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

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

扫码查看
原文链接
  • NSTL
  • Elsevier
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 degrees. 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 degrees 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.

AGBCarbon cycleMap validationUncertainty assessmentRemote sensingGROWING STOCK VOLUMEFOREST BIOMASSGROUND DATACARBONFIELDRETRIEVALMODELSERRORSAREA

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

展开 >

Univ Leicester

Joint Res Ctr JRC

Gamma Remote Sensing

Univ Edinburgh

Univ Leeds

Bangor Univ

Univ Ghent

Wageningen Univ & Res

Univ Manchester

Univ Tuscia

Univ Montpellier

Univ Brunei Darussalam

World Resources Inst Indonesia

UCL

Univ Dundee

Purdue Univ

Guyana Forestry Commiss

Int Inst Appl Syst Anal

Univ Zagreb

Inst Space Technol

Aberystwyth Univ

Natl Ctr Earth Observat NCEO

CNRS

展开 >

2022

10.1016/j.rse.2022.112917

Remote Sensing of Environment

Remote Sensing of Environment

EISCI
ISSN：0034-4257
年,卷(期)：2022.272
  • 12
  • 91