END_REF]Staver et al., 2011a;[START_REF] Gomes | Responses of Plant Biomass in the Brazilian Savanna to Frequent Fires[END_REF], also leaves conspicuous marks that can be successfully characterised from satellite images [START_REF] Daldegan | Spatial patterns of fire recurrence using remote sensing and GIS in the Brazilian savanna: Serra do Tombador Nature Reserve, Brazil[END_REF][START_REF] Daldegan | Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone[END_REF]. Thanks to the spatial resolution of Landsat image (30 m) the influence of fire on the vegetation structure can be investigated at a fine spatial details as compared to past studies that relied on burned area products derived from coarse (500 m) Moderate Resolution

Imaging Spectroradiometer (MODIS) (Staver et al., 2011a;[START_REF] Diouf | Relationships between fire history , edaphic factors and woody vegetation structure and composition in a semi-arid savanna landscape ( Niger , West Africa )[END_REF][START_REF] Axelsson | Rates of woody encroachment in African savannas reflect water constraints and fire disturbance[END_REF][START_REF] Venter | Drivers of woody plant encroachment over Africa[END_REF].

Emerging economies are currently experiencing a rapid expansion of built infrastructures (buildings, roads and hydroelectric dams); [START_REF] Ermgassen | The Role of "No Net Loss" Policies in Conserving Biodiversity Threatened by the Global Infrastructure Boom[END_REF][START_REF] Jones | Major dams and the challenge of achieving "No Net Loss" of biodiversity in the tropics[END_REF] for alleviating poverty and delivering economic growth [START_REF] Agenor | Public Infrastructure And Growth : New Channels And Policy Implications[END_REF][START_REF] Donaldson | Railroads of the Raj: Estimating the Impact of Transportation Infrastructure[END_REF]. Infrastructure can affect biodiversity in multiple ways, including direct habitat loss within the built infrastructure footprint, alteration of ecosystem properties or fragmentation [START_REF] Torres | Assessing large-scale wildlife responses to human infrastructure development[END_REF]. This is even more significant with hydroelectric dam construction which generally implies the permanent flooding of naturally drylands [START_REF] Jones | Major dams and the challenge of achieving "No Net Loss" of biodiversity in the tropics[END_REF]. Implementing such long-lived infrastructures without being accompanied by strong environmental safeguards can turn out to be drivers of biodiversity and ecosystem service loss [START_REF] Ranger | Addressing 'deep' uncertainty over long-term climate in major infrastructure projects: four innovations of the Thames Estuary 2100 Project[END_REF]. This is the case in the Central region of Cameroon where the hydroelectric dam construction over the Sanaga River crossing a FSE will undoubtedly have significant impact on the vegetation. The Nachtigal Hydropower Company (NHPC) in charge of the implementation of this project therefore took the engagement to avoid/reduce carbon and biodiversity loss as regards to the Sustainable Development Goals (SDGS). Understanding the dynamics of FSE in the area and induced changes in the vegetation structure and floristic composition is essential to evaluate the negative impacts of the dam within the construction area and to guide NHPC in setting up mitigation and compensation measures which will be implemented in nearby protected areas without a management plan: the Mpem et Djim national park (MDNP) and the community forest COPAL.

I.1.2 General and specific objectives

I.1.1.1. General objective

The general objective of this study is to characterize long-term (> 40 years) dynamics of forestsavanna ecotone (FSE) and induced changes in the vegetation structure and composition within two contrasted scenarios of anthropogenic pressures in the Guineo-Congolian transition area of the Central Region of Cameroon.

I.1.1.2. Specific objectives

Specifically, this study aimed to:

-Describe the variation in vegetation types and aboveground biomass over a forestsavanna transitional landscape;

-Quantify the rate of forest encroachment and the induced changes on the functional properties of the vegetation;

-Examine the role of fire in shaping the vegetation structure and dynamics;

-Describe the succession pattern in the vegetation structure and floristic composition as forest encroaches over savanna.

I.1.3 Research hypothesis and questions

This work relies on four research hypotheses:

1-Airborne LiDAR data improves the precision of landscape scale description of the vegetation types and AGB variation when used as intermediate level between field inventory data and optical satellite data;

2-Satellite data with high spatial and spectral resolution and cloud computing of Landsat image archives make it possible to automate the monitoring of land cover and characterise main drivers i.e. fire, in a simple, objective and reproducible way;

3-The frequency of fire determine the fate of forest-savanna transitions by shaping the structure and composition in the woody layer;

4-Both the aboveground biomass and floristic composition follow a successional gradient corresponding to the identified landscape dynamics.

We formulated four research questions with the view of answering to our research objectives:

1-What level of accuracy can we achieve when combining ALS data and different optical satellite imageries to describe vegetation types and structure in forest-savanna transitions?

2-What is the potential of Landsat image archives and current cloud computational capacities in monitoring long-term change in vegetation structure and characterise the frequency of fire across forest-savanna transitions?

3-How do the structure and composition of the vegetation vary along a fire frequency gradient?

4-What is the rate of AGB recovery; beta-diversity structuring and compositional shifts or trends in the floristic composition along a forest successional gradient?

I.2. LITERATURE REVIEW

I.2.1. Forest-savanna ecotone in tropical areas: definitions and concepts

Transitional areas between forest and savanna are the most widespread ecotone in tropical Africa [START_REF] Bouvet | An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR[END_REF] and this coexistence results into an area with evolutionary dynamism, storing genetic animal and plant diversity and acting as an important locus for the generation of new species [START_REF] Smith | Biodiversity hotspots and beyond: the need for preserving environmental transitions[END_REF]. At broad scales, the location of the transition is shaped by water availability, mediated strongly at local scales by disturbances such as fires, herbivory and spatial variation in soil properties. Uncertainties still remain on the relative contribution of the drivers and their interactions in determining the evolution of forest-savanna ecotones (FSE). FSE therefore constitutes a critical, yet poorly understood, component of tropical landscape dynamics. The distribution of major biomes of the world are controlled by climate, particularly the distribution of temperature and precipitation under which they are subjected [START_REF] Whittaker | Communities and ecosystems[END_REF][START_REF] Olson | Terrestrial Ecoregions of the World : A New Map of Life on Earth[END_REF][START_REF] Kottek | World map of the Köppen-Geiger climate classification updated[END_REF]. Savannas are ecosystems characterized by the coexistence of a discontinuous tree layer with an understory that is dominated by a continuous layer of herbaceous C4 grasses (Poaceae) and sedges (Cyperaceae) [START_REF] Archer | Tree-Grass Interactions In Savannas[END_REF][START_REF] Higgins | Fire, resprouting and variability: A recipe for grass-tree coexistence in savanna[END_REF][START_REF] Bond | What limits trees in C4 grasslands and savannas? Annual review of ecology[END_REF][START_REF] Ratnam | When is a "forest" a savanna, and why does it matter?[END_REF][START_REF] Parr | Tropical grassy biomes: misunderstood, neglected, and under threat[END_REF][START_REF] Oliveras | Many shades of green: the dynamic tropical forest-savannah transition zones[END_REF]. The openness of the canopy favours the establishment of light demanding C4 grasses in the understory [START_REF] Hoffmann | Comparative fire ecology of tropical savanna and forest trees[END_REF] which dry off during the dry season and produce sufficient biomass which promotes fires. Consequently the association with fire has led both C4 grasses and the trees that grow with them to develop fire-adaptive strategies. In contrast, forests are characterized by closed, shaded environments with cooler and more humid micro-climatic conditions which limits the development of light demanding C4 grasses reducing thus the probability of fire occurrence [START_REF] Ratnam | When is a "forest" a savanna, and why does it matter?[END_REF][START_REF] Hoffmann | Ecological thresholds at the savanna-forest boundary: How plant traits, resources and fire govern the distribution of tropical biomes[END_REF][START_REF] Parr | Tropical grassy biomes: misunderstood, neglected, and under threat[END_REF]. The tree species that dominate forest habitats are largely shade tolerant but very sensitive to fires (Hoffmann et al., 2009a;[START_REF] Murphy | What controls the distribution of tropical forest and savanna?[END_REF].

Savannas constitute the most spatially extensive biome in the world (Fig. 1) and are found along a broad range of precipitation, temperature and topo-edaphic regimes from tropical to temperate areas particularly in North America, Latin America, Africa, Australia and South East Asia [START_REF] Solbrig | The diversity of the savanna ecosystem[END_REF][START_REF] Archer | Tree-Grass Interactions In Savannas[END_REF][START_REF] Marchant | Understanding complexity in savannas : climate , biodiversity and people[END_REF].

Fig. 1: Distribution of the major savanna biome across the tropics [START_REF] Marchant | Understanding complexity in savannas : climate , biodiversity and people[END_REF].

In tropical humid areas like in Central Africa where mean annual rainfall varies between 1 500 and 1 800 mm moist humid forests are expected to dominate (Fig. 2; [START_REF] Whittaker | Communities and ecosystems[END_REF].

However, several experimental and modelling work have demonstrated that savannas can occur in areas where climate, soils, and topography suggest forest should dominate [START_REF] Swaine | The effects of fire exclusion on savanna vegetation at Kpong, Ghana[END_REF][START_REF] Moreira | Effects of fire protection on savanna structure in Central Brazil[END_REF][START_REF] Russell-Smith | Response of Eucalyptusdominated savanna to frequent fires: lessons from Munmarlary, 1973--1996[END_REF]Favier et al., 2004a[START_REF] Favier | Abrupt shifts in African savanna tree cover along a climatic gradient[END_REF]Bond et al., 2005;[START_REF] Bond | What limits trees in C4 grasslands and savannas? Annual review of ecology[END_REF]. Forest-savannah transitions in tropical areas have therefore been often referred to as zones of tension between two very different biomes where the stability and distribution of each vegetation at local scales depends on the spatial and temporal variability of abiotic and biotic drivers, and ecological processes and attributes (Fig. 3; [START_REF] Favier | Abrupt shifts in African savanna tree cover along a climatic gradient[END_REF]Hoffmann et al., 2009b;[START_REF] Lehmann | Deciphering the distribution of the savanna biome[END_REF].

Fig. 2: Functional distribution of the main terrestrial biomes in function of the mean annual precipitation and temperature as proposed by [START_REF] Whittaker | Communities and ecosystems[END_REF].

Unfortunately no agreement has been reached concerning the relative importance of climate, fire, hydrology, herbivory and soil characteristics in mediating the balance between these biomes [START_REF] Tinley | The influence of soil moisture balance on ecosystem patterns in southern Africa[END_REF][START_REF] Hopkins | Ecological processes at the forest-savanna boundary. Nature and dynamics of forest-savanna boundaries[END_REF][START_REF] Ruggiero | Soil-vegetation relationships in cerrado (Brazilian savanna) and semideciduous forest, Southeastern Brazil[END_REF][START_REF] Hirota | The climatic sensitivity of the forest, savanna and forest--savanna transition in tropical South America[END_REF][START_REF] Good | Climatological determinants of woody cover in Africa[END_REF]. At the global scale, and at large time scales, climate (mean annual temperature, precipitation seasonality and dry season length), fire regimes (frequency and intensity of fires)

and soil types determine distribution between forests (dark green; Fig. 3), grassy vegetation (dark purple as natural, light purple has human-modified; Fig. 3) and grassland biomes. At the community scale, fire regimes, soil properties and herbivory are the main drivers, and ecological processes are mostly reflected in tree-grass coexistence (see Fig. 3). At the local scale, many drivers and ecological processes affect the given vegetation existing at that precise point in space and time [START_REF] Oliveras | Many shades of green: the dynamic tropical forest-savannah transition zones[END_REF] Fig. 3: Main drivers and processes operating in shaping forest-savannah transitions and their scale of operability from global scale to local scale [START_REF] Oliveras | Many shades of green: the dynamic tropical forest-savannah transition zones[END_REF].

I.2.2. Major drivers and processes shaping the distribution of forest-savanna vegetation I.2.2.1. Role of climate

In tropical areas where mean annual rainfall exceeds the 650-1000 mm threshold increased rainfall would have minimal effect on forest savanna boundaries, as these systems are at a disequilibrium between vegetation and climate (Bond et al., 2005;[START_REF] Sankaran | Determinants of woody cover in African savannas[END_REF][START_REF] Bond | Carbon dioxide and the uneasy interactions of trees and savannah grasses[END_REF][START_REF] Favier | Abrupt shifts in African savanna tree cover along a climatic gradient[END_REF]. Changes in temperature could affect interactions between rainfall and woody cover as the raise in temperature increases transpiration which effectively counteract the effects of higher rainfall. Furthermore, widespread tree mortality recorded after extreme drought events offset increases in woody growth recorded during increased rainfall [START_REF] Fensham | Drought-induced tree death in savanna[END_REF]. Also empirical studies from [START_REF] February | Influence of competition and rainfall manipulation on the growth responses of savanna trees and grasses[END_REF] demonstrates that increasing rainfall favours the competition between trees and grasses, ultimately suppressing tree growth. In tropical areas an increase in the CO2 concentrations at the local scale will favour the development of trees that use the C3 photosynthesis pathway over the dominant grasses that use more costly C4 metabolically pathway [START_REF] Higgins | Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally[END_REF]. The long-term increase in atmospheric CO2 will thus favour trees over most abundant lowland savannah grasses [START_REF] Bond | Carbon dioxide and the uneasy interactions of trees and savannah grasses[END_REF]. Both woody thickening of savannahs and forest encroachment into savannahs is therefore expected at the regional scale due to increasing CO2. The net impact may be greater in mesic than in arid savannah environments [START_REF] Bond | Carbon dioxide and the uneasy interactions of trees and savannah grasses[END_REF][START_REF] O'connor | Bush encroachment in southern Africa: Changes and causes[END_REF]. The presence of a continuous grass layer in savanna favours frequent fires which regularly destroys the aerial biomass of woody saplings (Fig. 5;

topkill Hoffmann et al., 2009b). Repeated topkill results in a demographic bottleneck which limit successful tree seedling germination, establishment and/or transition to mature size classes [START_REF] Higgins | Fire, resprouting and variability: A recipe for grass-tree coexistence in savanna[END_REF][START_REF] Higgins | Effects of four decades of fire manipulation on woody vegetation structure in savanna[END_REF][START_REF] Ward | A century of woody plant encroachment in the dry Kimberley savanna of South Africa[END_REF] whereas saplings that lack the ability to regenerate are eliminated under frequent fires [START_REF] Fensham | Assessing woody vegetation cover change in north-west Australian savanna using aerial photography[END_REF].

I.2.2.2. Role of fire

Amongst the multiple factors that limit tree cover in savanna, fire appears to be the most widespread and universal in savannas worldwide to prevent from canopy closure [START_REF] Bond | Fire as a global ' herbivore ': the ecology and evolution of flammable ecosystems[END_REF][START_REF] Sankaran | Determinants of woody cover in African savannas[END_REF][START_REF] Bond | What limits trees in C4 grasslands and savannas? Annual review of ecology[END_REF][START_REF] Lehmann | Deciphering the distribution of the savanna biome[END_REF]Staver et al., 2011b;[START_REF] Reiche | Combining satellite data for better tropical forest monitoring[END_REF]. Grassy ecosystems in Central Africa have frequent fire occurrences (Fig. 4; [START_REF] Archibald | Defining pyromes and global syndromes of fire regimes[END_REF] therefore the distribution of savanna and forest cannot be adequately explained without explicitly considering the essential role of fire.

Fig. 4: Spatial distribution of pyromes [START_REF] Archibald | Defining pyromes and global syndromes of fire regimes[END_REF]. Pyromes represent regions of the globe that have similar fire frequencies, intensities, sizes, burned areas, and fire season lengths. Pixels with greater than 60% probability of being uniquely categorized are plotted (85% of the data). FIL = Frequent-intense-large; FCS = frequent-cool-small; RIL = rareintense-large; RCS = rare-cool-small; and ICS = intermediate-cool-small. The fire-suppression threshold [START_REF] Hoffmann | Ecological thresholds at the savanna-forest boundary: How plant traits, resources and fire govern the distribution of tropical biomes[END_REF].

A fire-free interval of sufficient duration is therefore required to ensure woody saplings to reach an adult state with a critical size at which they are no longer susceptible to topkill [START_REF] Bond | A proposed CO2-controlled mechanism of woody plant invasion in grasslands and savannas[END_REF]. At the individual level the point at which a tree has grown and is much less likely to be topkilled by subsequent fires can be referred to as a fire-resistance threshold and once a stem has surpassed this threshold it can continue to grow and extent his crown area leading to a gradual canopy closure [START_REF] Veenendaal | Structural , physiognomic and above-ground biomass variation in savannaforest transition zones on three continentshow different are co-occurring savanna and forest formations ?[END_REF][START_REF] Veenendaal | On the relationship between fire regime and vegetation structure in the tropics[END_REF]. The savannah gradually reaches a fire-suppression threshold due to its canopy closure that provides moist conditions with a low-light understory environment. The light demanding grass layer which was previously dominant is gradually replaced by fire resistant grasses (Aframomum spp., Chromolaena odorata) and shrub of light-demanding forest tree species such as Albizia spp.

( [START_REF] Youta-Happi | La disparition des savanes au Centre Cameroun entre 1950 et 1990[END_REF][START_REF] Youta-Happi | Arbres contre graminées : la lente invasion de la savane par la forêt au Centre-Cameroun[END_REF][START_REF] Youta-Happi | Bilan de la dynamique du contact forêt-savane en quarante ans ( 1950-1990 ) Dans la région du confluent du Mbam et du Kim, Centre-Cameroun. Peuplements anciens et actuels des forêts tropicales[END_REF][START_REF] Ibanez | Inferring savannahrainforest boundary dynamics from vegetation structure and composition: A case study in New Caledonia[END_REF] which limits the occurrence of fire. [START_REF] Veenendaal | Structural , physiognomic and above-ground biomass variation in savannaforest transition zones on three continentshow different are co-occurring savanna and forest formations ?[END_REF] evidenced an increase in the proportion of forest species with an increase in canopy closure and suggested that fire suppression serves to promote the likelihood of survival of forest species. Tropical forest is much less flammable [START_REF] Uhl | Deforestation, fire susceptibility, and potential tree responses to fire in the eastern Amazon[END_REF][START_REF] Hoffmann | Ecological thresholds at the savanna-forest boundary: How plant traits, resources and fire govern the distribution of tropical biomes[END_REF] and generally burns much less frequently and less intensely, allowing it to maintain a dense canopy with distinct ecosystem properties [START_REF] Eldridge | Impacts of shrub encroachment on ecosystem structure and functioning: towards a global synthesis[END_REF]. Savanna and forest have been frequently regarded to represent alternate stable states maintained by the positive feedback between fire and vegetation [START_REF] Hoffmann | Tree topkill, not mortality, governs the dynamics of savanna-forest boundaries under frequent fire in central Brazil[END_REF][START_REF] Hirota | The climatic sensitivity of the forest, savanna and forest--savanna transition in tropical South America[END_REF]Staver et al., 2011b;[START_REF] Dantas | Fire drives functional thresholds on the savannaforest transition[END_REF]. The alternative stable states theory postulates that two ecosystem states can occur under the same set of environmental conditions, and the presence of one or the other is determined by disturbance and historical pathway at the community and larger scale [START_REF] Whittaker | The role of mosaic phenomena in natural communities[END_REF]. Both fire-resistance and firesuppression thresholds represent a switch from a state maintained by frequent fires to a state that is not strongly influenced by fire. Once either threshold is surpassed, a return to the prethreshold state should become increasingly dependent on extreme events. Once a stem has passed the fire-resistance threshold, the probability of topkill will decline as the stem grows and accumulates more bark which protects the vessels from damages due to high temperatures generated by fires. Even so, extremely intense fires can cause some topkill of large trees that would otherwise be fire resistant [START_REF] Williams | Reproductive Phenology of Woody Species in a North Australian Tropical Savanna1[END_REF][START_REF] Ryan | How does fire intensity and frequency affect miombo woodland tree populations and biomass?[END_REF]. At the ecosystem level, once the fire-suppression threshold has been reached, subsequent fires will generally be less frequent, less intense and restricted to times of severe drought. Although lowintensity fires can be devastating in humid rainforest [START_REF] Cochrane | Fire as a recurrent event in tropical forests of the eastern Amazon: Effects on forest structure, biomass, and species composition[END_REF][START_REF] Barlow | Fire-mediated dieback and composi-tional cascade in an Amazonian forest[END_REF], forests of the seasonal tropics appear much more resilient to fire [START_REF] Hoffmann | Tree topkill, not mortality, governs the dynamics of savanna-forest boundaries under frequent fire in central Brazil[END_REF][START_REF] Balch | Size, species, and fire behavior predict tree and liana mortality from experimental burns in the Brazilian Amazon[END_REF]; therefore, the return to a savanna state probably requires multiple burns to cause successive reduction in tree cover and a gradual expansion of grasses.

I.2.2.3. Role of herbivory

Several authors have explored the influence of mammalian herbivores on savanna structure [START_REF] Augustine | Ungulate effects on the functional species composition of plant communities: herbivore selectivity and plant tolerance[END_REF][START_REF] Langevelde | Effects of fire and herbivory on the stability of savanna ecosystems[END_REF][START_REF] Augustine | Regulation of shrub dynamics by native browsing ungulates on East African rangeland[END_REF][START_REF] Sankaran | Woody cover in African savannas : The role of resources , fire and herbivory[END_REF]. An increase in the level of grazing leads to reduced fuel load, which makes fire less intense and, thus, less damaging to trees and, consequently, results in an increase in woody vegetation (Fig. 6 a and b; [START_REF] Langevelde | Effects of fire and herbivory on the stability of savanna ecosystems[END_REF].

The system then switches from a state with combined trees and grasses to a state with solely trees. This phenomenon has been observed in the region of Ilaha and Maracà in Guyana where forest species have become abundant in grazed savannas in contact with the forest [START_REF] Eden | Pasture development on cleared forest land in northern Amazonia[END_REF]. Likewise, an increase in the woody cover was observed on the margin of savannas exploited by extensive livestock farming in the north of Côte-d'Ivoire [START_REF] Buzon | Les savanes du Nord de la Côte d'Ivoire. Mésologie et dynamique : l'herbe[END_REF]). In the Sudano-Guinean savannas of the Adamawa plateau in Cameroon where extensive livestock farming is traditionally practiced, overgrazing led to a reduction in the grass cover which contributes to the decrease in the intensity of fires, would have favoured the multiplication savannah shrubs, and therefore an increase in the rate of woody cover [START_REF] Hurault | Surpaturage et transformation du milieu physique; formations vegetales, hydrologie de surface, geomorphologie, l'exemple des hauts plateaux de l'Adamaoua[END_REF]. In contrast large browsers (i.e. elephants) may enhance the effect of fire on trees because they reduce woody biomass, thus indirectly stimulating grass growth (Fig. 6 c andd; [START_REF] Accatino | Trees, grass, and fire in humid savannas-The importance of life history traits and spatial processes[END_REF][START_REF] Augustine | Regulation of shrub dynamics by native browsing ungulates on East African rangeland[END_REF]. This consequent increase in fuel load results in more intense fire and increased decline of biomass. The system then switches from a state with solely trees to a state with trees and grasses.

