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An Alternative Method of Cultivated Land Identification and Its Actual Change from 2009 to 2019: A Case Study of Gaochun, China
As the largest developing country, China has permanently attached great importance to cultivated land protection. However, due to the different rules of cultivated land identification in the second and third national land surveys, the cultivated land area in the two surveys has changed greatly. Some...
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Published in: | Land (Basel) 2023-03, Vol.12 (3), p.534 |
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description | As the largest developing country, China has permanently attached great importance to cultivated land protection. However, due to the different rules of cultivated land identification in the second and third national land surveys, the cultivated land area in the two surveys has changed greatly. Some agricultural lands in the south, such as plantations, forests, grasslands, aquaculture ponds, etc., belonged to cultivated land during the second survey, but they were identified as non-cultivated land in the third national land survey. This change has led to a sharp reduction in the area of cultivated land in some places. In order to calculate the actual change in the area of cultivated land since the second survey and provide a reasonable basis for the standard of cultivated land protection, this paper takes Gaochun District, a developed area in China, as an example; interprets the images of the second national land survey period with the deep learning network HRNet; and compares the results with the second and third national land survey rules. The results show that the actual reduction of cultivated land in Gaochun District in the past ten years accounts for 35.1% of the reduction of cultivated land in the two land surveys, while the reduction of cultivated land caused by the change of cultivated land identification rules accounts for 64.9% of the reduction of cultivated land in the two land surveys, indicating that the significant reduction in local cultivated land was mainly caused by the changes in the rules, and these cultivated land reduction behaviors existed before the second survey. |
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However, due to the different rules of cultivated land identification in the second and third national land surveys, the cultivated land area in the two surveys has changed greatly. Some agricultural lands in the south, such as plantations, forests, grasslands, aquaculture ponds, etc., belonged to cultivated land during the second survey, but they were identified as non-cultivated land in the third national land survey. This change has led to a sharp reduction in the area of cultivated land in some places. In order to calculate the actual change in the area of cultivated land since the second survey and provide a reasonable basis for the standard of cultivated land protection, this paper takes Gaochun District, a developed area in China, as an example; interprets the images of the second national land survey period with the deep learning network HRNet; and compares the results with the second and third national land survey rules. The results show that the actual reduction of cultivated land in Gaochun District in the past ten years accounts for 35.1% of the reduction of cultivated land in the two land surveys, while the reduction of cultivated land caused by the change of cultivated land identification rules accounts for 64.9% of the reduction of cultivated land in the two land surveys, indicating that the significant reduction in local cultivated land was mainly caused by the changes in the rules, and these cultivated land reduction behaviors existed before the second survey.</description><identifier>ISSN: 2073-445X</identifier><identifier>EISSN: 2073-445X</identifier><identifier>DOI: 10.3390/land12030534</identifier><language>eng</language><publisher>Basel: MDPI AG</publisher><subject>Agricultural land ; Agriculture ; Analysis ; Aquaculture ; Artificial intelligence ; Case studies ; change of cultivated land rules ; cultivated land protection ; Cultivated lands ; Cultivation ; Deep learning ; Developing countries ; Grasslands ; Land surveys ; LDCs ; Machine learning ; Methods ; national land survey ; Neural networks ; Plantations ; Polls & surveys ; Ponds ; Protection and preservation ; Provinces ; Reduction ; Remote sensing ; Semantics ; Surveys</subject><ispartof>Land (Basel), 2023-03, Vol.12 (3), p.534</ispartof><rights>COPYRIGHT 2023 MDPI AG</rights><rights>2023 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/). 