In past decades, the reckless exploitation of rare earth mines devastated the ecological environment. Under strict regulation and governance, the exploitation has gradually gotten back on track in recent years. In this regard, timely and accurate assessment of the ecological environment quality of rare earth management areas is indispensable for regional mine development planning, ecological protection, and sustainable development. Being relatively objective and providing instant results, the Remote Sensing Ecological Index (RSEI) is widely used in evaluating ecological environment quality. This paper combined Landsat 8 OLI multispectral imagery with meteorological, land type, and other data to set the Net Primary Productivity (NPP). The NPP reflects detailed regional vegetation destruction and climate variation, the greenness index of RSEI. We also used kernel principal component analysis (KPCA) to obtain the improved ecological index K-RSEI_NPP while evaluating the ecological environment quality of rare earth mining areas in southern Jiangxi and compared this with the traditional RSEI results. The results indicate that: (1) PC1 accounts for 88.51% of the results obtained based on K-RSEI_NPP, and the average correlation coefficient with each index reaches 0.757, which integrates the characteristics of the four indicators; (2) Compared with other indexes, the K-RSEI_NPP proposed in this paper can better display the detailed information of the ecological environment in the rare earth mining areas to differentiate mining areas under various statuses and cities with different vegetation coverage, and its results were consistent with the actual verification. Therefore, our K-RSEI_NPP can provide an effective basis for monitoring and evaluating the ecological environment of the mining area.

This paper investigates the impact of land use/cover type changes in the Haideigou open-pit coal mine on the evolution of the landscape patterns and ecological and environmental quality in the mine area, based on medium- and high-resolution remote sensing images in 2006, 2011, 2016, and 2021 using ArcGIS 10.5, Fragstats 4.2, and the Google Earth Engine platform. The results show that: (1) From 2006 to 2021, the area of cropland and waste dumps in the Heidaigou mining area changed significantly, the land use shifted in a single direction, and the overall land use change was unbalanced. (2) Through the analysis of landscape indicators, it was shown that the diversity of the landscape patches in the study area increased, connectivity decreased, and the patches became more fragmented. (3) Based on the changes in the mean value of the RSEI over the past 15 years, the ecological environment quality of the mining area deteriorated first and then improved. The quality of the ecological environment in the mining area was significantly affected by human activities. This study provides an important basis for achieving the sustainability and stability of ecological environmental development in mining areas.

Mineral exploiting information is an important indicator to reflect regional mineral activities. Accurate extraction of this information is essential to mineral management and environmental protection. In recent years, there are an increasingly large number of pieces of research on land surface information classification by conducting multi-source remote sensing data. However, in order to achieve the best classification result, how to select the optimal feature combination is the key issue. This study creatively combines Out of Bag data with Recursive Feature Elimination (OOB RFE) to optimize the feature combination of the mineral exploiting information of non-metallic building materials in Fujian province, China. We acquired and integrated Ziyuan-1-02D (ZY-1-02D) hyperspectral imagery, landsat-8 multispectral imagery, and Sentinel-1 Synthetic Aperture Radar (SAR) imagery to gain spectrum, heat, polarization, and texture features; also, two machine learning methods were adopted to classify the mineral exploiting information in our study area. After assessment and comparison on accuracy, it proves that the classification generated from our new OOB RFE method, which combine with random forest (RF), can achieve the highest overall accuracy 93.64% (with a kappa coefficient of 0.926). Comparing with Recursive Feature Elimination (RFE) alone, OOB REF can precisely filter the feature combination and lead to optimal result. Under the same feature scheme, RF is effective on classifying the mineral exploiting information of the research field. The feature optimization method and optimal feature combination proposed in our study can provide technical support and theoretical reference for extraction and classification of mineral exploiting information applied in other regions.

