## 4 Discussion
[79] highlight that the use of machine learning techniques, such as kNN, in combination with spectral bands and derived indices, can maximize classification accuracy, especially in areas where the distinction between classes is difficult due to complex spectral signatures. The accuracy of kNN in the years 2018 and 2023, with values above 99% and a Kappa index over 0.98, indicates excellent performance of the machine learning model in land use and cover classification. These results are consistent with the study by [PERSON] et al. [39], who also achieved high precision using machine learning algorithms to predict mineralization in mined areas. The robustness of the Kappa index in both studies demonstrates that, even in complex scenarios such as mining environments, kNN can provide consistent and reliable classifications. The precision of kNN in this study highlights the efficiency of combining NDVI and NDWI spectral indices with the machine learning algorithm, something also supported by [PERSON] et al. [10]. They pointed out that the integration of temporal spectral indices, such as NDVI, substantially improves the detection of changes in artisanal and small-scale mining areas. The high global accuracy values observed for 2018 and 2023 suggest that kNN can be a reliable alternative for monitoring land use changes, especially in regions with mining activities. However, OE and CE identified, particularly in the Constructions class, indicate room for improvement, especially in differentiating between urban areas and bare soil, as also reported by [PERSON] et al. [7]. The errors observed in this study may be related to the spatial resolution of the PlanetScope sensor and the spectral similarity between construction materials and bare soil, as discussed by [PERSON] et al. [40]. Improvements in spectral segmentation techniques or the use of sensors with higher spatial resolution could potentially reduce these errors. These results reinforce the relevance of using robust machine learning methodologies, such as kNN, for monitoring land use and cover in mining areas, but also point to the need for model adjustments to improve its sensitivity in certain classes, such as built-up areas and bare soil. The difficulties encountered in distinguishing between urban soil and bare soil classes are largely due to their spectral similarity and the sensor's spatial resolution. To overcome these limitations, future studies could explore integrating textural variables derived from high-resolution imagery or employing hybrid classifiers that combine machine learning with texture analysis. This approach could improve the accuracy of class separation in complex urban environments. The selection of land use and land cover classes was a critical step in the methodology. The delineation of categories was based on predominant features that were most relevant to the study's objectives, considering the spectral and spatial limitations of PlanetScope images. The integration of categories such as croplands was considered but proved unfeasible in the study area due to the low expressiveness of this class and the difficulty in distinguishing cultivated lands from native vegetation or bare soil. Future research could explore the use of sensors with higher spectral resolution or complementary techniques to enhance the detail of the classes. Evaluating surface mining areas through high spatial resolution satellite images is an efficient tool for monitoring and assessing land cover and use changes in mining complexes. It is important to highlight that high spatial resolution satellite images from the PlanetScope constellation have been freely available since 2017, making them an important data source for land use and cover monitoring in general. The analysis based on PlanetScope images, combined with visual validation through Google Earth, has proven to be a powerful tool for monitoring land use and land cover changes in areas affected by mining.