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

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.