TY - JOUR
T1 - An integrated pan-tropical biomass map using multiple reference datasets
AU - Avitabile, Valerio
AU - Herold, Martin
AU - Heuvelink, Gerard B.M.
AU - Lewis, Simon L.
AU - Phillips, Oliver L.
AU - Asner, Gregory P.
AU - Armston, John
AU - Ashton, Peter S.
AU - Banin, Lindsay
AU - Bayol, Nicola
AU - Berry, Nicholas J.
AU - Boeckx, Pascal
AU - de Jong, Bernardus H.J.
AU - Devries, Ben
AU - Girardin, Cecile A.J.
AU - Kearsley, Elizabeth
AU - Lindsell, Jeremy A.
AU - Lopez-Gonzalez, Gabriela
AU - Lucas, R.
AU - Malhi, Yadvinder
AU - Morel, Alexandra
AU - Mitchard, Edward T.A.
AU - Nagy, Laszlo
AU - Qie, Lan
AU - Quinones, Marcela J.
AU - Ryan, Casey M.
AU - Ferry, Slik J.W.
AU - Sunderland, Terry
AU - Laurin, Gaia Vaglio
AU - Gatti, Roberto Cazzolla
AU - Valentini, Riccardo
AU - Verbeeck, Hans
AU - Wijaya, Arief
AU - Willcock, Simon
N1 - cited By 58
PY - 2016/4/1
Y1 - 2016/4/1
N2 - 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.
AB - 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.
KW - aboveground biomass
KW - carbon cycle
KW - forest inventory
KW - forest plots
KW - REDD+
KW - remote sensing
KW - satellite mapping
KW - tropical forest
U2 - 10.1111/gcb.13139
DO - 10.1111/gcb.13139
M3 - Article
SN - 1354-1013
VL - 22
SP - 1406
EP - 1420
JO - Global Change Biology
JF - Global Change Biology
IS - 4
ER -