An integrated pan-tropical biomass map using multiple reference datasets

Valerio Avitabile, Martin Herold, Gerard B.M. Heuvelink, Simon L. Lewis, Oliver L. Phillips, Gregory P. Asner, John Armston, Peter S. Ashton, Lindsay Banin, Nicolas Bayol, Nicholas J. Berry, Pascal Boeckx, Bernardus H.J. de Jong, Ben Devries, Cecile A.J. Girardin, Elizabeth Kearsley, Jeremy A. Lindsell, Gabriela Lopez-Gonzalez, Richard Lucas, Yadvinder MalhiAlexandra Morel, Edward T.A. Mitchard, Laszlo Nagy, Lan Qie, Marcela J. Quinones, Casey M. Ryan, Slik J.W. Ferry, Terry Sunderland, Gaia Vaglio Laurin, Roberto Cazzolla Gatti, Riccardo Valentini, Hans Verbeeck, Arief Wijaya, Simon Willcock

Research output: Contribution to journalArticle

225 Scopus citations

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 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 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 5 Mg dry mass ha-1 vs. 21 and 28 Mg ha-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.

Original languageEnglish (US)
Pages (from-to)1406-1420
Number of pages15
JournalGlobal change biology
Volume22
Issue number4
DOIs
StatePublished - Apr 1 2016

Keywords

  • Aboveground biomass
  • Carbon cycle
  • Forest inventory
  • Forest plots
  • REDD+
  • Remote sensing
  • Satellite mapping
  • Tropical forest

ASJC Scopus subject areas

  • Global and Planetary Change
  • Environmental Chemistry
  • Ecology
  • Environmental Science(all)

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  • Cite this

    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., ... Willcock, S. (2016). An integrated pan-tropical biomass map using multiple reference datasets. Global change biology, 22(4), 1406-1420. https://doi.org/10.1111/gcb.13139