State of the art in remote sensing monitoring of carbon dynamics in African tropical forests
Thomas Bossy, Philippe Ciais, Solène Renaudineau, Liang Wan, Bertrand Ygorra, Elhadi Adam, Nicolas Barbier, Marijn Bauters, Nicolas Delbart, Frédéric Frappart, Tawanda W. Gara, Eliakim Hamunyela, Suspense A. Ifo, Gabriel Jaffrain, Philippe Maisongrande, Maurice Mugabowindekwe, Theodomir Mugiraneza, Cassandra Normandin, Conan Vassily Obame, Marc Peaucelle, Camille Pinet, Pierre Ploton, Le Bienfaiteur Sagang, Martin A. Schwartz, Valentine Sollier, Bonaventure Sonké, Paul Tresson, Aurélien De Truchis, An Vo Quang, Jean‐Pierre Wigneron
Abstract
African tropical forests play a crucial role in global carbon dynamics, biodiversity conservation, and climate regulation, yet monitoring their structure, diversity, carbon stocks and changes remains challenging. Remote sensing techniques, including multi-spectral data, lidar-based canopy height and vertical structure detection, and radar interferometry, have significantly improved our ability to map forest composition, estimate height and biomass, and detect degradation and deforestation features at a finer scale. Machine learning approaches further enhance these capabilities by integrating multiple data sources to produce improved maps of forest attributes and track changes over time. Despite these advancements, uncertainties remain due to limited ground-truth validation, and the structural complexity and large spatial heterogeneity of African forests. Future developments in remote sensing should examine how multi-sensor integration of high-resolution data from instruments such as Planet, Tandem-X, SPOT and improved AI methods can refine forest composition, carbon storage and function maps, enhance large-scale monitoring of tree height and biomass dynamics, and improve forest degradation and deforestation detection down to tree level. These advancements will be essential for supporting science-based decision-making in forest conservation and climate mitigation.