TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest
In forest ecosystem studies, tree stem structure variables (SSVs) proved to be an essential kind of parameters, and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon cycle. For thi...
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| Veröffentlicht in: | International journal of digital earth 2017-07, Vol.10 (7), p.701-718 |
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| creator | Lin, Yi Wei, Tian Yang, Bin Knyazikhin, Yuri Zhang, Yuhu Sato, Hisashi Fang, Xing Liang, Xinlian Yan, Lei Sun, Shanlin |
| description | In forest ecosystem studies, tree stem structure variables (SSVs) proved to be an essential kind of parameters, and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon cycle. For this newly emerging task, satellite imagery such as WorldView-2 panchromatic images (WPIs) is used as a potential solution for co-prediction of tree-level multifarious SSVs, with static terrestrial laser scanning (TLS) assumed as a 'bridge'. The specific operation is to pursue the allometric relationships between TLS-derived SSVs and WPI-derived feature parameters, and regression analyses with one or multiple explanatory variables are applied to deduce the prediction models (termed as Model1s and Model2s). In the case of Picea abies, Pinus sylvestris, Populus tremul and Quercus robur in a boreal forest, tests showed that Model1s and Model2s for different tree species can be derived (e.g. the maximum R
2
= 0.574 for Q. robur). Overall, this study basically validated the algorithm proposed for co-prediction of multifarious SSVs, and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling, which is useful for large-scale investigations of forest understory, macroecosystem ecology, global vegetation dynamics and global carbon cycle. |
| doi_str_mv | 10.1080/17538947.2016.1247473 |
| format | Article |
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2
= 0.574 for Q. robur). Overall, this study basically validated the algorithm proposed for co-prediction of multifarious SSVs, and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling, which is useful for large-scale investigations of forest understory, macroecosystem ecology, global vegetation dynamics and global carbon cycle.</description><identifier>ISSN: 1753-8947</identifier><identifier>EISSN: 1753-8955</identifier><identifier>DOI: 10.1080/17538947.2016.1247473</identifier><language>eng</language><publisher>Abingdon: Taylor & Francis</publisher><subject>Algorithms ; allometric relationship ; Allometry ; Boreal forests ; Bridges ; Carbon cycle ; Case studies ; co-prediction model ; Dynamics ; Ecological monitoring ; Ecology ; Ecosystem studies ; Equivalence ; Evolution ; Forest ecosystems ; Imagery ; Lasers ; Mathematical models ; Parameters ; Pine trees ; Plant species ; Prediction models ; Regression analysis ; Satellite imagery ; Satellites ; Scanning ; Spaceborne remote sensing ; static terrestrial laser scanning (TLS) ; Tests ; Tree stem structure variable (SSV) ; Understory ; Vegetation ; WorldView-2 panchromatic image (WPI)</subject><ispartof>International journal of digital earth, 2017-07, Vol.10 (7), p.701-718</ispartof><rights>2016 Informa UK Limited, trading as Taylor & Francis Group 2016</rights><rights>2016 Informa UK Limited, trading as Taylor & Francis Group</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c404t-c2693364f0c1dc5809535bf9b44ae4a164f8aa4ceb231ffb324ab126cf0e927e3</citedby><cites>FETCH-LOGICAL-c404t-c2693364f0c1dc5809535bf9b44ae4a164f8aa4ceb231ffb324ab126cf0e927e3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,780,784,27924,27925</link.rule.ids></links><search><creatorcontrib>Lin, Yi</creatorcontrib><creatorcontrib>Wei, Tian</creatorcontrib><creatorcontrib>Yang, Bin</creatorcontrib><creatorcontrib>Knyazikhin, Yuri</creatorcontrib><creatorcontrib>Zhang, Yuhu</creatorcontrib><creatorcontrib>Sato, Hisashi</creatorcontrib><creatorcontrib>Fang, Xing</creatorcontrib><creatorcontrib>Liang, Xinlian</creatorcontrib><creatorcontrib>Yan, Lei</creatorcontrib><creatorcontrib>Sun, Shanlin</creatorcontrib><title>TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest</title><title>International journal of digital earth</title><description>In forest ecosystem studies, tree stem structure variables (SSVs) proved to be an essential kind of parameters, and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon cycle. For this newly emerging task, satellite