Wood density (WD) relates to important tree functions such as stem mechanics and resistance against pathogens. This functional trait can exhibit high intraindividual variability both radially and vertically. With the rise of LiDAR-based methodologies allowing nondestructive tree volume estimations, failing to account for WD variations related to tree function and biomass investment strategies may lead to large systematic bias in AGB estimations. Here, we use a unique destructive dataset from 822 trees belonging to 51 phylogenetically dispersed tree species harvested across forest types in Central Africa to determine vertical gradients in WD from the stump to the branch tips, how these gradients relate to regeneration guilds and their implications for AGB estimations. We find that decreasing WD from the tree base to the branch tips is characteristic of shade-tolerant species, while light-demanding and pioneer species exhibit stationary or increasing vertical trends. Across all species, the WD range is narrower in tree crowns than at the tree base, reflecting more similar physiological and mechanical constraints in the canopy. Vertical gradients in WD induce significant bias (10%) in AGB estimates when using database-derived species-average WD data. However, the correlation between the vertical gradients and basal WD allows the derivation of general correction models. With the ongoing development of remote sensing products providing 3D information for entire trees and forest stands, our findings indicate promising ways to improve greenhouse gas accounting in tropical countries and advance our understanding of adaptive strategies allowing trees to grow and survive in dense rainforests.

Gabon is one of 11 high-forest, low-deforestation (HFLD) countries in the world. It has the highest proportion of preserved forests in the Congo Basin and is the first country to create large forest carbon offset credits in the market. However, about 60% of forests in Gabon is allocated to logging concessions, causing concerns for forest degradation and the sustainability of carbon credits. Here, we use a combination of air- and space-borne remote sensing data and the-state-of-the-art gradient boosted regression trees to estimate forest structure and aboveground biomass carbon density (ACD) of trees at 100 m resolution for the year circa 2020. Mapping spatial variations of ACD across floristically diverse landscapes, we estimate average density and total living carbon storage of trees at the national and sub-national levels. The estimated ACD of trees in forestlands within the country was 142.12 ± 7.3 Mg C ha⁻¹ with the highest values found in central Gabon (150.08 ± 5.8 Mg C ha⁻¹) and on highlands (161.18 ± 6.7 Mg C ha⁻¹). On average, in every region, ACD of forests found within logging concessions (149.89 ± 6.1 Mg C ha⁻¹) was higher than unmanaged forests of unprotected areas (122.81 ± 4.4 Mg C ha⁻¹), indicating the combined effects of logging in carbon-rich forests and increased productivity due to management. The country’s total estimated biomass carbon for trees (above and belowground) stored within the forests was 4.14 ± 0.3 Pg C with 68% found within logging concessions and 14% within protected areas. The map provides high precision and comprehensive assessment of carbon stocks of trees in Gabon’s forests, significantly improving the country’s prospects to implement climate mitigation policies and to participate in carbon markets.

Precise accounting of carbon stocks and fluxes in tropical vegetation using remote sensing approaches remains a challenging exercise, as both signal saturation and ground sampling limitations contribute to inaccurate extrapolations. Airborne LiDAR Scanning (ALS) data can be used as an intermediate level to radically increase sampling and enhance model calibration. Here we tested the potential of using ALS data for upscaling vegetation aboveground biomass (AGB) from field plots to a forest-savanna transitional landscape in the Guineo–Congolian region in Cameroon, using either a design-based approach or a model-based approach leveraging multispectral satellite imagery. Two sets of reference data were used: (1) AGB values collected from 62 0.16-ha plots distributed both in forests and savannas; and (2) an AGB map generated form ALS data. In the model-based approach, we trained Random Forest models using predictors from recent sensors of varying spectral and spatial resolutions (Spot 6/7, Landsat 8, and Sentinel 2), along with biophysical predictors derived after pre-processing into the Overland processing chain, following a forward variable selection procedure with a spatial 4-folds cross validation. The models calibrated with field plots lead to a systematic overestimation in AGB density estimates and a root mean squared prediction error (RMSPE) of up to 65 Mg.ha⁻¹ (90%), whereas calibration with ALS lead to low bias and a drop of ~30% in RMSPE (down to 43 Mg.ha⁻¹, 58%) with little effect of the satellite sensor used. Decomposing bias along the AGB density range, we show that multispectral images can (in some specific cases) be used for unbiased prediction at landscape scale on the basis of ALS-calibrated statistical models. However, our results also confirm that, whatever the spectral indices used and attention paid to sensor quality and pre-processing, the signal is not sufficient to warrant accurate pixelwise predictions, because of large relative RMSPE, especially above (200–250 t/ha). The design-based approach, for which average AGB density values were attributed to mapped land cover classes, proved to be a simple and reliable alternative (for landscape to region level estimations), when trained with dense ALS samples.

