Landsat-based long-term LUCC mapping in Xinlicheng Reservoir Basin using object-based classification
Rapid urbanization dramatically changes the local environment around Xinlicheng Reservoir Basin. Landsat images are suitable for the land use change caused by human impact. In order to obtain consistent land cover products, a hybrid classification method combining object-based classification and pre...
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| Veröffentlicht in: | IOP conference series. Earth and environmental science 2017-05, Vol.64 (1), p.12024 |
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| container_title | IOP conference series. Earth and environmental science |
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| creator | Su, Wei Liang, Dongmei Tang, Gula Xiao, Zundong Li, Jingxin Wan, Zhengyu Li, Ping |
| description | Rapid urbanization dramatically changes the local environment around Xinlicheng Reservoir Basin. Landsat images are suitable for the land use change caused by human impact. In order to obtain consistent land cover products, a hybrid classification method combining object-based classification and pre-classification alteration detection method was developed and applied to long-term multi-temporal Landsat images to obtain land cover change information. Object-based classification method was combined with Random forest (RF) classifier to classify the Landsat image in 2008. Then the changed areas in 2000, 2004, 2012, and 2016 were identified by comparing with the images in 2008 via the re-weighted multivariate alteration detection transformation method. The images in 2000, 2004, 2012 and 2016 were classified by RF classifier. Land cover maps for 2000, 2004, 2012, and 2016 were produced by combining the unchanged area in 2008 with the new classes of the changed areas in 2000, 2004, 2012 and 2016. According to the accuracy assessment, the overall accuracy of the land covers of the four periods are all greater than 93%. The accuracy assessment indicates that this hybrid method can produce consistent land cover datasets for a long time period. |
| doi_str_mv | 10.1088/1755-1315/64/1/012024 |
| format | Article |
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Landsat images are suitable for the land use change caused by human impact. In order to obtain consistent land cover products, a hybrid classification method combining object-based classification and pre-classification alteration detection method was developed and applied to long-term multi-temporal Landsat images to obtain land cover change information. Object-based classification method was combined with Random forest (RF) classifier to classify the Landsat image in 2008. Then the changed areas in 2000, 2004, 2012, and 2016 were identified by comparing with the images in 2008 via the re-weighted multivariate alteration detection transformation method. The images in 2000, 2004, 2012 and 2016 were classified by RF classifier. Land cover maps for 2000, 2004, 2012, and 2016 were produced by combining the unchanged area in 2008 with the new classes of the changed areas in 2000, 2004, 2012 and 2016. According to the accuracy assessment, the overall accuracy of the land covers of the four periods are all greater than 93%. The accuracy assessment indicates that this hybrid method can produce consistent land cover datasets for a long time period.</description><identifier>ISSN: 1755-1307</identifier><identifier>EISSN: 1755-1315</identifier><identifier>DOI: 10.1088/1755-1315/64/1/012024</identifier><language>eng</language><publisher>Bristol: IOP Publishing</publisher><subject>Accuracy ; Classification ; Classifiers ; Human impact ; Human influences ; Image classification ; Land cover ; Land use ; Landsat ; Landsat satellites ; Remote sensing ; Reservoirs ; Satellite imagery ; Urbanization</subject><ispartof>IOP conference series. Earth and environmental science, 2017-05, Vol.64 (1), p.12024</ispartof><rights>Published under licence by IOP Publishing Ltd</rights><rights>2017. This work is published under http://creativecommons.org/licenses/by/3.0/ (the “License”). Notwithstanding the ProQuest Terms and Conditions, you may use this content in accordance with the terms of the License.