## 4 Discussion  
Using GEE, the spatiotemporal evolution of kNDVI in the Huojitu mining area from 1999 to 2023 was analyzed. Compared to other studies that rely on NDVI index to monitor vegetation, this study used the kNDVI index to avoid saturation of vegetation pixels [55, 56]. Compared with the traditional NDVI index, kNDVI effectively addresses the issue of mixed pixels [57]. Although some studies have noted that kNDVI significantly outperforms NDVI, most of these studies directly apply kNDVI to vegetation growth without providing empirical evidence in their results [58, 59]. In Section 3.3.1, this study demonstrated the advantages of kNDVI over NDVI in applications involving mining areas with poor vegetation growth. Specifically, we compared the variation coefficients of the histogram and spatial distribution, proving that kNDVI can more accurately capture areas with poor vegetation growth and reflect the health status of vegetation more precisely. kNDVI smooths NDVI through kernel density estimation, effectively reducing noise interference and enhancing spatial continuity, thereby improving the accuracy of the vegetation index. This smoothing process enables kNDVI to better avoid the saturation effect in high-vegetation coverage areas, particularly when dealing with dense vegetation canopies [60, 61, 62]. By adjusting parameters of the kernel function, such as \(\sigma\), kNDVI can adaptively stretch the dynamic range to better capture extreme conditions in time series data, such as areas with extremely poor vegetation growth [23]. Additionally, kNDVI improves sensitivity to photosynthetic activity, allowing for a more detailed reflection of vegetation health and productivity [63]. This property makes kNDVI excellent for analyzing complex phenological cycles and an important tool for assessing the state of vegetation ecosystems [57]. Finally, kNDVI exhibited strong stability and robustness across different vegetation types and coverages [64]. The study area, located in the transition zone between the Loess Plateau and the Mu Us Desert, is characterized by its unique geographical environment, making it a representative mining region with significant implications for ecological restoration efforts [65]. Research findings indicate that the implementation of concurrent mining and restoration policies has led to substantial changes in vegetation growth within the mining area [66]. This study also showed that vegetation growth between 1999 and 2011 in the Huojitu mining area gradually improved but remained generally poor, with an average kNDVI of 0.063. During this period, approximately 300,000 drought-resistant seedlings were planted across 86,700 square meters in active mining zones; however, vegetation was predominantly composed of sparse desert steppe and shrubs [67]. Additionally, more than ten open-pit  
Figure 17: Land use change and its impact on vegetation growth between 1999 and 2023: **(a)** map of main land use transformation types, and **(b)** effects of land use change on vegetation growth. coal mines operated in the area during this time. From 2011 to 2018, there was a marked improvement in overall vegetation growth, reaching an annual average kNDVI of 0.151. This improvement can be attributed to the implementation of various ecological restoration projects and the closure of most open-pit coal mines in the Huoijitu mining area [68]. In 2015, vegetation growth was notably poor, likely due to the lowest recorded annual precipitation (376 mm) and average temperature (8.95 \({}^{\circ}\)C) during the study period. Between 2018 and 2023, vegetation growth showed a deteriorating trend, primarily because of intensified mechanized mining activities in the southeastern part of the Huoijitu mining area. Despite this, the establishment of artificial forests, grasslands, reservoirs, and agricultural land reforms contributed to relatively favorable vegetation conditions, with an annual average kNDVI of 0.173 [69].