An integrated pan‐tropical biomass map using multiple reference datasets

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…

• NS
  National Science Foundation

  Award: 1146206
• UD
  U.S. Department of State
• US
  United States Agency for International Development
• JD
  John D. and Catherine T. MacArthur Foundation
• AA
  Avatar Alliance Foundation
• LT
  Leverhulme Trust
• SR
  Sight Research UK

  Awards: ceh020002, NE/M021998/1, NE/I021217/1
• AU
  Aberystwyth University
• AA
  Australian Agency for International Development

  Award: 46167
• ES
  Epilepsy Society of Australia

  Award: 4000113100/14/I‐NB
• UO
  University of New South Wales
• EB
  Empresa Brasileira de Pesquisa Agropecuária
• BF
  Bundesministerium für Umwelt, Naturschutz und Reaktorsicherheit
• UF
  U.S. Forest Service
• DF
  Direktoratet for Utviklingssamarbeid

  Awards: QZA-10/0468, QZA‐10/0468
• NE
  Natural Environment Research Council

  Awards: NE/I021217/1, ceh020002, NE/M021998/1
• ER
  European Research Council
• SF
  Seventh Framework Programme

  Award: 283080
• DO
  Department of Science, Information Technology and Innovation, Queensland Government
• BF
  Bundesministerium für Umwelt, Naturschutz, nukleare Sicherheit und Verbraucherschutz