Fig. 6: Changes in the equilibrium woody and grass biomass with levels of grazing (A and B) and browsing (C and D). Sudden jumps of woody and grass biomass occur at distinct levels of grazing, and browsing. Solid lines give the stable equilibria, and dashed lines give the unstable equilibria. Arrows indicate the direction of development. (Modified from [START_REF] Langevelde | Effects of fire and herbivory on the stability of savanna ecosystems[END_REF].

I.2.2.4. Role of soil and topography

Locally, other edaphic factors, such as insufficient nutrient stocks, shallow, sandy or seasonally flooded soils, might prevent some sites from ever becoming forest during fire suppression. Soils can also vary spatially along the forest-savannah transition in fertility (e.g. organic matter, cation exchange capacity, macronutrients), soil physical properties (e.g. percentage of sand), and soil depth, the latter two factors combining to determine soil water availability. Soil fertility has long been recognized to be lower in savannah soils (Baillie, 1987) which is responsible for the lack of trees in savanna biomes [START_REF] Bond | Beyond the forest edge: Ecology, diversity and conservation of the grassy biomes[END_REF] as low nutrient availability limits tree growth. In contrast, other studies suggest that areas of high nutrient availability are associated with a reduction in woody cover, largely as a result of competition from grasses [START_REF] Mills | Constraint on woody cover in relation to nutrient content of soils in western southern Africa[END_REF]. The interaction of soil fertility with water availability and soil water holding capacity can strongly influence forest-savannah boundaries [START_REF] Veenendaal | Structural , physiognomic and above-ground biomass variation in savannaforest transition zones on three continentshow different are co-occurring savanna and forest formations ?[END_REF]. Forest patches are usually located in areas with higher soil moisture than savannah patches, but in seasonally flooded parts of the landscape the waterlogging can create anoxic conditions for roots, favouring short-lived grasses that grow in the non-flooded season. Conversely, a more regular water supply and shallower water table enables gallery forests to persist in savannah landscapes. [START_REF] Lehmann | Deciphering the distribution of the savanna biome[END_REF] show that areas of low rainfall and high nutrient availability facilitate the growth of palatable grasses, which in turn leads to increased herbivory and the maintenance of a savanna system. In contrast, high rainfall and nutrient availability facilitate rapid tree growth, resulting in a transition to a forests system. Soil nutrient availability and underlying geology may also alter vegetation communities through complex interactions with disturbance.

In tropical areas the great and very uniform depth of the ferrallitic soils which are dominant favour the growth of woody species. In fact, their water reserves are always largely sufficient to ensure a continuous plant growth, except locally when ferruginous crusts, dismantled or not, are superficial. In Kruger National Park woody cover was observed to increase in areas with nutrient-poor, granite soils and decrease in areas with nutrient-rich basalt soils over a sixty-year period, a pattern likely driven by herbivory [START_REF] Eckhardt | Trends in woody vegetation cover in the Kruger National Park, South Africa, between 1940 and 1998[END_REF]. It is thought that grasses in nutrient-poor soils do not recover as quickly under high grazing pressure as grasses in nutrientrich soils, resulting in decreased tree-grass competition. The findings of [START_REF] Levick | Context-dependent vegetation dynamics in an African savanna[END_REF], from a more northerly section of Kruger National Park (KNP) are similar, but point to a more ubiquitous increase. Here, woody cover increased in both nutrient-poor granite soils and nutrient-rich basalt soils but the increase was slightly more prominent in the basalt areas due to presumed higher levels of grazing and lower fire frequency, which encouraged tree growth [START_REF] Levick | Context-dependent vegetation dynamics in an African savanna[END_REF]. [START_REF] Buitenwerf | Increased tree densities in South African savannas: >50 years of data suggests CO 2 as a driver[END_REF] where AGB was high on crests, decreased midslope, and then increased near stream channels.

Overall, they observed 5-to-8-fold lower AGB on clayey, basalt-derived soil than on granites, and suggested this is due to herbivore-fire interactions rather than lower hydraulic conductivity or clay shrinkage/swelling, as previously hypothesized. Furthermore, [START_REF] Devine | Woody cover in wet and dry African savannas after six decades of experimental fires[END_REF] observed much higher woody cover in a wet granite-based region than a dry granite-based area of Kruger National park. It would appear, therefore, that though interactions between soils properties and disturbance play an important role in regulating woody cover. By mapping AGB within and outside fire and herbivore exclusion, they found that basalt-derived soils support tenfold higher AGB in the absence of fire and herbivory, suggesting high clay content alone is not a proximal limitation on AGB.

I.2.3. Dynamics of forest-savanna ecotone in Central Africa

Several authors have described a widespread woody encroachment into savannas in Central Africa (Fig. 7 and Fig. 8; [START_REF] Boulvert | Avancée ou recul de la forêt centrafricaine : changements climatiques, influence de l'homme et notamment des feux[END_REF][START_REF] Youta-Happi | La disparition des savanes au Centre Cameroun entre 1950 et 1990[END_REF][START_REF] Youta-Happi | Arbres contre graminées : la lente invasion de la savane par la forêt au Centre-Cameroun[END_REF][START_REF] Youta-Happi | Bilan de la dynamique du contact forêt-savane en quarante ans ( 1950-1990 ) Dans la région du confluent du Mbam et du Kim, Centre-Cameroun. Peuplements anciens et actuels des forêts tropicales[END_REF]Favier et al., 2004b;[START_REF] Mitchard | Measuring woody encroachment along a forest-savanna boundary in Central Africa[END_REF][START_REF] Mitchard | Woody encroachment and forest degradation in sub-Saharan Africa ' s woodlands and savannas 1982 -2006[END_REF][START_REF] Cuni-Sanchez | African savanna-forest boundary dynamics: A 20-year study[END_REF][START_REF] Aleman | Forest extent and deforestation in tropical Africa since 1900[END_REF][START_REF] Devine | Determinants of woody encroachment and cover in African savannas[END_REF][START_REF] Axelsson | Rates of woody encroachment in African savannas reflect water constraints and fire disturbance[END_REF][START_REF] Deklerck | Rate of forest recovery after fire exclusion on anthropogenic savannas in the Democratic Republic of Congo[END_REF] with significant impacts on the global carbon budget of the continent [START_REF] Poulter | Contribution of semi-arid ecosystems to interannual variability of the global carbon cycle[END_REF]. Modified from [START_REF] Mitchard | Woody encroachment and forest degradation in sub-Saharan Africa ' s woodlands and savannas 1982 -2006[END_REF]. [START_REF] Boulvert | Avancée ou recul de la forêt centrafricaine : changements climatiques, influence de l'homme et notamment des feux[END_REF] described a woody expansion occurring in the forest-savanna ecotones of Central Africa Republic and attributed it to be a consequence of the urbanization of the population. The example of the Bambari region shows that with the increase of the Fulani shepherd community and their herds, the fire regime has been totally modified. In the absence of high intensity bushfires trees took advantage over herbaceous plants, rendering pastures unsuitable and causing the migration of many herders to other areas [START_REF] Gautier | Agriculteurs et éleveurs des savanes d'Afrique centrale : de la coexistence à l'intégration territoriale[END_REF] especially towards humid savannas [START_REF] Boutrais | La géographie rurale à l'ORSTOM. Affiches de la géographie[END_REF]. While studying the dynamics of about 3000 years old isolated savannas enclosed by forest in the eastern part of the Congolese Mayombe, [START_REF] Schwartz | Present dynamics of the savanna-forest boundary in the Congolese Mayombe : A pedological , botanical and isotopic ( 13C and 14C ) study[END_REF] described an abrupt boundary between forest and savanna as a consequence of numerous savanna fires lit by hunters. did not change due to an increasing influx of recruits, while average diameter of savanna trees increased due to decreasing recruitment. Carbon stored by forest specialists increased from 3.12 to 5.60 MgC.ha -1 , suggesting a forest carbon recovery rate of 0.62 MgC.ha -1 .yr -1 . They estimated at least 150 years as the time required for a total forest recovery over savanna after fire exclusion. [START_REF] Jeffery | Fire management in a changing landscape: A case study from Lopé national park, Gabon[END_REF] reported in the Lopé National Park in Gabon that savannas can sufficiently thicken up over a 15 year period to reach a structure comparable to a colonising forest when protected from fires. Later [START_REF] Cardoso | A distinct ecotonal tree community exists at central African forest-savanna transitions[END_REF] evidenced the presence of an ecotonal community in the Loppé National Park that occupies a narrow belt between savanna and forest and stabilises the forest-savanna mosaic even when the savanna is burned regularly.

In Cameroon, Youta (1998) observed the encroachment of gallery forest into the surrounding savannas at a rate of 0.6-2 m.yr -1 between 1950 and 1990 in the Central Region. However the encroachment rate was slower in savannas neighbouring forest established on waterlogged soils compared to savannas that were close to forest found on well-drained ferrallitic soils dominated by Malvaceae and Ulmaceae. Youta (1998) also hypothesized that human presence in Guinean savannahs reduced fire occurrence which favoured the establishment of forest species and later contributed into the formation of forest patches within savannas. [START_REF] Mitchard | Measuring biomass changes due to woody encroachment and deforestation / degradation in a forestsavanna boundary region of central Africa using multi-temporal L-band radar backscatter[END_REF][START_REF] Mitchard | Measuring woody encroachment along a forest-savanna boundary in Central Africa[END_REF]Mitchard et al. ( , 2013) described a rapid woody encroachment of savannas in central Cameroon (Mbam et Djerem National Park) where forest edges were dominated by young pioneer trees, with dead and dying savanna trees prevalent, which is strong evidence that this constituted young encroaching forests. They hypothesize one of the causes to be either recent reduction in fire frequency due to a reduction in human pressure caused by urbanization, as rainfall did not alter significantly over the study period. Their alternative hypothesis was that increasing atmospheric CO2 concentrations were altering the competitive balance between grasses and trees.

In the coming decades, African biomes are predicted to experience profound changes whether through global drivers such as rainfall, temperature and atmospheric CO2 [START_REF] Malhi | Spatial patterns and recent trends in the climate of tropical rainforest regions[END_REF][START_REF] Lewis | Increasing human dominance of tropical forests[END_REF][START_REF] Hély | Sensitivity of African biomes to changes in the precipitation regime[END_REF][START_REF] Lewis | Tropical forests and the changing earth system[END_REF][START_REF] Zanne | Changes in the potential distribution of humid tropical forests on a warmer planet[END_REF][START_REF] Zeng | Committed changes in tropical tree cover under the projected 21st century climate change[END_REF][START_REF] Melissa | Carbon Conflicts and Forest Landscapes in Africa[END_REF][START_REF] Western | Predicting extreme droughts in savannah Africa: A comparison of proxy and direct measures in detecting biomass fluctuations, trends and their causes[END_REF][START_REF] Vieilledent | Bioclimatic envelope models predict a decrease in tropical forest carbon stocks with climate change in Madagascar[END_REF] or through local drivers such as fire and herbivory regime [START_REF] Oliveras | Many shades of green: the dynamic tropical forest-savannah transition zones[END_REF]. Although the main lines of the vegetation history in Central Africa are known, there are few studies which actually quantify the changes in the vegetation structure. By being the zone of tension between two very different biomes close to their climatic margins, a better understanding of forest-savanna ecosystem functioning is urgently required, as well as predictions of how they may respond to changes. The growing evidence of forest encroachment into savanna in Central Africa has profound implications for biodiversity; it decreases landscape heterogeneity, reducing the diversity of invertebrates, birds and large mammals [START_REF] Sirami | The impact of shrub encroachment on savanna bird diversity from local to regional scale[END_REF]Smit and Prins, 2020).

Large scale vegetation change also has consequences for energy, carbon and water budgets [START_REF] Woodward | Vegetation dynamics -Simulating responses to climatic change[END_REF][START_REF] Mitchard | Woody encroachment and forest degradation in sub-Saharan Africa ' s woodlands and savannas 1982 -2006[END_REF].

I.2.4. Remote sensing-based modelling of vegetation structure and dynamics in forest-savanna transitional areas

Conventionally, vegetation structure have been assessed using field-based inventory plots [START_REF] Jayakumar | Floristic inventory and diversity assessment-a critical review Vegetation analysis and Remote sensing View project Climate Change View project Floristic inventory and diversity assessment-a critical review[END_REF][START_REF] Higgins | Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally[END_REF][START_REF] Arellano | A Standard Protocol For Woody Plant Inventories And Soil Characterisation Using Temporary 0.1-Ha Plots In Tropical Forests on JSTOR[END_REF]. Field inventories are expensive, time consuming, labour intensive and they do not integrate the spatial heterogeneity of the vegetation structure within the landscape especially for remote areas with limited access [START_REF] Lu | The potential and challenge of remote sensing-based biomass estimation[END_REF][START_REF] Maniatis | The potential of using xylarium wood samples for wood density calculations: A comparison of approaches for volume measurement[END_REF][START_REF] Clark | Quantifying spatial and temporal dynamics of tropical forest structure using high resolution airborne lidar[END_REF][START_REF] Réjou-Méchain | Upscaling Forest Biomass from Field to Satellite Measurements : Sources of Errors and Ways to Reduce Them[END_REF]. In Central Africa historical field inventory data of forest-savanna ecotone over several decades are especially rare (Mitchard et al., 2013). Therefore it is difficult to use field plots alone in assessing natural and anthropogenic induced variation in vegetation structure and biomass over large areas. Given the extend of tropical ecosystems, access limitations and structural complexity, Remote Sensing (RS) methods have since long been used in assessing and characterize tropical ecosystems [START_REF] St-Onge | Automated forest structure mapping from high resolution imagery based on directional semivariogram estimates[END_REF][START_REF] Youta-Happi | Bilan de la dynamique du contact forêt-savane en quarante ans ( 1950-1990 ) Dans la région du confluent du Mbam et du Kim, Centre-Cameroun. Peuplements anciens et actuels des forêts tropicales[END_REF].

Moreover as new technologies emerged, RS provides a systematic and synoptic view of earth cover for changes in land cover and to reveal aspect of biological diversity directly. Satellite data are important tools in the interdisciplinary study of tropical forests that are increasingly integrated into studies that monitor changes in vegetation cover within tropical forests-savanna ecotones and also applied with other types of data (i.e. geographical, topography, hydrology)

to investigate the drivers of land cover changes.

Laser scanning methods such as LiDAR (Light Detection and Ranging; Fig. 9) have also emerged as a promising technology for estimating forest height, volume and AGB in boreal, temperate and tropical forests [START_REF] Drake | Sensitivity of largefootprint lidar to canopy structure and biomass in a neotropical rainforest[END_REF][START_REF] Clark | Quantifying mortality of tropical rain forest trees using high-spatial-resolution satellite data[END_REF]. Able to provide direct descriptors of forest structure including tree height, crown size, and tree density [START_REF] Heurich | Estimation of Forestry Stand Parameters Using Laser Scanning Data in Temperate, Structurally Rich Natural European Beech (Fagus Sylvatica) and Norway Spruce (Picea Abies) Forests[END_REF][START_REF] Bergen | Remote sensing of vegetation 3-D structure for biodiversity and habitat: review and implications for lidar and radar spaceborne missions[END_REF], LiDAR sensors are of particular interest for the estimation of forest biomass and carbon stocks [START_REF] Corona | Area-based lidar-assisted estimation of forest standing volume[END_REF][START_REF] Steinmann | Small area estimations of proportion of forest and timber volume combining Lidar data and stereo aerial images with terrestrial data[END_REF].

Airborne LiDAR are essential assets in the sense that they supply very high spatial and geometrical resolution (centimetre resolution) data on the tri-dimensional organization of the cover of areas that are too large or inaccessible. Recent studies proved the potentiality of very high resolution LiDAR images in accurate description of tropical forest cover, heterogeneity, cover change and biomass assessment [START_REF] Vincent | Détection des variations de structure de peuplements en forêt dense tropicale humide par Lidar aéroporté[END_REF]. Nevertheless assessments of tree species and forest structure at regional scales are thus usually interpolated from insitu measurements (field inventories) which can provide accurate estimates of species richness and vegetation structure at local scales (Fig. 10).

I.2.4.1. Mapping aboveground biomass

Despite the recommendation of [START_REF] Ciais | The carbon balance of Africa: synthesis of recent research studies[END_REF] to consider the contribution of savanna to the overall carbon budget, most of the studies focus only on closed forests [START_REF] Saatchi | Benchmark map of forest carbon stocks in tropical regions across three continents[END_REF][START_REF] Baccini | Estimated carbon dioxide emissions from tropical deforestation improved by carbon-density maps[END_REF][START_REF] Baccini | Improving pantropical forest carbon maps with airborne LiDAR sampling[END_REF][START_REF] Mitchard | Woody encroachment and forest degradation in sub-Saharan Africa ' s woodlands and savannas 1982 -2006[END_REF] and only few studies [START_REF] Mermoz | Biomass assessment in the Cameroon savanna using ALOS PALSAR data[END_REF][START_REF] Bouvet | An above-ground biomass map of African savannahs and woodlands at 25 m resolution derived from ALOS PALSAR[END_REF] have attempted to evaluate the amount of above-ground woody biomass (AGB) stored in savannah biomes. Congo Basin countries are still in the preparatory phase of implementing REDD+ [START_REF] Joseph | REDD+ readiness: Early insights on monitoring, reporting and verification systems of project developers[END_REF] which involve establishing administrative structures, determining reference levels for carbon stocks and development of credible monitoring, reporting and verification (MRV) systems, among others.

Reducing the uncertainty of AGB estimates within FS transitional area is not only essential to have a better knowledge the contribution of the country to the REDD+ but is also of increasing relevance for the African and global carbon budget.

Conventionally, credible estimation of biomass stocks are achieved through a design-based approach [START_REF] Kangas | Forest inventory: methodology and applications[END_REF][START_REF] Naesset | Model-assisted regional forest biomass estimation using LiDAR and InSAR as auxiliary data: A case study from a boreal forest area[END_REF]. The design-based approach consist in attributing to each vegetation type averaged AGB densities (referred to as Emission factors in this context) estimated from field-based inventories. Although design-based approach is still recommended in the Intergovernmental Panel on Climate Change (IPCC) guidelines for greenhouse gas inventories (IPCC 2019; Tier 1 and 2), precise estimation of these AGB densities require a rigorous sampling, for instance via a national forest inventory (McRoberts et al., 2019), which is still lacking in many tropical countries. Such carbon inventory systems relying exclusively on ground observations alone may not be feasible for large area surveys due to expense associated with installation and re-measurement of an exhaustive ground network. In addition, regional arrays of ground plots cannot always provide accurate local estimates, for instance at sub-regional and administrative unit level, or by land-use or cover classes [START_REF] Fahey | Forest carbon storage: ecology, management, and policy[END_REF].

Recent studies indicate that forest inventories involving combination of data from field-based samples and auxiliary information from remote sensing platforms, i.e., model-based, are being preferred because they tend to reduce costs while improving precision of the estimates [START_REF] Ene | Assessing the accuracy of regional LiDAR-based biomass estimation using a simulation approach[END_REF]Mcroberts et al., 2019). In fact auxiliary information provided by remote sensing systems has the potential to enhance the terrestrial surveys for forest carbon estimation [START_REF] Ene | Assessing the accuracy of regional LiDAR-based biomass estimation using a simulation approach[END_REF]. However no RS technology is capable of directly measuring vegetation AGB (e.g. [START_REF] Asner | High-fidelity national carbon mapping for resource management and REDD+[END_REF]. Instead, indirect relationships are established between RS indices that vary with plant's AGB and estimations of AGB in field sample plots (the 'ground truth'; Fig. 11). If we focus on multispectral optical remote sensing, the sensitivity of currently available satellite data sources (prior to the launch of GEDI and Biomass sensors) at high aboveground biomass (AGB) densities (100-200 Mg.ha -1 ) is known to be poor [START_REF] Avitabile | An assessment of forest biomass maps in Europe using harmonized national statistics and inventory plots[END_REF]Réjouméchain et al., 2019) due to signal saturation [START_REF] Huete | Overview of the radiometric and biophysical performance of the MODIS vegetation indices[END_REF][START_REF] Foody | Predictive relations of tropical forest biomass from Landsat TM data and their transferability between regions[END_REF]. Also in areas with a high degree of cloudiness like in western Central Africa [START_REF] King | Spatial and temporal distribution of tropospheric clouds observed by MODIS onboard the terra and aqua satellites[END_REF], atmospheric effects are responsible for spatial artefacts observed in the surface reflectance of spaceborne optical data [START_REF] Song | Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?[END_REF][START_REF] Morton | Amazon forests maintain consistent canopy structure and greenness during the dry season[END_REF]. Another issue is that the cost of field data acquisition of sufficient quality generally leads to a poor calibration of AGB prediction models in model-based approaches. At the forest-savanna transition zone, complex mosaics of contrasted land cover and land use types exacerbate the issue making the precise quantification of AGB stocks and stock change particularly difficult. To overcome this problem, several authors [START_REF] Asner | Tropical forest carbon assessment: integrating satellite and airborne mapping approaches[END_REF][START_REF] Baccini | Improving pantropical forest carbon maps with airborne LiDAR sampling[END_REF][START_REF] Asner | Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric[END_REF][START_REF] Xu | Spatial Distribution of Carbon Stored in Forests of the Democratic Republic of Congo[END_REF][START_REF] Réjou-Méchain | Upscaling Forest Biomass from Field to Satellite Measurements : Sources of Errors and Ways to Reduce Them[END_REF] recommended a multi-step upscaling approach based on airborne LiDAR scanning (a.k.a. ALS). Due to its ability to penetrate the canopy down to the ground surface ALS has indeed emerged as the reference technology for mapping vegetation AGB variations at landscape scale [START_REF] Asner | Mapping tropical forest carbon: Calibrating plot estimates to a simple LiDAR metric[END_REF][START_REF] Réjou-Méchain | Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest[END_REF][START_REF] Adhikari | Determinants of aboveground biomass across an afromontane landscape Mosaic in Kenya[END_REF][START_REF] Jha | Forest aboveground biomass stock and resilience in a tropical landscape of Thailand[END_REF], although cost still prevents wall-to-wall mapping at regional or national levels. The currently held assumption seems to be that the calibration of AGB mapping models based on MS imageries (model-based approach; Fig. 12) is improved when using the larger calibration (c) conversion of LiDAR structural data to aboveground carbon density estimates using new LiDAR allometrics along with a limited number of field plots; and (d) integration of the satellite map with the airborne LiDAR data to set a regional, high-resolution baseline carbon stock estimate (Fig. 12; [START_REF] Asner | Tropical forest carbon assessment: integrating satellite and airborne mapping approaches[END_REF]. When spatially explicit models are sufficiently accurate and precise (which is commonly interpreted as "when estimation uncertainty is no more than 20% of the mean", Zolkos et al., 2013), this approach would correspond to the highest quality tier of the IPCC (Tier 3).

