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Tropical forests store more than half of the world's forest carbon and are particularly threatened by deforestation and degradation processes, which together represent the second largest source of anthropogenic CO2 emissions. Consequently, tropical forests are the focus of international climate policies (i.e. Reducing emissions from deforestation and forest degradation, REDD) aiming at reducing forestrelated CO2 emissions. The REDD initiative lies on our ability to map forest carbon stocks (i.e. spatial dynamics) and to detect deforestation and degradations (i.e. temporal dynamics) at large spatial scales (e.g. national, forested basin), with accuracy and precision. Remote-sensing is as a key tool for this purpose, but numerous sources of error along the carbon mapping chain makes meeting REDD criteria an outstanding challenge. In the present thesis, we assessed carbon (quantified through aboveground biomass, AGB) estimation error at the tree- and plot-level using a widely used pantropical AGB model, and at the landscape-level using a remote sensing method based on canopy texture features from very high resolution (VHR) optical data. Our objective was to better understand and reduce AGB estimation error at each level using information on large canopy tree structure, distribution and spatial organization.Although large trees disproportionally contributed to forest carbon stock, they are under-represented in destructive datasets and subject to an under-estimation bias with the pantropical AGB model. We destructively sampled 77 very large tropical trees and assembled a large (pantropical) dataset to study how variation in tree form (through crown sizes and crown mass ratio) contributed to this error pattern. We showed that the source of bias in the pantropical model was a systematic increase in the proportion of tree mass allocated to the crown in canopy trees. An alternative AGB model accounting for this phenomenon was proposed. We also propagated the AGB model bias at the plot-level and showed that the interaction between forest structure and model bias, although often overlooked, might in fact be substantial. We further analyzed the structural properties of crown branching networks in light of the assumptions and predictions of the Metabolic Theory of Ecology, which supports the power-form of the pantropical AGB model. Important deviations were observed, notably from Leonardo's rule (i.e. the principle of area conservation), which, all else being equal, could support the higher proportion of mass in large tree crowns.A second part of the thesis dealt with the extrapolation of field-plot AGB via canopy texture features of VHR optical data. A major barrier for the development of a broad-scale forest carbon monitoring method based on canopy texture is that relationships between canopy texture and stand structure parameters (including AGB) vary among forest types and regions of the world. We investigated this discrepancy using a simulation approach: virtual canopy scenes were generated for 279 1-ha plots distributed on contrasted forest types across the tropics. We showed that complementing FOTO texture with additional descriptors of forest structure, notably on canopy openness (from a lacunarity analysis) and tree slenderness (from a bioclimatic proxy) allows developing a stable inversion frame for forest AGB at large scale. Although the approach we proposed requires further empirical validation, a first case study on a forests mosaic in the Congo basin gave promising results.Overall, this work increased our understanding of mechanisms behind AGB estimation errors at the tree-, plot- and landscape-level. It stresses the need to better account for variation patterns in tree structure (e.g. ontogenetic pattern of carbon allocation) and forest structural organization (across forest types, under different environmental conditions) to improve general AGB models, and in fine our ability to accurately map forest AGB at large scale. ; Les forêts tropicales séquestrent plus de la moitié du carbone forestier mondial et sont particulièrement menacées par les processus de déforestation et de dégradation, qui représentent la deuxième source d'émissions anthropogéniques de CO2. De fait, les forêts tropicales sont au centre de politiques climatiques internationales (i.e. Reducing emissions from deforestation and forest degradation, REDD) visant à réduire ces émissions. L'initiative REDD repose sur notre capacité à cartographier les stocks de carbone forestier (dynamique spatiale) et à détecter la déforestation et la dégradation (dynamique temporelle) à large échelle spatiale (e.g. nationale, bassin forestier), avec exactitude et précision. Dans ce cadre, la télédétection apparait comme un outil crucial, mais les nombreuses sources d'erreur dans la chaîne de cartographie du carbone font des objectifs du REDD un challenge ambitieux. Dans cette thèse, nous avons évalué les erreurs associées aux estimations de carbone forestier (quantifié au travers de la biomasse épigée, AGB) (1) aux échelles de l'arbre et du peuplement en utilisant un modèle pantropical largement employé et (2) à l'échelle du paysage en utilisant une méthode de télédétection basée sur les caractéristiques texturales d'images optiques à très haute résolution spatiale. Notre objectif général était de mieux comprendre et de réduire l'erreur d'estimation de l'AGB à chaque échelle par une meilleure prise en compte de la structure, de la distribution et de l'organisation spatiale des arbres de canopée.Malgré l'importance des grands arbres dans la dynamique du carbone forestier, ils sont sous-représentés dans les jeux de données destructifs et soumis à un biais de sous-estimation dans le modèle d'AGB pantropical. Nous avons assemblé une base de données pantropicale et étudié l'influence de la forme de l'arbre sur le patron d'erreur du modèle. Nos résultats montrent que la source de biais du modèle est une augmentation de la masse de l'arbre dans la couronne chez les arbres de canopée. Un modèle d'AGB prenant ce phénomène en compte a été proposé. Nous avons aussi propagé le biais du modèle à l'échelle du peuplement et montré que l'interaction entre la structure du peuplement et l'erreur du modèle, qui est souvent négligée, peut en fait être substantielle. Une analyse des propriétés structurelles des couronnes a également été menée au regard des hypothèses de la Théorie Métabolique de l'Ecologie Des déviations ont été observées, notamment à la loi de Léonardo (i.e. principe de conservation des aires), qui, toutes choses égales par ailleurs, pourraient justifier la grande proportion de masse trouvée dans les couronnes des arbres de canopée.Une seconde partie de la thèse porte sur l'extrapolation des estimations d'AGB des parcelles de terrain via les caractéristiques de texture des canopées extraites par transformée de Fourier (i.e. méthode FOTO). Un obstacle majeur au développement d'une méthode d'estimation de l'AGB à large échelle basée sur la texture tient au fait que la relation texture – paramètres de structure du peuplement varie entre types de forêt et régions du monde. Nous avons investigué cette question en simulant des scènes de canopées virtuelles pour 279 parcelles de 1 ha établies dans des types de forêts tropicales contrastés. Nous montrons qu'en complémentant les indices de texture FOTO avec d'autres descripteurs structuraux, notamment sur l'ouverture de la canopée (via une analyse de lacunarité) et l'élancement des arbres (via un proxy bioclimatique), il devrait être possible de développer un cadre d'inversion stable de l'AGB à large échelle. Un premier cas d'étude empirique dans une mosaïque forestière du bassin du Congo a donné des résultats prometteurs.Globalement, ce travail met en évidence le besoin de mieux prendre en compte les patrons de variation de structure de l'arbre (e.g. ontogénétique) et de la forêt afin d'améliorer les modèles génériques d'AGB.

