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An integrated pan‐tropical biomass map using multiple reference datasets
Author(s) -
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.,
DeVries Ben,
Girardin Cecile A. J.,
Kearsley Elizabeth,
Lindsell Jeremy A.,
LopezGonzalez Gabriela,
Lucas Richard,
Malhi Yadvinder,
Morel Alexandra,
Mitchard Edward T. A.,
Nagy Laszlo,
Qie Lan,
Quis 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
Publication year - 2016
Publication title -
global change biology
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 4.146
H-Index - 255
eISSN - 1365-2486
pISSN - 1354-1013
DOI - 10.1111/gcb.13139
Subject(s) - vegetation (pathology) , environmental science , biomass (ecology) , physical geography , tropics , amazon rainforest , reference data , geography , remote sensing , ecology , database , biology , medicine , pathology , computer science
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.