I.2.4.2. Mapping species diversity and vegetation types

Optical sensors with a series of contiguous bands covering narrow spectral ranges allows to record information related to a range of plant properties such as the age, the water content, the leaf pigment and the chemical composition [START_REF] Curran | Remote sensing of foliar chemistry[END_REF][START_REF] Martin | High spectral resolution remote sensing of forest canopy lignin, nitrogen, and ecosystem processes[END_REF][START_REF] Townsend | The biogeochemical heterogeneity of tropical forests[END_REF]. Recent studies in tropical forests showed that tree species often have unique spectral signatures based on their structural and biochemical properties (Colgan et al., 2012b;[START_REF] Rocchini | Satellite remote sensing to monitor species diversity: potential and pitfalls[END_REF]. Therefore the variability in high spatial resolution multispectral information can be used to differentiate species or group of species at a landscape scale, even in complex tropical ecosystems based on the optical traits corresponding to the reflectance of each pixel [START_REF] Clark | Hyperspectral discrimination of tropical rain forest tree species at leaf to crown scales[END_REF][START_REF] Ustin | Remote sensing of plant functional types[END_REF][START_REF] Mbobda | Diversity, structure and carbon storage potential of the Dja Wildlife Reserve vegetation cover[END_REF][START_REF] Neba | Assessing the spatial distribution of bamboo species using remote sensing in Cameroon[END_REF].

However the spectral species described from multispectral data do not directly refer to fieldbased species diversity but rather, the spectral species distribution within a specific area can approximate its biological species richness [START_REF] Féret | Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy[END_REF] according to the spectral variation hypothesis (SVH). The SVH postulates that the spectral variation of a site can be related to its ecosystem heterogeneity [START_REF] Palmer | Quantitative tools for perfecting species lists[END_REF] since greater heterogeneity allows a higher number of species to coexist [START_REF] Wilson | Heterogeneity, diversity and scale in plant communities. The ecological consequences of environmental heterogeneity[END_REF][START_REF] Huggett | Geoecology: an evolutionary approach[END_REF]. [START_REF] Féret | biodivMapR: An r package for αand β-diversity mapping using remotely sensed images[END_REF] generated α-and β-diversity indicators from Sentinel 2 optical imagery, based on SVH (Fig. 13). An alternative to mapping species diversity is to map habitats or vegetation types. Mapping vegetation types gives information on the spatial distribution of the land cover which is essential for planning and management activities [START_REF] Chauhan | Land use/land cover changes near Hazira Region, Gujarat using remote sensing satellite data[END_REF][START_REF] Papastergiadou | Land use changes and associated environmental impacts on the Mediterranean shallow Lake Stymfalia, Greece[END_REF][START_REF] Lin | The effects of changing the resolution of land-use modeling on simulations of land-use patterns and hydrology for a watershed land-use planning assessment in Wu-Tu, Taiwan[END_REF]. An accurate and up-to-date mapping of land cover is necessary to understand and quantify the influences of social, economic and environmental drivers over the vegetation patterns with time. However, land cover classification from satellite data remains a difficult task and it is especially challenging in heterogeneous landscapes with high degrees of cloudiness like western Central Africa. Therefore atmospheric effects needs to be corrected from the satellite images prior analysis [START_REF] Song | Classification and change detection using Landsat TM data: When and how to correct atmospheric effects?[END_REF]. Another issue when generating land cover maps from passive multispectral satellite imageries (Sentinel 2; Spot 6-7; Landsat) is the confusion of spectral responses from different vegetation types which limits the accuracy of the classification result [START_REF] Poursanidis | Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping[END_REF][START_REF] Ustuner | Application of support vector machines for landuse classification using high-resolution rapideye images: A sensitivity analysis[END_REF]. Integrating LiDAR data has clearly demonstrated the ability to accurately measure both spatial and vertical vegetation structure, even over dense tropical rainforest [START_REF] Goetz | Laser remote sensing of canopy habitat heterogeneity as a predictor of bird species richness in an eastern temperate forest, USA[END_REF][START_REF] Bergen | Remote sensing of vegetation 3-D structure for biodiversity and habitat: review and implications for lidar and radar spaceborne missions[END_REF][START_REF] Huang | The influence of vegetation height heterogeneity on forest and woodland bird species richness across the United States[END_REF]. [START_REF] Marselis | Distinguishing vegetation types with airborne waveform lidar data in a tropical forestsavanna mosaic: A case study in Lopé National Park, Gabon[END_REF] suggested that LiDAR-derived vegetation profiles can provide valuable information on vegetation type and successional stage in forest savanna transitional landscapes of Gabon which helped to improve vegetation classification.

I.2.4.3. Monitoring land cover changes and disturbances

A precise monitoring of forest change is a key component for the implementation of the REDD+ mechanism. Participating countries are required to establish robust Measuring, Reporting and Verification (MRV) systems with which to track activity data and land cover changes at regular timeframes [START_REF] Joseph | REDD+ readiness: Early insights on monitoring, reporting and verification systems of project developers[END_REF]. Remote sensing based approaches play a key role in monitoring land cover changes, as they provide the best opportunity for mapping vegetation change over large areas [START_REF] Sexton | Remote Sensing of Environment Long-term land cover dynamics by multi-temporal classi fi cation across the Landsat-5 record[END_REF][START_REF] Devries | Robust monitoring of small-scale forest disturbances in a tropical montane forest using Landsat time series[END_REF]. With the opening of the U.S.

Geological Service (USGS) data archive, large amounts of medium-resolution optical earth observation data have been made freely available to the public [START_REF] Wulder | The global Landsat archive: Status, consolidation, and direction[END_REF] which allowed for high temporal resolution forest change monitoring at unprecedented spatial scales [START_REF] Hansen | High-Resolution Global Maps of 21st-Century Forest Cover Change[END_REF]. The Landsat program comprises the longest continuous Earth

Observation mission which has been imaging the surface of the Earth for more than 40 years with consistent spatial (30 m) and temporal (16 day) resolutions since 1984 [START_REF] Wulder | The global Landsat archive: Status, consolidation, and direction[END_REF]. Landsat time series data have been used to produce long-term reliable forest versus nonforest land-cover classifications [START_REF] Hansen | High-Resolution Global Maps of 21st-Century Forest Cover Change[END_REF][START_REF] Ceccherini | Abrupt increase in harvested forest area over Europe after 2015[END_REF][START_REF] Jha | Forest aboveground biomass stock and resilience in a tropical landscape of Thailand[END_REF] to monitor land cover dynamics (Fig. 14). Also Landsat image archives have been used in mapping burned areas at a finer spatial resolution to quantify fire severity, trends and patterns in fire occurrence [START_REF] Bastarrika | BAMS: A tool for supervised burned area mapping using Landsat data[END_REF][START_REF] Quintano | Burn severity mapping from Landsat MESMA fraction images and Land Surface Temperature[END_REF][START_REF] Liu | Burned area detection based on Landsat time series in savannas of southern Burkina Faso[END_REF][START_REF] Daldegan | Spectral mixture analysis in Google Earth Engine to model and delineate fire scars over a large extent and a long time-series in a rainforest-savanna transition zone[END_REF]. Processing such amount of images over large extents and long-time-series has become considerably more cost-effective, given the powerful computational processing of Google Earth Engine (GEE ; [START_REF] Gorelick | Google Earth Engine: Planetary-scale geospatial analysis for everyone[END_REF]. GEE is a cloud-based platform (Fig. 15) for planetary-scale geospatial analysis that brings Google's massive computational capabilities to bear on a variety of high-impact societal issues including deforestation, drought, disaster, disease, food security, water management, climate monitoring and environmental protection. It is unique in the field as an integrated platform designed to empower not only traditional remote sensing scientists, but also a much wider audience that lacks the technical capacity needed to utilize traditional supercomputers or large-scale commodity cloud computing resources. It generates surface reflectance data from the Landsat Ecosystem Disturbance Adaptive Processing System (LEDAPS) which includes the calibration from at-sensor radiance to the top of atmosphere (TOA) reflectance and the atmospheric correction from TOA reflectance to surface reflectance [START_REF] Vermote | Atmospheric correction of visible to middle-infrared EOS-MODIS data over land surfaces: Background, operational algorithm and validation[END_REF][START_REF] Masek | A Landsat surface reflectance dataset for North America, 1990-2000[END_REF]. The study was conducted within a forest savanna mosaic of the Guineo-Congolian transitional area of the Mbam et Kim division in the Centre Region of Cameroon (Fig. 16). The area is under the influence of an equatorial climate of Guinean type [START_REF] Djoufack | Étude multi-échelles des précipitations et du couvert végétal au Cameroun : Analyses spatiales, tendances temporelles, facteurs climatiques et anthropiques de variabilité du NDVI[END_REF], which is hot and humid with an average annual temperature of 25°C. Mean annual rainfall is 1,500 mm, with a rainfall distribution characterized by a dry season lasting over three months (December-March), during which the monthly rainfall is less than 70 mm. The soils are deep mostly ferralitic red and yellow with a complete hydrolysis of minerals from granite-gneissic basement caused by hot rains [START_REF] Santoir | Atlas régional Sud-Cameroun[END_REF]. Youta (1998) concluded that soils under savanna vegetation are distinguished by a sandy texture that dominates the upper horizons with lower horizons being dominated by clay. This author also reports that forest soils display a decrease in the proportion of sand in favour of clay in the upper horizons as we move from the forest edge to its core.

On the human level, the Central Region of Cameroon is witnessing a rapid population growth and recent national surveys [START_REF] Anonyme | Annuaire Statistique du Cameroun[END_REF] have projected that the region would hold about 20% of the country's population by 2020. The sites sampled featured two different contexts of anthropogenic influences: the construction area of the Nachtigal hydroelectric dam (a.k.a

Nachtigal area) which is highly influenced by human activities in opposition to the protected area of the Mpem et Djim National Park (MDNP). The Nachtigal area extends over 216 km² (4°00'-4°30'N and 11°30'-12°00'E; Fig. 16) with a relief dominated by vast peneplains which slope gently towards the valleys of the Sanaga river. Topography does not pose insurmountable obstacles to human activities. As a consequence, the area has been targeted since 1950s for small and large scale agriculture such as artisanal cocoa (Theobroma cacao L.) and palm (Elaeis guineensis Jacq.) plantations [START_REF] Binet | Condition des femmes dans la région cacaoyère du Cameroun[END_REF][START_REF] Marticou | Les structures agricoles dans le Centre Cameroun, Direction[END_REF][START_REF] Jagoret | Afforestation of savannah with cocoa agroforestry systems: A small-farmer innovation in central Cameroon[END_REF] and industrial activities (e.g. hydroelectric dam construction) which result in a landscape strongly impacted by anthropogenic pressures. The MDNP was established in 2004 and covers an area of 975 km² (5°00'-5°30'N and 11°30'-12°00'E; Fig. 16).

II.1.1.2. Vegetation

Plant formations in the area belong to the Guineo-Congolian type [START_REF] White | La végétation de l'Afrique. Mémoire accompagnant la carte de végétation de l'Afrique[END_REF][START_REF] Santoir | Atlas régional Sud-Cameroun[END_REF] [START_REF] Santoir | Atlas régional Sud-Cameroun[END_REF] dominated by savanna formations interspersed by semi-deciduous forests or gallery forests along watercourses [START_REF] Letouzey | Etude phytogéographique du Cameroun[END_REF][START_REF] Youta-Happi | La disparition des savanes au Centre Cameroun entre 1950 et 1990[END_REF][START_REF] Youta-Happi | Bilan de la dynamique du contact forêt-savane en quarante ans ( 1950-1990 ) Dans la région du confluent du Mbam et du Kim, Centre-Cameroun. Peuplements anciens et actuels des forêts tropicales[END_REF]. [START_REF] Lewis | Above-ground biomass and structure of 260 African tropical forests[END_REF][START_REF] Libalah | The Role of Environmental Drivers in Tree Community Structure of Central African Lowland Forests[END_REF][START_REF] Duncanson | Committee on Earth Observation Satellites Working Group on Calibration and Validation Land Product Validation Subgroup Aboveground Woody Biomass Product Validation Good Practices Protocol[END_REF]. PSP locations were distributed among the main types of closed-canopy vegetation's found in the area had a dimension of 100 m x 100 m (Fig. 17) following the recommendation of [START_REF] Alder | Permanent sample plot techniques for mixed tropical forest[END_REF]. based on its taxonomy available in the Global Wood Density Database [START_REF] Chave | Towards a worldwide wood economics spectrum[END_REF][START_REF] Zanne | Changes in the potential distribution of humid tropical forests on a warmer planet[END_REF]. For trees identified at the species or genus level, the average WD of the respective taxonomic level was used. For trees identified at the family level, or unidentified trees, the plot average WD was used. Tree AGB was computed using allometric models based

Airborne LiDAR sampling

Light Detection And Ranging (LiDAR) is a remote sensing system that uses lasers to measure distances between a sensor and targets of interest (Fig. 21). LiDAR instrument consists of a high frequency laser that has the ability to emit tens of thousands of laser pulses per second, from which corresponding range measurements can be derived. Rate of laser pulse emission is quantified and referred to as the "pulse repetition frequency". Pulses are delivered by scanning, so that pulses are emitted within a set number of degrees from nadir; each laser pulse and associated returned energy has an associated "scan angle", which describes the angle from nadir of the pulse. LiDAR sensors for earth applications are frequently flown aboard fixed-wing aircraft at relatively low elevations (~1000 m above ground level when mounted on aircrafts), and unmanned aerial vehicles (UAVs) are becoming more popular as LiDAR platforms

(generally flown at elevations < 150 m). Because LiDAR systems use lasers to interact with targets, LiDAR is considered a form of active remote sensing, as opposed to passive remote sensing, such as sensors aboard satellites that passively measure reflected sunlight [START_REF] Klauberg | LiDAR Analysis in R and rLiDAR for Forestry Applications[END_REF].

II.2.2.2.2. Satellite data sampling

Products from different satellite sensors were used for specific purposes in this study as described in Table II. Land cover mapping remains a difficult task and it is especially challenging in heterogeneous landscapes such as forest-savanna transitions. In addition to its location within a forest-savanna transitional zone, the anthropogenic pressure over the Nachtigal area results into a complex combination of different landforms such as urban areas, agricultural fields, pasture/scrublands, bare areas, agroforest, forest and natural areas, water surfaces [START_REF] Alexandre | Analyse de l'usage du sol de la région de Bokito (Mbam et Inoubou, Cameroun ) à partir de données de télédétection et implications sur les systèmes de culture agroforestiers[END_REF] to name but a few. One of the main issues when generating land cover maps from such complex areas is the confusion of spectral responses from different features as it can be seen in Table III. The classification accuracy from satellite images therefore depends upon both the quality of the sensor and the classification method used [START_REF] Poursanidis | Landsat 8 vs. Landsat 5: A comparison based on urban and peri-urban land cover mapping[END_REF][START_REF] Ustuner | Application of support vector machines for landuse classification using high-resolution rapideye images: A sensitivity analysis[END_REF].

In this study Spot 6/7 image was used to map land cover types over the Nachtigal area as it has already proven its potentiality in mapping heterogeneous landscapes with good accuracy [START_REF] Kuzucu | Testing the potential of vegetation indices for land use/cover classification using high resolution data[END_REF]. Properties of the Spot 6/7 image were given in Table II. Additional vegetation indices i.e. the Normalized Difference Vegetation Index (NDVI) and the Enhanced Vegetation index (EVI) were computed and added as additional bands to enhance the spectral information and increase the spectral separability between classes.

II.2.2.2.1. Image classification

A supervised image classification was used to distinguish between the different land cover types as it usually warrants higher quality of the final mapping product [START_REF] Khatami | A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research[END_REF]. Supervised classification requires labelled training data for each land cover

Accuracy assessment

The accuracy of the different classifications was obtained from a confusion matrix. In a confusion matrix, the classification results are contrasted with reference ground truth pixels.

The quality of a confusion matrix is that it distinguishes the nature of the classification errors, and also their quantities. Several measurements such as Producer's, User's the Overall accuracy and Kappa index of agreement were derived for each class. The producer's accuracy is the fraction of correctly classified pixels with regard to all pixels of that ground truth class [START_REF] Olofsson | Good practices for estimating area and assessing accuracy of land change[END_REF]. For each class of ground truth pixels (row), the number of correctly classified pixels is divided by the total number of ground truth or test pixels of that class. The User's accuracy is the fraction of correctly classified pixels with regard to all pixels classified as this class in the classified image. For each class in the classified image (column), the number of correctly classified pixels is divided by the total number of pixels which were classified as this class [START_REF] Deng | Variations of wood basic density with tree age and social classes in the axial direction within Pinus massoniana stems in Southern China To cite this version : Variations of wood basic density with tree age and social classes in the axial direction within[END_REF]. The overall accuracy is the percentage of all correctly classified pixels (from all the classes) against the total number of pixels being checked [START_REF] Deng | Variations of wood basic density with tree age and social classes in the axial direction within Pinus massoniana stems in Southern China To cite this version : Variations of wood basic density with tree age and social classes in the axial direction within[END_REF]. The Cohen's Kappa (a.k.a Kappa) coefficient is a measure of agreement between two sets of datasets. Kappa coefficient measures the percentage of data values in the main diagonal of the table and then adjusts these values for the amount of agreement that could be expected due to chance alone.

II.2.2.2. Mapping functional attributes of the vegetation

II.2.2.2.1. Aboveground biomass a) AGB estimation from ALS data

To predict vegetation AGB density from the LiDAR canopy height model, we focused on data collected over the Nachtigal area and used the savanna plot size (40 * 40 m) as our minimum mapping unit (Fig. 24).

Baccini and Asner, 2013

Where n is the number of plots, 𝑦 𝑖 is the AGBFIELD estimate for plot i, and 𝑦 ̂𝑖 is its AGB prediction.

b) AGB estimation from satellite data -Design-based AGB estimates

As a reference AGB prediction method, we followed the recommendation of the IPCC (Calvo Buendia et al.; [START_REF] Särndal | Design-Based and Model-Based Inference in Survey Sampling [with Discussion and Reply[END_REF] by averaging AGB density values (either AGBALS or AGBFIELD) per (woody vegetation) land cover class; see section I.2.4.1 for details. In the case of ALS, the area was fully characterized, and hence not sampled stricto sensu.

-

Model-based AGB estimates

Three multispectral sensors were considered, namely Spot 6-7, Landsat 8, and Sentinel 2.

Among cloud-free images in sensors archives, we looked for dry-season images acquired: (i) at (approximately) the same date (to mitigate cross-sensor differences associated to change in land cover or vegetation phenology); and (ii) as close as possible to the ALS acquisition date. The year 2015 matched these criteria and we collected Level-1C images for Spot 6/7 (acquired on January 9th, Row/Column 4912/3514), Landsat 8 (acquired on December 1 st ; Path/Row 185/057) and Sentinel 2B (acquired on December 19th mosaic of T32NQK and T32NRL).

Spaceborne optical data were processed using the Overland algorithms [START_REF] Poilvé | geoland2 -BioPar Methods Compendium of MERIS FR Biophysical Products[END_REF]. Overland is a satellite image processing chain developed by AIRBUS DS Geo which aims to produce cloud and shadow masks and perform image atmospheric corrections, especially for areas with a high degree of cloudiness like western Central Africa. It is primarily coded in the IDL language (Harris) for image processing algorithms, with a core scene model and model inversion engine that has been developed in Matlab (MatWorks) Overland uses lookup tables from LOWTRAN and performs an inversion of a coupled atmospheric scene model [START_REF] Kneizys | Atmospheric Transmittance/Radiance: Computer Code LOWTRAN[END_REF] to estimate atmospheric parameters and discard influences from sky, aerosols, and clouds on the surface reflectance. Another feature of Overland is the ability to partition the reflectance of individual pixels into respective contributions of soil, photosynthetic vegetation (green matter), and the non-photosynthetic matter (dead wood), and characterize the self-cast shadows of the rough vegetation canopies. For this, it implements a vegetation model by combining PROSPECT [START_REF] Jacquemoud | PROSPECT: A model of leaf optical properties spectra[END_REF], SAIL [START_REF] Verhoef | Light scattering by leaf layers with application to canopy reflectance modeling: The SAIL model[END_REF], and a soil model. By inverting this vegetation model, it is possible to notably derive the fractional cover of green vegetation (fCover), the canopy shade factor (CSF), and the leaf area index (LAI).

To predict vegetation AGB, the three variables provided by Overland were considered, as well as images spectral bands and vegetation indices from corrected images summarized in Table VIII. The dataset of spaceborne optical variables thus consisted of 11 variables for Spot 6/7, 13 variables for Landsat 8 and 24 variables for Sentinel 2. Imagery products from the different satellite sensors were co-aligned to the AGBALS raster and aggregated to 40 m resolution. The Random Forest (RF) algorithm [START_REF] Breiman | Random forests[END_REF] was implemented in the randomForest R package [START_REF] Andy | Classification and Regression by randomForest[END_REF] to model vegetation AGB density from spaceborne optical variables. RF is a popular machine learning technique in remote sensing studies due to its ability to handle high-dimensional datasets, to account for nonlinear relationships between response and predictor variables, and to its relative robustness to multicollinearity, model overparameterization and overfitting [START_REF] Mariana | Random forest in remote sensing: A review of applications and future directions[END_REF].

Here decrease. The procedure is iterated as long as adding a supplementary variable in the model leads to relative RMSPE decrease larger than 1%. In the case of RFFIELD, the RMSPE at each iteration of the procedure was computed using a LOO-CV over all the AGBFIELD estimates. In the case of RFALS, the number of AGBALS estimates (i.e. 117415 pixels) makes the LOO-CV computationally prohibitive. A four-fold block CV was thus used for variable selection.

The ability of design-and model-based approaches was evaluated to predict vegetation AGB outside training areas using a four-fold cross-validation procedure (Fig. 25) with the LiDARbased AGBALS map as reference AGB density values.

For design-based approaches, average AGB densities per vegetation class were assessed in the three training folds (Fig. 25), either using ALS or field data from these folds. In the remaining fold, the mean fold AGB density was computed by multiplying the AGB density of each land cover class by its respective area, relative to the total woody vegetated area of the fold.

Validation could only be performed (qualitatively) at the scale of each land cover class or the whole fold.

For model-based approaches, in the case of RFALS, AGB density predictions were generated outside training areas using the four-fold block CV. In the case of RFFIELD, to circumvent the relatively small plot number, all AGBFIELD estimates were used to train the model and generate AGB density predictions in each of the four folds. The vector of independent AGB predictions was used to compute the CV statistics (i.e., R² and RMSPE). The mean signed deviation (MSD; Equation 5) was also computed as an indicator of model bias:

Equation 5 𝑀𝑆𝐷 = 𝟏 𝒏 ∑ (𝒚 ̂𝒊 -𝒚 𝒊 )
𝒏 𝒊=𝟏 [START_REF] Ploton | Toward a general tropical forest biomass prediction model from very high resolution optical satellite images[END_REF] where n is the number of pixels, 𝑦 ̂𝑖 is the AGB prediction of pixel i by RFFIELD or RFALS and 𝑦 𝑖 is its AGBALS value.

