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

Author

Listed:
  • Junjun Zhi

    (School of Geography and Tourism, Anhui Normal University, Wuhu 241002, China
    Engineering Technology Research Center of Resources Environment and GIS, Anhui Province, 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)

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.

Suggested Citation

  • Junjun Zhi & Xinyue Cao & Wangbing Liu & Yang Sun & Da Xu & Caiwei Da & Lei Jin & Jin Wang & Zihao Zheng & Shuyuan Lai & YongJiao Liu & Guohai Zhu, 2023. "Remote Sensing Monitoring and Spatial Pattern Analysis of Non-Grain Production of Cultivated Land in Anhui Province, China," Land, MDPI, vol. 12(8), pages 1-21, July.
    • Handle: RePEc:gam:jlands:v:12:y:2023:i:8:p:1497-:d:1204780
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    References listed on IDEAS

    as
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