## 4 Discussion
### Limitations  
Coal mining inevitably has a series of impacts on the regional ecological environment; therefore, conducting long-term, high-precision monitoring and spatiotemporal change analysis of the mining area is of great significance for ecological system restoration work. The Mann-Kendall mutation test and partial derivative methods were used to quickly and automatically obtain the trend of ecological environment quality changes in the Shen Dong mining area from 1990 to 2023 in this study. However, there are still some limitations. First, the methods used in this study did not extract the key information of time-series changes in various ecological environment indices (such as damage time and damage degree) on a per-pixel basis. Second, this study mainly relies on LANDSAT 30 m resolution remote sensing data, which has the advantage of a long time span but limited spatial resolution. Future research could integrate higher-resolution remote sensing data, such as Sentinel-2 (10-m resolution), to enhance the accuracy of assessments. However, since Sentinel-2 data has only been available since 2015, a combination method is proposed to cover the entire research period (1990-2023). For the period from 1990 to 2015, Landsat TM and ETM+ data (30-m resolution) will be utilized, as these provide continuous coverage. For the period from 2015 to 2023, Sentinel-2 data will be integrated to improve spatial resolution. To ensure consistency and accuracy, data fusion techniques (e.g., spatial downscaling and temporal interpolation) will be applied. Cross-comparisons between Landsat and Sentinel-2 data during overlapping periods will further validate the fused dataset. This approach will enable the full utilization of the advantages of both datasets while addressing the limitations of temporal coverage. Additionally, despite the Landsat imagery being radiometrically and geometrically corrected, the preprocessing process may still be affected by atmospheric influences such as clouds, shadows, and smoke, which can impact the image quality. Furthermore, the \(RSEI\) model, which relies on four indicators--greenness, heat, dryness, and wetness--captures the basic structure and function of the ecological system, but it may not fully address the complexity of the ecological environment. To enhance the model's accuracy and representativeness, future research could incorporate additional indicators based on the specific characteristics of the study area. This would allow for a more comprehensive reflection of ecological quality. Similarly, statistical modeling methods, such as Theil-Sen slope estimation and the Mann-Kendall trend test, are robust tools for detecting trends, but they may still be influenced by missing or irregular data, which can introduce bias into the results. Therefore, integrating more diverse datasets or employing advanced techniques for handling incomplete data could improve the reliability of the models, making them better suited to capturing the multifaceted nature of ecological change. Moreover, future research could integrate longer time-series remote sensing data to conduct in-depth analyses of the long-term impacts of climate change and policy adjustments on the ecological environment. Interdisciplinary research is also an important direction; combining social and economic data with ecological evaluations would help to deepen the understanding of the impact of human activities on the ecological environment and provide more comprehensive scientific support for policy making.