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Article Dans Une Revue International Journal of Applied Earth Observation and Geoinformation Année : 2023

Past decade above-ground biomass change comparisons from four multi-temporal global maps

David A Gibbs
Nancy Harris
  • Fonction : Auteur
Maurizio Santoro
  • Fonction : Auteur
Wigneron J.-P.
Hui Yang
Olga Brovkina
  • Fonction : Auteur
Hugh C.A. Brown
Milen Chanev
  • Fonction : Auteur
Zlatomir Dimitrov
  • Fonction : Auteur
Lachezar Filchev
Jonas Fridman
  • Fonction : Auteur
Mariano García
  • Fonction : Auteur
Alexander Gikov
  • Fonction : Auteur
Leen Govaere
Petar Dimitrov
  • Fonction : Auteur
Fardin Moradi
Adriane Esquivel Muelbert
  • Fonction : Auteur
Jan Novotný
  • Fonction : Auteur
Thomas a M Pugh
  • Fonction : Auteur
Mart-Jan Schelhaas
Dmitry Schepaschenko
  • Fonction : Auteur
Krzysztof Stereńczak

Résumé

consistently captured AGB losses in known deforestation hotspots. The comparisons also identified several carbon sink regions consistently detected by all maps. However, disagreement between maps is still large in key forest regions such as the Amazon basin. The overall ΔAGB map cross-correlation between maps varied in the range 0.11-0.29 (r). Reported ΔAGB magnitudes were largest in the high-resolution datasets including the CCI map differencing (stock change) and Flux model (gain-loss) methods, while they were smallest according to the coarser-resolution LVOD and JPL time series products, especially for AGB gains. Our results suggest that ΔAGB assessed from current maps can be biased and any use of the estimates should take that into account. Currently, ΔAGB reference data are sparse especially in the tropics but that deficit can be alleviated by upcoming LiDAR data networks in the context of Supersites and GEO-Trees.
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Licence : CC BY - Paternité

Dates et versions

hal-04070660 , version 1 (16-04-2023)

Licence

Paternité

Identifiants

Citer

Arnan Araza, Martin Herold, Sytze de Bruin, Philippe Ciais, David A Gibbs, et al.. Past decade above-ground biomass change comparisons from four multi-temporal global maps. International Journal of Applied Earth Observation and Geoinformation, 2023, 118, pp.103274. ⟨10.1016/j.jag.2023.103274⟩. ⟨hal-04070660⟩
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