A framework for evaluating forest ecological vulnerability in tropical deforestation fronts from the assessment of forest degradation in a landscape approach : Case studies from Brazil and Vietnam

The conservation of tropical forest cover is a key to ensuring sustainable provision of multiple ecosystem services. In human-modified landscapes, forest conservation must also be reconciled with agricultural productivity. However, increasing demography, demand for agricultural products and changes in land uses are affecting forest sustainability through degradation processes. A first step to tailor effective forest management by local decision makers is to identify most vulnerable forests and to characterize what is driving this vulnerability. The objective of this thesis is to develop a multidimensional approach using remote sensing to assess forest degradation and the relations with the broader dynamics of land use/cover towards the evaluation of forest ecological vulnerability. The thesis was applied in old-deforestation fronts of Paragominas (Brazil) and Di Linh (Vietnam) where large-scale deforestation driven by commercial agriculture shaped the landscape into land use mosaics with increasing degradation pressures. In Paragominas, degradation is linked with long-term accumulation of selective logging and fire implying changes in forest structure. We estimated the potential of multisource remote sensing to map forest aboveground biomass (AGB) from large-scale field assessment of carbon stock. We improved the accuracy of AGB mapping compared to pantropical datasets and revealed that 87% of forest was degraded. At a lower scale, we investigated the consequences of 33 years of degradation history from Landsat on forest structures using Unmanned Aerial Vehicle. We found that canopy textures captured canopy grain, heterogeneity and openness gradients, correlated with forest structure variability and could be used as proxies to characterize degraded forests. We also assessed the potential of very high resolution satellite images and derived canopy textures to upscale texture-structure relations at the municipality scale. Based on environmental, geographical factors and landscape structure metrics derived from land use/cover classification, we demonstrated that 80% of forest degradation was mainly driven by accessibility, geomorphology, fire occurrence and fragmentation. The drivers of degradation acted together and in sequence and clustering analysis disentangled different cascades of effects. Changes in landscape structure allowed reconstructing trajectories informing on agricultural frontier dynamics. The combination of current forest state, landscape dynamics and distribution of degradation drivers would be at the basis of ecological vulnerability assessment. In Di Linh, degradation mostly concerns forest edges and is driven by encroachment of coffee-based agriculture. Field inventory of the different forest types and other landscape elements combined with Sentinel-2 images allowed to map with high precision the current land cover. We then mapped land cover changes over 45 years using Landsat time series. We constructed trajectories of landscape structure dynamics from which we characterized the expansion of the agricultural frontier and highlighted heterogeneous agricultural encroachment on forested areas. We also identified degradation and fragmentation trajectories that affect forest cover at different rates and intensity. Combined, these indicators pinpointed hotspots of forest ecological vulnerability. Most vulnerable forest areas were experiencing rapid and recent forest cover loss associated with landscape fragmentation, land use competition due to coffee production and degradation. Through the developed remote sensing approaches and indicators at forest and landscape scales, we provided a holistic diagnosis of forests in human-modified landscapes encompassing forest state and broader dynamics and drivers. This thesis aims to pave the way for tailored and prioritized management of degraded forests at the landscape scale.