II.2.2.2.2. Spectral floristic assemblages

The progressive increase in spatial and spectral resolutions of open-access spaceborne images offers interesting prospects for the monitoring of biodiversity from space. Species turn-over in the geographical space (β-diversity) for instance, which is a central criterion to identify areas of high biodiversity in conservation programs, has been successfully assessed in tropical forest ecosystems using airborne high spatial and spectral resolution imaging spectroscopy [START_REF] Féret | Mapping tropical forest canopy diversity using high-fidelity imaging spectroscopy[END_REF][START_REF] Colgan | Mapping savanna tree species at ecosystem scales using support vector machine classification and BRDF correction on airborne hyperspectral and LiDAR data[END_REF]. The method relies on the assumption that species (or groups of species) should be separable based on their spectral signatures, so that the variance in spectral data is lower within than among groups of species. It provided coherent and promising preliminary results when implemented on high resolution multispectral Sentinel 2 data [START_REF] Féret | biodivMapR: An r package for αand β-diversity mapping using remotely sensed images[END_REF]. Here the method implemented in the BiodivMapR R package [START_REF] Féret | biodivMapR: An r package for αand β-diversity mapping using remotely sensed images[END_REF] was used to assess and map β-diversity on a cloud free Sentinel 2 image of the dry season (27-01-2020). Forest and savanna pixels were processed separately, using a forest -savanna mask. Within a given landcover subset (i.e., forest or savanna) an unsupervised k-means clustering was used to assign individual pixels to specific classes considered as "spectral species" based on their spectral signatures. [START_REF] Féret | biodivMapR: An r package for αand β-diversity mapping using remotely sensed images[END_REF] suggested using 40 -50 spectral species to accommodate for the high diversity of tropical forests.

However using a large number of spectral species did not change the overall pattern of βdiversity in the study area, therefore the number of spectral species was set to 10. The image was then gridded into 100 x 100 m windows and the dissimilarity between each pair of windows was computed using the Bray-Curtis dissimilarity metric. The resulting dissimilarity matrix was then submitted to a Principal Coordinate Analysis (PCoA, [START_REF] Legendre | Numerical ecology[END_REF] and the first axis was retained to reflect changes in (spectral) species composition, for either savanna or forest ecosystems.

II.2.3. Monitoring vegetation dynamics in Google Earth Engine

II.2.3.1. Aggregating temporal images for landcover change analysis

There are three common approaches to deal with archive imagery collections for land cover monitoring. The simplest one consists in using individual, high quality cloud free scenes. In tropical, frequently cloudy conditions, this approach can prove difficult. Alternatively, where sufficient archive is available, temporal aggregates can be produced around pivot dates, allowing the use of information contained in partially cloudy images, at the expense of a reduced precision on the timing of detected changes. Finally, for dense temporal series, time series analysis on pixels can be performed [START_REF] Hermosilla | An integrated Landsat time series protocol for change detection and generation of annual gap-free surface reflectance composites[END_REF], to optimize the use of the available data and the temporal precision of event detection. In this study, the temporal hindsight was prioritized, and therefore had to deal with very sparse series prior to the launch of Landsat 7. The third approach was thus excluded. Moreover, a major difficulty lies in interimage heterogeneity induced by directional and atmospheric effects. Correction models are now routinely applied to recover bottom-of-atmosphere reflectance (such as the LEDAPS algorithm for Landsat 5 to 8 TM, ETM and OLI data collections produced by USGS, 2020).

To work around the spectral stability issue, different approaches can be taken for image classification, mirroring of the temporal aggregation choices summarized above: (i) re-train a classifier for each image, either manually, or automatically, if the land cover classes are sufficiently distinct. (ii) use the time redundancy to stabilize the signal at the scene level by time-aggregating a sufficient number of images, (iii) use the time redundancy to stabilize the signal at pixel level, to classify stable land cover classes between major breakpoint events.

II.2.3.2. Mapping forest and savanna using supervised methods

A supervised classification approach was first adopted to generate the set of landcover maps used for the landcover change analysis in order to generate a reference product. In this approach, individual or aggregate Landsat images were retrieved from GEE and processed on a desktop computer using supervised, pixel-based classification. Supervised classification usually warrants higher quality of the final mapping product [START_REF] Khatami | A meta-analysis of remote sensing research on supervised pixel-based land-cover image classification processes: General guidelines for practitioners and future research[END_REF], but it comes at the cost of time-consuming human inputs. [START_REF] Huete | Overview of the radiometric and biophysical performance of the MODIS vegetation indices[END_REF] were also added to the set of predictors. Classification models were trained using visually-delineated polygons of each land cover class (i.e., forest and savanna) distributed throughout each image. Thus eight binary forest (value of 1) and savanna (value of 0) maps were obtained.

II.2.3.3. Mapping forest and savanna using automated-unsupervised method

An automated and unsupervised landcover mapping pipeline in GEE was developed.

Performing landcover classification directly in the GEE is appealing in that it alleviates data transfer between the GEE platform and the operator. Besides, the use of unsupervised classification -which does not require human inputs for the processing of each single imagetogether with the massive computational capabilities of GEE, offers prospects for large-scale land cover mapping, provided that the unsupervised algorithm can accurately predict the land cover classes of interest.

In the very convenient case of forest-savanna mosaics, the spectral contrast between the two classes is so important, with highly bi-modal radiometric distribution in most spectral bands (Fig. 27), that land-cover classification can easily be performed automatically at the individual image level. This is true as long as cloud, cloud shadows and strong haze can be masked, as well as very distinct land cover classes such as water bodies. Preliminary testing suggested that a simple K-means algorithm with two clusters successfully separated the two classes. On each image passing through the pipeline, we thus randomly drew 10,000 pixels to train a K-means algorithm and predicted the class of each image pixel (using the mean EVI of each cluster to determine which one was forest or savanna). This processing step was applied to the three images available before the year 1999, and to every individual image comprised within the 2years monitoring periods after 1999.

Unfortunately, the quality of cloud, cloud shadows and haze filters contained in image quality flags are variable across collections. In subsequent analyses, the option to perform automated classifications at the individual image level was thus followed, and then keep the modal class over a series of successive images, to account for possible remaining noise caused by imperfect masking. Similarly to the manual approach, the automated landcover mapping pipeline resulted in eight binary forest (value of 1) and savanna (value of 0) maps.

II.2.3.4. Generating transition maps

Landcover maps from the manual and automated classification approaches were processed separately. In each case, the eight landcover maps were stacked to generate a transition map representing landcover change (or vegetation dynamics) between the years 1975 and 2000.

Forest existing since 1975 were older than 45 years old (> 45). Pixels that witnessed a permanent change from savanna (0) to forest (1) or the opposite were classified as "forest gain"

or "forest loss" respectively and the year of transit detection was recorded. Pixels that underwent more than one transition throughout the monitoring period (i.e., 4% of the cases)

were classified as "unstable dynamics" and discarded from the analysis.

II.2.3.5. Mapping fire frequencies

II.2.2.2.1. Mapping fires scars

Amongst the numerous vegetation indices used to characterize fire occurrence, the Normalized Burn Ratio (NBR, [START_REF] Kane | Assessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National Park[END_REF]Fig. 28) has gained consideration for detecting burn scars left after a fire [START_REF] Miller | Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR)[END_REF][START_REF] Escuin | Fire severity assessment by using NBR (Normalized Burn Ratio) and NDVI (Normalized Difference Vegetation Index) derived from LANDSAT TM/ETM images[END_REF][START_REF] Sunderman | Remote sensing approaches for reconstructing fire perimeters and burn severity mosaics in desert spring ecosystems[END_REF][START_REF] Kane | Assessing fire effects on forest spatial structure using a fusion of Landsat and airborne LiDAR data in Yosemite National Park[END_REF]. The NBR index (Equation 6) is calculated using reflectance data from passive optical sensors, especially the near infrared (NIR) and Shortwave infrared (SWIR) bands:

Equation 6 NBR = (NIR-SWIR)/(NIR+SWIR) [START_REF] Key | The Normalized Burn Ratio (NBR): A Landsat TM radiometric measure of burn severity[END_REF] The scarcity in the data acquired before 1999 with Landsat 5 [START_REF] Tyukavina | Congo Basin forest loss dominated by increasing smallholder clearing[END_REF] limited the fire frequency analysis to the period between 1999 and 2019 (Landsat 7 and 8). Landsat 7 images collected after the 31 may 2003 were discarded due to the failure that occurred on the Scan Line Corrector (SLC) which led to data gaps in images. As the availability of Landsat 8 products started in 2013, three fire monitoring intervals were considered: from 1999 to 2003 ( 5years of Landsat 7, before SLC off), from 2015 to 2019 (5 years of Landsat 8) and the entire period of data availability (12 years; 99-03//13-19; see Fig. 26). NBR was computed for each pixel and each observation in their respective time series in GEE. A semi-automated approach was then adopted to map fire scars from NBR time series. First, at least two images in Landsat 7 and Landsat 8 collections with apparent fire scars were selected and polygons on burnt and unburnt areas was visually delineated in each image. Based on a visual interpretation of NBR distribution in burnt and unburnt polygons (Fig. 29), an NBR threshold of 0.1 was fixed below

The fire frequency was quantified per fire monitoring period (i.e., 1999 -2003 and 2014 -2018; 5 years) by computing a Burn Area Index (BAI). To account for the irregular distribution of spaceborne observations within and among years resulting from the filtering of cloudy or shadowed observations, the information contained in pixels' fire scars time series was first aggregated into yearly observations. For the period between 1999 and 2003, a year was considered as a "burned" year (in which the focal pixel was burned) when at least one fire scar was detected within that year. For the period after 2013 the smaller revisit time of L8 over L7 allowed fixing a more stringent threshold, with at least two consecutive fire scar detections necessary for a year to be considered as "burned". The BAI for a pixel was then computed as the ratio between the numbers of burned years over the total number of years in a given fire

II.2.2.2.3. Comparing Landsat-and MODIS-derived fire frequencies

The MODIS burned area monthly product (MCD64A1.006 Collection 6) at 500 m resolution [START_REF] Giglio | MCD64A1 MODIS/Terra+Aqua Burned Area Monthly L3 Global 500m SIN Grid V006[END_REF] is a very popular data source that was used for the sake of comparison for the same monitoring interval as our Landsat dataset (2014)(2015)(2016)(2017)(2018). Comparison aimed at assessing the interest of increased resolution in the context of our forest-savanna mosaic landscape (30 vs. 500 m pixels).

II.2.2.2.4. Relating land cover change to vegetation structure and floristic composition

Multivariate analysis was made use of to identify the main gradient of floristic composition between forest and savanna plots. Multivariate analysis (or multivariate ordination) has become a well-established framework that facilitates the arrangement of objects that are characterized by multiple variables so that similar objects are closer each other and dissimilar objects are farther from each other [START_REF] Gauch | Multivariate analysis in community ecology[END_REF][START_REF] Ter Braak | A theory of gradient analysis[END_REF][START_REF] Dray | Community ecology in the age of multivariate multiscale spatial analysis[END_REF]. Major floristic groups were identified by the means of a non-symmetric correspondence (NSCA) analysis. NSCA is a correspondence analysis developed to integrate species diversity indices in a multispecies (plot-by-species floristic table) framework with the aim to capture the prominent features of the species composition of a community [START_REF] Pélissier | Consistency between ordination techniques and diversity measurements: two strategies for species occurrence data ¨[END_REF][START_REF] Couteron | Additive apportioning of species diversity: towards more sophisticated models and analyses[END_REF]. NSCA was suited for this study as it emphasizes abundant species from the plot profile limiting the influence of unknown and not correctly identified species. Species scores from the NSCA was extracted and groups of species were generated based on a k-means clustering. All the statistical analysis was performed in R statistical software (R Core Team, The landcover map obtained over the Nachtigal study area (Fig. 31) had an accuracy of 96.5%

with a kappa index of 0.95. Shrubby savanna and woody savanna were vegetation with the highest omission errors (8.14 and 8.29% respectively; Table IX) whereas the highest confusion between classes was observed for Woody savanna and Cropland. The highest confusions were made between Woody savanna and Cropland (31 pixels as omission errors a 24 pixels as commission errors)

Table IX Commission (%) 0 0 4.9 7.7 2.67 0 0 6.79 0 3.37

The Nachtigal area was dominated by Agroforests (25%) followed by Woody savanna and Shrubby savanna (~22%; Table IX and Fig. 31). Old-growth secondary forests has a low proportion occupying only 11% of the entire landscape. The LiDAR-based model used to predict vegetation AGB (AGBALS map; res = 40 m) over the Nachtigal area from the vegetation median canopy height (MCH) yielded a good result with an R² of 0.81 and a RMSPE of 52.7 Mg.ha -1 after a leave-one-out cross validation (Fig. 32).

AGBALS map (Fig. 33); constituted our intermediary scale for the calibration and validation of spaceborne-based and design based AGB models (see next section). Mg.ha -1 ) into equally sized bins of 25 Mg.ha -1 . We decomposed error and bias along the axes of both predicted and observed AGB. From a prediction perspective, RFFIELD models lead to a systematic overestimation of AGBALS (MSD > 0, hence predictions are higher than observations) across the whole predicted AGB range, with both increasing bias and error towards large AGB values (Fig. 35 a, b andc). This means that in any predicted AGB bin, To have a better understanding of the implication of the different AGB prediction approaches outside of training areas, we had a closer look at the distribution of predicted AGB density in the four blocks used in the 4-fold CV (Fig. 38 b). We first investigated the case of ALS-trained approaches (Fig. 39). Using RFALS, (model-based approach, based on Sentinel 2 imagery data), we could use the continuous distribution of biomass predictions. In all folds, but more markedly in the 3 rd and 4 th folds, the shape of the density curve diverged from the reference (ALS) curve over the higher range of AGB density values, with the expected drop around 250 Mg.ha -1 . Folds 3 and 4 comprise a much higher proportion of forest vegetation than the two others (Fig. 38 b), which explains the difficulty of the model-based approach to faithfully reproduce de AGB density distribution. At the fold scale however (coloured arrows beneath density plots), the mean predicted AGB density was close (below +/-10%) to the reference ALS value, except for the 4 th fold (+13% difference). This illustrates that a well-trained statistical model, even with poor per-pixel predictive power, can provide unbiased prediction at the landscape level, at least as long as the landscape matrix is not too different from the training conditions. The design-based approach, which makes the economy of a biomass prediction model and 'blindly' apply an average LiDAR-based AGB density value to each land cover class, appeared to perform equally well, with mean AGB densities between -7.2% and +12% of the ALS reference value. When focusing on specific land cover classes (histogram insets in Fig. 39) the model-based approach, as could be expected, systematically underestimated AGB density relative to LiDARbased estimates in the old-growth secondary forest land cover type. The expected opposite trend (overestimation) at the other end of the biomass gradient, was more subtle in low biomass vegetation's such as woody savanna, with higher overestimations on the 4 th fold. Design-based predictors did not present a systematic bias tendency in any vegetation type. A similar analysis with models calibrated with AGBFIELD is presented in Fig. 40. Here model-based predictions showed clearly aberrant density curves, and produced fold-level mean AGB density predictions comprised between +36.6% and +35.4% of the ALS reference. The design-based approach performed better in three folds with mean overall AGB density below 12%, but showed a 30.9% bias in fold 3. These variations can be explained by the poor sampling rate (and design) in the training folds due to the small number of available plots, which results in some large errors in the estimation of the AGB density of some of the land cover classes (up to 100%). and average rate of forest progression (i.e., c. 1 %.year -1 , or equivalently c. 6 km².year -1 ; Fig. 42). While the opposite transition (i.e., forest loss) was also casually observed in the transition map, it only applied to 0.2 % of the pixels. Since 1975, the MDNP has thus lost about 50% of its initial savanna area, and linear extrapolation of the progression rate suggests that savannas will completely disappear from the park within the next 30 years. The automated-unsupervised land cover classification implemented in GEE provided similar results than the supervised approach in terms of vegetation dynamics, capturing the steady increase of forest cover through time and leading to an average rate of forest encroachment virtually equal (i.e., c. 1 %.year -1 ; Fig. 42). There was, however, a systematic difference in terms of absolute forest cover between the two classification approaches. The automated approach classified a higher proportion of the park area as forests when the forest cover was low (i.e., at the beginning of the study period), and this difference progressively decreased with the increase of forest cover (i.e., toward the end of the study period) which was also concomitant with the increase in density in the image series. The spatial structure of forest spectral diversity matched the pattern of forest encroachment, with younger and older stands found on the positive and negative sides of PCoA 1 (Fig. 44-a), respectively. We also observed consistent changes in forest structure (Fig. 44-b), with an increase in forest AGB from 63 ± 47 Mg.ha -1 for younger forests up to 210 ± 90 Mg.ha -1 for the oldest forests. The transition map thus suggested an average linear increase of about 3.3 Mg.ha - 1 .year -1 in MDNP forests (Fig. 44-b). However young regenerating forests (≤ 20 years) seemed to show a higher AGB accumulation rate of 4.3 Mg.ha -1 .year -1 whereas older forest successions (>20 years) have a lower AGB accumulation rate of 3.2 Mg.ha -1 .year -1 . The burned areas detected by the 500 m spatial resolution burn area MODIS product proved higher for low fire frequencies (BAI ≤ 0.2; Fig. 46 a) with an overestimation of up to 98% (Fig. 46 b) compared to the 30 m Landsat estimates. A reverse trend was observed for higher fire frequencies (BAI ≥ 0.4), where Landsat captured higher proportions of burned pixels with the MODIS product even failing to detect yearly fire frequencies (BAI = 1). Overall the BAIs quantified using Landsat were on average 28% higher than the ones detected from MODIS. The first axis of the PCoA on the dissimilarity matrix of windows spectral species composition depicted a gradual change in savanna composition from woody savanna (negative scores on PCoA 1, blue tone in Fig. 47) to grassy savanna (positive scores on PCoA 1, red tone in Fig. 47). Savanna's spectral β-diversity indices (PCoA 1) showed a marked bimodality (Fig. 48), with a first mode around ca. -0.5 of PCoA 1 which corresponds to unfrequently burnt savannas (BAI close to 0) and a second mode around ca.

Types

III.1.4.1. Gradients in floristic composition

Non-symmetric correspondence analysis on the floristic table revealed two patterns of floristic distribution (Fig. 50). The gradient revealed by the first axis of the NSCA (explains 21% of total variance) separated plots dominated with savanna species (red and orange Fig. 50 The species distribution from the NSCA axis revealed four groups of floristic assemblages as depicted by the k-means clustering (Fig. 50) with the first axes separating savanna species (red and orange) from the ecotone species (cyan; dominated with Albizia zygia, Bridelia micranta, Nauclea latifolia, Oncoba glauca, and Treculia obovoidea) and forest species (green; dominated with Celtis mildbraedii, Celtis zenkeri, Glyphaea brevis, Sterculia rhinopetala and Trilepisium madagascariense). This axis is therefore an indicator of the succession trend within the area. The second axis from the NSCA separates the two groups of savanna; the first group (orange) with Hymenocardia acida, Crossopteryx febrifuga and Bridelia ferruginea which dominates Nachtigal and the second group (red) with Terminalia glaucescens, Piliostigma thonningii and Annona senegalensis which dominate the (MDNP). This second axis is therefore an indicator of savavanna structure.

III.1.4.2. Species succession

The relative species abundance within plots for the savanna floristic groups (orange and red for savannas in Nachtigal and Mpem et Djim respectively) is higher in plots with low woody AGB (AGBWood) and gradually decreases towards plots with higher AGBWood values up till 40 Mg.ha - 1 from which they become scarce (Fig. 51-a). Unlike the savanna floristic groups the relative abundance of species characterizing ecotone (cyan) is low in plots with low AGBWood (≤ 40 Mg.ha -1 ) and increases towards higher AGBWood values. However the relative abundance of forest species (green) becomes significant in plots with AGBWood ≥ 90 Mg.ha -1 . The savanna floristic groups display no clear pattern with the AGB of the grass layer (AGBGrass) whereas the relative abundance of forest species decreases towards higher AGBGrass. The presence of forest species is low in plots with high fire frequency (Fig. 51-c andd) where savanna species dominated with T. glaucescens, P. thonningii and A. senegalensis are more abundant as compared to savanna species dominated with H. acida, C. febrifuga and B. ferruginea (Fig. 51-c andd). Ecotone species are however present in all plots irrespective of the fire frequencies.

The basal area (BA) of savanna species increases towards higher AGBWood values up till 40 Mg.ha -1 where it decreases (Fig. 52-a). The BA of savanna species is higher than the one from forest species for plot's AGBWood < 40 Mg.ha -1 from which the tendency reverses. Forest species display an increase in their BA towards higher AGBWood values. The basal area for all woody species decreases with the increase in AGBGrass (Fig. 52-b). The basal area for forest species is high for plots with no fire occurrence (BAI =0) and drastically decreases as BAI increases. Savanna species dominated with T. glaucescens, P. thonningii and A. senegalensis have the highest basal area toward higher fire frequencies (Fig. 52-c andd). Detailed information on land cover and land cover change remains a top priority for land managers and decision-makers [START_REF] Anderson | A Land Use and Land Cover Classification System for Use with Remote Sensor Data[END_REF]. But this information still remains a challenge especially in heterogeneous landscapes such as forest-savanna transitions. In parallel with technological and computational developments, Spot 6/7 satellite data have proven its

III.2.1.2. Landscape-scale AGB estimation

Despite known limitations, multispectral spaceborne data remain broadly used for AGB estimations over the landscape. A widely held assumption is that Airborne LiDAR scanning (ALS) data ensure better model calibration, and hence partly compensates signal limitations [START_REF] Asner | Tropical forest carbon assessment: integrating satellite and airborne mapping approaches[END_REF][START_REF] Réjou-Méchain | Using repeated small-footprint LiDAR acquisitions to infer spatial and temporal variations of a high-biomass Neotropical forest[END_REF][START_REF] Timothy | Remote sensing of aboveground forest biomass: A review[END_REF][START_REF] Asner | Carbon declines along tropical forest edges correspond to heterogeneous effects on canopy structure and function[END_REF].

These results indeed show that model fit and error can be drastically improved, with an R² of 0.7 and a RMSE decrease of 30%, when using a Random Forest model calibrated with AGBALS reference data (RFALS) instead of field plots (RFFIELD). Predictions of the latter moreover proved highly biased (inaccurate) and imprecise along the whole range of AGB densities. Looking closely at the AGB predictions of RFALS, we can however see that the signal of multispectral data, whatever the sensor, does not in fact allow accurate AGB predictions in low and high ranges of actual vegetation biomass (i.e., AGBALS). Indeed, low biomass ranges are systematically overestimated, and high biomass ranges are underestimated. This behaviour has been already evidenced across the tropics (Zhang et al., 2017;[START_REF] Csillik | Monitoring tropical forest carbon stocks and emissions using Planet satellite data[END_REF] in similar studies, highlighting the fact that errors in AGB estimations when using optical signal is unfortunately still both unavoidable and crippling for large scale wall to wall AGB mapping [START_REF] Réjou-Méchain | Upscaling Forest Biomass from Field to Satellite Measurements : Sources of Errors and Ways to Reduce Them[END_REF]. It is thus important to better benchmark value and limitations of optical signals in varying contexts through landscape-scale studies integrating highly informative ALS data. Here we showed that improved calibration of spaceborne models by ALS data did ensure unbiased estimation of AGB overall. In other words, although the signal does not allow accurate predictions of AGB in a given forest or savanna location, RFALS models still provide accurate prediction of average AGB levels across the landscape. This is only true, as long as the balance between land cover types in the predicted landscape is comparable to that of the training area. Notably, the underestimation of total AGB will plummet with the share of land harbouring high AGB forests, above the hinge point of saturation of about 225-250 Mg.ha - 1 . This threshold ought to be kept in mind in any further application.

Regarding possible effects of the compromise between spatial and spectral resolution allowed by different spaceborne sensors, RFALS models based on lower spatial resolution and narrowwavelength spaceborne images (i.e. Landsat 8 and Sentinel 2) seemed to perform slightly better than models based on higher spatial resolution broad-wavelength imagery (Spot 6/7) with a minor decrease of 10% in RMSPE. Similarly, the variability in the spectral predictors selected by our forward model selection procedure does not allow being conclusive regarding the relative interest of any given spectral index over others.

