Bossy T., Ciais P., Renaudineau S., Wan L., Ygorra B., Adam E., Barbier Nicolas, Bauters M., Delbart N., Frappart F., Gara T. W., Hamunyela E., Ifo S. A., Jaffrain G., Maisongrande P., Mugabowindekwe M., Mugiraneza T., Normandin C., Obame C. V., Peaucelle M., Pinet C., Ploton Pierre, Sagang L., Schwartz M., Sollier V., Sonké B., Tresson Paul, De Truchis A., Quang A. V., Wigneron J. P. (2025). State of the art and for remote sensing monitoring of carbon dynamics in African tropical forests. Frontiers in Remote Sensing, 6, p. 1532280 [19 p.].
Titre du document
State of the art and for remote sensing monitoring of carbon dynamics in African tropical forests
Année de publication
2025
Auteurs
Bossy T., Ciais P., Renaudineau S., Wan L., Ygorra B., Adam E., Barbier Nicolas, Bauters M., Delbart N., Frappart F., Gara T. W., Hamunyela E., Ifo S. A., Jaffrain G., Maisongrande P., Mugabowindekwe M., Mugiraneza T., Normandin C., Obame C. V., Peaucelle M., Pinet C., Ploton Pierre, Sagang L., Schwartz M., Sollier V., Sonké B., Tresson Paul, De Truchis A., Quang A. V., Wigneron J. P.
Source
Frontiers in Remote Sensing, 2025,
6, p. 1532280 [19 p.]
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.
Plan de classement
Etudes, transformation, conservation du milieu naturel [082]
;
Télédétection [126]
Description Géographique
REPUBLIQUE DEMOCRATIQUE DU CONGO ; CONGO ; CENTRAFRIQUE ; CAMEROUN ; GUINEE EQUATORIALE ; CONGO CUVETTE ; ZONE TROPICALE
Localisation
Fonds IRD [F B010093352]
Identifiant IRD
fdi:010093352
