Artículos de revistas
An Integrated Pan-tropical Biomass Map Using Multiple Reference Datasets
Registro en:
Global Change Biology. Wiley-blackwell, v. 22, p. 1406 - 1420, 2016.
1354-1013
1365-2486
WOS:000371515300008
10.1111/gcb.13139
Autor
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; de Jong
Bernardus H. J.; 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
Institución
Resumen
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 14477 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 5Mg dry massha(-1) vs. 21 and 28Mgha(-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. 22 4 1406 1420 EU FP7 GEOCARBON [283080] NORAD [QZA-10/0468] ESA GlobBiomass project [4000113100/14/I-NB] AusAID within CIFOR's Global Comparative Study on REDD+ [46167] German Federal Ministry for the Environment Nature Conservation and Nuclear Safety (BMU) International Climate Initiative (IKI) through the project 'From Climate Research to Action under Multilevel Governance: Building Knowledge and Capacity at Landscape Scale' Brazilian Agricultural Research Corporation (EMBRAPA) US Forest Service USAID US Department of State Aberystwyth University University of New South Wales (UNSW) Queensland Department of Science, Information Technology and Innovation (DSITI) Avatar Alliance Foundation John D. and Catherine T. MacArthur Foundation NSF [1146206] European Research Council (T-FORCES) CIFOR/USAID Philip Leverhulme Prize