Satellite-based mapping of annual canopy height and aboveground biomass in African dense forests

作者：Liang Wan, Philippe Ciais, Aurélien de Truchis, Ewan Sean, Fabian Jörg Fischer, David Purnell, Gabriel Belouze, Ibrahim Fayad, Martin Schwartz, Yidi Xu, Yang Su, Maxime Réjou-Méchain, Nicolas Barbier, Paul Tresson, Jean‐François Bastin, Jan Bogaert, Arthur Vander Linden, Antoine Plumacker, Bhély Angoboy Ilondea, Dieu-Merci Assumani, Thalès de Haulleville, Le Bienfaiteur Sagang, Laurent Durieux, Youngryel Ryu, Tackang Yang, Conan Vassily Obame, Thomas Bossy, Frédéric Frappart, Marc Peaucelle, Jean‐Pierre Wigneron, Jérôme Chave, Aida Cuní‐Sanchez, Wannes Hubau, Hans Verbeeck, Pascal Boeckx, Jean‐Remy Makana, Corneille E. N. Ewango, Elizabeth Kearsley, Bonaventure Sonké, Moses B. Libalah, Pierre Ploton · 发表于：Frontiers in Remote Sensing · 年份：2025 · DOI：10.3389/frsen.2025.1724950 · 被引用次数：2 · 研究领域：Remote Sensing and LiDAR Applications、Remote Sensing in Agriculture、Forest ecology and management

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. Interp...