Rodda S. R., Fararoda R., Gopalakrishnan R., Jha N., Réjou-Méchain Maxime, Couteron Pierre, Barbier Nicolas, Alfonso A., Bako O., Bassama P., Behera D., Bissiengou P., Biyiha H., Brockelman W. Y., Chanthorn W., Chauhan P., Dadhwal V. K., Dauby Gilles, Deblauwe V., Dongmo N., Droissart Vincent, Jeyakumar S., Jha C. S., Kandem N. G., Katembo J., Kougue R., Leblanc Hugo, Lewis S., Libalah M., Manikandan M., Martin-Ducup O., Mbock G., Memiaghe H., Mofack G., Mutyala P., Narayanan A., Nathalang A., Ndjock G. O., Ngoula F., Nidamanuri R. R., Pélissier Raphaël, Saatchi S., Sagang L., Salla P., Simo-Droissart M., Smith T. B., Sonke B., Stevart T., Tjomb D., Zebaze D., Zemagho L., Ploton Pierre. (2024). LiDAR-based reference aboveground biomass maps for tropical forests of South Asia and Central Africa. Scientific Data - Nature, 11 (1), p. 334 [15 p.].
Titre du document
LiDAR-based reference aboveground biomass maps for tropical forests of South Asia and Central Africa
Année de publication
2024
Auteurs
Rodda S. R., Fararoda R., Gopalakrishnan R., Jha N., Réjou-Méchain Maxime, Couteron Pierre, Barbier Nicolas, Alfonso A., Bako O., Bassama P., Behera D., Bissiengou P., Biyiha H., Brockelman W. Y., Chanthorn W., Chauhan P., Dadhwal V. K., Dauby Gilles, Deblauwe V., Dongmo N., Droissart Vincent, Jeyakumar S., Jha C. S., Kandem N. G., Katembo J., Kougue R., Leblanc Hugo, Lewis S., Libalah M., Manikandan M., Martin-Ducup O., Mbock G., Memiaghe H., Mofack G., Mutyala P., Narayanan A., Nathalang A., Ndjock G. O., Ngoula F., Nidamanuri R. R., Pélissier Raphaël, Saatchi S., Sagang L., Salla P., Simo-Droissart M., Smith T. B., Sonke B., Stevart T., Tjomb D., Zebaze D., Zemagho L., Ploton Pierre
Source
Scientific Data - Nature, 2024,
11 (1), p. 334 [15 p.]
Accurate mapping and monitoring of tropical forests aboveground biomass (AGB) is crucial to design effective carbon emission reduction strategies and improving our understanding of Earth's carbon cycle. However, existing large-scale maps of tropical forest AGB generated through combinations of Earth Observation (EO) and forest inventory data show markedly divergent estimates, even after accounting for reported uncertainties. To address this, a network of high-quality reference data is needed to calibrate and validate mapping algorithms. This study aims to generate reference AGB datasets using field inventory plots and airborne LiDAR data for eight sites in Central Africa and five sites in South Asia, two regions largely underrepresented in global reference AGB datasets. The study provides access to these reference AGB maps, including uncertainty maps, at 100 m and 40 m spatial resolutions covering a total LiDAR footprint of 1,11,650 ha [ranging from 150 to 40,000 ha at site level]. These maps serve as calibration/validation datasets to improve the accuracy and reliability of AGB mapping for current and upcoming EO missions (viz., GEDI, BIOMASS, and NISAR).
Plan de classement
Sciences fondamentales / Techniques d'analyse et de recherche [020]
;
Etudes, transformation, conservation du milieu naturel [082]
;
Télédétection [126]
Description Géographique
AFRIQUE CENTRALE ; ASIE DU SUD ; ZONE TROPICALE
Localisation
Fonds IRD [F B010090581]
Identifiant IRD
fdi:010090581
