Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches
•Grassland biomass estimation and uncertainty were analyzed in Northern China.•Uncertainty was traced to data sources, model forms and model parameters.•Biomass allocation was most influential to grassland biomass estimation. Accurate estimation of grassland biomass and its dynamics are crucial not...
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Veröffentlicht in: | Ecological indicators 2016-01, Vol.60, p.1031-1040 |
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description | •Grassland biomass estimation and uncertainty were analyzed in Northern China.•Uncertainty was traced to data sources, model forms and model parameters.•Biomass allocation was most influential to grassland biomass estimation.
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±99.71g/m2) 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. |
doi_str_mv | 10.1016/j.ecolind.2015.09.001 |
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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±99.71g/m2) 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.</description><identifier>ISSN: 1470-160X</identifier><identifier>EISSN: 1872-7034</identifier><identifier>DOI: 10.1016/j.ecolind.2015.09.001</identifier><language>eng</language><publisher>Elsevier Ltd</publisher><subject>Allocations ; Biomass ; China ; Data sources ; dry matter partitioning ; Dynamics ; Grassland biomass ; Grasslands ; NDVI ; Northern China ; Remote sensing ; root shoot ratio ; Root-to-shoot ratio ; spatial data ; terrestrial ecosystems ; Uncertainty ; Uncertainty analysis</subject><ispartof>Ecological indicators, 2016-01, Vol.60, p.1031-1040</ispartof><rights>2015 Elsevier Ltd</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c446t-77f6557229b721aa80ae213cb6a8c6828044bf143489c432719cebaf8335737d3</citedby><cites>FETCH-LOGICAL-c446t-77f6557229b721aa80ae213cb6a8c6828044bf143489c432719cebaf8335737d3</cites><orcidid>0000-0001-5426-7390</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktohtml>$$Uhttps://dx.doi.org/10.1016/j.ecolind.2015.09.001$$EHTML$$P50$$Gelsevier$$H</linktohtml><link.rule.ids>314,780,784,3550,4024,27923,27924,27925,45995</link.rule.ids></links><search><creatorcontrib>Jia, Wenxiao</creatorcontrib><creatorcontrib>Liu, Min</creatorcontrib><creatorcontrib>Yang, Yuanhe</creatorcontrib><creatorcontrib>He, Honglin</creatorcontrib><creatorcontrib>Zhu, Xudong</creatorcontrib><creatorcontrib>Yang, Fang</creatorcontrib><creatorcontrib>Yin, Cai</creatorcontrib><creatorcontrib>Xiang, Weining</creatorcontrib><title>Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches</title><title>Ecological indicators</title><description>•Grassland biomass estimation and uncertainty were analyzed in Northern China.•Uncertainty was traced to data sources, model forms and model parameters.•Biomass allocation was most influential to grassland biomass estimation.
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±99.71g/m2) 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.</description><subject>Allocations</subject><subject>Biomass</subject><subject>China</subject><subject>Data sources</subject><subject>dry matter partitioning</subject><subject>Dynamics</subject><subject>Grassland biomass</subject><subject>Grasslands</subject><subject>NDVI</subject><subject>Northern China</subject><subject>Remote sensing</subject><subject>root shoot ratio</subject><subject>Root-to-shoot ratio</subject><subject>spatial data</subject><subject>terrestrial ecosystems</subject><subject>Uncertainty</subject><subject>Uncertainty