The well-accepted design-based approach, on the other end, provided a simple and accurate alternative for landscape-level AGB estimation, when trained with a dense sample of ALS data.

As a single AGB value is attributed to each land cover class, this approach does not provide a detailed intra-class variation map. But if the model-based approach does provide such a map, it is unreliable anyways, as it has been shown, as long as the available predictors remain poorly correlated to AGB. It might be better not to lure the user with the pretence of a high-resolution product, when the estimates are only valid at large scale. the former displays a vegetation which is more diverse (species richness) and structurally stable compared to the latter. However the vegetation in the Nachtigal area still supply some ecological and ecosystem services thanks to its less disturbed vegetation that is still maintaining.

The Nachtigal area is dominated by Agroforest which covers 25.2% of the surface area while relatively undisturbed Old-growth forests cover only 11%. This ratio is expected to shift towards man-influenced forest types (Agroforest and degraded secondary forest) as forest are the most susceptible to experience profound changes in their structure due to land-use changes in Central Africa [START_REF] Aleman | Forest extent and deforestation in tropical Africa since 1900[END_REF]. In fact the local population directly rely on forest and savanna for their livelihoods in Africa (FAO, 2007). The expansion and intensification of agriculture, timber and biofuel monocultures to feed and supply the material aspirations of growing and more affluent human populations, the unsustainable harvesting of timber and non-timber forest products (including widespread over-hunting of game vertebrates) and urbanization processes will lead to drastic changes in vegetation structure [START_REF] Gardner | A multi-region assessment of tropical forest biodiversity in a human-modified world[END_REF]. The hydroelectric dam construction in the area over the Sanaga River will favour a massive flow of people towards the area with a big interest in small and large scale agriculture activities such as artisanal cocoa farms, palm plantations and cropland. Increasing cropland will reduce woody cover in savanna vegetation [START_REF] Nacoulma | Impacts of land-use on West African savanna vegetation: a comparison between protected and communal area in Burkina Faso[END_REF]. Land-use changes are expected to have the largest effect on changes in land cover distributions and biodiversity [START_REF] Sala | Global biodiversity scenarios for the year 2100[END_REF][START_REF] Aleman | Forest extent and deforestation in tropical Africa since 1900[END_REF]. (50s -80s, Youta, 1998) or a more recent one (1986( -2006[START_REF] Mitchard | Measuring woody encroachment along a forest-savanna boundary in Central Africa[END_REF]. Youta (1998) measured an encroachment rate of 3%.year -1 after 39 years ) to that obtained with supervised approach. However the performance of automated approaches was dependent upon the number of images available within the timeframe of the analysis and the best performances were achieved after year 1999 from when Landsat sensors had smaller revisit time.

III

III.2.2.3. Spectral composition structuring along a forest succession

As forest encroaches over savanna, differences in the functional composition of plant communities are apparent in the spectral reflectance of forest canopies captured by Sentinel 2 satellite data. Spectral species diversity appeared structured along a forest-age gradient. This reflects the successional gradient of floristic assemblages where fast growing pioneers species with low aboveground biomass yet strong photosynthetic activity dominate recent transitions and are then gradually replaced by long-lived species in old regenerating forests [START_REF] Fairhead | Contested forests: Modern conservation and historical land use in guinea's ziama reserve[END_REF][START_REF] Youta-Happi | Bilan de la dynamique du contact forêt-savane en quarante ans ( 1950-1990 ) Dans la région du confluent du Mbam et du Kim, Centre-Cameroun. Peuplements anciens et actuels des forêts tropicales[END_REF][START_REF] Ibanez | Inferring savannahrainforest boundary dynamics from vegetation structure and composition: A case study in New Caledonia[END_REF][START_REF] Cuni-Sanchez | African savanna-forest boundary dynamics: A 20-year study[END_REF][START_REF] Deklerck | Rate of forest recovery after fire exclusion on anthropogenic savannas in the Democratic Republic of Congo[END_REF].

III.2.2.4. AGB recovery along a forest succession

Thanks to the integration between spaceborne image series and local UAV-borne LiDAR data, it was able to quantify the pace of AGB increment from recent forest transitions to older forests.

AGB was found to steadily increase with a hint of levelling-off after 20 years. The rates of AGB recovery found in this study of 4.3 Mg.ha -1 .yr -1 found for young secondary forests (≤ 20 years as defined by the Intergovernmental Panel on Climate Change; IPCC) is 57% lower than the IPCC 2006 default AGB accumulation rates for young tropical rainforest in Africa (~ 10 Mg.ha - 1 .year -1 ; Suarez et al., 2019). Refined IPCC 2006 default AGB accumulation rates proposed by [START_REF] Suarez | Estimating aboveground net biomass change for tropical and subtropical forests : Refinement of IPCC default rates using forest plot data[END_REF] are still above our own estimates by 43.4% (7.6 ± 5.9 Mg.ha -1 .yr -1 ). This would reflect a contrasted scenario of AGB uptake depending on whether the forest recovers after disturbances (reforestation) or encroaches over savanna and woodland (afforestation). The former regeneration process would imply an average AGB uptake 2-fold greater than in the latter scenario. The type of previous land cover or disturbance significantly influences the carbon accumulation rate [START_REF] Moran | Effects of soil fertility and land-use on forest succession in Amazônia[END_REF][START_REF] Cook-Patton | Mapping carbon accumulation potential from global natural forest regrowth[END_REF]. Reforestation actually occurs on previously forested soils [START_REF] Janzen | The Soil Remembers[END_REF] keeping characteristics (soil fertility and structural properties) favourable to forest seedlings establishment and growth [START_REF] Viani | Savanna soil fertility limits growth but not survival of tropical forest tree seedlings[END_REF]. Afforestation on the other hand generally occurs on nutrient-poor savanna soils leading to a slower forest recovery [START_REF] Moran | Effects of soil fertility and land-use on forest succession in Amazônia[END_REF]. As expected, older forest successions (> 20 years) had a lower AGB recovery rate of 3.2 Mg.ha -1 .year -1 compared to younger forest successions. This is similar to what was found by [START_REF] Suarez | Estimating aboveground net biomass change for tropical and subtropical forests : Refinement of IPCC default rates using forest plot data[END_REF]3.5 ± 3.3 Mg.ha -1 .yr -1 ) and close to the IPCC 2006 default rates (~ 3 Mg.ha -1 .yr -1 ) for older secondary tropical rainforests in Africa.

III.2.3. Influence of fire on vegetation dynamics

III.2.3.1. Performance of Landsat data in characterising fire frequency

The Burn Area Index (BAI) obtained in this study using 30 m Landsat was shown to better retrieve the variability of fire frequencies than the 500 m MODIS fire product. The latter indeed estimated fire frequencies (BAI) 28% lower than the former on average, and totally failed to detect areas with the highest frequency (yearly) of fire regime. This finding accords with [START_REF] Chuvieco | Historical background and current developments for mapping burned area from satellite Earth observation[END_REF] and [START_REF] Ramo | African burned area and fire carbon emissions are strongly impacted by small fires undetected by coarse resolution satellite data[END_REF] who found that recent BA products covering Africa with Sentinel-2 images (at 20 m spatial resolution) over a single year reached estimates 1.8 and 3.2 times higher, respectively, than the estimates from MODIS products. This strong discrepancy is mostly caused by insufficient spatial resolution leading to the omission of small fires (< 100 ha) [START_REF] Roteta | Development of a Sentinel-2 burned area algorithm: Generation of a small fire database for sub-Saharan Africa[END_REF] as the study area is likely dominated by small low-intensity fires [START_REF] Mitchard | Measuring woody encroachment along a forest-savanna boundary in Central Africa[END_REF], in accordance with the fire biomes typology [START_REF] Archibald | Defining pyromes and global syndromes of fire regimes[END_REF]. This raises a caveat considering that several studies (Staver et al., 2011a;[START_REF] Diouf | Relationships between fire history , edaphic factors and woody vegetation structure and composition in a semi-arid savanna landscape ( Niger , West Africa )[END_REF][START_REF] Axelsson | Rates of woody encroachment in African savannas reflect water constraints and fire disturbance[END_REF][START_REF] Venter | Drivers of woody plant encroachment over Africa[END_REF] fully relied on MODIS to assess fire frequency influence on the vegetation structure.

III.2.3.2. Fire influence on savanna structure and dynamics

Under sufficient rainfall, savannas are considered as unstable systems [START_REF] Langevelde | Effects of fire and herbivory on the stability of savanna ecosystems[END_REF][START_REF] Sankaran | Determinants of woody cover in African savannas[END_REF] where regular disturbances such as fire are absolutely required to maintain tree-grass coexistence in the system by buffering against transition to a closed-canopy state [START_REF] Veenendaal | On the relationship between fire regime and vegetation structure in the tropics[END_REF][START_REF] Venter | Drivers of woody plant encroachment over Africa[END_REF]. Under very high rainfall comparable to the MDNP situation, even fires may not be sufficient to ensure savanna stability [START_REF] Jeffery | Fire management in a changing landscape: A case study from Lopé national park, Gabon[END_REF][START_REF] Djeumen | A minimalistic model of vegetation physiognomies in the savanna biome[END_REF]. On average, no fire occurrence was recorded for savanna pixels that transited to forest between two consecutive monitoring periods. Fire-free intervals facilitate tree recruitment and allow trees to approach canopy closure which suppress fire by excluding grasses [START_REF] Bond | A proposed CO2-controlled mechanism of woody plant invasion in grasslands and savannas[END_REF][START_REF] Veenendaal | Structural , physiognomic and above-ground biomass variation in savannaforest transition zones on three continentshow different are co-occurring savanna and forest formations ?[END_REF][START_REF] Veenendaal | On the relationship between fire regime and vegetation structure in the tropics[END_REF]. In the Lopé National Park in Gabon [START_REF] Jeffery | Fire management in a changing landscape: A case study from Lopé national park, Gabon[END_REF] savannas newly protected from fire can sufficiently thicken up over a 15 year period to reach a structure comparable to a colonising forest. In the absence of fire, the system switches from a state of co-occurrence of fire-adapted trees and heliophytic grasses to a state with fire resistant, shade-bearing giant herbs (Aframomum spp.) and forbs (Chromolaena odorata, a well-known invasive woody grass; Youta, 1996;Olivieras et al., 2016) along with saplings of light-demanding forest tree species such as Albizia spp., Macaranga spp. [START_REF] Youta-Happi | La disparition des savanes au Centre Cameroun entre 1950 et 1990[END_REF][START_REF] Youta-Happi | Arbres contre graminées : la lente invasion de la savane par la forêt au Centre-Cameroun[END_REF][START_REF] Youta-Happi | Bilan de la dynamique du contact forêt-savane en quarante ans ( 1950-1990 ) Dans la région du confluent du Mbam et du Kim, Centre-Cameroun. Peuplements anciens et actuels des forêts tropicales[END_REF][START_REF] Ibanez | Inferring savannahrainforest boundary dynamics from vegetation structure and composition: A case study in New Caledonia[END_REF]. [START_REF] Cardoso | A distinct ecotonal tree community exists at central African forest-savanna transitions[END_REF] evidenced the presence of an ecotone community in the Loppé National Park that occupies a narrow belt between savanna and forest and stabilises the forestsavanna mosaic even when the savanna is burned regularly. Such a belt accelerates woody encroachment of the savanna if fire were to be suppressed.

The spectral diversity of savannas still observable in 2020 evidenced two contrasting dominant classes either related to low (1 fire every 5 years) or high (about yearly fire) fire frequencies.

Interestingly, we observed a decreasing relationship between AGB and the fire frequency that led from high AGB (median of ca. 40 Mg.ha -1 ) with low fire frequencies to low AGB (ca. 20

Mg.ha -1 ) with high fire frequencies situations. This suggests that savanna with low fire frequency accumulates ~ 50% more AGB than with high fire frequency. We may here note that we observed a few savannas displaying intermediate fire frequencies (BAI of 0. [START_REF] Hoffmann | Ecological thresholds at the savanna-forest boundary: How plant traits, resources and fire govern the distribution of tropical biomes[END_REF][START_REF] Veenendaal | On the relationship between fire regime and vegetation structure in the tropics[END_REF] that fuels fires after drying-up thereby indirectly limiting fire intensity and propagation [START_REF] Lehmann | Deciphering the distribution of the savanna biome[END_REF] It was noticed that some persistent savannas (i.e. which did not witness any forest transition) that still displayed low fire frequencies (BAI < 0.2 i.e. one fire occurrence in five years) over a 5-years of monitoring period (1999-2003 and 2014-2018). Such class of BAI relates to the largest variability in AGB. This suggests that at a local scale other factors like topo-edaphic controls (Colgan et al., 2012a), and herbivory [START_REF] Langevelde | Effects of fire and herbivory on the stability of savanna ecosystems[END_REF][START_REF] Sankaran | Woody cover in African savannas : The role of resources , fire and herbivory[END_REF][START_REF] Venter | Drivers of woody plant encroachment over Africa[END_REF] may also have influenced forest transition. In addition, fire characteristics also matter, since fires occurring early in the dry season are known to have less impact on woody vegetation than fires taking place after several dry months [START_REF] Walters | The Land Chief's embers: ethnobotany of Batéké fire regimes, savanna vegetation and resource use in Gabon[END_REF][START_REF] Jeffery | Fire management in a changing landscape: A case study from Lopé national park, Gabon[END_REF]). Yet all those factors were not documented in this study.

III.2.4. Species succession dynamics

A compositionally and structurally distinct tree community existed in the forest-savanna transition sampled in the Central region of Cameroon. The species composition of this transitional landscape characterize transitions between semi-deciduous forests in the south and the soudano-guinean savannas in of the Adamaoua region as described by [START_REF] Letouzey | Carte phytogéographique du Cameroun, 1:500 000, 8 feuillets + 5 notices[END_REF][START_REF] Letouzey | Etude phytogéographique du Cameroun[END_REF] and Youta (1998). The floristic transition is gradual as species composition changes to suit the changing environment (Huston, 1994b

III.2.5. Implication for conservation and management

Woody encroachment is a major conservation challenge across tropical Africa and likely to only become worse in the future with increasing global atmospheric carbon dioxide and increasing rainfall in many parts of central Africa expected to favour the growth of trees over grasses [START_REF] Bond | Carbon dioxide and the uneasy interactions of trees and savannah grasses[END_REF][START_REF] Higgins | Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally[END_REF][START_REF] Stevens | Woody encroachment over 70 years in South African savannahs: Overgrazing, global change or extinction aftershock[END_REF][START_REF] Stevens | Savanna woody encroachment is widespread across three continents[END_REF]. The ). During that period shepherds regularly burn the savanna to favour grass flush (Youta and Bonval, 1996;[START_REF] Mitchard | Measuring woody encroachment along a forest-savanna boundary in Central Africa[END_REF]. Poachers also set fire to flush small grasses (as described by [START_REF] Walters | The Land Chief's embers: ethnobotany of Batéké fire regimes, savanna vegetation and resource use in Gabon[END_REF].

Frequent fires limits the growth of pyrophilous woody species and woody build-up in savannas which makes the establishment of forest species unlikely [START_REF] Jeltsch | Ecological buffering mechanisms in savannas: A unifying theory of long-term tree-grass coexistence[END_REF][START_REF] Dantas | Fire drives functional thresholds on the savannaforest transition[END_REF][START_REF] Venter | Drivers of woody plant encroachment over Africa[END_REF] delaying thus forest expansion. On the other hand cattle grazing and trampling limit the accumulation of grass fuel and is liable to depress both fire frequency and intensity. Our results suggest the existence of a fire frequency threshold of 0.4 (interpreted as biennials fires) is a necessary condition to limit savanna woody encroachment and maintain the co-occurrence of grassy and woody layers. But considering the dramatic forest expansion that was evidenced, the current, unplanned fire regimes seem unable to preserve the mosaic landscape.

Our findings have important management implications as they provide insight into the ecological paradox associated with woody encroachment, which is a pervasive phenomenon and a growing concern for managers of African savannas [START_REF] Stevens | Savanna woody encroachment is widespread across three continents[END_REF][START_REF] Venter | Drivers of woody plant encroachment over Africa[END_REF]. Fire frequency in the MDNP is generally the product of non-managed fires as the park still lacks an effective fire management program. Therefore managers in the MDNP have little influence on how much of the park burns on an annual basis, and that area burnt is largely dictated by uncontrolled actions from transhumant shepherds and poachers. It is very likely that fire frequencies below a 2-year cycle would allow bush encroachment and at longer run afforestation. Even annual, early fires also would. Forest expansion appeared indeed as a continuous and steady process in the MDNP and if we extrapolate the observed fairly constant rate of forest gain into savanna, the area will lose all its savanna in less than 30 years. This is even more certain when we consider the rise in CO2 concentration and climate change predictions for tropical Africa that expect a rise in precipitations over the next decades [START_REF] Pachauri | Climate Change 2014: Synthesis Report. Contribution of Working Groups I, II and III to the Fifth Assessment Report of the Intergovernmental Panel on Climate Change[END_REF] favouring forest expansion [START_REF] Higgins | Atmospheric CO2 forces abrupt vegetation shifts locally, but not globally[END_REF][START_REF] Ma | Spatial patterns and temporal dynamics in savanna vegetation phenology across the north australian tropical transect[END_REF][START_REF] Stevens | Woody encroachment over 70 years in South African savannahs: Overgrazing, global change or extinction aftershock[END_REF][START_REF] Stevens | Savanna woody encroachment is widespread across three continents[END_REF]. Local factors including fire management, soil fertility and herbivory pressure are expected to mediate this general prediction. Although carbon mitigation programs such as REDD+ scheme tend to encourage forest expansion, the loss of savanna ecosystems in the area will drastically modify landscape-level diversity [START_REF] Veldman | Where Tree Planting and Forest Expansion are Bad for Biodiversity and Ecosystem Services[END_REF][START_REF] Bond | Ancient grasslands at risk[END_REF] and ecosystem services, including hydrology [START_REF] Jackson | Atmospheric science: Trading water for carbon with biological carbon sequestration[END_REF][START_REF] Acharya | Woody plant encroachment impacts on groundwater recharge: A review[END_REF] and soil nutrient cycles [START_REF] Berthrong | A global meta-analysis of soil exchangeable cations, pH, carbon, and nitrogen with afforestation[END_REF], and it would markedly alter community assemblages [START_REF] Bremer | Does plantation forestry restore biodiversity or create green deserts? A synthesis of the effects of land-use transitions on plant species richness[END_REF][START_REF] Veldman | Tyranny of trees in grassy biomes[END_REF][START_REF] Abreu | The biodiversity cost of carbon sequestration in tropical savanna[END_REF] especially those of savanna plant and animal specialists, including iconic large mammals and big cats. However, some of those valuable data will need spatial interpolation (GEDI or Ice) from optical data or will not be available for long-term monitoring. Therefore improving the use of optical data will remain an issue. Attributing average AGB density values to broad land-cover classes (referred to as design-based sampling here) will continue to remain a valid alternative to obtain regional unbiased AGB estimations. Anthropogenic influence is lower in the Mpem et Djim NP compared to the Nachtigal area and the former displays a vegetation which is more diverse (species richness) and structurally stable compared to the latter. However the vegetation in the Nachtigal area still supply some ecological and ecosystem services thanks to its less disturbed vegetation that is still maintaining. We demonstrated the potential of automated unsupervised [START_REF] Romijn | Assessing change in national forest monitoring capacities of 99 tropical countries[END_REF].

The burn area product derived from this study using Landsat (spatial resolution of 30 m) is more appropriate in capturing frequent fires compared to 500 m MODIS products and showed good correlation with the vegetation structure with savanna displaying two dominant states of woody cover mediated by the frequency of fire. Two fire occurrences in five years was found to be the threshold below which savanna stability becomes highly unlikely. The relative abundance of savanna specialist species gradually decreases in favour of forest specialist species as succession occurs. The presence of forest pioneers was evidenced within savanna stands with high fire frequency. Fire therefore fails to ensure savanna stability within the landscape. The forest-savanna ecotone in the area appears to encourage an active forest encroachment over savanna.

IV.2. PERSPECTIVES

A useful step forward would be standardization of such methodologies in different forestsavanna transition areas to enable cross-site comparisons. Localized ALS data distributed within different vegetation types are recommended to improve the characterization of the structure of the vegetation over large areas. Integrating multispectral satellite images with smaller revisit time and higher spatial accuracy (i.e. Sentinel 2; Planet) into lancover change analysis can improve the monitoring of forest-savanna transition and drivers of change (i.e. fire) at fine spatial (< 10 m) and temporal (bi-monthly) resolution. Nevertheless fire monitoring was restricted to yearly fire statistics ignoring the seasonality of fire within the year (early or late fires) which have significant influence on the fate of the savanna structure (Bucini et al., 2002;[START_REF] Diouf | Relationships between fire history , edaphic factors and woody vegetation structure and composition in a semi-arid savanna landscape ( Niger , West Africa )[END_REF]

Fig. 1 :Fig. 2 :Fig. 3 :Fig. 4 :Fig. 5 :Fig. 6 : 12 Fig. 7 : 14 Fig. 8 :Fig. 9 :Fig. 12 :Fig. 14 :Fig. 24 :Fig. 44 :

 123456127148912142444 Fig. 1: Distribution of the major savanna biome across the tropics. .......................................... 6

  height model: mean (min-max). ............................................................................................... 57 Table IX : Structure and performances of the different models selected for each spaceborne optical sensor. ........................................................................................................................... 58 Table X : Structure and diversity of the main vegetation types derived from plot data within the Nachtigal and the Mpem et Djim National Park (MDNP). ...................................................... 65 Table XI : Summary statistics of 40 m x 40 m field plot data used in depicting compositional shifts or trends in the floristic composition: mean. .................................................................. 73 Near Infra-Red Green Ratio; NSCA: Non-Symmetric Correspondence Analysis NU-REDD+: National Unit in charge of REDD + xiv of global change (combining anthropic and climatic pressures) on biome distribution needs innovative approaches allowing to address the large spatial scales involved and the scarcity of available ground data. Characterizing vegetation dynamics at landscape to regional scale requires both a high level of spatial detail (resolution), generally obtained through precise field measurements, and a sufficient coverage of the land surface (extent) provided by satellite images. The difficulty usually lies between these two scales as both signal saturation from satellite data and ground sampling limitations contribute to inaccurate extrapolations. Airborne laser scanning (ALS) data has revolutionized the trade-off between spatial detail and landscape coverage as it gives accurate information of the vegetation's structure over large areas which can be used to calibrate satellite data. Also recent satellite data of improved spectral and spatial resolutions (Sentinel 2) allow for detailed characterizations of compositional gradients in the vegetation, notably in terms of the abundance of broad functional/optical plant types. Another major obstacle comes from the lack of a temporal perspective on dynamics and disturbances. Growing satellite imagery archives over several decades (45 years; Landsat) and available computing facilities such as Google Earth Engine (GEE) provide new possibilities to track long term successional trajectories and detect significant disturbances (i.e. fire) at a fine spatial detail (30m) and relate them to the current structure and composition of the vegetation. With these game changing tools our objective was to track long-term dynamics of forest-savanna ecotone in the Guineo-Congolian transition area of the Central Region of Cameroon with induced changes in the vegetation structure and composition within two contrasted scenarios of anthropogenic pressures: 1) the Nachtigal area which is targeted for the dam construction and subject to intense agricultural activities and 2) the Mpem et Djim National Park (MDNP) which has no management plan.The maximum likelihood classification of the Spot 6/7 image aided with the information from the canopy height derived from ALS data discriminated the vegetation types within the Nachtigal area with good accuracy (96.5%). Using field plots data in upscaling aboveground biomass (AGB) form field plots estimates to the satellite estimates with model-based approaches lead to a systematic overestimation in AGB density estimates and a root mean squared prediction error (RMSPE) of up to 65 Mg.ha -1 (90%), whereas calibration with ALS data (AGBALS) lead to low bias and a drop of ~30% in RMSPE (down to 43 Mg.ha-1 , 58%)

Fig. 5 :

 5 Fig. 5: The two critical thresholds that govern the response of savanna systems to fire. (a) The fire-resistance threshold and (b) The fire-suppression threshold[START_REF] Hoffmann | Ecological thresholds at the savanna-forest boundary: How plant traits, resources and fire govern the distribution of tropical biomes[END_REF].

  also demonstrate that woody encroachment occurs rapidly on granite-based soils but is predominantly absent in basalt-based soils. Colgan et al. (2012) recorded lower woody aboveground biomass (AGB) levels on crests which linearly increased toward streams in the KNP. The observed pattern was different on granite substrates,

Fig. 7 :

 7 Fig. 7: Location of studies finding woody encroachment in Central Africa. (a) Overlaid on the Global Inventory Modelling and Mapping Studies (GIMMS) dataset with average threemonthly minimum NDVI from 1982 to 1986 in magenta and from 2002 to 2006. (b) Areas with significant changes in NDVI trends as increasing (green) and decreasing (red) trends (right).Modified from[START_REF] Mitchard | Woody encroachment and forest degradation in sub-Saharan Africa ' s woodlands and savannas 1982 -2006[END_REF].