</rights><lds50>peer_reviewed</lds50><oa>free_for_read</oa><woscitedreferencessubscribed>false</woscitedreferencessubscribed><citedby>FETCH-LOGICAL-c402t-faf07353a86434ffa05b456d6755e7b8f95d9f8ab2e6ec3e1410f5aa797104ac3</citedby><cites>FETCH-LOGICAL-c402t-faf07353a86434ffa05b456d6755e7b8f95d9f8ab2e6ec3e1410f5aa797104ac3</cites></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://iopscience.iop.org/article/10.1088/1755-1315/64/1/012024/pdf$$EPDF$$P50$$Giop$$Hfree_for_read</linktopdf><link.rule.ids>314,780,784,27924,27925,38868,38890,53840,53867</link.rule.ids></links><search><creatorcontrib>Su, Wei</creatorcontrib><creatorcontrib>Liang, Dongmei</creatorcontrib><creatorcontrib>Tang, Gula</creatorcontrib><creatorcontrib>Xiao, Zundong</creatorcontrib><creatorcontrib>Li, Jingxin</creatorcontrib><creatorcontrib>Wan, Zhengyu</creatorcontrib><creatorcontrib>Li, Ping</creatorcontrib><title>Landsat-based long-term LUCC mapping in Xinlicheng Reservoir Basin using object-based classification</title><title>IOP conference series. Earth and environmental science</title><addtitle>IOP Conf. Ser.: Earth Environ. Sci</addtitle><description>Rapid urbanization dramatically changes the local environment around Xinlicheng Reservoir Basin. Landsat images are suitable for the land use change caused by human impact. In order to obtain consistent land cover products, a hybrid classification method combining object-based classification and pre-classification alteration detection method was developed and applied to long-term multi-temporal Landsat images to obtain land cover change information. Object-based classification method was combined with Random forest (RF) classifier to classify the Landsat image in 2008. Then the changed areas in 2000, 2004, 2012, and 2016 were identified by comparing with the images in 2008 via the re-weighted multivariate alteration detection transformation method. The images in 2000, 2004, 2012 and 2016 were classified by RF classifier. Land cover maps for 2000, 2004, 2012, and 2016 were produced by combining the unchanged area in 2008 with the new classes of the changed areas in 2000, 2004, 2012 and 2016. According to the accuracy assessment, the overall accuracy of the land covers of the four periods are all greater than 93%. The accuracy assessment indicates that this hybrid method can produce consistent land cover datasets for a long time period.</description><subject>Accuracy</subject><subject>Classification</subject><subject>Classifiers</subject><subject>Human impact</subject><subject>Human influences</subject><subject>Image classification</subject><subject>Land cover</subject><subject>Land use</subject><subject>Landsat</subject><subject>Landsat satellites</subject><subject>Remote sensing</subject><subject>Reservoirs</subject><subject>Satellite imagery</subject><subject>Urbanization</subject><issn>1755-1307</issn><issn>1755-1315</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2017</creationdate><recordtype>article</recordtype><sourceid>O3W</sourceid><sourceid>ABUWG</sourceid><sourceid>AFKRA</sourceid><sourceid>AZQEC</sourceid><sourceid>BENPR</sourceid><sourceid>CCPQU</sourceid><sourceid>DWQXO</sourceid><sourceid>GNUQQ</sourceid><recordid>eNp9kMtKxDAUhoMoOI4-glBw4ao2aXNplzqMFygI6oC7cJomY4ZOU5OO4NvbUm8LcXVu3_nP4UfolOALgvM8IYKxmGSEJZwmJMEkxSndQ7Pv_v53jsUhOgphgzEXNCtmqC6hrQP0cQVB11Hj2nXca7-NytViEW2h62y7jmwbPdu2sepFD9WDDtq_OeujKwjDaBdGxlUbrb50VAMhWGMV9Na1x-jAQBP0yWeco9X18mlxG5f3N3eLyzJWFKd9bMBgkbEMck4zagxgVlHGaz78rkWVm4LVhcmhSjXXKtOEEmwYgCgEwRRUNkdnk27n3etOh15u3M63w0mZMo5xLlhOBopNlPIuBK-N7Lzdgn-XBMvRUDmaJUfjJKeSyMnQYe982rOu-xFeLh9_U7KrzUCSP8j_1T8AVq-ETQ</recordid><startdate>20170501</startdate><enddate>20170501</enddate><creator>Su, Wei</creator><creator>Liang, Dongmei</creator><creator>Tang, Gula</creator><creator>Xiao, Zundong</creator><creator>Li, Jingxin</creator><creator>Wan, Zhengyu</creator><creator>Li, Ping</creator><general>IOP