imagery such as WorldView-2 panchromatic images (WPIs) is used as a potential solution for co-prediction of tree-level multifarious SSVs, with static terrestrial laser scanning (TLS) assumed as a 'bridge'. The specific operation is to pursue the allometric relationships between TLS-derived SSVs and WPI-derived feature parameters, and regression analyses with one or multiple explanatory variables are applied to deduce the prediction models (termed as Model1s and Model2s). In the case of Picea abies, Pinus sylvestris, Populus tremul and Quercus robur in a boreal forest, tests showed that Model1s and Model2s for different tree species can be derived (e.g. the maximum R
2
= 0.574 for Q. robur). Overall, this study basically validated the algorithm proposed for co-prediction of multifarious SSVs, and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling, which is useful for large-scale investigations of forest understory, macroecosystem ecology, global vegetation dynamics and global carbon cycle.</description><subject>Algorithms</subject><subject>allometric relationship</subject><subject>Allometry</subject><subject>Boreal forests</subject><subject>Bridges</subject><subject>Carbon cycle</subject><subject>Case studies</subject><subject>co-prediction model</subject><subject>Dynamics</subject><subject>Ecological monitoring</subject><subject>Ecology</subject><subject>Ecosystem studies</subject><subject>Equivalence</subject><subject>Evolution</subject><subject>Forest ecosystems</subject><subject>Imagery</subject><subject>Lasers</subject><subject>Mathematical models</subject><subject>Parameters</subject><subject>Pine trees</subject><subject>Plant species</subject><subject>Prediction models</subject><subject>Regression analysis</subject><subject>Satellite imagery</subject><subject>Satellites</subject><subject>Scanning</subject><subject>Spaceborne remote sensing</subject><subject>static terrestrial laser scanning (TLS)</subject><subject>Tests</subject><subject>Tree stem structure variable (SSV)</subject><subject>Understory</subject><subject>Vegetation</subject><subject>WorldView-2 panchromatic image (WPI)</subject><issn>1753-8947</issn><issn>1753-8955</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>DOA</sourceid><recordid>eNp9Uctu2zAQFIoWaOrkEwIQ6Fkun6LUU4ugaQMY6CHpmaDIpUNDMt2l5MA_ku8tbac55sIlhtyZ3ZmqumZ0yWhLvzCtRNtJveSUNUvGpZZavKsujnjddkq9f71L_bH6lPOG0oZKKS6q54fVfd1j9GvwxKV6h-Cjm2LakhTIhAD1AHsYyDgPUwwWY5ozyROM5cDZTTMC2RfY9gNkEjCN5Cnh4PcRnmpOdnbrHgtop-hIHO0a8PCVWOJshsIw-8NJ5xFInxDsQEIpebqsPgQ7ZLh6qYvqz-2Ph5tf9er3z7ub76vaSSqn2vGmE6KRgTrmnWppp4TqQ9dLaUFaVl5aa6WDngsWQi-4tD3jjQsUOq5BLKq7M69PdmN2WCbEg0k2mhOQcG0sltEHMJw5z5RqfOtAtpr3ulWsY7IIU-21L1yfz1w7TH_nsoTZpBm3ZXzDOipbJnmxfFGp8y-HKWeE8KrKqDnGaf7HaY5xmpc4S9-3c1_cFotGezLZTPYwJAxYXI7ZiLcp_gEwvqkS</recordid><startdate>20170703</startdate><enddate>20170703</enddate><creator>Lin, Yi</creator><creator>Wei, Tian</creator><creator>Yang, Bin</creator><creator>Knyazikhin, Yuri</creator><creator>Zhang, Yuhu</creator><creator>Sato, Hisashi</creator><creator>Fang, Xing</creator><creator>Liang, Xinlian</creator><creator>Yan, Lei</creator><creator>Sun, Shanlin</creator><general>Taylor & Francis</general><general>Taylor & Francis Ltd</general><general>Taylor & Francis Group</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7ST</scope><scope>7UA</scope><scope>8FD</scope><scope>C1K</scope><scope>F1W</scope><scope>FR3</scope><scope>H8D</scope><scope>H96</scope><scope>KR7</scope><scope>L.G</scope><scope>L7M</scope><scope>SOI</scope><scope>DOA</scope></search><sort><creationdate>20170703</creationdate><title>TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest</title><author>Lin, Yi ; Wei, Tian ; Yang, Bin ; Knyazikhin, Yuri ; Zhang, Yuhu ; Sato, Hisashi ; Fang, Xing ; Liang, Xinlian ; Yan, Lei ; Sun, Shanlin</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c404t-c2693364f0c1dc5809535bf9b44ae4a164f8aa4ceb231ffb324ab126cf0e927e3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Algorithms</topic><topic>allometric relationship</topic><topic>Allometry</topic><topic>Boreal forests</topic><topic>Bridges</topic><topic>Carbon cycle</topic><topic>Case studies</topic><topic>co-prediction model</topic><topic>Dynamics</topic><topic>Ecological monitoring</topic><topic>Ecology</topic><topic>Ecosystem studies</topic><topic>Equivalence</topic><topic>Evolution</topic><topic>Forest ecosystems</topic><topic>Imagery</topic><topic>Lasers</topic><topic>Mathematical