An integrated pan-tropical biomass map using multiple reference datasets

Published on
February 11, 2016

Valerio Avitabile and Martin Herold, among others published a paper: An integrated pan-tropical biomass map using multiple reference datasets in Global Change Biology.

doi: 10.1111/gcb.13139

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 Nā€“23.4 S) of 375 Pg dry mass, 9ā€“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 15ā€“21% lower than that of the input maps and, most importantly,
nearly unbiased estimates (mean bias 5 Mg dry mass per ha vs. 21 and 28 Mg per ha 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.

Keywords: aboveground biomass; carbon cycle; forest inventory; forest plots; REDD+; remote sensing; satellite mapping;
tropical forest