In the context of the climate and biodiversity crisis facing our planet, tropical forests playing a key role in global carbon flux and containing over half of Earth’s species are important to preserve. They are today threatened by deforestation but also by forest degradation, which is more difficult to study. Here, we performed a systematic review of studies on moist tropical forest degradation using remote sensing and fitting indicators of forest resilience to perturbations. Geographical repartition, spatial extent and temporal evolution were analyzed. Indicators of compositional, structural and regeneration criteria were noted as well as remote sensing indices and metrics used. Tropical moist forest degradation is not extensively studied especially in the Congo basin and in southeast Asia. Forest structure (i.e., canopy gaps, fragmentation and biomass) is the most widely and easily measured criteria with remote sensing, while composition and regeneration are more difficult to characterize. Mixing LiDAR/Radar and optical data shows good potential as well as very high-resolution satellite data. The awaited GEDI and BIOMASS satellites data will fill the actual gap to a large extent and provide accurate structural information. LiDAR and unmanned aerial vehicles (UAVs) form a good bridge between field and satellite data. While the performance of the LiDAR is no longer to be demonstrated, particular attention should be brought to the UAV that shows great potential and could be more easily used by local communities and stakeholders.

A third of the forest area in Central Africa has been granted to logging companies. Logging is highly selective in the region, with an average of 0.7–4.0 trees harvested per ha, but its direct impact on forest structure and the spatial variation of this impact remain understudied. Here, we investigated the direct impact of logging on forest structure, we related this impact to logging intensity and canopy opening. We compiled unique datasets collecting field measurements and aerial observations in four FSC certified concessions. Our data includes pre- and post-logging inventory of forest plots covering 38 ha, records of over 6000 harvested trees, and drone RGB images covering over 6000 ha. In average, logging activities reduced forest above-ground biomass by 8.8%, stem density by 6.5%, basal-area by 8.5% and canopy cover by 4.4%. Strong relationships were found between the reduction in biomass, stem density, or basal area with logging intensity, canopy opening and the number and volume of harvested trees (relative root mean squared error (rRMSE) between 0.128 and 0.164). Additionally, we demonstrated that canopy opening can be a good indicator to monitor and upscale logging intensity (rRMSE between 0.0005 and 0.0022). This study is the first covering extensive inventory plots and uninhabited aerial vehicle images before and after logging in different locations in Central Africa, providing a valuable reference to evaluate the impact of logging on forest structure. It demonstrates how canopy opening can be used to estimate measurements usually collected in the field and provides to the remote sensing community a unique dataset that will help improving monitoring systems (Dupuis et al 2024 (available at: https://hdl.handle.net/2268/323683)). These findings also have significant implications to control and manage logging activities, especially for certification standards, forest administrations, and European regulations.