Opencast coal mine production and construction activities have a certain impact on the ecological environment, while the development and utilization of large coal bases distributed in semi-arid steppe regions may have a more direct and significant impact on the eco-environment. Therefore, in-depth studies of the ecological impacts of human activities and natural environmental elements in opencast coal mines in typical semi-arid steppe regions and analyses of their driving forces are of great significance for protecting and restoring regional fragile steppe ecosystems. In this paper, the mining area southwest of the Shengli coalfield, a typical ore concentration area in eastern Inner Mongolia, was selected as the research object. Its remote sensing ecological index (RSEI) was calculated using the Google Earth Engine (GEE) platform to analyze the eco-environmental quality in the mining area and its surrounding 2 km from 2005 to 2021. The geographically weighted artificial neural network model (GWANN) was combined with the actual situation of mining activity and ecological restoration to discuss the driving factors of eco-environmental quality change in the study area. The results showed that: (1) the proportion of the study area with excellent and good eco-environmental quality increased from 20.96% to 23.93% from 2005 to 2021, and the proportions of areas with other quality grades fluctuated strongly. (2) The change in eco-environmental quality in the interior of the mining area was closely related to the reclamation of dump sites and migration of the mining area. (3) The maximum contribution rate of the mining activity factor to the external eco-environmental quality of the mining area reached 43.33%, with an annual average contribution rate of 34.48%; as the distance from the mining area increased, its contribution gradually decreased. This quantitative analysis of the driving forces of RSEI change in the mining area will complement future work in ecological evaluations of mining areas while also improving the practicality of ecological evaluation at the mining scale, thereby further helping the ecological management of mining areas.

Timely and accurate monitoring of lakes’ water quality is crucial for assessing regional ecological health and implementing targeted conservation activities. Compared with traditional in situ water quality measurement methods, satellite remote sensing technology is more cost-effective and convenient, and also enables long-term time-series monitoring. This study utilizes Sentinel-2 multispectral imagery, selects East Juyan Lake as the study area, and employs measured water quality data from 30 in situ sampling points as training and testing samples. Using the correlation coefficient, root mean square error, and mean absolute error as evaluation metrics, a Grid Search-based XGBoost machine-learning method is applied to invert the concentration of total phosphorus (TP), a key parameter for water quality assessment. The experiments demonstrate that: (1) The XGBoost model, after parameter tuning via Grid Search, achieved the highest inversion accuracy, with R², RMSE, and MRE values of 0.856, 0.017, and 7.20%, respectively; The average TP concentration retrieved for the lake was 0.231 mg/L. This method requires minimal manual setting of numerous training parameters, reducing human intervention. (2) The spatial distribution shows that TP is primarily enriched in the deeper central and eastern parts of the lake, while concentrations are relatively lower in the near-shore vegetation zones and the western shallow water areas. The findings provide a significant reference for remote sensing monitoring of lake water quality and can be used to predict and regulate salinity, eutrophication, and similar conditions in comparable lakes.

Scientific and rational monitoring of eco-environmental effects induced by mining activities is a prerequisite for optimizing mining planning and contributes to the advancement of ecological civilization. Remote sensing and multi-source data provide advanced methods for long-term dynamic evaluation of mining-induced eco-environmental effects. This study systematically constructs eco-environmental effect indicators tailored to mining characteristics and establishes quantitative extraction methods based on Landsat data and spectral indices. The Mine Eco-environmental Effect Index (MEEI) was developed using kernel principal component analysis (KPCA). The Heidaigou Open-pit Coal Mine in Jungar Banner was selected as the study area to validate the MEEI’s performance and analyze ecological dynamics across five key temporal phases. Results indicate the following: (1) the KPCA-based MEEI effectively integrates multi-indicator features, offering an objective representation of comprehensive eco-environmental impacts; (2) from 1990 to 2020, the ecological trajectory of the coal mine followed a pattern of “sharp deterioration → gradual slowdown → relative stabilization”, with post-mining restoration and management measures significantly mitigating negative impacts and improving regional ecological quality. This study provides a methodological framework for dynamic evaluation of mining-related eco-environmental effects, supporting sustainable mining practices and ecological governance.