Fig. 8 :

 8 Fig. 8: Woody plant cover dynamics over sub-Saharan Africa revealing a dominant increasing trend over 30 years (1986-2016). Histogram alongside coloured scales indicate data distribution. Grey areas represent urban surfaces, wetland, cropland and forest. Modified from Venter et al. (2018).

Fig. 9 :

 9 Fig. 9: Illustration of tree-dimensional data obtained from an airborne LiDAR scan over a forest-savanna transitional area. (a) Aerial view of a forest-savanna transition captured from an optical camera mounted on a Dji Mavic pro and (b) Aerial view of a forest-savanna transition captured from an airborne Laser scanner mounted on a Dji Matrice 600. The colour gradient corresponds to the variation from low (blue) to high (red) height values.

Fig. 10 :

 10 Fig. 10: Spatial resolution and cost effectiveness of field-based and remotely-sensed methods for monitoring vegetation structure (modified from Ganivet and Bloomberg, 2019).

Fig. 11 :

 11 Fig. 11: General workflow of remote sensing-based AGB mapping methods. Regardless of the remote sensing data type, remote sensing indices (s) are extracted over forest sample plots (a) and used a predictor (s) of in situ AGB estimations (b).Once calibrated, the model can be used to predict forest AGB over the entire study area (c). (modified from[START_REF] Ploton | Improving tropical forest aboveground biomass estimations: insights from canopy trees structure and spatial organization[END_REF]

Fig. 12 :

 12 Fig. 12: General workflow of the methodology used in upscaling aboveground biomass from plot scale to satellite scale with model-based and design-based approaches from this study.

Fig. 13 :

 13 Fig. 13: Sentinel 2 satellite image (a) and derived (b) α and (c) β-diversity maps produced with biodivMapR (modified from Féret and de Boissieu (2020).

Fig. 14 :

 14 Fig. 14: Illustration of the potential offered by Landsat image archives in monitoring land cover change dynamics (modified from Young, 2017).

Fig. 15 :

 15 Fig. 15: Illustration of the Earth engine interactive development environment.

Fig. 16 :

 16 Fig. 16: Study area. (a) Location (red dot) within the Guineo-Congolian transitional area (light green) of the Mbam et Kim division in the Central region of Cameroon (b). (c) Boundaries of the Mpem et Djim National Park (yellow); the different rivers (blue); the Nachtigal area (pink) and the ALS LiDAR sampling footprint (orange).

Fig. 17 :

 17 Fig.17: Demarcation of a one ha plot for data collection. A: length and direction of first baselines; B: complete demarcation of quadrats[START_REF] Libalah | The Role of Environmental Drivers in Tree Community Structure of Central African Lowland Forests[END_REF] displaying the cartesian coordinates of the poles of the baselines and the diagonal of the plot.

Fig. 18 :

 18 Fig. 18: Field data collection in the forest. (a) Plot delimitation; (b and c) tree measurements; (d and e) data collection; (f) voucher specimens.

Fig. 19 :

 19 Fig. 19: Field data collection in the savanna vegetation. (a) Savanna sampling; (b and c) data collection of the woody layer.

Fig. 21 :

 21 Fig. 21: Principles guiding vegetation sampling with airborne LiDAR systems.

Fig. 23 :

 23 Fig. 23: Spot 6/7 image of a forest (a) with the corresponding canopy height model showing the elevation of the vegetation (b) derived from the normalized LiDAR point cloud.

  type to establish the statistics to identify spectral classes (or clusters) in a multiband image. All classes have to be derived usually through a training stage with the use of training samples.Training samples for each land cover type was therefore manually delineated based on (i) the knowledge of the land cover type from the field; (ii) their spectral responses from Spot 6/7 satellite image and (iii) the height distribution of their canopies as obtained through the CHM (TableIII). A total of approximately 2640 pixels for the different land-cover types was generated. The image was classified into 13 land cover types using the maximum likelihood algorithm from the ENVI 5.0 image classification software. Finally, a 3 x 3 majority filter was applied to each classification to recode isolated pixels classified differently than the majority class of the window. Table III : Illustration of the relationships between the structure of the canopy of different land cover types, the height distribution from the Canopy Height Model (CHM) and the spectral reflectance from Spot 6/7 satellite image (Red-Green-Blue colour composite = NIR-Red-Cameroon has proposed categories of land cover types in order to improve its capacities to report on their forest carbon stocks changes following the recommendation of the Intergovernmental Panel on Climate Change (IPCC). Correlations between the land cover categories proposed in this study were established with what was expected at the national level as described by the national unit in charge of REDD + (UN-REDD+). Table VI summarizes the correspondence between the land cover classes proposed by the UN-REDD+ and the classes obtained from this study.

Fig. 24 :

 24 Fig. 24: Canopy height data extraction within 40 m x 40 m subplots (grey hatched squares, a) for each 1 ha forest plots (solid black square). The image represent canopy height variation obtained from airborne LiDAR (b).

Fig. 25 :

 25 Fig. 25: The principles of the applied cross validation methods used in this study: leave one-out and 4-fold cross-validation.

  Red)/(NIR + Red + L)*1.5 with L = 0.5[START_REF] Huete | Overview of the radiometric and biophysical performance of the MODIS vegetation indices[END_REF] x IRECI (NIR -Red)/(Red1/Red2)[START_REF] Frampton | Evaluating the capabilities of Sentinel-2 for quantitative estimation of biophysical variables in vegetation[END_REF] and vegetation indices are candidate predictors retained for each model. NIR = near infra-red; Red 1, 2, 3 = red-Edge 1, 2, 3; Red 4 = NIR narrow; SWIR 1, 2 = shortwave infra-red 1, 2; RGR = red green ratio; NIRGR = near infra-red green ratio; NDVI = normalized difference vegetation index; EVI = enhanced vegetation index; SR = simple ratio; SAVI = soil-adjusted vegetation index; IRECI = inverted red-edge chlorophyll index; S2REP = Sentinel 2 red-edge position.

Fig. 26 :

 26 Fig. 26: Temporal domains covered by spaceborne data for vegetation and fire monitoring. Orange and yellow bands represent the years of fire monitoring with Landsat sensors and the hatched band represent the fire monitoring interval from MODIS sensor. The green dotted boxes represent the intervals within which images were selected for landcover monitoring.

Fig. 27 :

 27 Fig. 27: EVI distribution displaying the bimodality of the forest-savanna landscape from a Landsat level 2 (BOA reflectance) median images of the dry season (Nov. -Mar.) from 2008 to 2010.

Fig. 28 :

 28 Fig. 28: Example of fire scar discrimination from the Normalized burn ratio (NBR). (a and b) RGB true colour composite of two forest-savanna landscapes from Landsat 7 (LE07_185056_20000315) and Landsat 8 (LC08_185056_20180205) with burn scars appearing in violet. (c and d) NBR of the same areas highlighting burn scars in red, dry savanna grasses in yellow and photosynthetically active savanna vegetation in green. Forest vegetation is masked out (white).

1 .

 1 Spatial distribution of the vegetation types from satellite data

:

  Confusion matrix obtained from the landcover classification using Spot 6/7 multispectral image with 2640 training pixels. AF = Agroforests; BR = Bareground; CL = Croplan; DF = Degraded secondary forests / Fallows; EG = Elaeis guineensis form; OF = Oldgrowth secondary forests; RA = Raphia spp. form; SS = Shrubby savanna; SF = Swampy forest; WS = Woody savanna.

Fig. 31 :

 31 Fig. 31: Spatial distribution of the landcover types within the Nachtigal area.

Fig. 32 :

 32 Fig. 32: Scatterplot of field-derived biomass (AGBFIELD, Mg.ha -1 ) vs biomass predicted from the ALS model (AGBALS, Mg.ha -1 ) in the leave-one-out cross-validation. Solid black line represents the 1:1 line. AF = Agroforest; DF = Degraded secondary forest; OF = Old-growth secondary forest; SS = Shrubby savanna; WS = Woody savanna plots.When observing the AGB variation for the main vegetation types mapped within the study area (Fig.33), Old-growth secondary forests store the highest amount of AGB (≈ 200 Mg.ha -1 ) while Shrubby Savannas store the lowest (≈ 40 Mg.ha -1 ).

Fig. 34 :

 34 Fig. 34: Results of the spatial forward variable selection procedure for RFFIELD model (left column) and RFALS Models (right column). (a,b) models based on Spot 6/7; (c,d) models based on Landsat 8 predictors; (e,f) models based Sentinel 2 predictors. The red dot marks the last variable leading to a decrease larger than 1% in model RMSPE.

Fig. 37 :

 37 Fig. 37: Performance of RF AGB prediction models (RFFIELD: top row; RFFIELD: bottom row) based on Landsat 8 optical data. (a, d) Heat plots showing the relationships between Lidarbased AGB estimates vs RFFIELD (panel a) and RFALS (panel d) predictions. Solid black lines represent the 1:1 line. Dashed grey lines represent the fit of simple linear models between observed and predicted AGBs. (b, c, e, f) Scatterplots showing the relationships between statistics of models prediction error (namely the root mean squared prediction error, RMSPE, in red and the mean signed deviation, MSD, in blue) along the ranges of predicted AGB (panels b and e) and LiDAR-based AGB (panels c and f)

Fig. 38 :

 38 Fig. 38: Subset of the study area. (a) The most dominant vegetation classes and distribution of the field plots. (b) Folding of the study area in four spatial folds for cross-validation of the aboveground biomass prediction models. Proportion of the different vegetation types (with a significant woody component) located within each fold.

Fig. 39 :III. 1 . 1 . 4 .

 39114 Fig. 39: Detailed analysis of model-based (Sentinel 2 sensor) and design-based AGB predictions in each fold and land-cover class, in the case of ALS trained approaches. Density curves show the distribution of Lidar-based AGB (red) and model-based predicted AGB values (green). The arrows represent the mean AGB density values obtained by the different approaches. Inset barplots represent mean AGB density estimates for each vegetation class. AF = Agroforest; DF = Degraded secondary forest; OF = Old-growth secondary forest; SS = Shrubby savanna; WS = Woody savanna; GS = Grassy savanna.

Fig. 41 :

 41 Fig. 41: Vegetation transition map of the Mpem et Djim National Park from 1975 to 2020. (a, b, c) Illustrative Landsat images (RGB colour composites, bands 3, 4, 2) of the MDNP for the years 1975, 2000 and 2020 respectively. Forests appear in green and savannas in violet. Transition map for the 1975-2020 study period using manual (d; ENVI) and automated (e; GEE) land cover classification approaches. Existing forests from 1975's landcover classification are more than 45 years old (>45). From 1975 onward, a gradient of green nuances represents the estimated establishment's year (forest age) of forest patches. Perpetuating savannas are displayed in orange, and pixels with inconsistent dynamics (i.e., more than one transition during the study period) in red.

Fig. 42 :

 42 Fig.42: Change in forest encroachment rate (in %.year -1 , black) and forest cover (in % of total park area, dark green) throughout the study period for the supervised (ENVI; circles and full lines) and unsupervised (GEE; triangles and dashed lines) classification approaches. Green lines represent the fit of simple linear models between forest cover and years, while black lines represent the average forest progression rate, which fluctuated around ca. 1%.

Fig. 43 :

 43 Fig. 43: Relationship between vegetation history and spectral species composition of the forest on an illustrative subset of the study area. (a) Age of the forest transition and associated (b) spectral β-diversity index, derived from the first axis of the Principal coordinate ordination analysis.

Fig. 44 : 1 .

 441 Fig. 44: Relationships between forest age, spectral diversity and structure. (a) Boxplot of pixels spectral β-diversity index, derived from the first axis of the Principal coordinate ordination analysis (PCoA 1), by forest age bins. Labelled letters represent the results of a Tukey honest significant difference (HSD) test, with different letters for boxes having different means at the probability cut-off value of p < 0.05. (b) Boxplot of pixels' aboveground biomass (AGB) by forest age bins. Lines represent the fit of simple linear models between forest age and AGB for young secondary forests (≤ 20years, solid blue line), old secondary forests (> 20 years; solid red line) and all successional stages (dotted black line) with their respective 95% confidence interval ribbons included. Note that in panels a and b, x axes after year 20 are represented with broken lines to mark a change in the interval between age bins.

Fig. 46 :

 46 Fig. 46: Burn Area Index (BAI) derived from Landsat at 30 m (LST_30; grey) and the MODIS burn area product at 500 m (MOD_500; black) for the 2014-2018 period over areas classified as savannas in 2020. (a) Burned area (in km²) disaggregated by BAI classes. (b) Difference between the burned area estimated by Landsat and MODIS relative to the Landsat estimate (solid black line). The red line represents the average overall difference.

  0.4 corresponding to frequently burned savannas (BAI close to 0.4; Fig.48-a). The two modes in savanna's composition are consistent with distinct gradients in biomass accumulation (Fig.48-b), with higher AGB values found on unfrequently burnt savannas (c. 49 ± 40 Mg.ha -1 for leftmost box in Fig.48-b) than on frequently burnt savannas (21 ± 18 Mg.ha -1 for the rightmost box).

Fig. 48 :Fig. 49 :

 4849 Fig. 48: Relationships between savanna spectral diversity, fire frequency and savanna structure. (a) Density plot of pixels spectral β-diversity score on the first axis of the Principal coordinate analysis (PCoA 1) and associated recent (2015-2019) fire frequency, represented by a smooth average of pixels Burn Area Index (BAI, red line). (b) Boxplot of AGB variation by PCoA 1 bins. The labelled letters represent the results of a Tukey honest significant difference (HSD) test, with different letters for boxes having different means at the probability cut-off value of p < 0.05. Red numbers above the plots represent the average BAI for each PCoA 1 bin.

  ) from plots dominated by forest species (green). The second axis (explains 10% of total variance) separated between plots sampled in the Nachtigal area (circles) and the ones in Mpem et Djim national park (MDNP).

Fig. 50 :

 50 Fig. 50: Floristic pattern depicted by first two axes of a Non-Symmetric correspondence Analysis (NSCA) of the floristic table derived from the 40 x 40 m plots distributed in forest and savanna in the Mpem et Djim National Park (triangle) and Nachtigal (circled). Species colours represent groups from k-means clustering (savanna group 1 = red, savanna group 2 = orange; ecotone species = cyan; and forest species = green). Only species with high contributions on the NSCA 1 and 2 axes are displayed for visualization purposes. See Appendix 4 for the entire list of species belonging to each floristic group.

Fig. 51 :

 51 Fig. 51: Relationship between the relative abundance of the different group of species distinguished from the NSCA analysis; the aboveground biomass from the woody (a) and grassy (b) layers and the fire frequencies for (c) 5-year monitoring and (d) 7 years monitoring. Ecotone species (Eco; cyan); forest species (For; green); savanna species from Mpem et Djim national park (Sav 1, orange) and savanna species from Nachtigal (Sav 2, red).

Fig. 52 : 1 .

 521 Fig. 52: Relationship between the basal area of the different group of species distinguished from the NSCA analysis and the aboveground biomass (a) woody and (b) grassy layers and fire frequency (c) 5-year monitoring and (d) 7 years monitoring. Ecotone species (Eco; cyan); forest species (For; green); savanna species from Mpem et Djim national park (Sav 1, orange) and savanna species from Nachtigal (Sav 2, red).

  potentiality in land cover classification to produce data with high accuracy (96.5%) in this study. The level of details achieved in the land cover types proposed was thanks to both the fine spatial resolution of the Spot 6/7 image (3 m) and the contribution of the canopy structure of the different vegetation obtained from the canopy height model (CHM) derived from LiDAR. Exploiting those two source of information helped in defining training samples ensuring a good spectral separability between classes. The methodology used to obtain this degree of details in land cover classes constitute a reliable reference to implement national scale land cover mapping in Cameroon. This is even more significant in the context of ongoing climate mitigation efforts to Reduce Emissions from Deforestation and Forest Degradation (REDD+) where Measuring, Reporting and Verification (MRV) of carbon stocks and pools are expected for different vegetation types. Achieving unbiased estimation of carbon stocks and fluxes in these highly dynamic environments, characterized by mosaics of very different land uses and covers, is a critical challenge.

III. 2 . 1 . 3 .

 213 Differences in the structure and floristic composition between the Nachtigal area and the Mpem et Djim NP Anthropogenic influence is lower in the Mpem et Djim NP compared to the Nachtigal area and

  potential ability of the ecotone to transform into the frontier of woody encroachment emphasizes how close forest-savanna mosaics may be to a sudden and practically irreversible state shift, and how important regular burning is for preventing this. The creation of the Mpem et Djim National Park (MDNP) in 2004 may have hindered anthropogenic activities principally at its less accessible savanna core where we observed intense forest expansion. A lower proportion of encroaching forests is observed in peripheral savannas, which are more accessible especially in the northern part of the MDNP where natural boundaries constituted by the Mpem et Djim rivers are absent. From the various observations made during field campaigns conducted between 2019 and 2020; we noticed that those savannas were subject to substantial livestock transhumance during the dry season (nov. -mar.

  to track long-term dynamics of forest-savanna ecotone in the Guineo-Congolian transition area of the Central Region of Cameroon and characterise induced changes in the vegetation structure and composition with the compensation needs for a hydroelectric dam construction. The study area displayed two contrasted scenarios of anthropogenic influences: 1) the Nachtigal area which is subject to intense agricultural activities and targeted for the construction of a hydroelectric dam and 2) the Mpem et Djim National Park which had no management plan since its creation in 2004. The conceptual framework consisted in a nested design to fill in the gap between localised field data and large scale satellite data with intermediate airborne laser (ALS) data. Ground truth data together with canopy structure of the vegetation derived from ALS data helped to discriminate vegetation types with high accuracy (96.5%) within the Nachtigal area using high spatial resolution satellite imagery i.e. Spot 6/7 (3m). The landscape is dominated with Agroforests (25.2%) while undisturbed forests only covered 11% of the total area. Achieving unbiased landscape-scale aboveground biomass (AGB) estimations from spaceborne multispectral data, was significantly improved when integrating intermediate ALS-based AGB data in prediction models with an error reduction of ~30% compared to a field-based AGB calibration. It is however crucial to acknowledge that, due to signal limitations, irrespective of the multispectral sensor and mix of spectral indices used, predictions are only unbiased at the landscape or regional level, and for land cover conditions similar to the training area. Data from upcoming new radar-based (BIOMASS, NISAR) and LiDAR-based space missions (GEDI, ICESat-2, MOLI) are expected to improve our extrapolation capacities by providing both global coverage and signals with better relationships to vegetation structure (Réjou-Méchain et al., 2019; Quegan et al., 2019).

Répartition chiffrée des Enseignants de la Faculté des Sciences de l'Université de Yaoundé I

			Maître de Conférences	En poste
	16 MKOUNGA Pierre	Maître de Conférences	En poste
	17 NOTE LOUGBOT Olivier Placide NOMBRE D'ENSEIGNANTS Maître de Conférences DÉPARTEMENT Professeurs Maîtres de Conférences Chargés de Cours Assistants Chef Service/MINESUP Total
	18 NGO MBING Joséphine BCH 9 (1) BPA 13 (1)	13 (09) Maître de Conférences 14 (06) 09 (06) 19 (05)	Sous/Direct. 3 (2) MINERESI (2)	39 (18) 46 (14)
	19 BPV NGONO BIKOBO Dominique 07 (1) 10 (01) Maître de Conférences 15 (08) CI 10 (1) 9 (02) 12 (02) Serge 20 NOUNGOUE TCHAMO Diderot Maître de Conférences CO 7 (0) 17 (04) 09 (03) IN 2 (0) 1 (0) 13 (01)	(0) En poste (0) (0) En poste 09 (01)		33 (10) 34 (5) 35(7) 25 (2)
	21 TABOPDA KUATE Turibio MAT 1 (0)	Maître de Conférences 5 (0) 19 (01)	En poste 05 (02)		30 (3)
	22 TCHOUANKEU Jean-Claude MIB 1 (0) PHY 12 (0)	Maître de Conférences 5 (02) 06 (01) 15 (02) 10 (03)	06 (02) Doyen /FS/ UYI 18 (5) (0) 40 (5)
	ST	8 (1)	14 (01)	19 (05)	(0)		43(7)
	Total	70 (5)	98 (27)	136 (35)	(9)	343 (76)
	Soit un total de		344 (76) dont :
	-Professeurs		70 (5)
	-Maîtres de Conférences	98 (27)
	-Chargés de Cours	136 (35)
	-Assistants		38 (9)
	( ) = Nombre de Femmes	76

Table I :

 I Selection of studies using airborne LiDAR scanning data to parameterize model-based approaches and generate wall-to-wall AGB and tree height maps from spaceborne optical imagery. .................................................................................................................................... 22

Table II :

 II Characteristics, properties and purposes of different satellite sensors used in this study .................................................................................................................................................. 36

Table III :

 III Illustration of the relationships between the structure of the canopy of different land cover types, the height distribution from the Canopy Height Model (CHM) and the spectral reflectance from Spot 6/7 satellite image. ................................................................................ 38

Table IV :

 IV Land cover types proposed by the national unit in charge of REDD + (UN-REDD +) and correspondence to the land cover types proposed from this study ............................... 40

Table V :

 V Band names and vegetation indices used to generate different aboveground biomass predictive models based on satellite data: Spot 6/7 (S. 6/7), Landsat 8 (L. 8), and Sentinel 2 (S.

