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An integrated pan-tropical biomass map using multiple reference datasets

(2016) GLOBAL CHANGE BIOLOGY. 22(4). p.1406-1420
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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.
Keywords
satellite mapping, RAIN-FOREST, DENSITY, DEFORESTATION, carbon cycle, forest inventory, forest plots, REDD, remote sensing, aboveground biomass, FOREST CARBON STOCKS, ABOVEGROUND BIOMASS, tropical forest, satellite mapping, EMISSIONS, AMAZONIAN FORESTS, PATTERNS, REGIONS, PLOTS

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Citation

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Chicago
Avitabile, Valerio, Martin Herold, Gerard BM Heuvelink, Simon L Lewis, Oliver L Phillips, Gregory P Asner, John Armston, et al. 2016. “An Integrated Pan-tropical Biomass Map Using Multiple Reference Datasets.” Global Change Biology 22 (4): 1406–1420.
APA
Avitabile, V., Herold, M., Heuvelink, G. B., Lewis, S. L., Phillips, O. L., Asner, G. P., Armston, J., et al. (2016). An integrated pan-tropical biomass map using multiple reference datasets. GLOBAL CHANGE BIOLOGY, 22(4), 1406–1420.
Vancouver
1.
Avitabile V, Herold M, Heuvelink GB, Lewis SL, Phillips OL, Asner GP, et al. An integrated pan-tropical biomass map using multiple reference datasets. GLOBAL CHANGE BIOLOGY. 2016;22(4):1406–20.
MLA
Avitabile, Valerio, Martin Herold, Gerard BM Heuvelink, et al. “An Integrated Pan-tropical Biomass Map Using Multiple Reference Datasets.” GLOBAL CHANGE BIOLOGY 22.4 (2016): 1406–1420. Print.
@article{7125056,
  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.},
  author       = {Avitabile, Valerio and Herold, Martin and Heuvelink, Gerard BM and Lewis, Simon L and Phillips, Oliver L and Asner, Gregory P and Armston, John and Asthon, Peter and Banin, Lindsay and Bayol, Nicolas and Berry, Nicholas J and Boeckx, Pascal and de Jong, Bernardus HJ and DeVries, Ben and Girardin, Cecile AJ and Kearsley, Elizabeth and Lindsell, Jeremy A and Lopez-Gonzalez, Gabriela and Lucas, Richard and Malhi, Yadvinder and Morel, Alexandra and Mitchard, Edward TA and Nagy, Laszlo and Qie, Lan and Quinones, Marcela J and Ryan, Casey M and Slik, JW Ferry and Sunderland, Terry and Vaglio Laurin, Gaia and Cazzola Gatti, Roberto and Valentini, Riccardo and Verbeeck, Hans and Wijaya, Arief and Willcock, Simon},
  issn         = {1354-1013},
  journal      = {GLOBAL CHANGE BIOLOGY},
  keyword      = {satellite mapping,RAIN-FOREST,DENSITY,DEFORESTATION,carbon cycle,forest inventory,forest plots,REDD,remote sensing,aboveground biomass,FOREST CARBON STOCKS,ABOVEGROUND BIOMASS,tropical forest,satellite mapping,EMISSIONS,AMAZONIAN FORESTS,PATTERNS,REGIONS,PLOTS},
  language     = {eng},
  number       = {4},
  pages        = {1406--1420},
  title        = {An integrated pan-tropical biomass map using multiple reference datasets},
  url          = {http://dx.doi.org/10.1111/gcb.13139},
  volume       = {22},
  year         = {2016},
}

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