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<p>Unstable cultivated land (UCL) significantly impacts national food security and socio-economic stability. However, due to the lack of operational identification criteria, large-scale and long-time studies are limited, leading to unclear understanding of the characteristics, quantity and spatial distribution of UCL. This research fills these gaps by constructing the first publicly available national-scale UCL dataset in China from 2000 to 2023. According to the&#xa0;classification system established by national standards, UCL is categorized into six types: sandy desertified cultivated land (SDCL), rocky desertified cultivated land (RDCL), sloping cultivated land (SCL) exceeding 25°, riverine and lacustrine cultivated land (RLCL), forest zone cultivated land (FZCL), and pasture zone cultivated land (PZCL), with refined identification criteria. Subsequently, the spatial distribution information of various cultivated land was extracted based on multi-source remote sensing data, vector data, and statistical data, combined with ecological sensitivity assessment and GIS analysis techniques, such as slope analysis, extraction analysis, overlay analysis, and proximity analysis. The results indicate that UCL in China accounts for approximately 10% of the total cultivated land, predominantly located in regions with poor farming conditions and fragile ecological backgrounds. Approximately two-thirds of this UCL is contributed by FZCL, SCL and SDCL. Data accuracy was evaluated using random sampling and visual interpretation techniques. The findings reveal that the overall accuracy of each assessed year exceeded 93%, with kappa coefficients all greater than 0.9. This dataset has a&#xa0;wide range of applications in unstable cultivated land assessment and management, as well as cultivated land protection and utilization.</p>

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A 30 m unstable cultivated land dataset in China from 2000 to 2023

  • 期刊论文

  • 出版模式：

    开放获取

  • 发表日期：

    2026年8月4日

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  • Feifei Jiang,
  • Zhanbin Luo,
  • Jian Hao,
  • Jiang Li,
  • Jiayuan Guo,
  • Weihong Guo,
  • Jing Ma,
  • Fu Chen

摘要

Unstable cultivated land (UCL) significantly impacts national food security and socio-economic stability. However, due to the lack of operational identification criteria, large-scale and long-time studies are limited, leading to unclear understanding of the characteristics, quantity and spatial distribution of UCL. This research fills these gaps by constructing the first publicly available national-scale UCL dataset in China from 2000 to 2023. According to the classification system established by national standards, UCL is categorized into six types: sandy desertified cultivated land (SDCL), rocky desertified cultivated land (RDCL), sloping cultivated land (SCL) exceeding 25°, riverine and lacustrine cultivated land (RLCL), forest zone cultivated land (FZCL), and pasture zone cultivated land (PZCL), with refined identification criteria. Subsequently, the spatial distribution information of various cultivated land was extracted based on multi-source remote sensing data, vector data, and statistical data, combined with ecological sensitivity assessment and GIS analysis techniques, such as slope analysis, extraction analysis, overlay analysis, and proximity analysis. The results indicate that UCL in China accounts for approximately 10% of the total cultivated land, predominantly located in regions with poor farming conditions and fragile ecological backgrounds. Approximately two-thirds of this UCL is contributed by FZCL, SCL and SDCL. Data accuracy was evaluated using random sampling and visual interpretation techniques. The findings reveal that the overall accuracy of each assessed year exceeded 93%, with kappa coefficients all greater than 0.9. This dataset has a wide range of applications in unstable cultivated land assessment and management, as well as cultivated land protection and utilization.