Comparison of forest above-ground biomass from dynamic global vegetation models with spatially explicit remotely sensed observation-based estimates.
AGB density deficits
carbon cycle
forest ecosystems
human disturbances
model evaluation
remote sensing-based biomass
Journal
Global change biology
ISSN: 1365-2486
Titre abrégé: Glob Chang Biol
Pays: England
ID NLM: 9888746
Informations de publication
Date de publication:
07 2020
07 2020
Historique:
received:
21
12
2019
accepted:
19
03
2020
pubmed:
20
5
2020
medline:
27
11
2020
entrez:
20
5
2020
Statut:
ppublish
Résumé
Gaps in our current understanding and quantification of biomass carbon stocks, particularly in tropics, lead to large uncertainty in future projections of the terrestrial carbon balance. We use the recently published GlobBiomass data set of forest above-ground biomass (AGB) density for the year 2010, obtained from multiple remote sensing and in situ observations at 100 m spatial resolution to evaluate AGB estimated by nine dynamic global vegetation models (DGVMs). The global total forest AGB of the nine DGVMs is 365 ± 66 Pg C, the spread corresponding to the standard deviation between models, compared to 275 Pg C with an uncertainty of ~13.5% from GlobBiomass. Model-data discrepancy in total forest AGB can be attributed to their discrepancies in the AGB density and/or forest area. While DGVMs represent the global spatial gradients of AGB density reasonably well, they only have modest ability to reproduce the regional spatial gradients of AGB density at scales below 1000 km. The 95th percentile of AGB density (AGB
Types de publication
Journal Article
Langues
eng
Sous-ensembles de citation
IM
Pagination
3997-4012Subventions
Organisme : European Space Agency
Pays : International
Organisme : Horizon 2020
ID : 821003
Pays : International
Organisme : SNSF
ID : 20020_172476
Pays : International
Informations de copyright
© 2020 John Wiley & Sons Ltd.
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