The Shendong mining area is the largest coal production base in western China. Due to long-term mining activities, the ecological environment quality (EEQ) of the Shendong mining area has undergone significant changes. Investigating the evolution of EEQ during the process of mineral resource exploitation is of great importance for the sustainable development of the mining area. However, current research lacks a quantitative assessment of the contributions of climate change and human activities to the spatiotemporal variations in EEQ in the Shendong mining area. In this study, the Remote Sensing Ecological Index (RSEI) was used as an EEQ evaluation metric. The Theil–Sen slope estimation and Mann–Kendall test were applied to analyze the spatiotemporal changes of EEQ from 1990 to 2023. Additionally, the partial derivative method was used to investigate the response characteristics of EEQ to climatic factors and human activities and to quantify the relative contributions of these two driving factors. The results indicate that, over the past 34 years, the overall EEQ in the study area has shown an improving trend. Compared to 1990, the proportions of areas with good-grade and excellent-grade EEQ in 2023 increased by 28% and 23.78%, respectively. Additionally, in the second phase (2011–2023), the average RSEI time series value significantly increased compared to the first phase (1990–2010). Among the climatic factors, annual precipitation had the greatest impact on EEQ, with an average contribution rate of 0.085. The conversion of unused land to forestland significantly improved the EEQ, with the area showing a very significant increase in RSEI, accounting for 82.30%. The areas in the mining region showing very significant, significant, and slight increases in RSEI were smaller than the overall study area. In conclusion, the overall EEQ in the study area has shown an improving trend, with climate change being the dominant factor in 71.52% of the areas where RSEI increased, while human activities were the dominant factor in 26.89% of the areas where RSEI decreased.

Plant species classification in semi-arid mining areas is of great significance in assessing the environmental impacts and ecological restoration effects of coal mining. However, in semi-arid mining areas characterized by mixed arbor–shrub–herb vegetation, the complex vegetation distribution patterns and spectral features render single-sensor approaches inadequate for achieving fine classification of plant species in such environments. How to effectively fuse hyperspectral images (HSI) data with light detection and ranging (LiDAR) to achieve better accuracy in classifying vegetation in semi-arid mining areas is worth exploring. There is a lack of precise evaluation regarding how these two data collection approaches impact the accuracy of fine-scale plant species classification in semi-arid mining environments. This study established two experimental scenarios involving the synchronous and asynchronous acquisition of HSI and LiDAR data. The results demonstrate that integrating LiDAR data, whether synchronously or asynchronously acquired, significantly enhances classification accuracy compared to using HSI data alone. The overall classification accuracy for target vegetation increased from 71.7% to 84.7% (synchronous) and 80.2% (asynchronous), respectively. In addition, the synchronous acquisition mode achieved a 4.5% higher overall accuracy than asynchronous acquisition, with particularly pronounced improvements observed in classifying vegetation with smaller canopies (Medicago sativa L.: 17.4%, Pinus sylvestris var. mongholica Litv.: 11.7%, and Artemisia ordosica Krasch.: 7.5%). This study can provide important references for ensuring classification accuracy and error analysis of land cover based on HSI-LiDAR fusion in similar scenarios.