1. An integrated pan-tropical biomass map using multiple reference datasets
- Author
-
Hans Verbeeck, Slik J.W. Ferry, Terry Sunderland, Cécile A. J. Girardin, Pascal Boeckx, John Armston, Lindsay F. Banin, Lan Qie, Marcela J. Quinones, Bernardus H. J. de Jong, Gabriela Lopez-Gonzalez, Richard Lucas, Edward T. A. Mitchard, Riccardo Valentini, Martin Herold, Valerio Avitabile, Laszlo Nagy, Jeremy A. Lindsell, Elizabeth Kearsley, Simon L. Lewis, Arief Wijaya, Nicolas Bayol, Nicholas J. Berry, Casey M. Ryan, Gaia Vaglio Laurin, Ben DeVries, Roberto Cazzolla Gatti, Yadvinder Malhi, Gerard B. M. Heuvelink, Oliver L. Phillips, Alexandra C. Morel, Peter S. Ashton, Gregory P. Asner, Simon Willcock, Avitabile V., Herold M., Heuvelink G.B.M., Lewis S.L., Phillips O.L., Asner G.P., Armston J., Ashton P.S., Banin L., Bayol N., Berry N.J., Boeckx P., de Jong B.H.J., Devries B., Girardin C.A.J., Kearsley E., Lindsell J.A., Lopez-Gonzalez G., Lucas R., Malhi Y., Morel A., Mitchard E.T.A., Nagy L., Qie L., Quinones M.J., Ryan C.M., Ferry S.J.W., Sunderland T., Laurin G.V., Cazzolla Gatti R., Valentini R., Verbeeck H., Wijaya A., and Willcock S.
- Subjects
0106 biological sciences ,010504 meteorology & atmospheric sciences ,Mean squared error ,Forest plot ,Climate change ,Datasets as Topic ,Structural basin ,010603 evolutionary biology ,01 natural sciences ,Ecology and Environment ,Trees ,Laboratory of Geo-information Science and Remote Sensing ,Tropical forest ,Environmental Chemistry ,Satellite imagery ,Laboratorium voor Geo-informatiekunde en Remote Sensing ,Biomass ,Aboveground bioma ,0105 earth and related environmental sciences ,General Environmental Science ,Remote sensing ,Global and Planetary Change ,Tropical Climate ,Forest inventory ,Ecology ,Tropics ,Aboveground biomass ,Carbon cycle ,15. Life on land ,Models, Theoretical ,Sensor fusion ,PE&RC ,Forest plots ,Satellite mapping ,13. Climate action ,Spatial ecology ,Environmental science ,Physical geography ,REDD+ ,ISRIC - World Soil Information ,Maps as Topic - 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 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.
- Published
- 2016