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A new approach for land cover classification and change analysis: Integrating backdating and an object-based method
Accurate information on land use and land cover (LULC) change is crucial for ecosystem monitoring and environmental change studies. Updating/backdating approaches have been increasingly used for LULC classification and change analysis, but mostly based on pixels. Here, we presented a new approach, a...
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Published in: | Remote sensing of environment 2016-05, Vol.177, p.37-47 |
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description | Accurate information on land use and land cover (LULC) change is crucial for ecosystem monitoring and environmental change studies. Updating/backdating approaches have been increasingly used for LULC classification and change analysis, but mostly based on pixels. Here, we presented a new approach, an object-based backdating approach which integrates the backdating approach with an object-based method, and further compared it with the pixel-based backdating approach. We tested the new approach by using Landsat TM data collected in 2001 and 2009 at the Beijing metropolitan region. We found that: 1) an object-based backdating approach achieved higher accuracy for change detection, LULC classification and change analysis than the pixel-based backdating approach. With the object-based approach, the overall accuracies for the classification and change analysis were 84.33% (versus 69.33% for a pixel-based approach), and 80.00% (versus 70.50% for a pixel-based approach), respectively. 2) The object-based backdating approach greatly increases the efficiency because classification and change analysis are only conducted for locations with changes. The increase in efficiency is particularly important for LULC classification and change analysis conducted at a large area, for example, at the national or global scale.
•We present a new approach for land cover classification and change analysis.•This new approach integrates backdating and an object-based method.•The object-based backdating method has higher accuracy and efficiency.•Errors caused by spatial misregistration are greatly reduced by the new method.•The new method effectively incorporates spatial information and expert knowledge. |
doi_str_mv | 10.1016/j.rse.2016.02.030 |
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•We present a new approach for land cover classification and change analysis.•This new approach integrates backdating and an object-based method.•The object-based backdating method has higher accuracy and efficiency.•Errors caused by spatial misregistration are greatly reduced by the new method.•The new method effectively incorporates spatial information and expert knowledge.</description><identifier>ISSN: 0034-4257</identifier><identifier>EISSN: 1879-0704</identifier><identifier>DOI: 10.1016/j.rse.2016.02.030</identifier><language>eng</language><publisher>Elsevier Inc</publisher><subject>Backdating ; Beijing ; Change analysis ; Change detection ; Classification ; Ecosystems ; Environmental monitoring ; Land cover ; Land cover classification ; Land use ; Object-based method ; Pixels ; Remote sensing</subject><ispartof>Remote sensing of environment, 2016-05, Vol.177, p.37-47</ispartof><rights>2016 Elsevier Inc.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c472t-e628b2422c95c909765a35a3fc19b7550db2e1e4137b5fd0f489891f59709e1f3</citedby><cites>FETCH-LOGICAL-c472t-e628b2422c95c909765a35a3fc19b7550db2e1e4137b5fd0f489891f59709e1f3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><link.rule.ids>314,776,780,27901,27902</link.rule.ids></links><search><creatorcontrib>Yu, Wenjuan</creatorcontrib><creatorcontrib>Zhou, Weiqi</creatorcontrib><creatorcontrib>Qian, Yuguo</creatorcontrib><creatorcontrib>Yan, Jingli</creatorcontrib><title>A new approach for land cover classification and change analysis: Integrating backdating and an object-based method</title><title>Remote sensing of environment</title><description>Accurate information on land use and land cover (LULC) change is crucial for ecosystem monitoring and environmental change studies. Updating/backdating approaches have been increasingly used for LULC classification and change analysis, but mostly based on pixels. Here, we presented a new approach, an object-based backdating approach which integrates the backdating approach with an object-based method, and further compared it with the pixel-based backdating approach. We tested the new approach by using Landsat TM data collected in 2001 and 2009 at the Beijing metropolitan region. We found that: 1) an object-based backdating approach achieved higher accuracy for change detection, LULC classification and change analysis than the pixel-based backdating approach. With the object-based approach, the overall accuracies for the classification and change analysis were 84.33% (versus 69.33% for a pixel-based approach), and 80.00% (versus 70.50% for a pixel-based approach), respectively. 2) The object-based backdating approach greatly increases the efficiency because classification and change analysis are only conducted for locations with changes. The increase in efficiency is particularly important for LULC classification and change analysis conducted at a large area, for example, at the national or global scale.