2). .............................................................................................................................................. 44 Table VI : Confusion matrix obtained from the landcover classification using Spot 6/7 multispectral image with 2640 training pixels. ........................................................................ 55 Table VII : Proportion of the different landcover types located in the study area ................... 55 Table VIII : Summary statistics of field plot data used in predicting AGB from the canopy

  The automated unsupervised long-term (45 years) land cover change monitoring from Landsat image archives based on GEE captured a consistent and regular pattern of forest progression into savanna at an average rate of 1% (ca. 6 km².year - 1 ). No fire occurrence was captured for savanna that transited to forest within five years of monitoring. Distinct assemblages of spectral species are apparent in forest vegetation which is consistent with the age of transition. As forest gets older AGBALS recovers at a rate of 4.3 Mg.ha - Comprendre les effets des changements globaux (pressions anthropiques et climatiques) sur la distribution des biomes passe par des approches innovantes qui prennent en compte la large étendue spatiale de même que la faible disponibilité des données de terrain. Caractériser la dynamique de la végétation de l'échelle locale à celle régionale nécessite à la fois un niveau de détail (résolution) élevé, acquis grâce aux mesures précises sur le terrain de même qu'une couverture suffisante de la zone (étendue) obtenue par les images satellitaires. La complexité se trouve généralement entre ces deux échelles ; avec un signal satellitaire limité par la saturation additionné aux données de terrain localisées qui impactent la précision lors des extrapolations. Les données acquises grâce au scanner laser aéroporté (Airborne laser scaning ; la qualité du capteur et le prétraitement des images pour tous les satellites. Le signal du satellite ne garantit pas une précision dans les estimations à l'échelle du pixel au-delà de 200-250 Mg.ha -1 à cause des valeurs élevées du RMSPE relatif. L'approche basée sur la conception (design-based approach) pour laquelle les valeurs moyennes d'AGB sont attribuées aux différents types de végétation constitue une alternative simple et fiable pour des estimations d'AGB à l'échelle du paysage pour des données ALS distribuées dans les différentes typologies.

	L'AGB et la diversité spécifique mesurées au sein de 74 parcelles d'inventaires (distribuées le
	long d'un gradient de succession de la savane vers la forêt) sont élevées pour les végétations
	localisées dans le PNMD comparées aux végétations semblables échantillonnées à Nachtigal.
	L'approche automatique non supervisée du suivi à long terme (45 ans) des changements de
	couvert à partir d'archives Landsat dans GEE a mesuré un avancement constant et régulier de

1

.year -1 in young forest stands (< 20 years) compared to 3.2 Mg.ha -1 .year -1 recorded for older forest successions (≥ 20 years). In savannas, two modes could be identified along the gradient of spectral species assemblage, corresponding to distinct AGBALS levels, where woody savannas with low fire frequency store 50% more carbon than open grassy savannas with high fire frequency. At least two fire occurrences in five years is found to be the fire regime threshold below which woody savannas start to dominate over grassy ones. Four distinct plant communities were found distributed along a fire frequency gradient. However the presence of fire-sensitive pioneer forest species in all scenarios of fire frequencies (from low to high fire frequencies) would suggest that the limiting effect of fire on woody vegetation is not sufficient to hinder woody encroachment in the area bringing therefore sufficient humidity required for the establishment of pioneer forest saplings within open savannas. These results have implications for carbon sequestration and biodiversity conservation policies. The maintenance of the savanna ecosystem in the region would require active management actions, and contradicts reforestation goals (REDD+, Bonn challenge, etc.).

Keywords

: Forest-savanna ecotone, Google earth Engine, Airborne laser scanning, aboveground biomass, fire, plant communities. xvii RÉSUMÉ ALS) apportent une alternative au compromis entre la précision et la surface couverte nécessaire pour améliorer les données satellitaires. L'avènement des satellites récents (Sentinel 2) avec une meilleure résolution spatiale et spectrale permettent une caractérisation détaillée des groupes floristiques. Un autre obstacle repose sur le manque de perspective temporelle sur les dynamiques et les perturbations. La disponibilité d'archives d'images satellitaires sur plusieurs décennies (>45 ans ; Landsat) de même que l'avènement des méthodes de traitements automatiques tels que Google Earth Engine (GEE) offrent de nouvelles possibilités dans le suivi à long terme des trajectoires de succession à une fine résolution spatiale (30 m) et les relier à la structure et la composition de la végétation. À l'aide de ces outils révolutionnaires l'objectif de cette étude a été de caractériser la dynamique de la végétation dans l'écotone forêt-savane de la zone de transition Guinéo-Congolaise de la région du Centre Cameroun en lien avec des changements induits sur la structure et la composition de la végétation dans la zone de construction du barrage hydroélectrique de Nachtigal et le parc national du Mpem et Djim (PNMD). La classification supervisée avec l'algorithme de maximum de vraisemblance d'une image du satellite Spot 6/7 complété par les informations sur la hauteur de canopée dérivée des données ALS ont servi à discriminer les types de végétation dans la zone de Nachtigal avec une grande précision (96,5 %). L'utilisation des données de terrain afin d'estimer la biomasse aérienne ligneuse (AGB) pour la zone de Nachtigal à partir d'un modèle statistique (modelbased approach) calibré sur les données satellitaires a conduit à une surestimation systématique des estimations d'AGB avec une erreur résiduelle moyenne des prédictions (RMSPE) allant jusqu'à 65 Mg.ha -1 (90 %), tandis que l'utilisation les données ALS (AGBALS) a conduit à un faible biais et une réduction du RMSPE d'environ 30 % (43 Mg.ha -1 , 58 %) avec un faible effet du type de satellite utilisé. Cependant ces résultats confirment qu'une attention particulière doit xviii être portée sur la forêt sur la savane à une vitesse moyenne d'environ 6 km²an -1 . L'archive d'image Landsat est la mieux appropriée pour le suivi de la fréquence des feux dans la zone d'étude en comparaison avec les produits MODIS. Entre deux périodes de monitoring successifs (5 ans)

Table I :

 I Selection of studies using airborne LiDAR scanning data to parameterize model-based approaches and generate wall-to-wall AGB and tree height maps from spaceborne optical imagery. Statistics of model predictive performance (i.e. R² and Root Mean Square Prediction Error, or RMSPE) are derived from a variety of model validation strategies, including (1) withholding of a given proportion of data for model testing, with test data selected at random (strat. 1), (2) 10fold cross validation, with random split of data into folds (strat. 2), (3) Monte Carlo cross-validation, with random or spatial split of data into folds (strat. 3 and 4, respectively). Details of cross-validation strategies can be found in the original studies. RMSPE provided in Mg of carbon per hectare in original studies are converted to Mg of AGB per hectare with a carbon-to-AGB conversion factor of 0.5

	Country	Multispectral	Spatial scale (m)	Model type	Model validation	R²	RMSPE (Mg.ha -1 )	References
	Colombia and Peru	MODIS	500	Random Forest	strat 1 (10%)	0.86	31.4-35.2 (Baccini and Asner, 2013)
	China	Sentinel 2	10	Random Forest	strat 2	0.62	50.36	(Wang et al., 2020)
	Panama	Landsat 5,7	100	Random Forest	strat 1 (30%)	0.62	45	(Asner et al., 2013)
	Cambodia	QuickBird	1.5	Multiple regression	-	0.73	42.8	(Hirata et al., 2018)
	Democratic Republic of Congo	Landsat 8	100	Maximum Entropy	strat 3 strat 4	0.76 0.65	61.29 62.16	(Xu et al., 2017)
	Pantropical	MODIS	500	Random Forest	strat 1 (10%) 0.71-0.83	38-50	(Baccini et al., 2012)
	Peru	Planet Dove	100	Random Forest	strat 1 (20%)	0.7	50.76	(Csillik et al., 2019)
	Malaysian Borneo	Landsat 8	30	Deep learning	strat 2	0.7	83.2	(Asner et al., 2018)
	Panama	Landsat 7	30	Generalized Linear model strat 1 (16%)	0.51	3.42 m	(Caughlin et al., 2016)

2.2.2.1. Forest sampling Vegetation

  Young colonizing forests in direct contact with savanna are dominated by Albizia adianthifolia (Schumach.) W.Wight and Albizia ferruginea (Guill. and Perr.) Benth.), Albizia zygia (DC.) J.F.Macbr., Alchornea cordifolia (Schumach. and Thonn.) Müll.Arg.), Macaranga spinosa Müll. Arg., while emergent trees species such as Mansonia altissima (A.Chev.) A.Chev. Terminalia superba Engl. and Diels and Triplochiton scleroxylon K. Schum dominate in older forest successions. A distinct set of mostly pyrophilous

tree species characterizes the savanna, mainly: Annona senegalensis Pers., Bridelia ferruginea Benth., Crossopterix febrifuga (Afzel. ex G.Don) Benth., Hymenocardia lyrata Tul., Lannea kerstingii (Enql.) K. Krause., Piliostigma thonningii (Schumach.) Milne-Redh, Psorospermum febrifugum Spach. And Terminalia glaucescens Planch. ex Benth. Grass cover is dominated by species of the Poaceae/Andropogonae: Andropogon spp., Hyparrhenia diplandra (Hack.) Stapf., Hyparrhenia rufa (Nees.) Stapf. And Pennisetum purpureum Schumach. (Youta, 1998). II.2. METHODS II.2.1. Data collection II.2.1.1. Field data collection Field data acquisition campaigns were conducted from February 2018 to December 2020. II.communities there were sampled within Permanent Sampling Plots (PSP) of one hectare. Permanent sampling plots refer to sampling units where trees are individually and permanently identified and tagged. The use of PSP have received much attention as a tool to assessing vegetation dynamics (radial growth; mortality, carbon accumulation, etc.) especially in tropical forests

  on DBH, H and WD. For forest trees, the pantropical AGB model of[START_REF] Chave | Improved allometric models to estimate the aboveground biomass of tropical trees[END_REF] Equation 1) was used. For trees from savanna plots, an AGB model developed for semi-arid savanna by[START_REF] Colgan | Harvesting tree biomass at the stand level to assess the accuracy of field and airborne biomass estimation in savannas[END_REF] Equation 2) was used. Lastly, plot woody AGB density (AGBFIELD) was computed as the sum of individual tree AGBs over the plot area (expressed in Mg.ha -1 ).[START_REF] Colgan | Harvesting tree biomass at the stand level to assess the accuracy of field and airborne biomass estimation in savannas[END_REF] where DBH is the diameter at breast height (cm); H is the total height (m) and WD is the wood specific density of each individual (g.cm -3 ).

	Equation 1	AGB = 0.0673 * (WD * H * DBH^2)^0.976
		Chave et al., 2014
	Equation 2 AGB = exp[-2.215 + 1.393*ln(DBH) + 0.144(ln(DBH))² + 0.729*ln(H) + 0.805*ln(WD)]

II.2.1.2. Spatial data collection II.2.2.2.1.

Table II :

 II Characteristics, properties and purposes of different satellite sensors used in this study. Res = sensor resolution (in m); nBands = number of spectral bands for each sensor.

Source Acquisition period Res(m) nBands Purpose

	II.2
					-Landcover classification in
	Spot 6-7	2015	3	4	the Nachtigal area
					-AGB modelling
					-AGB modelling
	Sentinel 2	2015-2020	10	10	-Mapping spectral species
					diversity in the Mpem et Djim NP
	Landsat (Lst) Collection	Lst 2 1975-1978 Lst 5 1986 Lst 7 1999-2013 Lst 8 2013-2020	60 30 30 30	5 6 6 8	-AGB modelling -Land cover dynamics -Fire frequency mapping
	MODIS	2000-2019	500	5	-Fire frequency mapping

.2. Spatial and statistical analysis II.2.2.1. Land cover mapping

Table VI :

 VI Land cover types proposed by the national unit in charge of REDD + (UN-REDD +) and correspondence to the land cover types proposed from this study

Ecological zone Classes from the UN-REDD + Classes from this study

	Table VII : Continuation
	Ecological zone	Classes from the UN-REDD +	Classes from this study
			8-Bareground
		7-Buildings
			9-Urban areas
		8-Multi annual crops	10-Elaeis guineensis form
	Crop lands
		9-Annual crops	11-Cropland
	Flooded forests and	10-Swamp forests	12-Swamp forest
	wetlands	11-Raphia palm swamp forests	13-Raphia sp. stand
	II.2.2.2.2.
			1-Agroforests
		1-Young secondary moist forests	2-Degraded secondary
	Moist humid forest		forest / Fallow
		2-Old-growth secondary moist forests 3-Old-growth secondary
			forest
		3-Woody savanna	4-Woody savanna
	Vegetation with a	4-Shrubby savanna	5-Shrubby savanna
	dominant grass layer	5-Grassy savanna	6-Grassy hydromorphic
			savanna
	Vegetation free areas	6-Continental waters	7-Water

Table VIII

 VIII

	Red2	-	-	B6			x
	Red3	-	-	B7			x
	NIR	B4	B5	B8	x	x	x
	Red4	-	-	B8a			x
	SWIR1	-	B6	B11		x	x
	SWIR2	-	B7	B12		x	x
			Vegetation indices
		Equations	References
	RGR	(Red/Green)		(Sims A. and Gamon, 2002)	x	x	x
	NIRGR	(NIR/Green)			x	x	x
	NDVI	(NIR -Red)/(NIR + Red)		(Tuchker, 1979)	x	x	x
	EVI	2.5*[(NIR -Red)/(1 + NIR + 6*Red -7.5*Blue)]	(Huete et al., 2002)	x	x	x
	SR	(NIR/RED)		(Jordan, 1969)
		: Band names and vegetation indices used to generate different aboveground
	biomass predictive models based on satellite data: Spot 6/7 (S. 6/7), Landsat 8 (L. 8), and
	Sentinel 2 (S. 2).
			Spectral band		Candidate predictors
	Designation
		S. 6/7	L. 8	S. 2	S. 6/7	L. 8	S. 2
	Blue	-	B2	B2
	Green	B2	B3	B3	x	x	x
	Red	B3	B4	B4	x	x	x
	Red1	-	-	B5			x

Table X :

 X Proportion of the different landcover types located in the study area

	Land cover types	Area (km²) Proportion (%)
	Agroforests	130.9	25.2
	Woody savanna	114.9	22.1
	Shrubby savanna	112.0	21.6
	Old-growth secondary forests	58.5	11.3
	Degraded secondary forests / Fallow	37.8	7.3
	River	29.1	5.6
	Bareground	13.1	2.5
	Elaeis guineensis plantation	8.1	1.6
	Swampy forest	7.8	1.5
	Grassy hydromorphic savanna	6.3	1.2
	Cropland	0.9	0.2
	Raphia sp. stand	0.2	0.0
	Urban areas	0.2	0.0

Table XI :

 XI Summary statistics of field plot data used in predicting AGB from the canopy height model: mean (min-max). The sites consisted in closed canopy (forest) and open canopy (savanna) vegetation as observed in the field.

	Sites	No. Stem (ind.ha -1 )	Lorey's height (m)	Basal area (m².ha -1 )	Woody biomass (Mg.ha -1 )
	Forest	392 (216 -538)	27 (24 -33)	28 (19 -35)	234 (80 -422)
	Savanna	239 (50 -550)	7 (5 -10)	18 (12 -28)	21 (1 -133)

Table XII

 XII

	90%), whereas RFALS models greatly improved the prediction accuracy (drop of ~30% in
	RMSPE and relative RMSPE). RFALS models based on Landsat 8 or Sentinel 2 predictors lead
	to a decrease of 10% both in RMSPE and relative RMSPE (R² = 0.7; RMSPE of c. 43 Mg.ha -1
	and relative RMSPE of c. 60%) compared to RFALS models based on Spot 6/7 predictors (R² =
	0.6; RMSPE = 48 Mg.ha -1 and Relative RMSPE = 66.5%)

shows the variables selected (RFFIELD and RFALS) and the model performances in cross-validation for the different models. Independently of the satellite sensor, RFFIELD models gave the poorest performances, with R² values around 0.6, and RMSPE of up to 65 Mg.ha -1 (i.e.

Table XII :

 XII Structure and performances (squared correlation: R², root mean squared prediction error: RMSPE in Mg.ha -1 and the relative RMSPE in %) of the different models selected for each spaceborne optical sensor. Performance metrics are based on 4-fold cross validation.

								Spot
	6/7 (S. 6/7); Landsat 8 (L. 8) and Sentinel 2 (S. 2). Candidate spectral bands and vegetation
	indices retained for each models: EVI = enhanced vegetation index; IRECI = inverted red-edge
	chlorophyll index; NDVI = normalized difference vegetation index; SAVI = soil-adjusted
	vegetation index; S2REP = Sentinel 2 red-edge position; SWIR 2 = short-wave infra-red 2.
	Sensor	RFFIELD models	R²	RMSPE	Relative RMSPE	RFALS models	R²	RMSPE	Relative RMSPE
	S. 6/7	Green + CSF + EVI 0.58	65	90	Red + CSF + LAI	0.62	48.3	66.5
	L. 8	Red + SWIR 2 + SAVI	0.61	64.8	88	Red + SWIR 2 + Green + fCover + EVI	0.7	43.1	60
	S. 2	S2REP + SWIR 2 + NDVI 2	0.58	63.2	85	NDVI 2 + SWIR 2 + IRECI + NDVI 4 + Red + NDVI 3	0.7	42.8	58

.2.2. Vegetation change patterns III.2.2.1. Long-term (1975-2020) forest expansion

	Forest-savanna ecotones (FSE) of northern hemisphere tropical Africa are currently
	experiencing forest encroachment over savannas. Several studies, scattered from Guinea to the
	Central African Republic (and even central Gabon) have illustrated this trend, while referring
	to time windows of variable lengths within the last six decades. However, in all documented
	sites, no consistent picture over more than 30 years was available. The conclusion of
	progressive forest encroachment qualitatively agrees with previous studies in nearby areas of
	the Guineo-Congolian region in Central Africa that stretched over either an early time window

Performance of automated cloud computing and Landsat image archives in modelling land cover dynamics

  This highly benefited to intermediate CAI classes (interpretable as both dense savanna and young forest) with a marginal increase of upper CAI class (>1 m²/m² which unequivocally relates to close canopy forest)[START_REF] Mitchard | Measuring woody encroachment along a forest-savanna boundary in Central Africa[END_REF].It was shown in the present study that simple principles for distinguishing contrasted vegetation types allow benefitting from massive high spatial resolution image series and cloud computing to consistently document the dynamics of forest-savanna boundaries with unsupervised methods. An integrated picture of vegetation changes was indeed achieved in a protected area in Central Cameroon (MDNP, ca. 100,000 ha) from 1975 to 2020, that is all over the timeframe covered by the Landsat (sensu lato) series. The automated, unsupervised landcover classification implemented in GEE provided similar results in terms of vegetation dynamics, capturing the steady increase of forest cover through time and leading to an average rate of forest encroachment virtually equal (i.e., c. 1%.year-1

	of monitoring in a nearby area in Cameroon (Mbam et Kim confluent). Mitchard et al. (2009) expressed forest encroachment qualitatively as increases in canopy area index (CAI) and observed a shrinking of low CAI areas (0.2 m²/m² area, interpreted as "grasslands") by 43% over 20 years (0.9% y -1 for 1986-2000 and 1.29% for 2000-2006). III.2.2.2.

  of savanna species under the current conditions experienced by humid savannas. Reciprocally, increased woody biomass and cover is known to depress grass production

	2-0.3 year -1 )
	and intermediate AGB (~ 30 Mg.ha -1 ). Analogously, Mitchard et al. (2009) already found that

savannas of intermediate CAI were relatively scarce in the nearby Mbam-Djerem region. Our data suggest the relative instability of savanna physiognomies in presence of intermediate fire frequencies (BAI < 0.4 year -1 ) which do not seem sufficient to prevent biomass build-up and stand thickening

  . The shift towards wooded savannas and associated ineffective fire regimes seems here to occur for AGB values around 30 Mg.ha -1 . Once this threshold is overpassed, the AGB build-up towards 40 to 60 Mg.ha -1 (i.e. towards a savanna woodland) seems inexorable and prefigures the floristic shift towards stands dominated by forest pioneer species that displayed AGB values in the range 50 -100 Mg.ha -1 . Field prospection allowed us to frequently observe dense woodlands characterized by tall savanna trees (mainly Terminalia glaucescens) frequently fringing young forests dominated by species such as Albizia adianthifolia and Macaranga sp. that overtopped surviving T. glaucescens engulfed in dense thickets of C. odorata. All this strongly suggests recent afforestation.

  Bridelia micranta, Nauclea latifolia, Oncoba glauca, and Treculia obovoidea) and old-growth forest species (Celtis mildbraedii, Celtis zenkeri, Glyphaea brevis, Sterculia rhinopetala and Trilepisium madagascariense) in the later forest succession. The presence of pioneer forest saplings (low basal area) in more open savannas (AGBWood ≤ 40 Mg.ha -1 ) and their occurrence in grassy savanna would suggest that forest is gradually taking advantage over savanna by secondary succession. During forest expansion, the establishment and the growth of pioneer forest species are likely to shade savanna specialists which are shade-intolerant species. This explains the decrease in the abundance of savanna specialist with the increase in pioneer forest species and their scarcity in forest stands (AGBWood > 50 Mg.ha -1 ). However the presence of fire-sensitive pioneer forest species in all scenarios of fire frequencies (from low to high fire frequencies) would suggest that the limiting effect of fire on woody vegetation is not sufficient to hinder woody encroachment in the area bringing therefore sufficient humidity required for the establishment of pioneer forest saplings within open savannas. Unlike distinct ecotonal communities found between forest and savanna in the Lopé National Park[START_REF] Cardoso | A distinct ecotonal tree community exists at central African forest-savanna transitions[END_REF], the forest savanna ecotone in the area is a mixture of forest pioneers and savanna trees which likely accelerate forest encroachment over savanna even in contexts of frequent fires.

