ArticleScientific data2020
A map of African humid tropical forest aboveground biomass derived from management inventories.
Article in Scientific data, 2020. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 6 papers.
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Who cites it
6 citing papers in PubMed.
- Managed rainforests support higher carbon density and sequestration in the Congo Basin.Nature communications · 2026Article
- Aboveground biomass in seven tropical forest patches of Western-Africa: comparison of manual inventory and terrestrial laser scanning.Annals of forest science · 2026Article
- Tackling unresolved questions in forest ecology: The past and future role of simulation models.Ecology and evolution · 2021Review
- Evidence of past forest fragmentation in the Congo Basin from the phylogeography of a shade-tolerant tree with limited seed dispersal: Scorodophloeus zenkeri (Fabaceae, Detarioideae).BMC ecology and evolution · 2021Article
- Spatial validation reveals poor predictive performance of large-scale ecological mapping models.Nature communications · 2020Article
- A map of African humid tropical forest aboveground biomass derived from management inventories.Scientific data · 2020Article
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Authors and funding
25 authors.
Funding
No grant is acknowledged in the PubMed record.
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.
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