Monitoring and Analysis of Relocation and Reclamation of Residential Areas Based on Multiple Remote Sensing Indices

Huiping Huang, Yingqi Wang, Chao Yuan, Wenlu Zhu and Yichen Tian ()
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Huiping Huang: Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Yingqi Wang: Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Chao Yuan: Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Wenlu Zhu: Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China
Yichen Tian: Aerospace Information Research Institute, Chinese Academy of Sciences, No. 9 Dengzhuang South Road, Haidian District, Beijing 100094, China

Land, 2025, vol. 14, issue 2, 1-21

Abstract: The relocation of residents from high-risk areas is a critical measure to address safety and development issues in the floodplain regions of Henan Province in China. Whether the old villages can be reclaimed as farmland after demolition concerns Henan Province’s ability to maintain its farmland red line. This paper integrated multiple remote sensing indices and proposed a remote sensing identification method for monitoring the progress status of village relocation and reclamation that adapted to data characteristics and application scenarios. Firstly, it addressed the issue of missing target bands in GF-2 (GaoFen-2) by employing a band downscaling method; secondly, it combined building and vegetation indices to identify changes in land cover in the old villages within the floodplain, analyzing the implementation effects of the relocation and reclamation policies. Results showed that using a Random Forest regression model to generate a 4 m resolution shortwave infrared band not only retains the original target band information of Landsat-8 but also enhances the spatial detail of the images. Based on the optimal thresholds of multiple remote sensing indices, combined with human footprint data and POI (Points of Interest) identified village boundaries, the overall accuracy of identifying the progress status of resident relocation and reclamation reached 93.5%. In the floodplain region of Henan, the implementation effect of resident relocation was relatively good, with an old village demolition rate of 77%, yet the farmland reclamation rate was only 23%, indicating significant challenges in land conversion, lagging well behind the pilot program schedule requirements. Overall, this study made two primary contributions. First, to distinguish between rural construction and bare soil, thereby improving the accuracy of construction land extraction, an Enhanced Artifical Surface Index (EASI) was proposed. Second, the monitoring results of land use changes were transformed from pixel-level to village-level, and this framework can be extended to other specific land use change monitoring scenarios, demonstrating broad application potential.

Keywords: remote sensing indices; Landsat-8; GaoFen-2; Henan floodplain; relocation and reclamation; remote sensing identification (search for similar items in EconPapers)
JEL-codes: Q15 Q2 Q24 Q28 Q5 R14 R52 (search for similar items in EconPapers)
Date: 2025
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