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Mapping Cropland Abandonment in the Cloudy Hilly Regions Surrounding the Southwest Basin of China

Author

Listed:
  • Yali Wei

    (College of Resource, Sichuan Agricultural University, Chengdu 611130, China)

  • Junjie Wen

    (College of Resource, Sichuan Agricultural University, Chengdu 611130, China)

  • Qunchao Zhou

    (College of Resource, Sichuan Agricultural University, Chengdu 611130, China)

  • Yan Zhang

    (College of Resource, Sichuan Agricultural University, Chengdu 611130, China)

  • Gaocheng Dong

    (College of Resource, Sichuan Agricultural University, Chengdu 611130, China)

Abstract

Cropland is a vital resource intricately connected to food security. Currently, the issue of cropland abandonment poses a serious threat to food production and supply, presenting a significant challenge to rural economies and the stability of the food supply chain. The hilly and cloudy regions of southwest China are particularly affected by cropland abandonment, presenting significant challenges in accurately mapping the distribution of abandoned cropland due to fragmentation and heavy cloud pollution. Therefore, this study focuses on Mingshan County, located in Ya’an City, Sichuan Province, China, as the study area. Utilizing Google Earth Engine (GEE) and a random forest algorithm, a method integrating multi-source data from Landsat 8, Sentinel-2, and Sentinel-1 is proposed to extract abandoned cropland spanning from 2018 to 2022. This study analyzes spatial and temporal characteristics, employing the Geodetector with optimal parameters to explore the underlying mechanisms. The findings reveal the following: (1) The method achieves an overall accuracy of land use classification surpassing 88.67%, with a Kappa coefficient exceeding 0.87. Specifically, the accuracy for identifying abandoned cropland reaches 87.00%. (2) From 2018 to 2022, the abandonment rate in Mingshan County fluctuated between 4.58% and 5.77%, averaging 5.03%. The lowest abandonment rate occurred in 2019–2020, while the highest was observed in 2020–2021. (3) Cropland abandonment is influenced by both natural and social factors. Elevation and slope are the main driving factors, alongside factors such as distance to road, town, and residential settlement that all significantly contribute to abandonment trends. These five factors exhibit positive correlation with the abandonment rate, with distance to the river showing relatively weaker explanatory power.

Suggested Citation

  • Yali Wei & Junjie Wen & Qunchao Zhou & Yan Zhang & Gaocheng Dong, 2024. "Mapping Cropland Abandonment in the Cloudy Hilly Regions Surrounding the Southwest Basin of China," Land, MDPI, vol. 13(5), pages 1-25, April.
    • Handle: RePEc:gam:jlands:v:13:y:2024:i:5:p:586-:d:1385083
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    References listed on IDEAS

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    1. Prishchepov, Alexander V. & Schierhorn, Florian & Löw, Fabian, 2021. "Unraveling the diversity of trajectories and drivers of global agricultural land abandonment," EconStor Open Access Articles and Book Chapters, ZBW - Leibniz Information Centre for Economics, vol. 10(2).
    2. Yanwei Wang & Wei Song, 2021. "Mapping Abandoned Cropland Changes in the Hilly and Gully Region of the Loess Plateau in China," Land, MDPI, vol. 10(12), pages 1-16, December.
    3. Shi, Tiechou & Li, Xiubin & Xin, Liangjie & Xu, Xiaohong, 2018. "The spatial distribution of farmland abandonment and its influential factors at the township level: A case study in the mountainous area of China," Land Use Policy, Elsevier, vol. 70(C), pages 510-520.
    4. Xiaobin Jin & Xiaomin Xiang & Xu Guan & Xiaowei Wu & Qing Bai & Yinkang Zhou, 2017. "Assessing the relationship between the spatial distribution of land consolidation projects and farmland resources in China, 2006–2012," Food Security: The Science, Sociology and Economics of Food Production and Access to Food, Springer;The International Society for Plant Pathology, vol. 9(5), pages 889-905, October.
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    Cited by:

    1. Xiaojian Li & Linbing Ma & Xi Liu, 2025. "Identification, Mechanism and Countermeasures of Cropland Abandonment in Northeast Guangdong Province," Land, MDPI, vol. 14(2), pages 1-21, January.

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