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<p>The accurate quantification and comprehension of idle rural residential land (IRRL) distribution characteristics form the bedrock for governmental land management optimization and institutional reform. However, large-scale IRRL monitoring poses significant challenges. This study proposed a novel IRRL identification method, the threshold selection—mean fusion method, which integrated multi-source land use/cover data with nighttime light data so as to swiftly and precisely analyze its spatiotemporal changes and key influencing factors from 2000 to 2020. In the past 20&#xa0;years, the IRRL area in China decreased from 32,137.99 km<sup>2</sup> to 28,911.33 km<sup>2</sup>, with the idle rate of rural residential land (IRRRL) falling from 37.28% to 20.16%. IRRL were predominantly located in the central and northern regions of China, with high-density regions in the Northeast China, North China, and Central China, encompassing provinces such as Heilongjiang, Inner Mongolia, and Shandong. At the meso and micro scales, significant differences in IRRL area were observed across the Heihe-Tengchong line, but the IRRRL differences were minor. This study addresses a notable gap in research by utilizing multi-source data to comprehensively examine the spatiotemporal dynamics of large-scale IRRL over extended periods. It contributes not only to advancing theoretical insights into regional rural development but also aligns closely with practical imperatives like rural revitalization and land consolidation strategies.</p>

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Identification of idle rural residential land (IRRL) using nighttime light data in China

  • 期刊论文

  • 发表日期：

    2026年1月14日

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  • Si Wu,
  • Qianwen Gong

摘要

The accurate quantification and comprehension of idle rural residential land (IRRL) distribution characteristics form the bedrock for governmental land management optimization and institutional reform. However, large-scale IRRL monitoring poses significant challenges. This study proposed a novel IRRL identification method, the threshold selection—mean fusion method, which integrated multi-source land use/cover data with nighttime light data so as to swiftly and precisely analyze its spatiotemporal changes and key influencing factors from 2000 to 2020. In the past 20 years, the IRRL area in China decreased from 32,137.99 km2 to 28,911.33 km2, with the idle rate of rural residential land (IRRRL) falling from 37.28% to 20.16%. IRRL were predominantly located in the central and northern regions of China, with high-density regions in the Northeast China, North China, and Central China, encompassing provinces such as Heilongjiang, Inner Mongolia, and Shandong. At the meso and micro scales, significant differences in IRRL area were observed across the Heihe-Tengchong line, but the IRRRL differences were minor. This study addresses a notable gap in research by utilizing multi-source data to comprehensively examine the spatiotemporal dynamics of large-scale IRRL over extended periods. It contributes not only to advancing theoretical insights into regional rural development but also aligns closely with practical imperatives like rural revitalization and land consolidation strategies.