The southern regions of China are rich in ion-adsorbed rare earth mineral resources, primarily distributed in ecologically fragile red soil hilly areas. Recent decades of mining activities have caused severe environmental damage, exacerbating ecological security (ES) risks due to the inherent fragility of the red soil hilly terrain. However, the mechanisms through which multiple interacting factors influence the ES of rare earth mining areas (REMA) remain unclear, and an effective methodological framework to evaluate these interactions dynamically is still lacking. To address these challenges, this study develops an innovative dynamic ES evaluation and earlywarning simulation framework, integrating Variable Weight (VW) theory and the Bayesian Network (BN) model. This framework enhances cross-stage comparability and adapts to evolving ecological conditions while leveraging the BN model's diagnostic inference capabilities for precise ES predictions. A case study was conducted in the Lingbei REMA. The main findings of the study are as follows: (1) From 2000 to 2020, the overall ES of the mining area exhibited a dynamic trend of deterioration, followed by improvement, and ultimately stabilization. (2) Scenario S27 (high vegetation health status and high per capita green space coverage) significantly reduces the probability of the ES reaching the extreme warning level. (3) The evaluation and simulation framework developed in this study provides a more accurate representation of the ES level distribution and its variations, with probabilistic predictions of ES demonstrating high accuracy. This study is of great significance for improving regional ES, supporting the optimization of ecological restoration strategies under multi-objective scenarios, and promoting the coordinated development of nature and resource utilization.

In China, ion-adsorbing rare earth minerals are mainly located in the southern hilly areas and are important strategic resources. Extensive long-term mining has severely damaged the land cover in mining areas, caused soil pollution and terrain fragmentation, disrupted the balance between mining and agriculture, severely restricted agricultural development, and affected ecological development. Precise and detailed classification of land use within mining areas is crucial for monitoring the sustainable development of agricultural ecology in these areas. In this study, we leverage the high spatial and high spectral resolution characteristics of the Zhuhai-1 (OHS) hyperspectral image datasets. We create four types of datasets based on spectral, vegetation, red edge, and texture characteristics. These datasets are optimized for multifaceted features, considering the complex land use scenario in rare earth mining areas. Additionally, we design seven optimal combination schemes for features. This is performed to examine the impact of different schemes on land use classification in rare earth mining areas and the accuracy of identifying agricultural land classes from broken blocks. The results show that (1) the inclusion of texture features has the most obvious effect on the overall classification accuracy; (2) the red edge feature has the worst effect on improving the overall accuracy of the surface classification; however, it has a prominent effect on the identification of agricultural lands such as farmland, orchards, and reclaimed vegetation; and (3), following the combination of various optimization features, the land use classification yielded the highest overall accuracy, at 88.16%. Furthermore, the comprehensive identification of various agricultural land classes, including farmland, orchards, and greenhouse vegetables, yielded the most desirable outcomes. The research results not only highlight the advantages of hyperspectral images for complex terrain classification and recognition but also address the previous limitations in the application of hyperspectral datasets over wide mining areas. Additionally, the results underscore the reliability of feature selection methods in reducing information redundancy and improving classification accuracy. The proposed feature selection combination, based on OHS hyperspectral datasets, offers technical support and guidance for the detailed classification of complex land use in mining areas and the accurate monitoring of agroecological environments.