International audience ; Africa is forecasted to experience large and rapid climate change1 and population growth2 during the twenty-first century, which threatens the world's second largest rainforest. Protecting and sustainably managing these African forests requires an increased understanding of their compositional heterogeneity, the environmental drivers of forest composition and their vulnerability to ongoing changes. Here, using a very large dataset of 6 million trees in more than 180,000 field plots, we jointly model the distribution in abundance of the most dominant tree taxa in central Africa, and produce continuous maps of the floristic and functional composition of central African forests. Our results show that the uncertainty in taxon-specific distributions averages out at the community level, and reveal highly deterministic assemblages. We uncover contrasting floristic and functional compositions across climates, soil types and anthropogenic gradients, with functional convergence among types of forest that are floristically dissimilar. Combining these spatial predictions with scenarios of climatic and anthropogenic global change suggests a high vulnerability of the northern and southern forest margins, the Atlantic forests and most forests in the Democratic Republic of the Congo, where both climate and anthropogenic threats are expected to increase sharply by 2085. These results constitute key quantitative benchmarks for scientists and policymakers to shape transnational conservation and management strategies that aim to provide a sustainable future for central African forests.

International audience ; Africa is forecasted to experience large and rapid climate change1 and population growth2 during the twenty-first century, which threatens the world's second largest rainforest. Protecting and sustainably managing these African forests requires an increased understanding of their compositional heterogeneity, the environmental drivers of forest composition and their vulnerability to ongoing changes. Here, using a very large dataset of 6 million trees in more than 180,000 field plots, we jointly model the distribution in abundance of the most dominant tree taxa in central Africa, and produce continuous maps of the floristic and functional composition of central African forests. Our results show that the uncertainty in taxon-specific distributions averages out at the community level, and reveal highly deterministic assemblages. We uncover contrasting floristic and functional compositions across climates, soil types and anthropogenic gradients, with functional convergence among types of forest that are floristically dissimilar. Combining these spatial predictions with scenarios of climatic and anthropogenic global change suggests a high vulnerability of the northern and southern forest margins, the Atlantic forests and most forests in the Democratic Republic of the Congo, where both climate and anthropogenic threats are expected to increase sharply by 2085. These results constitute key quantitative benchmarks for scientists and policymakers to shape transnational conservation and management strategies that aim to provide a sustainable future for central African forests.

International audience ; Africa is forecasted to experience large and rapid climate change1 and population growth2 during the twenty-first century, which threatens the world's second largest rainforest. Protecting and sustainably managing these African forests requires an increased understanding of their compositional heterogeneity, the environmental drivers of forest composition and their vulnerability to ongoing changes. Here, using a very large dataset of 6 million trees in more than 180,000 field plots, we jointly model the distribution in abundance of the most dominant tree taxa in central Africa, and produce continuous maps of the floristic and functional composition of central African forests. Our results show that the uncertainty in taxon-specific distributions averages out at the community level, and reveal highly deterministic assemblages. We uncover contrasting floristic and functional compositions across climates, soil types and anthropogenic gradients, with functional convergence among types of forest that are floristically dissimilar. Combining these spatial predictions with scenarios of climatic and anthropogenic global change suggests a high vulnerability of the northern and southern forest margins, the Atlantic forests and most forests in the Democratic Republic of the Congo, where both climate and anthropogenic threats are expected to increase sharply by 2085. These results constitute key quantitative benchmarks for scientists and policymakers to shape transnational conservation and management strategies that aim to provide a sustainable future for central African forests.

International audience ; Africa is forecasted to experience large and rapid climate change1 and population growth2 during the twenty-first century, which threatens the world's second largest rainforest. Protecting and sustainably managing these African forests requires an increased understanding of their compositional heterogeneity, the environmental drivers of forest composition and their vulnerability to ongoing changes. Here, using a very large dataset of 6 million trees in more than 180,000 field plots, we jointly model the distribution in abundance of the most dominant tree taxa in central Africa, and produce continuous maps of the floristic and functional composition of central African forests. Our results show that the uncertainty in taxon-specific distributions averages out at the community level, and reveal highly deterministic assemblages. We uncover contrasting floristic and functional compositions across climates, soil types and anthropogenic gradients, with functional convergence among types of forest that are floristically dissimilar. Combining these spatial predictions with scenarios of climatic and anthropogenic global change suggests a high vulnerability of the northern and southern forest margins, the Atlantic forests and most forests in the Democratic Republic of the Congo, where both climate and anthropogenic threats are expected to increase sharply by 2085. These results constitute key quantitative benchmarks for scientists and policymakers to shape transnational conservation and management strategies that aim to provide a sustainable future for central African forests.