). As the woody layer within savanna thickens (increase in woody AGB; AGBWood) savanna species (Hymenocardia acida, Crossopteryx febrifuga, Bridelia ferruginea, Terminalia glaucescens, Piliostigma thonningii and Annona senegalensis) are gradually replaced by fast growing pioneer forest species (Albizia zygia,

  approaches based on open source cloud-based platforms such as Google Earth Engine for longterm (45 years) land cover change monitoring. A distinct functional (β-diversity) structure of the vegetation was described along a forest transitional gradient. AGB recovery rate of regenerating forests was higher (4.3 Mg.ha -1 .year -1 ) in young forest stands (< 20 years old)

	compared to older forest successions (≥ 20 years old) where AGB recovery is lower (3.2 Mg.ha -
	1 .year -1 ). This gives way to a new approach which guarantees an improved measurements of
	national level carbon stocks (Sagang et al., 2020) and carbon stock changes in forest-savanna
	transitional landscapes (in relation to Tier 2 and Tier 3 accuracy levels), in compliance with the
	United Nations Framework Convention on Climate Change (UNFCCC) and IPCC requirements
	for countries still reporting at Tier 1 level especially in Central Africa

  . Subsequent efforts are therefore needed to temporally disaggregate annual BAI products. A useful step forward would be standardization of such methodologies in different forest-savanna transition areas to enable cross-site comparisons. Coordinates and structure of the 74 plots sampled within the study area. N ind.: number of individuals within 1-ha; N sp.: number of species within 1-ha; Lorey's H = averaged height of trees weighted by the basal area; G: basal area and AGB = aboveground biomass; N Sp.: Number of species. Species list and total number of inviduals (N) sampled from the 10 1-ha forest plots and 35 0.16-ha savanna plots installed in the forest-savanna transition area of Cameroon

	Plant press 30 sav_Mpem009 Index Species Index Species Index Species	2 Press vouchers 0.16 11.6325 5.0293 17 Familly N Index Species Familly N Index Species Familly N Index Species	7	3	Familly Familly Familly	7	N N N
	Alcohol 70-95° Envelopes and silica gel Markers, ordinary pencils, eraser Notebook with hard cover * Average quantity for each plot Appendix 2. ID Plot name Size (ha) 1 Mpem001 1 11.6558 5.0181 371 5 L Conserve specimens 100 To dry the samples for genetic analysis, if desire 2 To mark and take notes 1 Take notes in the field lon lat N ind Lorey's H (m) G (m²/ha) AGB (Mg/ha) 33 37 377 2 Mpem002 1 11.6479 5.0324 313 33 39 511 3 Mpem003 1 11.6479 5.0223 278 25 24 208 4 Mpem004 1 11.6520 5.0143 354 30 24 250 5 Mpem005 1 11.5615 5.1790 568 23 28 239 6 Mpem006 1 11.5848 5.1683 353 21 17 122 7 Mpem007 1 11.5860 5.1806 397 29 36 361 8 Mpem008 1 11.7411 5.1172 590 28 44 494 9 Mpem009 1 11.7368 5.1229 535 25 28 233 10 Mpem010 1 11.7272 5.1237 342 22 17 118 11 nachtigal001 1 11.7229 4.3782 356 28 30 247 12 nachtigal002 1 11.7084 4.3570 429 33 26 237 13 nachtigal003 1 11.7418 4.3747 330 25 23 180 14 nachtigal004 1 11.7040 4.3678 538 24 35 240 15 nachtigal005 1 11.7198 4.3612 216 29 28 220 16 nachtigal006 1 11.6642 4.3246 353 28 26 250 17 nachtigal007 1 11.7229 4.4177 368 27 29 268 18 nachtigal008 1 11.7200 4.4093 337 29 19 102 19 nachtigal009 1 11.8143 4.4540 485 28 30 272 20 nachtigal010 1 11.8232 4.4601 509 24 28 225 21 nachtigal011 1 11.8133 4.4440 386 23 24 161 22 sav_Mpem001 0.16 11.6529 5.0213 26 8 5 14 23 sav_Mpem002 0.16 11.6452 5.0270 40 8 7 23 24 sav_Mpem003 0.16 11.6521 5.0277 46 8 22 72 25 sav_Mpem004 0.16 11.6527 5.0306 18 6 3 7 26 sav_Mpem005 0.16 11.6545 5.0302 77 21 24 203 27 sav_Mpem006 0.16 11.6457 5.0307 29 9 8 25 28 sav_Mpem007 0.16 11.6413 5.0267 19 6 3 6 29 sav_Mpem008 0.16 11.6394 5.0311 27 8 6 20 31 sav_Mpem010 0.16 11.6288 5.0349 37 6 4 8 32 sav_Mpem011 0.16 11.6269 5.0356 41 6 3 7 33 sav_Mpem012 0.16 11.6337 5.0364 53 9 11 42 34 sav_Mpem013 0.16 11.6339 5.0308 40 6 6 13 35 sav_Mpem014 0.16 11.6569 5.0395 73 15 19 101 36 sav_Mpem015 0.16 11.7199 5.2837 51 8 9 31 37 sav_Mpem016 0.16 11.7178 5.2928 18 9 4 15 38 sav_Mpem017 0.16 11.7116 5.2938 21 4 2 2 39 sav_Mpem018 0.16 11.7201 5.2882 32 9 6 19 40 sav_Mpem019 0.16 11.7212 5.2878 25 9 5 18 41 sav_Mpem020 0.16 11.7062 5.2853 29 6 3 7 42 sav_Mpem021 0.16 11.7060 5.2860 126 6 15 28 43 sav_Mpem022 0.16 11.7557 5.2682 64 6 10 22 44 sav_Mpem023 0.16 11.7635 5.2695 10 7 2 7 45 sav_Mpem024 0.16 11.7601 5.2671 27 5 3 4 46 sav_Mpem025 0.16 11.7602 5.2727 18 5 2 5 47 sav_Mpem026 0.16 11.7550 5.2777 14 5 3 4 48 sav_Mpem027 0.16 11.7519 5.2772 46 5 6 11 49 sav_Mpem028 0.16 11.7538 5.2697 38 8 11 30 50 sav_Mpem029 0.16 11.7437 5.2624 7 5 1 1 51 sav_Mpem030 0.16 11.7452 5.2639 50 7 9 24 52 sav_Mpem031 0.16 11.7649 5.2714 11 5 1 2 53 sav_Mpem032 0.16 11.7476 5.2706 22 7 6 17 54 sav_Mpem033 0.16 11.7634 5.2532 2 7 1 2 55 sav_Mpem034 0.16 11.7492 5.2679 32 7 4 10 56 sav_Mpem035 0.16 11.7789 5.2706 66 6 8 13 57 sav_nachtigal001 0.16 11.6884 4.4056 51 5 4 6 58 sav_nachtigal002 0.16 11.6847 4.4034 57 8 11 31 59 sav_nachtigal003 0.16 11.7049 4.3978 55 5 5 9 60 sav_nachtigal004 0.16 11.7049 4.4011 77 6 11 23 61 sav_nachtigal005 0.16 11.7099 4.4002 17 7 2 4 62 sav_nachtigal006 0.16 11.6501 4.3729 8 5 1 1 63 sav_nachtigal007 0.16 11.6490 4.3702 16 6 2 4 64 sav_nachtigal008 0.16 11.6479 4.3721 20 6 3 7 65 sav_nachtigal009 0.16 11.6586 4.3685 15 6 2 4 66 sav_nachtigal010 0.16 11.6366 4.3577 18 6 4 9 67 sav_nachtigal011 0.16 11.6382 4.3559 18 5 2 2 68 sav_nachtigal012 0.16 11.5967 4.4120 57 8 7 25 69 sav_nachtigal013 0.16 11.5988 4.4102 28 7 5 13 70 sav_nachtigal014 0.16 11.5911 4.4127 51 24 17 150 71 sav_nachtigal015 0.16 11.7293 4.4177 40 7 6 16 72 sav_nachtigal016 0.16 11.7306 4.4214 46 6 5 9 73 sav_nachtigal017 0.16 11.7306 4.4238 88 24 35 300 74 sav_nachtigal018 0.16 11.6843 4.4058 27 13 6 38 156 Mallotus Euphorbiaceae 37 198 Plagiostyles Euphorbiaceae 39 Blighia sapida Sapindaceae 9 7 Alchornea Euphorbiaceae 182 Oncoba welwitschii Salicaceae 3 28 Antrocaryon Anacardiaceae Appendix 3 : Index Species Familly N 244 Theobroma cacao Malvaceae 706 255 Trilepisium madagascariense Moraceae 528 238 Terminalia glaucescens Combretaceae 271 226 Sterculia rhinopetala Malvaceae 254 17 Annona senegalensis Annonaceae 247 78 Crossopteryx febrifuga Rubiaceae 212 239 Terminalia ivorensis Combretaceae 203 45 Bridelia ferruginea Phyllanthaceae 194 196 Piliostigma thonningii Fabaceae 186 136 Hymenocardia acida Phyllanthaceae 182 56 Celtis mildbraedii Cannabaceae 179 59 Celtis zenkeri Cannabaceae 175 209 Ricinodendron heudelotii Euphorbiaceae 170 236 Syzygium guineense Myrtaceae 150 206 Pycnanthus angolensis Myristicaceae 146 127 Glyphaea brevis Malvaceae 139 21 Anthonotha macrophylla Fabaceae 122 55 Celtis africana Cannabaceae 115 260 Uapaca paludosa Phyllanthaceae 111 73 Cola lateritia Malvaceae 108 24 Antidesma membranaceum Phyllanthaceae 106 36 Berlinia bracteosa Fabaceae 106 268 Xylopia aethiopica Annonaceae 101 121 Funtumia africana Apocynaceae 99 256 Triplochiton scleroxylon Malvaceae 98 58 Celtis tessmannii Cannabaceae 84 158 Mansonia altissima Malvaceae 84 241 Terminalia superba Combretaceae 84 6 Albizia zygia Fabaceae 81 160 Mareyopsis longifolia Euphorbiaceae 79 128 Greenwayodendron suaveolens Annonaceae 77 250 Trichilia prieuriana Meliaceae 76 Index Species Familly 172 Myrianthus arboreus Urticaceae 46 Bridelia micrantha Phyllanthaceae Sp. N 180 Oncoba glauca Salicaceae 207 Rauvolfia macrophylla Apocynaceae 253 Trichilia welwitschii Meliaceae 71 Cola cordifolia Malvaceae 229 Strombosia pustulata Olacaceae 246 Treculia obovoidea Moraceae 3 Albizia adianthifolia Fabaceae 53 Ceiba pentandra Malvaceae 153 Macaranga spinosa Euphorbiaceae 163 Milicia excelsa Moraceae 171 Musanga cecropioides Urticaceae 61 Chrysophyllum boukokoense Sapotaceae 176 Newbouldia laevis Bignoniaceae 129 Grewia coriacea Malvaceae 135 Hylodendron gabunense Fabaceae 222 Sorindeia juglandifolia Anacardiaceae 86 Desplatsia dewevrei Malvaceae 213 Rothmannia longiflora Rubiaceae 170 Morus mesozygia Moraceae 147 Lannea welwitschii Anacardiaceae 68 Cola acuminata Malvaceae 110 Eribroma oblongum Malvaceae 116 Fernandoa adolfi-friderici Bignoniaceae 117 Ficus exasperata Moraceae 118 Ficus mucuso Moraceae 259 Uapaca guineensis Phyllanthaceae 27 Antrocaryon klaineanum Anacardiaceae 70 Cola chlamydantha Malvaceae 197 Piptadeniastrum africanum Fabaceae 41 Bombax brevicuspe Malvaceae oppositifolius 167 Millettia sanagana Fabaceae 36 227 Sterculia tragacantha Malvaceae 36 57 Celtis philippensis Cannabaceae 35 12 Angylocalyx pynaertii Fabaceae 34 242 Tetrapleura tetraptera Fabaceae 34 251 Trichilia rubescens Meliaceae 33 2 Afzelia bipindensis Fabaceae 32 103 Elaeis guineensis Arecaceae 31 122 Funtumia elastica Apocynaceae 30 144 Klaineanthus gaboniae Euphorbiaceae 30 161 Margaritaria discoidea Phyllanthaceae 30 201 Pseudospondias microcarpa Anacardiaceae 30 252 Trichilia tessmannii Meliaceae 29 134 Homalium letestui Salicaceae 28 186 Pancovia laurentii Sapindaceae 28 10 Amphimas Fabaceae 27 africana 266 Vitex rivularis Lamiaceae 69 Cola altissima Malvaceae 76 Cordia platythyrsa Boraginaceae 145 Klainedoxa gabonensis Irvingiaceae 214 Rothmannia talbotii Rubiaceae 34 Barteria fistulosa Passifloraceae 168 Monodora myristica Annonaceae 204 Pterocarpus soyauxii Fabaceae 228 Strombosia grandifolia Olacaceae 8 Alstonia boonei Apocynaceae 150 Lophira alata Ochnaceae 199 Pouteria alnifolia Sapotaceae 225 Staudtia kamerunensis Myristicaceae 94 Diospyros suaveolens Ebenaceae 181 Oncoba mannii Salicaceae 263 Vitex cienkowskii Lamiaceae 133 Holoptelea grandis Ulmaceae 9 139 Irvingia grandifolia Irvingiaceae 9 157 Mangifera indica Anacardiaceae 9 274 Zanthoxylum macrophyllum Rutaceae 9 4 Albizia ferruginea Fabaceae 8 20 Anthonotha cladantha Fabaceae 8 81 Dacryodes klaineana Burseraceae 8 114 Erythroxylum mannii Erythroxylaceae 8 208 Rauvolfia vomitoria Apocynaceae 8 254 Tridesmostemon omphalocarpoides Sapotaceae 8 5 Albizia glaberrima Fabaceae 7 19 Anthocleista nobilis Gentianaceae 7 74 Cola lepidota Malvaceae 7 87 Detarium macrocarpum Fabaceae 7 92 Diospyros crassiflora Ebenaceae 7 96 Dovyalis zenkeri Salicaceae 7 cordifolia 16 Annickia Annonaceae 249 Tricalysia Rubiaceae micraster 3 29 Aoranthe cladantha Rubiaceae pangolina chlorantha 33 Balanites Zygophyllaceae 258 Uapaca acuminata Phyllanthaceae 3 37 Berlinia hollandii Fabaceae 9 Amphimas Fabaceae 2 38 Bersama abyssinica Greyiaceae wilsoniana 109 Entandrophragma utile Meliaceae 132 Harungana madagascariensis Hypericaceae 190 Pentaclethra Fabaceae ferrugineus 22 Antiaris toxicaria Moraceae 44 Brenania brieyi Rubiaceae 2 25 Antidesma venosum Phyllanthaceae 47 Burkea africana Fabaceae 2 32 Baikiaea insignis Fabaceae 48 Calpocalyx Fabaceae 2 dinklagei 80 Dacryodes edulis Burseraceae 2 49 Campylospermum Ochnaceae macrophylla 217 Sapium ellipticum Euphorbiaceae 240 Terminalia macroptera Combretaceae 275 Zanthoxylum tessmannii Rutaceae 26 Antidesma vogelianum Phyllanthaceae 42 Bombax buonopozense Malvaceae 52 Casearia aculeata Salicaceae 63 Chrysophyllum Sapotaceae 82 Dacryodes Burseraceae mannii 2 51 Canarium Burseraceae macrophylla 99 Drypetes klainei Putranjivaceae schweinfurtii 2 100 Drypetes leonensis Putranjivaceae 64 Citrus sinensis Rutaceae 2 104 Elaeophorbia Euphorbiaceae 65 Cleistopholis Annonaceae 2 glauca drupifera 112 Erythrophleum Fabaceae 79 Cylicodiscus Fabaceae 2 gabunensis ivorense 115 Euphorbia Euphorbiaceae 85 Desplatsia Malvaceae 2 chrysochlamys drupifera 119 Ficus sycomorus Moraceae 98 Drypetes Putranjivaceae 2 gossweileri pruniforme 72 Cola flavovelutina Malvaceae 123 Gambeya africana Sapotaceae 2 105 Entada africana Fabaceae 97 Drypetes capillipes Putranjivaceae 124 Gambeya Sapotaceae 2 107 Entandrophragma Meliaceae boukokoensis candollei pterocarpoides 111 Eriocoelum macrocarpum Sapindaceae 18 Anonidium mannii Annonaceae 108 Entandrophragma Meliaceae 7 126 Garcinia ovalifolia Clusiaceae 141 Keayodendron Phyllanthaceae 2 120 Ficus vogeliana Moraceae 27 174 Nauclea latifolia Rubiaceae 27 164 Millettia barteri Fabaceae 26 179 Olax subscorpioidea Olacaceae 25 205 Pterygota bequaertii Malvaceae 25 175 Nesogordonia papaverifera Malvaceae 24 231 Strombosiopsis tetrandra Olacaceae 24 257 Turraeanthus africanus Meliaceae 24 264 Vitex doniana Lamiaceae 24 40 Blighia welwitschii Sapindaceae 23 183 Ongokea gore Olacaceae 23 62 Chrysophyllum lacourtianum Sapotaceae 22 91 Diospyros canaliculata Ebenaceae 22 270 Xylopia quintasii Annonaceae 22 245 Treculia africana Moraceae 19 265 Vitex grandifolia Lamiaceae 19 101 Duboscia macrocarpa Malvaceae 18 148 Lasiodiscus mannii Rhamnaceae 18 192 Pericopsis laxiflora Fabaceae 89 Dialium guineense Fabaceae 90 Diospyros bipindensis Ebenaceae 130 Guarea cedrata Meliaceae 178 Ochtocosmus africanus 50 Canarium schweinfurthii Burseraceae 84 Desbordesia insignis Irvingiaceae 88 Dialium bipindense Fabaceae 95 Discoglypremna caloneura Euphorbiaceae 113 Erythrophleum cylindricum 125 Gambeya lacourtiana Sapotaceae 7 155 Maesopsis eminii Rhamnaceae 7 193 Persea americana Lauraceae 7 233 Symphonia globulifera Clusiaceae 7 262 Uvariastrum pierreanum Annonaceae 7 271 Xylopia rubescens Annonaceae 7 13 Aningeria altissima Sapotaceae 6 54 Celtis adolfi-friderici Cannabaceae 6 66 Cleistopholis patens Annonaceae 6 162 Markhamia lutea Bignoniaceae 173 Nauclea diderrichii Rubiaceae 187 Panda oleosa Pandaceae 195 Picralima nitida Apocynaceae 220 Scottellia klaineana Achariaceae 235 Synsepalum Sapotaceae bridelioides 142 Khaya anthotheca Meliaceae 131 Guarea thompsonii Meliaceae 2 159 Maranthes glabra 140 Irvingia robur Irvingiaceae Chrysobalanaceae 2 166 Millettia Fabaceae 146 Klainedoxa Irvingiaceae 2 microphylla macrophylla 188 Pauridiantha floribunda Rubiaceae 151 Lophira lanceolata Ochnaceae 2 152 Lovoa trichilioides Meliaceae 200 Pseudospondias Anacardiaceae 2 154 Maesobotrya Phyllanthaceae stipulatum 273 Zanthoxylum heitzii Rutaceae longifolia klaineana 30 Aulacocalyx caudata Rubiaceae 211 Rinorea Violaceae 2 165 Millettia laurentii Fabaceae 35 Beilschmiedia congolana Lauraceae 43 Borassus aethiopum Arecaceae oblongifolia 215 Rothmannia Rubiaceae 169 Morinda lucida Rubiaceae 2 184 Oxyanthus Rubiaceae whitfieldii 216 Santiria trimera Burseraceae oliganthus 2 232 Swartzia fistuloides Leguminosae 185 Pachypodanthium Annonaceae 2 staudtii Fabaceae 75 Cola verticillata Malvaceae 6 60 Centroplacus Centroplacaceae 234 Synsepalum Sapotaceae 2 189 Pausinystalia Rubiaceae suaveolens 138 Irvingia gabonensis Irvingiaceae 77 Corynanthe pachyceras Rubiaceae 210 Rinorea dentata Violaceae 247 Trema orientalis Cannabaceae 269 Xylopia hypolampra Annonaceae 15 Annickia affinis Annonaceae 31 Aulacocalyx jasminiflora Rubiaceae 83 Daniellia oliveri Fabaceae glaucinus dulcificum macroceras 6 106 Entandrophragma Meliaceae 67 Coelocaryon Myristicaceae 243 Tetrorchidium Euphorbiaceae 2 191 Pentadesma Clusiaceae 6 preussii didymostemon butyracea angolense 202 Pteleopsis hylodendron Combretaceae 93 Diospyros mannii Ebenaceae 267 Voacanga africana Apocynaceae 2 194 Petersianthus Lecythidaceae 6 223 Spathodea campanulata Bignoniaceae 6 230 Strombosia zenkeri Olacaceae 6 237 Tabernaemontana crassa Apocynaceae 6 272 Zanthoxylum gilletii Rutaceae 6 102 Duguetia staudtii Annonaceae 137 Hymenostegia afzelii Fabaceae 143 Kigelia africana Bignoniaceae 149 Leptactina involucrata Rubiaceae 177 Ochna calodendron Ochnaceae 1 Afrostyrax Huaceae macrocarpus 1 203 Pterocarpus Fabaceae kamerunensis 11 Angylocalyx Fabaceae mildbraedii 1 212 Rothmannia Rubiaceae oligophyllus 14 Aningeria robusta Sapotaceae lateriflora 1 23 Antidesma Phyllanthaceae 218 Schumanniophyton Rubiaceae 1 magnificum laciniatum 219 Scottellia coriacea Achariaceae	5 N 5 5 5 5 5 5 5 5 4 4 4 4 4 4 4 4 4 4 4 4 4 4 3 3 3 3 3 3 3 3 3 3 3
						iii iv v vi vii

Blaise Chargé de Cours En poste EWANE Cécile Anne Chargée de Cours En poste FONKOUA Martin Chargé de Cours En poste BEBEE Fadimatou Chargée de Cours En poste KOTUE KAPTUE Charles Chargé de Cours En poste LUNGA Paul KEILAH Chargé de Cours En poste MANANGA Marlyse Joséphine Chargée de Cours En poste MBONG ANGIE M. Mary Anne Chargée de Cours En poste PECHANGOU NSANGOU Sylvain Chargé de Cours

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ACKNOWLEDGEMENTS

It is a genuine pleasure to express my deep sense of thanks and gratitude for the financial, technical, material and moral support which ensured the completion of this Ph.D. Thesis. I am

J. épse NOUMBEM Chargée de Cours Chef.cell / Supervision committee: -Prof. SONKÉ Bonaventure for co-supervision. You warmly welcomed me in the Plant gave me to work on and manage such a fascinating project, teaching how to cooperate and raising in me the art of scientific research.

-Dr. COUTERON Pierre for co-supervision. I would acknowledge your valuable input providing indispensable advice, information and support that helped to shape this project.

Institutions and funders:

-The University of Yaoundé I for providing me a doctorate position.

-Institut de Recherche pour le Développement (IRD) through the "Allocations de recherche pour une Thèse au Sud (ARTS)" for providing a scholarship throughout my PhD program.

Special thanks to Mrs BRÛLÉ Gaëlle for all logistic assistance in Europe , Dr. BRAUN Jean-Jacques, Dr. HOUGARD Jean-Marc and all the administrative staff of IRD in Cameroon for their administrative assistance.

-The Nachtigal Hydropower Company for providing funding, administrative and logistical assistance for field work. I am grateful to Mrs ARDORINO Florence, Mrs DAUCHEZ Laurene, Mr CHEBEN Pierre, and Mrs DOUMBE Christine for the nice collaboration. -The Mpem et Djim National Park's staff starting from the park warden in the person of BISSECK Jean-Pierre and his dynamic team researchers. I sincerely thank Dr. PÉLISSIER Raphaël for providing all assistance during my visits. Dr. PLOTON Pierre I am grateful for your support, guidance and thank you. I say thank you to Mrs VIENNOIS Gaëlle for her mapping expertise; Dr. RÉJOU-MÉCHAIN Maxime

LIST OF APPENDICES

which pixels were classified as burned and above which pixels were classified as unburned in the entire image collection. This threshold of 0.1 was later comforted when observing the NBR variation with time for frequently and rarely burned pixels (Fig. 30), which showed the progressive fading of fire scars' influence on pixel spectral properties. Fig. 29: Density plot of the Normalised Burned Ratio from savanna pixels with fire scars (grey colour) and savanna pixels without fire scars (green colour). The blue vertical line represents the NBR threshold of 0.1 which separates burned from unburned pixels.

For each pixel, the result of this classification process was thus a time series with observations classified as burned or unburned and featuring, for each observation, the associated image acquisition date.