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Type: Artigo
Title: An integrated pan‐tropical biomass map using multiple reference datasets
Author: Avitabile, Valerio
Herold, Martin
Heuvelink, Gerard B. M.
Lewis, Simon L.
Phillips, Oliver L.
Asner, Gregory P.
Armston, John
Ashton, Peter S.
Banin, Lindsay
Bayol, Nicolas
Berry, Nicholas J.
Boeckx, Pascal
Jong, Bernardus H. J. de
DeVries, Ben
Girardin, Cecile A. J.
Kearsley, Elizabeth
Lindsell, Jeremy A.
Lopez‐Gonzalez, Gabriela
Lucas, Richard
Malhi, Yadvinder
Morel, Alexandra
Mitchard, Edward T. A.
Nagy, Laszlo
Qie, Lan
Quinones, Marcela J.
Ryan, Casey M.
Ferry, Slik J. W.
Sunderland, Terry
Laurin, Gaia Vaglio
Gatti, Roberto Cazzolla
Valentini, Riccardo
Verbeeck, Hans
Wijaya, Arief
Willcock, Simon
Abstract: We combined two existing datasets of vegetation aboveground biomass (AGB) (Proceedings of the National Academy of Sciences of the United States of America, 108, 2011, 9899; Nature Climate Change, 2, 2012, 182) into a pan‐tropical AGB map at 1‐km resolution using an independent reference dataset of field observations and locally calibrated high‐resolution biomass maps, harmonized and upscaled to 14 477 1‐km AGB estimates. Our data fusion approach uses bias removal and weighted linear averaging that incorporates and spatializes the biomass patterns indicated by the reference data. The method was applied independently in areas (strata) with homogeneous error patterns of the input (Saatchi and Baccini) maps, which were estimated from the reference data and additional covariates. Based on the fused map, we estimated AGB stock for the tropics (23.4 N–23.4 S) of 375 Pg dry mass, 9–18% lower than the Saatchi and Baccini estimates. The fused map also showed differing spatial patterns of AGB over large areas, with higher AGB density in the dense forest areas in the Congo basin, Eastern Amazon and South‐East Asia, and lower values in Central America and in most dry vegetation areas of Africa than either of the input maps. The validation exercise, based on 2118 estimates from the reference dataset not used in the fusion process, showed that the fused map had a RMSE 15–21% lower than that of the input maps and, most importantly, nearly unbiased estimates (mean bias 5 Mg dry mass ha−1 vs. 21 and 28 Mg ha−1 for the input maps). The fusion method can be applied at any scale including the policy‐relevant national level, where it can provide improved biomass estimates by integrating existing regional biomass maps as input maps and additional, country‐specific reference datasets.
Subject: Biomassa
Ciclo de carbono (Biogeoquímica)
Sensoriamento remoto
Country: Reino Unido
Editor: Wiley
Rights: Fechado
Identifier DOI: 10.1111/gcb.13139
Date Issue: 2016
Appears in Collections:IB - Artigos e Outros Documentos

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