Abstract
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.
Highlights
Background & SummaryQuantifying forest aboveground biomass (AGB) has been the focus of considerable interest in the last century for both commercial and scientific[1] purposes
Given the escalation of carbon emissions associated with deforestation[8] and forest degradation[9], fires[10] and drought events[11] in the tropics, it is more than ever important to increase our understanding of tropical forest AGB geography and of its ecological determinants
While DRC’s national forest inventories (NFI) is a tremendous undertaking and an important step forward for this country, it is obvious that it will only provide a crude picture of forest AGB spatial distribution
Summary
Background & SummaryQuantifying forest aboveground biomass (AGB) has been the focus of considerable interest in the last century for both commercial (e.g. timber, fiber, energy productions) and scientific (e.g. forest ecosystem functioning and productivity)[1] purposes. We validated the AGB computation scheme using data from scientific plots (Fig. 2), allowing a detailed characterization of uncertainty sources and their propagation to plot-level estimations.
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