analysis</subject><issn>1470-160X</issn><issn>1872-7034</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNqNkc9u1DAQxiMEEqXwCAgfuWxqJ47_cEFoVaBSBQeoxM1ynMmuV4kdPE6lfRGeF0fbO5w81vzmG833VdVbRmtGmbg51eDi5MNQN5R1NdU1pexZdcWUbHaStvx5qbmkOybor5fVK8RTAYTW4qr6c4vZzzb7GIgNA1mDg5StD_lc_nY6IyCJIzkkizhtRO_jXGriA_kWUz5CCmR_9MF-IPs4LzZ5LFplZF6n7JcJSII5ZiAIAX04kMFmSzCuyRXpTXGOA0xbxy5LitYdAV9XL0Y7Ibx5eq-rh8-3P_dfd_ffv9ztP93vHOci76QcRdfJptG9bJi1ilpoWOt6YZUTqlGU835kvOVKO942kmkHvR1V23aylUN7Xb2_6JbFv1fAbGaPDqZyKcQVDZNSUaqF0v-Bdi2XjKmuoN0FdSkiJhjNkorJ6WwYNVtk5mSeIjNbZIZqUxIpc-8uc6ONxh6Kk-bhRwFEaWraMVWIjxcCiimPHpJB56FkNvgELpsh-n_s-AvVxq3X</recordid><startdate>201601</startdate><enddate>201601</enddate><creator>Jia, Wenxiao</creator><creator>Liu, Min</creator><creator>Yang, Yuanhe</creator><creator>He, Honglin</creator><creator>Zhu, Xudong</creator><creator>Yang, Fang</creator><creator>Yin, Cai</creator><creator>Xiang, Weining</creator><general>Elsevier Ltd</general><scope>FBQ</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>7U6</scope><scope>C1K</scope><scope>8FD</scope><scope>FR3</scope><scope>KR7</scope><orcidid>https://orcid.org/0000-0001-5426-7390</orcidid></search><sort><creationdate>201601</creationdate><title>Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches</title><author>Jia, Wenxiao ; Liu, Min ; Yang, Yuanhe ; He, Honglin ; Zhu, Xudong ; Yang, Fang ; Yin, Cai ; Xiang, Weining</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c446t-77f6557229b721aa80ae213cb6a8c6828044bf143489c432719cebaf8335737d3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Allocations</topic><topic>Biomass</topic><topic>China</topic><topic>Data sources</topic><topic>dry matter partitioning</topic><topic>Dynamics</topic><topic>Grassland biomass</topic><topic>Grasslands</topic><topic>NDVI</topic><topic>Northern China</topic><topic>Remote sensing</topic><topic>root shoot ratio</topic><topic>Root-to-shoot ratio</topic><topic>spatial data</topic><topic>terrestrial ecosystems</topic><topic>Uncertainty</topic><topic>Uncertainty analysis</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jia, Wenxiao</creatorcontrib><creatorcontrib>Liu, Min</creatorcontrib><creatorcontrib>Yang, Yuanhe</creatorcontrib><creatorcontrib>He, Honglin</creatorcontrib><creatorcontrib>Zhu, Xudong</creatorcontrib><creatorcontrib>Yang, Fang</creatorcontrib><creatorcontrib>Yin, Cai</creatorcontrib><creatorcontrib>Xiang, Weining</creatorcontrib><collection>AGRIS</collection><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Sustainability Science Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Civil Engineering Abstracts</collection><jtitle>Ecological indicators</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jia, Wenxiao</au><au>Liu, Min</au><au>Yang, Yuanhe</au><au>He, Honglin</au><au>Zhu, Xudong</au><au>Yang, Fang</au><au>Yin, Cai</au><au>Xiang, Weining</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches</atitle><jtitle>Ecological indicators</jtitle><date>2016-01</date><risdate>2016</risdate><volume>60</volume><spage>1031</spage><epage>1040</epage><pages>1031-1040</pages><issn>1470-160X</issn><eissn>1872-7034</eissn><abstract>•Grassland biomass estimation and uncertainty were analyzed in Northern China.•Uncertainty was traced to data sources, model forms and model parameters.•Biomass allocation was most influential to grassland biomass estimation.
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±99.71g/m2) 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.</abstract><pub>Elsevier Ltd</pub><doi>10.1016/j.ecolind.2015.09.001</doi><tpages>10</tpages><orcidid>https://orcid.org/0000-0001-5426-7390</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Allocations Biomass China Data sources dry matter partitioning Dynamics Grassland biomass Grasslands NDVI Northern China Remote sensing root shoot ratio Root-to-shoot ratio spatial data terrestrial ecosystems Uncertainty Uncertainty analysis |
title | Estimation and uncertainty analyses of grassland biomass in Northern China: Comparison of multiple remote sensing data sources and modeling approaches |
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