Remote Sensing Monitoring and Spatial Pattern Analysis of Non-Grain Production of Cultivated Land in Anhui Province, China

Junjun Zhi, Xinyue Cao, Wangbing Liu (), Yang Sun, Da Xu, Caiwei Da, Lei Jin, Jin Wang, Zihao Zheng, Shuyuan Lai, YongJiao Liu and Guohai Zhu
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Junjun Zhi: School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
Xinyue Cao: School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
Wangbing Liu: Key Laboratory of Jianghuai Arable Land Resources Protection and Eco-Restoration, Hefei 230088, China
Yang Sun: School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
Da Xu: Urban and Rural Planning Management Service Center of Jin’an District, Lu’an 237100, China
Caiwei Da: School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
Lei Jin: Key Laboratory of Jianghuai Arable Land Resources Protection and Eco-Restoration, Hefei 230088, China
Jin Wang: Key Laboratory of Jianghuai Arable Land Resources Protection and Eco-Restoration, Hefei 230088, China
Zihao Zheng: College of Letters and Science, University of California, Santa Barbara, CA 93106, USA
Shuyuan Lai: School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
YongJiao Liu: School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
Guohai Zhu: School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China

Land, 2023, vol. 12, issue 8, 1-21

Abstract: In recent years, non-grain production of cultivated land (NGPCL) has become increasingly prominent in China, seriously affecting food production and threatening the country’s food security. However, there is a lack of large-scale and high-precision methods for remote sensing identification of NGPCL. From the perspective of effective management of cultivated land resources, the characteristics of the spatial patterns of NGPCL, both on a large scale and at a patch scale, need to be further studied. For solving this problem, this paper uses the Google Earth engine (GEE) cloud computing platform and multi-source remote sensing data with a machine learning algorithm to determine the occurrence of NGPCL in Anhui province in 2019, and then uses nine selected landscape pattern indexes to analyze the spatial patterns of NGPCL from two aspects, specifically, economic development level and topography. The results show that: (1) terrain features, radar features, and texture features are beneficial to the extraction of NGPCL; (2) the degree of separation obtained by using an importance evaluation approach shows that spectral features have the highest importance, followed by index features with red edges, texture features, index features without red edges, radar features, and terrain features; and (3) the cultivated land in Anhui province in 2019 is mainly planted with food crops, and the phenomenon of NGPCL is more likely to occur in areas with high economic development levels and flat terrain. Aided by the GEE cloud platform, multi-source remote sensing data, and machine learning algorithm, the remote sensing monitoring approach utilized in this study could accurately, quickly, and efficiently determine NGPCL on a regional scale.

Keywords: non-grain production; Google Earth engine; remote sensing; landscape pattern; land use; cultivated land (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2023
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (2)

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