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

作者：Arnan B. Araza, Sytze de Bruin, Martin Herold, S. Quegan, Nicolas Labrière, Pedro Rodríguez‐Veiga, Valerio Avitabile, Maurizio Santoro, Edward T. A. Mitchard, Casey M. Ryan, Oliver Lawrence Phillips, Simon Willcock, Hans Verbeeck, João M. B. Carreiras, Lars Hein, Mart‐Jan Schelhaas, Ana María Pacheco-Pascagaza, Polyanna da Conceição Bispo, Gaia Vaglio Laurin, Ghislain Vieilledent, Ferry J.W. Slik, Arief Wijaya, Simon L. Lewis, A. Morel, Jingjing Liang, Hansrajie Sukhdeo, Dmitry G. Schepaschenko, Jura Čavlović, Hammad Gilani, Richard Lucas · 发表于：Remote Sensing of Environment · 年份：2022 · DOI：10.1016/j.rse.2022.112917 · 被引用次数：152 · 研究领域：Remote Sensing and LiDAR Applications、Forest ecology and management、Remote Sensing in Agriculture

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...