气候变化领域集成服务门户(CCPortal): A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps

CCPortal

DOI	10.1016/j.rse.2022.112917
	A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps
	Araza, Arnan; de Bruin, Sytze; Herold, Martin; Quegan, Shaun; Labriere, Nicolas; 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
发表日期	2022
ISSN	0034-4257
EISSN	1879-0704
卷号	272
英文摘要	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.
英文关键词	AGB; Carbon cycle; Map validation; Uncertainty assessment; Remote sensing
语种	英语
WOS研究方向	Environmental Sciences ; Remote Sensing ; Imaging Science & Photographic Technology
WOS类目	Science Citation Index Expanded (SCI-EXPANDED)
WOS记录号	WOS:000760311600002
来源期刊	REMOTE SENSING OF ENVIRONMENT
文献类型	期刊论文
条目标识符	http://gcip.llas.ac.cn/handle/2XKMVOVA/281128
作者单位	Wageningen University & Research; Wageningen University & Research; University of Sheffield; University of Sheffield; Centre National de la Recherche Scientifique (CNRS); CNRS - Institute of Ecology & Environment (INEE); Universite de Toulouse; Universite Federale Toulouse Midi-Pyrenees (ComUE); Universite Toulouse III - Paul Sabatier; Ecole Nationale Formation Agronomique (ENSFEA); Institut de Recherche pour le Developpement (IRD); University of Leicester; University of Leicester; European Commission Joint Research Centre; EC JRC ISPRA Site; GAMMA Remote Sensing AG; University of Edinburgh; University of Leeds; Bangor University; UK Research & Innovation (UKRI); Biotechnology and Biological Sciences Research Council (BBSRC); Rothamsted Research; Ghent University; Wageningen University & Research; University of Manchester; Tuscia University; CIRAD; Universite de Montpellier; Institut de Recherche pour le Developpement (IRD); INRAE; Centre National de la Recherche Scientifique (CNRS); University Brunei Dar...
推荐引用方式 GB/T 7714	Araza, Arnan,de Bruin, Sytze,Herold, Martin,et al. A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps[J],2022,272.
APA	Araza, Arnan.,de Bruin, Sytze.,Herold, Martin.,Quegan, Shaun.,Labriere, Nicolas.,...&Lucas, Richard.(2022).A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps.REMOTE SENSING OF ENVIRONMENT,272.
MLA	Araza, Arnan,et al."A comprehensive framework for assessing the accuracy and uncertainty of global above-ground biomass maps".REMOTE SENSING OF ENVIRONMENT 272(2022).