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Les écosystèmes forestiers d’Afrique sont en proie à la déforestation et subissent des dégradations, ayant pour conséquences une pression importante sur les services écosystémiques et une contribution à l’émission de gaz à effet de serre dans l’atmosphère. L’accessibilité et le faible coût des technologies liées à la télédétection en font des outils valorisables et prometteurs à la caractérisation de forêts claires de type miombos, première étape vers le suivi temporel des stocks de biomasse végétale et de carbone. L’objectif général de ce travail de fin d’étude est de caractériser la structure forestière d’un miombo en périphérie de la ville de Lubumbashi (RDC) à l’aide d’imageries drone et satellitaires. Une approche arbre au départ du Modèle Numérique de Hauteur de la zone d’étude de 10 hectares construit sur base des imageries acquises par drone a été mise en place, d’une part pour évaluer la performance de l’algorithme de détection des arbres individuels, et d’autre part pour valider les hauteurs extraites du Modèle Numérique de Hauteur en les comparant avec les mesures faites sur le terrain. L’identification des arbres individuels est jugée satisfaisante (score F = 0.76) de même que les hauteurs extraites du Modèle Numérique de Hauteur (R² = 0.835, RMSE% = 10.99%). Ensuite, une approche surface a permis de mettre en évidence un modèle d’estimation de la biomasse aérienne au départ du volume sous le Modèle Numérique de Hauteur volha et du coefficient de variation de la hauteur hcv (variables surfaciques extraites du Modèle Numérique de Hauteur) pour une taille de parcelle de 0.25 ha (R² aj = 0.65, RMSE% = 14.43%). Finalement, un test de corrélation linéaire de Pearson a été fait entre la biomasse aérienne et, d’un côté, cinq indices de végétation calculés au départ des bandes spectrales des imageries Sentinel-2 et, d’un autre côté, l’intensité de rétrodiffusion en polarisations VV et VH d’imageries Sentinel-1. Les hautes valeurs de biomasse aérienne ainsi que leur faibles gammes n’ont pas permis de mettre en évidence une relation linéaire probante. De ce fait, aucun modèle d’estimation de la biomasse aérienne au départ d’imageries satellitaires n’a été construit. Les outils de télédétection appliqués aux miombos humides sont prometteurs au vu des résultats obtenus, ayant néanmoins permis de mettre en évidence l’importance d’un échantillonnage adéquat. Il est dès lors recommandé de combiner les technologies satellitaires, d’étendre la gamme de biomasse aérienne et d’augmenter le nombre de parcelles afin d’optimiser la représentativité de l’échantillonnage et de pouvoir construire des modèles d’estimation de la biomasse aérienne valides. Africa’s forest ecosystems are suffering from deforestation and degradation, resulting in significant pressure on ecosystem services and a contribution to the emission of greenhouse gases into the atmosphere. The accessibility and low cost of remote sensing technologies make them valuable and promising tools for characterizing miombo woodlands, a first step towards temporal monitoring of plant biomass and carbon stocks. The general objective of this master thesis is to characterize the forest structure of a miombo on the outskirts of Lubumbashi (DRC) using UAV and satellite imagery. A tree-based approach based on the Canopy Height Model of the 10 hectares study area derived from UAV images was implemented, on the one hand to evaluate the performance of the algorithm for detecting individual trees, and on the other hand to validate the heights extracted from the Canopy Height Model by comparing them with the measurements made in the field. The identification of individual trees is considered satisfactory (score F = 0.76) as well as the heights extracted from the Canopy Height Model (R² = 0.835, RMSE% = 10.99%). Then, an area-based approach made possible the development of a model for estimating above-ground biomass from the volume under the Canopy Height Model volha and the coefficient of variation of height hcv (metrics derived from the Canopy Height Model) for a plot size of 0.25 ha (R² adj = 0.65, RMSE% = 14.43%). Finally, a Pearson linear correlation test was performed between the above-ground biomass and, on the one hand, five vegetation indices computed from the spectral bands of Sentinel-2 imageries and, on the other hand, the backscatter intensity in VV and VH polarizations of Sentinel-1 imageries. The high values of above-ground biomass and their low ranges did not reveal a convincing linear relationship. As a result, no model for estimating above-ground biomass from satellite imagery has been developed. Remote sensing tools applied to wet miombos are promising in view of the results obtained, but have nevertheless highlighted the importance of adequate sampling. It is therefore recommended to combine satellite technologies, extend the range of above-ground biomass and
increase the number of plots in order to optimise the representativeness of the sampling and to be able to build valid above-ground biomass estimation models.

