Abstract
Accurate maps of canopy height (CH) and aboveground biomass (AGB) are needed for monitoring forests over large regions. Producing such data is particularly challenging over the complex, diverse and dense humid tropical forests of Africa where signal saturation observed from optical and radar satellites and complex responses in LiDAR data require advanced mapping techniques to capture high biomass and tall height values. Here, we trained a deep learning (U-Net) model to generate the first annual maps (2019–2022) of top CH at 10 m resolution over the African dense forest region, using Sentinel-1/-2 images trained on LiDAR-derived height data from the Global Ecosystem Dynamics Investigation mission (GEDI). To predict AGB from CH on a 30-m grid, we calibrated allometric models combining AGB data from field inventories, CH from our map, and wood density from a new high-resolution (1 km) map. The CH map has a mean absolute error (MAE) of 4.54 m and an underestimation bias of 1.54 m compared to independent airborne LiDAR data (5.93 m and 1.40 m compared to independent GEDI data). Evaluation of the AGB map against independent measurements from field sites suggests an improved accuracy (MAE = 79.65 Mg/ha, bias = 6.47 Mg/ha) compared to recent datasets such as ESA-CCI, NCEO, and GEDI L4B. Our map also captures the large-scale spatial gradients of AGB across African dense forests, as observed in a comprehensive dataset of forest concession measurements aggregated at a 1-km scale. Interpretable machine learning was used to assess the contribution of ancillary variables (e.g., climate, soil, forest type) to biomass prediction. While some variables were relevant, their inclusion failed to improve AGB estimates in high and low biomass extremes and introduced spatial artifacts, limiting their utility for consistent annual mapping. Together, our annual CH and AGB maps offer an open, scalable tool for monitoring forest disturbances and interannual biomass dynamics. Future work will focus on refining biomass–height relationships to further improve AGB estimation.
