Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches

doi:10.1016/j.ecolind.2015.09.001

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	Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches
	Jia, Wenxiao ; Liu, Min ; Yang, Yuanhe 2; He, Honglin 3; Zhu, Xudong 4,5; Yang, Fang ; Yin, Cai 6; Xiang, Weining
刊名	ECOLOGICAL INDICATORS
	2016
卷号	60 页码:1031-1040
关键词	Grassland biomass NDVI Root-to-shoot ratio Uncertainty analysis Northern China
ISSN号	1470-160X
DOI	10.1016/j.ecolind.2015.09.001
文献子类	Article
英文摘要	Accurate estimation of grassland biomass and its dynamics are crucial not only for the biogeochemical dynamics of terrestrial ecosystems, but also for the sustainable use of grassland resources. However, estimations of grassland biomass on large spatial scale usually suffer from large variability and mostly lack quantitative uncertainty analyses. In this study, the spatial grassland biomass estimation and its uncertainty were assessed based on 265 field measurements and remote sensing data across Northern China during 2001-2005. Potential sources of uncertainty, including remote sensing data sources (DATsrc), model forms (MODfrm) and model parameters (biomass allocation, BMallo, e.g. root:shoot ratio), were determined and their relative contribution was quantified. The results showed that the annual grassland biomass in Northern China was 1268.37 +/- 180.84Tg (i.e., 532.02 +/- 99.71 g/m(2)) during 2001-2005, increasing from western to eastern area, with a mean relative uncertainty of 19.8%. There were distinguishable differences among the uncertainty contributions of three sources (BMallo >DATsrc>MODfrm), which contributed 52%, 27% and 13%, respectively. This study highlighted the need to concern the uncertainty in grassland biomass estimation, especially for the uncertainty related to BMallo. (C) 2015 Elsevier Ltd. All rights reserved.
学科主题	Biodiversity Conservation ; Environmental Sciences
电子版国际标准刊号	1872-7034
出版地	AMSTERDAM
WOS关键词	NET PRIMARY PRODUCTIVITY ; MODIS TIME-SERIES ; ABOVEGROUND BIOMASS ; ALPINE GRASSLANDS ; TIBETAN PLATEAU ; CARBON STORAGE ; VEGETATION ; PATTERNS ; AVHRR ; PERFORMANCE
WOS研究方向	Science Citation Index Expanded (SCI-EXPANDED)
语种	英语
出版者	ELSEVIER SCIENCE BV
WOS记录号	WOS:000367407000104
资助机构	Non-profit Special Research fund of National Environmental Protection of China [201109030] ; Science and Technology Projects of China on Certified carbon budget affected by climate change and related issues [XDA05050700] ; National Natural Science Foundation of ChinaNational Natural Science Foundation of China (NSFC) [41201092, 41471076]
内容类型	期刊论文
源URL	[http://ir.ibcas.ac.cn/handle/2S10CLM1/25218]
专题	植被与环境变化国家重点实验室
作者单位	1.E China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R China 2.E China Normal Univ, Sch Ecol & Environm Sci, Shanghai Key Lab Urban Ecol Proc & EcoRestorat, Shanghai 200241, Peoples R China 3.Chinese Acad Sci, Inst Bot, State Key Lab Vegetat & Environm Change, Beijing 100093, Peoples R China 4.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, Peoples R China 5.Colorado State Univ, Nat Resource Ecol Lab, Ft Collins, CO 80523 USA 6.Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Div Earth Sci, Berkeley, CA 94720 USA
推荐引用方式 GB/T 7714	Jia, Wenxiao,Liu, Min,Yang, Yuanhe,et al. Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches[J]. ECOLOGICAL INDICATORS,2016,60:1031-1040.
APA	Jia, Wenxiao.,Liu, Min.,Yang, Yuanhe.,He, Honglin.,Zhu, Xudong.,...&Xiang, Weining.(2016).Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches.ECOLOGICAL INDICATORS,60,1031-1040.
MLA	Jia, Wenxiao,et al."Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches".ECOLOGICAL INDICATORS 60(2016):1031-1040.