Forest expansion into savanna is a pervasive phenomenon in West and Central Africa, warranting comparative studies under diverse environmental conditions. We collected vegetation data from the woody and grassy components within 73 plots of 0.16 ha distributed along a successional gradient from humid savanna to forest in Central Africa. We associated spatially collocated edaphic parameters and fire frequency derived from remote sensing to investigate their combined influence on the vegetation. Soil texture was more influential in shaping savanna structure and species distribution than soil fertility, with clay-rich soils promoting higher grass productivity and fire frequency. Savanna featuring woody aboveground biomass surpassing 40 Mg ha ⁻¹ could escape the grass–fire feedback loop, by depressing grass biomass below 4 Mg ha ⁻¹ . This thicker woody layer also favoured the establishment of fire-tolerant forest pioneers, which synergically contributed to the expansion of forests. Conversely, savannas below this fire suppression threshold sustained a balance between trees and grasses through the grass–fire feedback mechanism. This hysteresis loop, particularly pronounced on clayey soils, suggests that the contrast between grassy savanna and young forests might represent alternative ecosystem states, although savannas with low woody biomass remained vulnerable to forest edge encroachment.

The Congo Basin and its contiguous forests harbor globally significant carbon stocks, estimated at 65 gigatons of C (GtC) above and belowground. Despite rising temperatures and intensifying droughts, they have remained a carbon sink, albeit weak: 0.26–0.50 GtC yr. ⁻¹ carbon uptake since 1980. However, these forests' carbon stocks and fluxes, including gross primary productivity, respiration, net primary productivity, and riverine carbon transport, remain poorly quantified. This limits understanding of the region's role in the global carbon cycle, its vulnerability to environmental change, and its potential as a long‐term carbon sink. We review and quantify Congo Basin and contiguous forest carbon stocks and fluxes and synthesize the current knowledge on how key global change drivers shape the region's carbon cycle. We find limited responses to long‐term precipitation variability, but declining stocks and fluxes in response to long‐term and increasing temperature and drought frequency. Land cover and land use changes, largely driven by small‐scale agriculture, logging, and agro‐industrial expansion, reduce carbon stocks, ecosystem structure and functioning, and animal‐mediated ecosystem services. In contrast, large‐scale savanna biomass burning delivers phosphorus and nitrogen to Congo Basin forests via cross‐equatorial winds, providing additional nutrients and supporting carbon sequestration. In situ studies suggest that CO ₂ fertilization has increased intrinsic water‐use efficiency, although its effects may be modulated by climate change, and its impacts on biomass accumulation remain uncertain. Legacy effects from historical land use and climate change likely shaped present‐day vegetation structure, yet their relative influence is unclear. High‐resolution carbon monitoring, improved remote sensing, and strengthened in situ measurement networks are needed to quantify the impacts of these key drivers and their interactions on the Central African carbon cycle. This is needed to inform conservation strategies and advance understanding of the region's future as a carbon sink under global change pressures.