Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches
文献类型：期刊论文
| 作者 | Jia, Wenxiao; Liu, Min; Yang, Yuanhe2 ; He, Honglin3; Zhu, Xudong1,4; Yang, Fang; Yin, Cai5; Xiang, Weining
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| 刊名 | ECOLOGICAL INDICATORS
 |
| 出版日期 | 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 |
| 出版地 | AMSTERDAM |
| 电子版国际标准刊号 | 1872-7034 |
| 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 |
| WOS记录号 | WOS:000367407000104 |
| 出版者 | ELSEVIER SCIENCE BV |
| 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] |
| 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.Chinese Acad Sci, Inst Geog Sci & Nat Resources Res, Key Lab Ecosyst Network Observat & Modeling, Beijing 100101, 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.Colorado State Univ, Nat Resource Ecol Lab, Ft Collins, CO 80523 USA 5.Univ Calif Berkeley, Lawrence Berkeley Natl Lab, Div Earth Sci, Berkeley, CA 94720 USA 6.E China Normal Univ, Sch Geog Sci, Shanghai 200241, Peoples R China |
| 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. |
入库方式： OAI收割
来源：植物研究所
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