A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

Arnan Araza (Lead / Corresponding author), Sytze de Bruin, Martin Herold, Shaun Quegan, Nicolas Labriere, Pedro Rodriguez-Veiga, Valerio Avitabile, Maurizio Santoro, Edward T.A. Mitchard, Casey M. Ryan, Oliver L. Phillips, Simon Willcock, Hans Verbeeck, Joao Carreiras, Lars Hein, Mart Jan Schelhaas, Ana Maria Pacheco-Pascagaza, Polyanna da Conceição Bispo, Gaia Vaglio Laurin, Ghislain VieilledentFerry Slik, Arief Wijaya, Simon L. Lewis, Alexandra Morel, Jingjing Liang, Hansrajie Sukhdeo, Dmitry Schepaschenko, Jura Cavlovic, Hammad Gilani, Richard Lucas

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    16 Citations (Scopus)
    47 Downloads (Pure)


    Over the past decade, several global maps of above-ground biomass (AGB) have been produced, but they exhibit significant differences that reduce their value for climate and carbon cycle modelling, and also for national estimates of forest carbon stocks and their changes. The number of such maps is anticipated to increase because of new satellite missions dedicated to measuring AGB. Objective and consistent methods to estimate the accuracy and uncertainty of AGB maps are therefore urgently needed. This paper develops and demonstrates a framework aimed at achieving this. The framework provides a means to compare AGB maps with AGB estimates from a global collection of National Forest Inventories and research plots that accounts for the uncertainty of plot AGB errors. This uncertainty depends strongly on plot size, and is dominated by the combined errors from tree measurements and allometric models (inter-quartile range of their standard deviation (SD) = 30–151 Mg ha−1). Estimates of sampling errors are also important, especially in the most common case where plots are smaller than map pixels (SD = 16–44 Mg ha−1). Plot uncertainty estimates are used to calculate the minimum-variance linear unbiased estimates of the mean forest AGB when averaged to 0.1. These are used to assess four AGB maps: Baccini (2000), GEOCARBON (2008), GlobBiomass (2010) and CCI Biomass (2017). Map bias, estimated using the differences between the plot and 0.1 map averages, is modelled using random forest regression driven by variables shown to affect the map estimates. The bias model is particularly sensitive to the map estimate of AGB and tree cover, and exhibits strong regional biases. Variograms indicate that AGB map errors have map-specific spatial correlation up to a range of 50–104 km, which increases the variance of spatially aggregated AGB map estimates compared to when pixel errors are independent. After bias adjustment, total pantropical AGB and its associated SD are derived for the four map epochs. This total becomes closer to the value estimated by the Forest Resources Assessment after every epoch and shows a similar decrease. The framework is applicable to both local and global-scale analysis, and is available at https://github.com/arnanaraza/PlotToMap. Our study therefore constitutes a major step towards improved AGB map validation and improvement.

    Original languageEnglish
    Article number112917
    Pages (from-to)1-16
    Number of pages16
    JournalRemote Sensing of Environment
    Early online date9 Feb 2022
    Publication statusPublished - Apr 2022


    • AGB
    • Carbon cycle
    • Map validation
    • Remote sensing
    • Uncertainty assessment

    ASJC Scopus subject areas

    • Soil Science
    • Geology
    • Computers in Earth Sciences


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