models</topic><topic>Parameters</topic><topic>Pine trees</topic><topic>Plant species</topic><topic>Prediction models</topic><topic>Regression analysis</topic><topic>Satellite imagery</topic><topic>Satellites</topic><topic>Scanning</topic><topic>Spaceborne remote sensing</topic><topic>static terrestrial laser scanning (TLS)</topic><topic>Tests</topic><topic>Tree stem structure variable (SSV)</topic><topic>Understory</topic><topic>Vegetation</topic><topic>WorldView-2 panchromatic image (WPI)</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Lin, Yi</creatorcontrib><creatorcontrib>Wei, Tian</creatorcontrib><creatorcontrib>Yang, Bin</creatorcontrib><creatorcontrib>Knyazikhin, Yuri</creatorcontrib><creatorcontrib>Zhang, Yuhu</creatorcontrib><creatorcontrib>Sato, Hisashi</creatorcontrib><creatorcontrib>Fang, Xing</creatorcontrib><creatorcontrib>Liang, Xinlian</creatorcontrib><creatorcontrib>Yan, Lei</creatorcontrib><creatorcontrib>Sun, Shanlin</creatorcontrib><collection>CrossRef</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Technology Research Database</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Civil Engineering Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Advanced Technologies Database with Aerospace</collection><collection>Environment Abstracts</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>International journal of digital earth</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Lin, Yi</au><au>Wei, Tian</au><au>Yang, Bin</au><au>Knyazikhin, Yuri</au><au>Zhang, Yuhu</au><au>Sato, Hisashi</au><au>Fang, Xing</au><au>Liang, Xinlian</au><au>Yan, Lei</au><au>Sun, Shanlin</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest</atitle><jtitle>International journal of digital earth</jtitle><date>2017-07-03</date><risdate>2017</risdate><volume>10</volume><issue>7</issue><spage>701</spage><epage>718</epage><pages>701-718</pages><issn>1753-8947</issn><eissn>1753-8955</eissn><abstract>In forest ecosystem studies, tree stem structure variables (SSVs) proved to be an essential kind of parameters, and now simultaneously deriving SSVs of as many kinds as possible at large scales is preferred for enhancing the frontier studies on marcoecosystem ecology and global carbon cycle. For this newly emerging task, satellite imagery such as WorldView-2 panchromatic images (WPIs) is used as a potential solution for co-prediction of tree-level multifarious SSVs, with static terrestrial laser scanning (TLS) assumed as a 'bridge'. The specific operation is to pursue the allometric relationships between TLS-derived SSVs and WPI-derived feature parameters, and regression analyses with one or multiple explanatory variables are applied to deduce the prediction models (termed as Model1s and Model2s). In the case of Picea abies, Pinus sylvestris, Populus tremul and Quercus robur in a boreal forest, tests showed that Model1s and Model2s for different tree species can be derived (e.g. the maximum R
2
= 0.574 for Q. robur). Overall, this study basically validated the algorithm proposed for co-prediction of multifarious SSVs, and the contribution is equivalent to developing a viable solution for SSV-estimation upscaling, which is useful for large-scale investigations of forest understory, macroecosystem ecology, global vegetation dynamics and global carbon cycle.</abstract><cop>Abingdon</cop><pub>Taylor & Francis</pub><doi>10.1080/17538947.2016.1247473</doi><tpages>18</tpages><oa>free_for_read</oa></addata></record> |
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| ispartof | International journal of digital earth, 2017-07, Vol.10 (7), p.701-718 |
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| language | eng |
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| subjects | Algorithms allometric relationship Allometry Boreal forests Bridges Carbon cycle Case studies co-prediction model Dynamics Ecological monitoring Ecology Ecosystem studies Equivalence Evolution Forest ecosystems Imagery Lasers Mathematical models Parameters Pine trees Plant species Prediction models Regression analysis Satellite imagery Satellites Scanning Spaceborne remote sensing static terrestrial laser scanning (TLS) Tests Tree stem structure variable (SSV) Understory Vegetation WorldView-2 panchromatic image (WPI) |
| title | TLS-bridged co-prediction of tree-level multifarious stem structure variables from worldview-2 panchromatic imagery: a case study of the boreal forest |
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