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><cites>FETCH-LOGICAL-c363t-8d8e74ca870ca3dc8c829c7aed996bbbd474f8e3cb01b7e39ccba3650add6cae3</cites><orcidid>0000-0003-4734-5535 ; 0000-0002-2466-9468</orcidid></display><links><openurl>$$Topenurl_article</openurl><openurlfulltext>$$Topenurlfull_article</openurlfulltext><thumbnail>$$Tsyndetics_thumb_exl</thumbnail><linktopdf>$$Uhttps://www.proquest.com/docview/2791671617/fulltextPDF?pq-origsite=primo$$EPDF$$P50$$Gproquest$$Hfree_for_read</linktopdf><linktohtml>$$Uhttps://www.proquest.com/docview/2791671617?pq-origsite=primo$$EHTML$$P50$$Gproquest$$Hfree_for_read</linktohtml><link.rule.ids>314,780,784,25753,27924,27925,37012,44590,75126</link.rule.ids></links><search><creatorcontrib>Jiang, Zhuoran</creatorcontrib><creatorcontrib>Jiang, Ming</creatorcontrib><creatorcontrib>Wang, Yahua</creatorcontrib><creatorcontrib>Ma, Can</creatorcontrib><creatorcontrib>Qiao, Weifeng</creatorcontrib><title>An Alternative Method of Cultivated Land Identification and Its Actual Change from 2009 to 2019: A Case Study of Gaochun, China</title><title>Land (Basel)</title><description>As the largest developing country, China has permanently attached great importance to cultivated land protection. However, due to the different rules of cultivated land identification in the second and third national land surveys, the cultivated land area in the two surveys has changed greatly. Some agricultural lands in the south, such as plantations, forests, grasslands, aquaculture ponds, etc., belonged to cultivated land during the second survey, but they were identified as non-cultivated land in the third national land survey. This change has led to a sharp reduction in the area of cultivated land in some places. In order to calculate the actual change in the area of cultivated land since the second survey and provide a reasonable basis for the standard of cultivated land protection, this paper takes Gaochun District, a developed area in China, as an example; interprets the images of the second national land survey period with the deep learning network HRNet; and compares the results with the second and third national land survey rules. The results show that the actual reduction of cultivated land in Gaochun District in the past ten years accounts for 35.1% of the reduction of cultivated land in the two land surveys, while the reduction of cultivated land caused by the change of cultivated land identification rules accounts for 64.9% of the reduction of cultivated land in the two land surveys, indicating that the significant reduction in local cultivated land was mainly caused by the changes in the rules, and these cultivated land reduction behaviors existed before the second survey.</description><subject>Agricultural land</subject><subject>Agriculture</subject><subject>Analysis</subject><subject>Aquaculture</subject><subject>Artificial intelligence</subject><subject>Case studies</subject><subject>change of cultivated land rules</subject><subject>cultivated land protection</subject><subject>Cultivated lands</subject><subject>Cultivation</subject><subject>Deep learning</subject><subject>Developing countries</subject><subject>Grasslands</subject><subject>Land surveys</subject><subject>LDCs</subject><subject>Machine learning</subject><subject>Methods</subject><subject>national land survey</subject><subject>Neural networks</subject><subject>Plantations</subject><subject>Polls & surveys</subject><subject>Ponds</subject><subject>Protection and preservation</subject><subject>Provinces</subject><subject>Reduction</subject><subject>Remote sensing</subject><subject>Semantics</subject><subject>Surveys</subject><issn>2073-445X</issn><issn>2073-445X</issn><fulltext>true</fulltext><rsrctype>article</rsrctype><creationdate>2023</creationdate><recordtype>article</recordtype><sourceid>PIMPY</sourceid><sourceid>DOA</sourceid><recordid>eNpNkUGLFDEQhRtRcBn35g8IeN1Zk0660_HWNLoOjHhQwVtTXanMZOhJ1nRa2JN_3eyOyFYdKjxefRR5VfVW8FspDX8_Q7Ci5pI3Ur2ormqu5Vap5ufLZ-_X1fWynHgpI2SnmqvqTx9YP2dKAbL_TewL5WO0LDo2rHNRIJNl-4JmO0she-exGGNgT1JeWI95hZkNRwgHYi7FM6sLnuVYpjAfWM8GWIh9y6t9eOTeQcTjGm7Kig_wpnrlYF7o-t_cVD8-ffw-fN7uv97thn6_RdnKvO1sR1ohdJojSIsddrVBDWSNaadpskor15HEiYtJkzSIE8i24WBti0ByU-0uXBvhNN4nf4b0MEbw45MQ02GElD3ONHIOk1AAWLdaWcEnWZNB13R6ckY2qrDeXVj3Kf5aacnjKa7lA-dlrLURrRat0MV1e3EdoEB9cDEnwNKWzh5jIOeL3mslddvyktqmurksYIrLksj9P1Pw8THi8XnE8i8_X5em</recordid><startdate>20230301</startdate><enddate>20230301</enddate><creator>Jiang, Zhuoran</creator><creator>Jiang, Ming</creator><creator>Wang, Yahua</creator><creator>Ma, Can</creator><creator>Qiao, Weifeng</creator><general>MDPI AG</general><scope>AAYXX</scope><scope>CITATION</scope><scope>7SN</scope><scope>7ST</scope><scope>ABUWG</scope><scope>AFKRA</scope><scope>ATCPS</scope><scope>AZQEC</scope><scope>BENPR</scope><scope>BHPHI</scope><scope>C1K</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>PYCSY</scope><scope>SOI</scope><scope>DOA</scope><orcidid>https://orcid.org/0000-0003-4734-5535</orcidid><orcidid>https://orcid.org/0000-0002-2466-9468</orcidid></search><sort><creationdate>20230301</creationdate><title>An Alternative Method of Cultivated Land Identification and Its Actual Change from 2009 to 2019: A Case Study of Gaochun, China</title><author>Jiang, Zhuoran ; Jiang, Ming ; Wang, Yahua ; Ma, Can ; Qiao, Weifeng</author></sort><facets><frbrtype>5</frbrtype><frbrgroupid>cdi_FETCH-LOGICAL-c363t-8d8e74ca870ca3dc8c829c7aed996bbbd474f8e3cb01b7e39ccba3650add6cae3</frbrgroupid><rsrctype>articles</rsrctype><prefilter>articles</prefilter><language>eng</language><creationdate>2023</creationdate><topic>Agricultural