•We present a new approach for land cover classification and change analysis.•This new approach integrates backdating and an object-based method.•The object-based backdating method has higher accuracy and efficiency.•Errors caused by spatial misregistration are greatly reduced by the new method.•The new method effectively incorporates spatial information and expert knowledge.</description><subject>Backdating</subject><subject>Beijing</subject><subject>Change analysis</subject><subject>Change detection</subject><subject>Classification</subject><subject>Ecosystems</subject><subject>Environmental monitoring</subject><subject>Land cover</subject><subject>Land cover classification</subject><subject>Land use</subject><subject>Object-based method</subject><subject>Pixels</subject><subject>Remote sensing</subject><issn>0034-4257</issn><issn>1879-0704</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2016</creationdate><recordtype>article</recordtype><recordid>eNqFkUFv3CAQhVHUSNkm-QG5cezFzoDBmPYURU26UqRemjPCeNhl4zVb8KbKvw8b99yKkXjSfA_p8Qi5YVAzYO3trk4Za15kDbyGBs7IinVKV6BAfCIrgEZUgkt1QT7nvANgslNsRfIdnfAPtYdDitZtqY-JjnYaqIuvmKgbbc7BB2fnECf6sdjaaYNF2vEth_yVrqcZN6kA04b21r0MizyxdqKx36Gbq95mHOge520crsi5t2PG67_3JXl--P7r_kf19PNxfX_3VDmh-Fxhy7ueC86dlk6DVq20TRnvmO6VlDD0HBkK1qhe-gG86HSnmZdagUbmm0vyZXm3ZPt9xDybfcgOx5IP4zEb1kE5LRfd_1HVSS5a3rKCsgV1Keac0JtDCnub3gwDc-rC7Ezpwpy6MMBN6aJ4vi0eLHFfAyaTXcDJ4RBS-R0zxPAP9zsmU5Gh</recordid><startdate>201605</startdate><enddate>201605</enddate><creator>Yu, Wenjuan</creator><creator>Zhou, Weiqi</creator><creator>Qian, Yuguo</creator><creator>Yan, Jingli</creator><general>Elsevier Inc</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>7UA</scope><scope>C1K</scope><scope>F1W</scope><scope>H96</scope><scope>L.G</scope><scope>SOI</scope><scope>8FD</scope><scope>FR3</scope><scope>H8D</scope><scope>KR7</scope><scope>L7M</scope></search><sort><creationdate>201605</creationdate><title>A new approach for land cover classification and change analysis: Integrating backdating and an object-based method</title><author>Yu, Wenjuan ; Zhou, Weiqi ; Qian, Yuguo ; Yan, Jingli</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c472t-e628b2422c95c909765a35a3fc19b7550db2e1e4137b5fd0f489891f59709e1f3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2016</creationdate><topic>Backdating</topic><topic>Beijing</topic><topic>Change analysis</topic><topic>Change detection</topic><topic>Classification</topic><topic>Ecosystems</topic><topic>Environmental monitoring</topic><topic>Land cover</topic><topic>Land cover classification</topic><topic>Land use</topic><topic>Object-based method</topic><topic>Pixels</topic><topic>Remote sensing</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Yu, Wenjuan</creatorcontrib><creatorcontrib>Zhou, Weiqi</creatorcontrib><creatorcontrib>Qian, Yuguo</creatorcontrib><creatorcontrib>Yan, Jingli</creatorcontrib><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>Water Resources Abstracts</collection><collection>Environmental Sciences and Pollution Management</collection><collection>ASFA: Aquatic Sciences and Fisheries Abstracts</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) 2: Ocean Technology, Policy & Non-Living Resources</collection><collection>Aquatic Science & Fisheries Abstracts (ASFA) Professional</collection><collection>Environment Abstracts</collection><collection>Technology Research Database</collection><collection>Engineering Research Database</collection><collection>Aerospace Database</collection><collection>Civil Engineering Abstracts</collection><collection>Advanced Technologies Database with Aerospace</collection><jtitle>Remote sensing of environment</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Yu, Wenjuan</au><au>Zhou, Weiqi</au><au>Qian, Yuguo</au><au>Yan, Jingli</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>A new approach for land cover classification and change analysis: Integrating backdating and an object-based method</atitle><jtitle>Remote sensing of environment</jtitle><date>2016-05</date><risdate>2016</risdate><volume>177</volume><spage>37</spage><epage>47</epage><pages>37-47</pages><issn>0034-4257</issn><eissn>1879-0704</eissn><abstract>Accurate information on land use and land cover (LULC) change is crucial for ecosystem monitoring and environmental change studies. Updating/backdating approaches have been increasingly used for LULC classification and change analysis, but mostly based on pixels. Here, we presented a new approach, an object-based backdating approach which integrates the backdating approach with an object-based method, and further compared it with the pixel-based backdating approach. We tested the new approach by using Landsat TM data collected in 2001 and 2009 at the Beijing metropolitan region. We found that: 1) an object-based backdating approach achieved higher accuracy for change detection, LULC classification and change analysis than the pixel-based backdating approach. With the object-based approach, the overall accuracies for the classification and change analysis were 84.33% (versus 69.33% for a pixel-based approach), and 80.00% (versus 70.50% for a pixel-based approach), respectively. 2) The object-based backdating approach greatly increases the efficiency because classification and change analysis are only conducted for locations with changes. The increase in efficiency is particularly important for LULC classification and change analysis conducted at a large area, for example, at the national or global scale.
•We present a new approach for land cover classification and change analysis.•This new approach integrates backdating and an object-based method.•The object-based backdating method has higher accuracy and efficiency.•Errors caused by spatial misregistration are greatly reduced by the new method.•The new method effectively incorporates spatial information and expert knowledge.</abstract><pub>Elsevier Inc</pub><doi>10.1016/j.rse.2016.02.030</doi><tpages>11</tpages><oa>free_for_read</oa></addata></record> |
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subjects | Backdating Beijing Change analysis Change detection Classification Ecosystems Environmental monitoring Land cover Land cover classification Land use Object-based method Pixels Remote sensing |
title | A new approach for land cover classification and change analysis: Integrating backdating and an object-based method |
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