## 4 Discussion  
The experimental results revealed significant changes in land use and land cover between 2018 and 2023, with a 3.28% decrease in vegetation cover and a 6.62% increase in urbanized areas. These figures highlight the accelerated impact of mining activities, especially in the direct influence area along water bodies, where an increase in riparian vegetation loss was observed. The effectiveness of the kNN algorithm, demonstrated by an Overall Accuracy above 99% and a Kappa index of 0.98, reinforces its applicability in mining-impacted scenarios. However, the identified challenges, such as spectral overlap between urban area and exposed soil, reflect the need for complementary methods, such as textural variables or images with higher spatial resolution. The reduction in the \"water\" class may be related to the decrease in precipitation recorded in October 2023, a month with little or no rain in the region. However, data variations may also be attributed to possible classification errors between bare soil and constructions, caused by the spectral similarity of the objects due to the sensor's spatial resolution. According to [PERSON] et al. (2018), different types of land cover have distinct spectral signatures, but the similarity of these signatures under certain conditions can result in classification errors. This phenomenon is particularly challenging in RS, demanding refined techniques to ensure greater accuracy. Figure 9: Example of a constructed area in the study region: (**a**) sample built-up area (PlanetScope scene) and (**b**) sample built-up area (Google Earth image base). The separability analysis between land use and land cover classes, using JMD associated with the NDVI and NDWI spectral indices, demonstrated high effectiveness, especially in distinguishing between Water and Vegetation classes. The results corroborate studies such as those by [PERSON] et al. [7], who also observed a strong correlation between mining activities and their influence on the environmental degradation process. However, while other studies often report difficulties in detecting water in mined areas, the use of the NDWI in this work ensured clear separability for the water class, as evidenced by the high Jeffries-Matusita distance values (>1.95). This result is consistent with the findings of [PERSON] [75], who emphasized the importance of vegetation indices like NDVI in improving the separation of classes with distinct spectral characteristics, such as dense vegetation and water bodies. Another relevant point is the high separability observed for the Bare soil vs. Vegetation class, particularly with the Red and NIR bands. [PERSON] et al. [76] point out that the use of these bands, associated with vegetation indices, significantly improves the discrimination of bare soil due to the high reflectance in the red and near-infrared bands. These authors also suggest that the combination of spectral indices and specific bands can improve the accuracy of classification in mined and deforested areas, as observed in their study. However, the low separability between Constructions and Vegetation, especially for NDWI, reflects a frequent challenge in using spectral indices in urban areas. [PERSON] et al. [77] identify similar limitations when using spectral indices to separate urban areas from vegetation, pointing to the need for post-processing techniques, such as the integration of textural variables, to overcome spectral mixing problems. Additionally, [PERSON] and [PERSON] [78] also discuss how the spectral similarity between construction materials and bare soil can complicate classification, requiring more refined adjustments to the classification algorithm. The use of multiple spectral bands in combination with NDVI and NDWI indices, as evaluated through JMD, proved to be an effective strategy for improving class separability. [PERSON] et al.