1. Arid and semi-arid ecosystems cover 40.6% of Earth's land surface and support 2.3 billion people (UNCCD 2024), providing critical ecosystem services including wood production, carbon sequestrati...

Wood density stands as one of the most integrative functional traits of trees, reflecting fundamental trade-offs in adaptive strategies and closely connected to ecosystem history and dynamics. Wood density is linked to multiple key processes including carbon accumulation and mechanical support, determining how plants allocate resources between growth and survival. Yet, despite its recognized importance for understanding forest ecology and improving carbon stock estimates, spatially explicit knowledge of local wood density variation remains severely limited, particularly in tropical rainforests.Community wood density integrates ecological processes operating at the stand scale, primarily through species composition. Regeneration guild strategies drive much of this variation: fast-growing pioneer species exhibit low wood density while shade-tolerant species show substantially higher values. These compositional differences are also expressed through distinct forest structure patterns —early succession stages with pioneer-dominated composition generally develop lower, more uniform canopies, whereas mature forests with non-pioneer light demanding and shade-tolerant species build taller, more complex vertical architectures. We hypothesized that vertical canopy opening profiles, capturing the proportion of gaps at successive height aboveground, contain the ecological signatures of floristic composition and successional stages that ultimately determine community wood density.These ecological relationships create opportunities to leverage high-resolution airborne LiDAR for detecting wood density variation at large scale through canopy structure. To test these hypotheses, we modeled community wood density at the stand level across 76 one-hectare plots in 18 Central African forests. We derived canopy stratification metrics from cumulative gap proportion curves extracted from Canopy Height Models, characterizing vertical opening patterns.We show that canopy opening profiles effectively capture structural signatures associated with community wood density variation. Canopy openness at approximately 20 m and overall canopy stratification emerged as the strongest predictors. Variance partitioning and structural equation modelling reveal that this structure-wood density relationship is entirely mediated by floristic composition and successional stage, which jointly determine both forest structure and wood density. Canopy structure thus acts as a proxy for species composition.These findings have direct implications for remote sensing applications in tropical forests such as Central African ones. The strong covariation between vertical stratification and species assemblages opens a pathway to account for local wood density variation when mapping AGB through LiDAR-derived indicators. This approach could substantially reduce uncertainties in carbon stock estimates and improve our technique for monitoring forest degradation and successional dynamics across this critical biome.

Central african forests are subject to increasing anthropic disturbance, with slash and burn agriculture and wood extraction as the leading causes of disturbance. The extent of resulting fallows and secondary forests are currently poorly investigated in the region while they are crucial to map and to monitor to better understand forest state, functioning and potential recovery of the second largest tropical forest of the world.In this study, we explore the hypothesis that recovery rate of carbon and functional diversity can be modelized by change in canopy structure. To estimate the carbon recovery rate in secondary forest, 15 permanent forest inventories of 1ha each are conducted in the Mabali research centre of the Equateur province in DRC. We use space for time substitution method to create a chronosequence covering forests of different ages and/or different types of past disturbance. UAV’s LIDAR data acquired during the field campaign over the plots and the surrounding forest are processed to extract meaningful canopy features. By exploring the relationship between forest structure and forest recovery rate, we aim to deepen the understanding of the complexity of secondary forests and provide datasets for the calibration of Land Surface Modeling and satellite-based biomass estimation.