land</topic><topic>Agriculture</topic><topic>Analysis</topic><topic>Aquaculture</topic><topic>Artificial intelligence</topic><topic>Case studies</topic><topic>change of cultivated land rules</topic><topic>cultivated land protection</topic><topic>Cultivated lands</topic><topic>Cultivation</topic><topic>Deep learning</topic><topic>Developing countries</topic><topic>Grasslands</topic><topic>Land surveys</topic><topic>LDCs</topic><topic>Machine learning</topic><topic>Methods</topic><topic>national land survey</topic><topic>Neural networks</topic><topic>Plantations</topic><topic>Polls & surveys</topic><topic>Ponds</topic><topic>Protection and preservation</topic><topic>Provinces</topic><topic>Reduction</topic><topic>Remote sensing</topic><topic>Semantics</topic><topic>Surveys</topic><toplevel>peer_reviewed</toplevel><toplevel>online_resources</toplevel><creatorcontrib>Jiang, Zhuoran</creatorcontrib><creatorcontrib>Jiang, Ming</creatorcontrib><creatorcontrib>Wang, Yahua</creatorcontrib><creatorcontrib>Ma, Can</creatorcontrib><creatorcontrib>Qiao, Weifeng</creatorcontrib><collection>CrossRef</collection><collection>Ecology Abstracts</collection><collection>Environment Abstracts</collection><collection>ProQuest Central (Alumni)</collection><collection>ProQuest Central</collection><collection>Agricultural & Environmental Science Collection</collection><collection>ProQuest Central Essentials</collection><collection>AUTh Library subscriptions: ProQuest Central</collection><collection>Natural Science Collection</collection><collection>Environmental Sciences and Pollution Management</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>Environmental Science Collection</collection><collection>Environment Abstracts</collection><collection>DOAJ Directory of Open Access Journals</collection><jtitle>Land (Basel)</jtitle></facets><delivery><delcategory>Remote Search Resource</delcategory><fulltext>fulltext</fulltext></delivery><addata><au>Jiang, Zhuoran</au><au>Jiang, Ming</au><au>Wang, Yahua</au><au>Ma, Can</au><au>Qiao, Weifeng</au><format>journal</format><genre>article</genre><ristype>JOUR</ristype><atitle>An Alternative Method of Cultivated Land Identification and Its Actual Change from 2009 to 2019: A Case Study of Gaochun, China</atitle><jtitle>Land (Basel)</jtitle><date>2023-03-01</date><risdate>2023</risdate><volume>12</volume><issue>3</issue><spage>534</spage><pages>534-</pages><issn>2073-445X</issn><eissn>2073-445X</eissn><abstract>As the largest developing country, China has permanently attached great importance to cultivated land protection. However, due to the different rules of cultivated land identification in the second and third national land surveys, the cultivated land area in the two surveys has changed greatly. Some agricultural lands in the south, such as plantations, forests, grasslands, aquaculture ponds, etc., belonged to cultivated land during the second survey, but they were identified as non-cultivated land in the third national land survey. This change has led to a sharp reduction in the area of cultivated land in some places. In order to calculate the actual change in the area of cultivated land since the second survey and provide a reasonable basis for the standard of cultivated land protection, this paper takes Gaochun District, a developed area in China, as an example; interprets the images of the second national land survey period with the deep learning network HRNet; and compares the results with the second and third national land survey rules. The results show that the actual reduction of cultivated land in Gaochun District in the past ten years accounts for 35.1% of the reduction of cultivated land in the two land surveys, while the reduction of cultivated land caused by the change of cultivated land identification rules accounts for 64.9% of the reduction of cultivated land in the two land surveys, indicating that the significant reduction in local cultivated land was mainly caused by the changes in the rules, and these cultivated land reduction behaviors existed before the second survey.</abstract><cop>Basel</cop><pub>MDPI AG</pub><doi>10.3390/land12030534</doi><orcidid>https://orcid.org/0000-0003-4734-5535</orcidid><orcidid>https://orcid.org/0000-0002-2466-9468</orcidid><oa>free_for_read</oa></addata></record> |
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subjects | Agricultural land Agriculture Analysis Aquaculture Artificial intelligence Case studies change of cultivated land rules cultivated land protection Cultivated lands Cultivation Deep learning Developing countries Grasslands Land surveys LDCs Machine learning Methods national land survey Neural networks Plantations Polls & surveys Ponds Protection and preservation Provinces Reduction Remote sensing Semantics Surveys |
title | An Alternative Method of Cultivated Land Identification and Its Actual Change from 2009 to 2019: A Case Study of Gaochun, China |
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