Net carbon losses in Central African forests revealed by high-resolution biomass change maps

Abstract

Dense humid forests in Central Africa hold vast biomass carbon stocks but are increasingly affected by small-scale disturbances that are difficult to detect with current satellite observations. Here, we generate 10 m canopy height maps for 2019–2022 using deep learning with spaceborne lidar and Sentinel-1/−2 imagery, and derive aboveground biomass changes at 30 m resolution. The maps reveal widespread small disturbance patches, 87% smaller than 1 ha. These account for 48% of biomass carbon gains in regrowing forests and 37% of carbon losses from deforestation. We estimate a net forest carbon loss of −58 ± 14 Tg C yr−1, with gross losses of −126 ± 21 Tg C yr−1 partially offset by gains of 68 ± 10 Tg C yr−1. The Democratic Republic of the Congo emerges as a net carbon source (−46 ± 11 Tg C yr−1), representing 79% of the regional loss, primarily due to degradation, despite having the largest carbon gains in young secondary forests. Our remote-sensing estimates of national carbon budgets are broadly consistent with country-level inventories and bookkeeping model estimates, paving the way for spatially explicit and transparent carbon monitoring. High-resolution biomass changes maps reveal net carbon losses across Central African forests, show that small disturbances drive carbon changes, and provide spatially explicit national carbon budgets broadly consistent with independent estimates.