Forest canopy gaps are key indicators of forest dynamics, yet their vertical organization remains poorly quantified across forest types. While canopy gaps are commonly interpreted as signatures of disturbance regimes, their size distribution and vertical organization may also reflect intrinsic differences in stand structure, crown architecture, and density-dependent interactions. We hypothesize that the vertical distribution of canopy gap size differs among forest types, reflecting contrasting structural organization and developmental trajectories. Using airborne LiDAR, we quantified vertical gap size distributions in two Central African old-growth forest types: monodominant Gilbertiodendron dewevrei (GIL) and MIXED forests, across two sites in the Democratic Republic of the Congo (Lileko and Yangambi). We also derived structural ratios normalizing gap area by basal area and canopy height. Results show clear divergence in vertical gap profiles. GIL forests consistently exhibit lower canopy openness than MIXED forests. Between heights of 10 and 20 m, total gap area per hectare ranged from 39.11 (95% CI: 19.84–83.5) to 567.56 (95% CI: 229.44–707.48) m² ha⁻¹ in GIL forests, compared with 69.10 (95% CI: 55.28–123.51) to 1244.97 (95% CI: 784.73–1360.45) m² ha⁻¹ in MIXED forests across both sites. Structural ratios further confirm that GIL forests maintain smaller gap areas relative to basal area and canopy height. Overall, the results support the three study hypotheses, demonstrating that vertical gap size distributions and their normalization by stand structural attributes provide robust and transferable indicators for discriminating among Central African old-growth forest types and developmental stages, highlighting the importance of structural context for interpreting tropical forest dynamics.

Dense humid forests in Central Africa hold vast biomass carbon stocks but face increasing pressure from logging, clearings, and fire disturbances. Many of these disturbance events have a small spatial scale and are missed by current satellite observations. To address this knowledge gap we generated annual 10 meter resolution maps of canopy height from 2019 to 2022 using a deep learning model fusing spaceborne lidar height measurements with Sentinel radar and optical imagery. The maps reveal widespread previously undetected small disturbance patches, with 87% of all the disturbances being of less than 1 ha in size. These disturbance patches smaller than 1 ha account for 48% of the biomass carbon gains in regrowing forests and for 37% of carbon losses from deforestation. We found a net carbon loss of −58 ± 9 Tg C yr−1 composed of a gross loss of −126 ± 7 Tg C yr−1 partially offset by a gain of 68 ± 6 Tg C yr−1, implying a turnover of biomass carbon from disturbances of 0.52% per year. The Democratic Republic of the Congo is a small net source of carbon (−46 ± 6 Tg C yr−1) due to degradation, despite having the largest carbon gains in young secondary forests. Across the Congo Basin, protected areas show a net biomass loss of −4 ± 1 Tg C yr−1, with a gross loss of −14 ± 1 Tg C yr−1, highlighting uneven conservation outcomes. Our remote-sensing data aggregated into national carbon budgets align well with country-level inventories and bookkeeping model estimates, paving the way for spatially explicit and transparent carbon monitoring.

Open woody formations provide critical dryland ecosystem services—fuelwood, charcoal, construction materials, and livestock fodder—yet remain systematically underestimated by global monitoring systems prioritizing dense forests. Conventional binary forest/non-forest classifications fail to effectively detect these formations, creating a disconnect between forest cover assessments and ground reality. This limitation highlights fundamental challenges in detecting sparse woody vegetation where deciduous phenology, spectral confusion with herbaceous vegetation, and shadows compromise traditional remote sensing.We mapped continuous woody cover (0-100%) across 36 years (1989-2025) using Landsat imagery and Random Forest regression calibrated on 505 photo-interpreted plots and validated on 41 field plots. Images acquired during rainy season/early dry season maximized detection when deciduous species retain foliage. We analyzed fire regimes (2000-2024) and human pressure via distance gradients from habitations (1-10 km) to identify degradation drivers.Intact woody thickets declined dramatically from 60% (1989) to 35% (2025), with accelerating loss after 2010. Fire is the primary degradation driver: burned areas lose four times more woody cover than unburned zones. Crucially, fire frequency—not single events—determines degradation severity. Natural recovery is very limited even after 10 years, insufficient to offset immediate post-fire losses. Human proximity also shows significant impacts: woody cover is approximately 20% lower near habitations than in remote areas.Continuous woody cover mapping successfully quantifies sparse woody vegetation dynamics in heterogeneous dryland environments. This approach is transferable to similar dryland ecosystems globally facing comparable detection challenges. Results reveal that fire frequency exceeds natural recovery capacity, providing scientific evidence to inform conservation and restoration strategies in drylands under increasing anthropogenic pressure.