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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
Main Authors: Jiang, Zhuoran, Jiang, Ming, Wang, Yahua, Ma, Can, Qiao, Weifeng
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Jiang, Ming
Wang, Yahua
Ma, Can
Qiao, Weifeng
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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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 &amp; 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/). 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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. 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ispartof Land (Basel), 2023-03, Vol.12 (3), p.534
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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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