AMAP, Univ Montpellier, IRD, CNRS, INRAE, CIRAD, Montpellier, France.
CIRAD, UPR Forêts et Sociétés, F-34398 Montpellier, France; Université de Montpellier, F-34000, Montpellier, France.
Sci Data. 2020 Jul 8;7(1):221. doi: 10.1038/s41597-020-0561-0.
Forest biomass is key in Earth carbon cycle and climate system, and thus under intense scrutiny in the context of international climate change mitigation initiatives (e.g. REDD+). In tropical forests, the spatial distribution of aboveground biomass (AGB) remains, however, highly uncertain. There is increasing recognition that progress is strongly limited by the lack of field observations over large and remote areas. Here, we introduce the Congo basin Forests AGB (CoFor-AGB) dataset that contains AGB estimations and associated uncertainty for 59,857 1-km pixels aggregated from nearly 100,000 ha of in situ forest management inventories for the 2000 - early 2010s period in five central African countries. A comprehensive error propagation scheme suggests that the uncertainty on AGB estimations derived from c. 0.5-ha inventory plots (8.6-15.0%) is only moderately higher than the error obtained from scientific sampling plots (8.3%). CoFor-AGB provides the first large scale view of forest AGB spatial variation from field data in central Africa, the second largest continuous tropical forest domain of the world.
森林生物量是地球碳循环和气候系统的关键,因此在国际气候变化缓解倡议(如 REDD+)的背景下受到了强烈关注。然而,在热带森林中,地上生物量(AGB)的空间分布仍然高度不确定。人们越来越认识到,由于缺乏对大面积和偏远地区的实地观测,进展受到了严重限制。在这里,我们引入了刚果盆地森林 AGB(CoFor-AGB)数据集,该数据集包含了来自五个中非国家 2000 年至 2010 年代初期近 10 万公顷森林管理清查的近 10 万个 1 公里像素的 AGB 估算值及其相关不确定性。一个全面的误差传播方案表明,从大约 0.5 公顷的清查样地得出的 AGB 估算值的不确定性(8.6-15.0%)仅比从科学采样样地获得的误差略高。CoFor-AGB 提供了从中非实地数据中获得的森林 AGB 空间变化的第一个大规模视图,这是世界上第二大连续的热带森林区域。