Land Use Classification in Rare Earth Mining Areas Based on Multi-Source Remote Sensing and Feature Optimization

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Abstract
Rare earth elements are vital, non-renewable strategic resources, and their exploitation has significant impacts on regional ecological security and sustainable development. To address the issue of insufficient accuracy in land use classification in rare earth mining areas, this study takes the Lingbei rare earth mining area in Dingnan County, Jiangxi Province, as a case study. Multi-source remote sensing data, including Sentinel-2 imagery, Sentinel-1 SAR data, nighttime light data, and DEM data, were integrated to construct a feature set combining spectral, textural, and topographic information. On this basis, this study developed a feature optimization framework that combines recursive feature elimination (RFE), mean decrease accuracy (MDA), and K-fold cross-validation (CV), termed RFE-MDA-CV. We designed nine feature combination schemes and compared them with the optimal feature subset. Their performance was systematically evaluated across four classifiers: RF, SVM, CART, and GBDT. The results were as follows: (1) the optimized feature set combined with the RF classifier consistently achieved the highest classification performance, with a mean OA of approximately 93.2% and a kappa coefficient of about 0.916, outperforming CART and SVM by around 4-5 percentage points; (2) land use remained generally stable between 2016 and 2023, but frequent conversions occurred between forest land, cropland, and impervious surfaces, mainly driven by urban expansion and mining activities; and (3) cross-regional experiments demonstrated that the proposed feature optimization framework has good applicability and transferability in mining areas with similar geomorphological and metallogenic conditions. Overall, the proposed RFE-MDA-CV method can be effectively implemented on the Google Earth Engine platform, significantly improving the accuracy and robustness of land use classification in rare earth mining areas, while providing reliable technical support for ecological monitoring and land resource management.
Funding Information
  • National Natural Science Foundation of China (42004158)
  • National Natural Science Foundation of China (42461051)
  • Natural Science Foundation of Jiangxi Province (20224BAB212025)
  • Science and Technology Research Project of Education Department of Jiangxi Province (GJJ2200803)

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