TY - JOUR

T1 - A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

AU - Araza, Arnan

AU - de Bruin, Styze

AU - Herold, Martin

AU - Quegan, Shaun

AU - Labriere, Nicolas

AU - Veiga, Pedro Rodriguez

AU - Avitabile, Valerio

AU - Santoro, Maurizio

AU - Mitchard, Edward T.A.

AU - Ryan, Casey M.

AU - Phillips, Oliver L.

AU - Willcock, Simon

AU - Verbeeck, Hans

AU - Carreiras, Joao

AU - Hein, Lars

AU - Schelhaas, Mart-Jan

AU - Pascagaza, Ana Maria Pacheco

AU - da Concecao Bispo, Polyanna

AU - Laurin, Gaia Vaglio

AU - Vieilledent, Ghislain

AU - Slik, Ferry

AU - Wijaya, Arief

AU - Lewis, Simon L.

AU - Morel, Alexandra

AU - Liang, Jingjing

AU - Sukhdeo, Hansrajie

AU - Schepaschenko, Dimitry

AU - Cavlovic, Jura

AU - Gilani, Hammad

AU - Lucas, Richard

PY - 2022/4

Y1 - 2022/4

N2 - 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 mapsis 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 collectionof 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 commoncase 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 errorshave 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 every after 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 constitutesa major step towards improved AGB map validation and improvement.

AB - 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 mapsis 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 collectionof 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 commoncase 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 errorshave 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 every after 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 constitutesa major step towards improved AGB map validation and improvement.

KW - AGB

KW - carbon cycle

KW - map validation

KW - uncertainty assessment

KW - remote sensing

U2 - https://doi.org/10.1016/j.rse.2022.112917

DO - https://doi.org/10.1016/j.rse.2022.112917

M3 - Article

VL - 272

JO - Remote Sensing of the Environment

JF - Remote Sensing of the Environment

SN - 0034-4257

M1 - 112917

ER -