Publishing</general><scope>O3W</scope><scope>TSCCA</scope><scope>AAYXX</scope><scope>CITATION</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>CCPQU</scope><scope>DWQXO</scope><scope>GNUQQ</scope><scope>HCIFZ</scope><scope>PATMY</scope><scope>PIMPY</scope><scope>PQEST</scope><scope>PQQKQ</scope><scope>PQUKI</scope><scope>PRINS</scope><scope>PYCSY</scope></search><sort><creationdate>20170501</creationdate><title>Landsat-based long-term LUCC mapping in Xinlicheng Reservoir Basin using object-based classification</title><author>Su, Wei ; Liang, Dongmei ; Tang, Gula ; Xiao, Zundong ; Li, Jingxin ; Wan, Zhengyu ; Li, Ping</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c402t-faf07353a86434ffa05b456d6755e7b8f95d9f8ab2e6ec3e1410f5aa797104ac3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2017</creationdate><topic>Accuracy</topic><topic>Classification</topic><topic>Classifiers</topic><topic>Human impact</topic><topic>Human influences</topic><topic>Image classification</topic><topic>Land cover</topic><topic>Land use</topic><topic>Landsat</topic><topic>Landsat satellites</topic><topic>Remote sensing</topic><topic>Reservoirs</topic><topic>Satellite imagery</topic><topic>Urbanization</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Su, Wei</creatorcontrib><creatorcontrib>Liang, Dongmei</creatorcontrib><creatorcontrib>Tang, Gula</creatorcontrib><creatorcontrib>Xiao, Zundong</creatorcontrib><creatorcontrib>Li, Jingxin</creatorcontrib><creatorcontrib>Wan, Zhengyu</creatorcontrib><creatorcontrib>Li, Ping</creatorcontrib><collection>Institute of Physics Open Access Journal Titles</collection><collection>IOPscience (Open Access)</collection><collection>CrossRef</collection><collection>ProQuest Central (Alumni Edition)</collection><collection>ProQuest Central UK/Ireland</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>ProQuest Central</collection><collection>Natural Science Collection</collection><collection>ProQuest One Community College</collection><collection>ProQuest Central Korea</collection><collection>ProQuest Central Student</collection><collection>SciTech Premium Collection</collection><collection>Environmental Science Database</collection><collection>Publicly Available Content Database</collection><collection>ProQuest One Academic Eastern Edition (DO NOT USE)</collection><collection>ProQuest One Academic</collection><collection>ProQuest One Academic UKI Edition</collection><collection>ProQuest Central China</collection><collection>Environmental Science Collection</collection><jtitle>IOP conference series. Earth and environmental science</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Su, Wei</au><au>Liang, Dongmei</au><au>Tang, Gula</au><au>Xiao, Zundong</au><au>Li, Jingxin</au><au>Wan, Zhengyu</au><au>Li, Ping</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>Landsat-based long-term LUCC mapping in Xinlicheng Reservoir Basin using object-based classification</atitle><jtitle>IOP conference series. Earth and environmental science</jtitle><addtitle>IOP Conf. Ser.: Earth Environ. Sci</addtitle><date>2017-05-01</date><risdate>2017</risdate><volume>64</volume><issue>1</issue><spage>12024</spage><pages>12024-</pages><issn>1755-1307</issn><eissn>1755-1315</eissn><abstract>Rapid urbanization dramatically changes the local environment around Xinlicheng Reservoir Basin. Landsat images are suitable for the land use change caused by human impact. 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| subjects | Accuracy Classification Classifiers Human impact Human influences Image classification Land cover Land use Landsat Landsat satellites Remote sensing Reservoirs Satellite imagery Urbanization |
| title | Landsat-based long-term LUCC mapping in Xinlicheng Reservoir Basin using object-based classification |
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