forêt claire, miombo, biomasse aérienne, AGB, drone, Sentinel-1, Sentinel-2, Modèle Numérique de Hauteur --- miombo woodland, above-ground biomass, AGB, UAV, Sentinel-1, Sentinel-2, Canopy Height Model, Individual Tree Detection, Area-based approach --- Sciences du vivant > Multidisciplinaire, généralités & autres --- Ingénierie, informatique & technologie > Multidisciplinaire, généralités & autres

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Savannas and woodlands are one of the most challenging targets for remote sensing. This book provides a current snapshot of the geographical focus and application of the latest sensors and sensor combinations in savannas and woodlands. It includes feature articles on terrestrial laser scanning and on the application of remote sensing to characterization of vegetation dynamics in the Mato Grosso, Cerrado and Caatinga of Brazil. It also contains studies focussed on savannas in Europe, North America, Africa and Australia. It should be important reading for environmental practitioners and scientists globally who are concerned with the sustainability of the global savanna and woodland biome.

Savanna --- alternative stable states --- MODIS VCF --- land surface temperature --- albedo --- Cerrado --- Amazon --- vegetation type --- optical --- sar --- synergism --- mapping --- savanna --- post oak --- vegetation index --- ecosystem function --- phenology --- encroachment --- evergreen --- deciduous --- vegetation --- grassland --- fractional cover --- trend --- ecoregion --- bare soil --- livestock --- production systems --- remote sensing --- vegetation dynamics --- vegetation persistence --- conservation --- savannas --- Africa --- vegetation indices --- oak-grass savanna --- hydrology --- Sentinel-2 --- land cover --- grasslands --- forests --- monitoring --- random forest --- spectral indexes --- vegetation seasonality --- aboveground biomass --- Cerrado ecosystem --- SAR --- allometry --- biomass --- carbon --- cost-effective --- LiDAR --- TLS --- plant water availability --- tree phenology --- phenocams --- MODIS --- terrestrial laser scanning (TLS) --- Above Ground Biomass (AGB) --- 3D point cloud --- vegetation structure --- n/a

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Above ground biomass has been listed by the Intergovernmental Panel on Climate Change as one of the five most prominent, visible, and dynamic terrestrial carbon pools. The increased awareness of the impacts of climate change has seen a burgeoning need to consistently assess carbon stocks to combat carbon sequestration. An accurate estimation of carbon stocks and an understanding of the carbon sources and sinks can aid the improvement and accuracy of carbon flux models, an important pre-requisite of climate change impact projections. Based on 15 research topics, this book demonstrates the role of remote sensing in quantifying above ground biomass (forest, grass, woodlands) across varying spatial and temporal scales. The innovative application areas of the book include algorithm development and implementation, accuracy assessment, scaling issues (local–regional–global biomass mapping), and the integration of microwaves (i.e. LiDAR), along with optical sensors, forest biomass mapping, rangeland productivity and abundance (grass biomass, density, cover), bush encroachment biomass, and seasonal and long-term biomass monitoring.

NDLMA --- n/a --- multi-angle remote sensing --- TerraSAR-X --- above ground biomass --- stem volume --- regression analysis --- ground-based remote sensing --- sensor fusion --- pasture biomass --- grazing management --- livestock --- mixed forest --- SPLSR --- estimation accuracy --- Bidirectional Reflectance Distribution Factor --- forage crops --- Land Surface Phenology --- climate change --- vegetation index --- dry biomass --- mapping --- rangeland productivity --- vegetation indices --- error analysis --- broadleaves --- remote sensing --- applicability evaluation --- ultrasonic sensor --- chlorophyll index --- alpine meadow grassland --- forest biomass --- anthropogenic disturbance --- fractional vegetation cover --- alpine grassland conservation --- carbon mitigation --- conifer --- short grass --- grazing exclusion --- MODIS time series --- random forest --- aboveground biomass --- NDVI --- AquaCrop model --- inversion model --- wetlands --- field spectrometry --- spectral index --- yield --- foliage projective cover --- lidar --- correlation coefficient --- Sahel --- biomass --- dry matter index --- Niger --- Landsat --- grass biomass --- particle swarm optimization --- winter wheat --- carbon inventory --- rice --- forest structure information --- MODIS --- light detection and ranging (LiDAR) --- ALOS2 --- ecological policies --- above-ground biomass --- Wambiana grazing trial --- food security --- forest above ground biomass (AGB) --- Atriplex nummularia --- regional sustainability --- CIRed-edge