## 5 Conclusions  
The spatiotemporal analysis of mining areas in the semi-arid region of Pernambuco, utilizing high-resolution images from 2018 and 2023 and machine learning techniques, highlighted the magnitude of the environmental transformations occurring in the region. The data revealed a reduction in vegetation cover and a significant increase in urban areas and bare soil, which are direct reflections of the expansion of mining activities. These results underscore the continuous pressure that these activities exert on local ecosystems, especially in sensitive regions like the Caatinga, where biodiversity is already naturally adapted to extreme climate and soil conditions. The applied methodology, which combined the kNN algorithm and the NDVI and NDWI spectral indices, demonstrated accuracy in image classification and landscape change identification. With an accuracy exceeding 99% and a Kappa index above 0.98, the methodology was effective in detecting impacted areas, confirming the potential of these tools in environmental monitoring in mining areas. However, some challenges were observed, such as the separability between the classes of urban area and bare soil, suggesting that future adjustments in modeling may further increase the precision of the results. The findings indicate that between 2018 and 2023, there was a marked degradation of vegetation and a significant increase in built areas, especially near water bodies. This trend reflects the intense human intervention in the region and reinforces the need for public policies aimed at mitigating these impacts, as well as promoting environmental recovery in affected areas. The uncontrolled expansion of mining poses a threat to environmental sustainability, endangering local communities that rely on natural resources for their livelihoods. The results obtained in this study demonstrated that mining activities significantly influencedchanges in land use and cover in the analyzed region. However, this study reaffirms the importance of using RS and machine learning technologies in environmental monitoring, especially in vulnerable areas like the Brazilian semi-arid region. Furthermore, it highlights the need for regulation and responsible management of mining activities, to adopt more sustainable practices that balance economic development and environmental preservation. This work provided an in-depth understanding of spatiotemporal changes in land cover, emphasizing the importance of RS and spatial data analysis in environmental monitoring. The classification system adopted in this study was suitable for representing the main land use and land cover transformations in the mining area under investigation. However, we acknowledge that the inclusion of additional categories, such as croplands, could enrich the analysis in regions where such features are more prominent, especially using higher-resolution images and refined methodologies. For future research, it is suggested to expand the training dataset and explore other machine learning techniques to enhance classification. Additionally, it is recommended to conduct further studies to investigate the impact of land use policies and climate change on vegetation dynamics in mining areas, aiming to contribute to conservation strategies and sustainable development. It is also noted that the methodology tested in this study could be implemented to assess the spatiotemporal behavior of land cover in other mining regions with arid and/or semi-arid climatic characteristics. Conceptualization, A.d.P.P., J.A.S.d.N., A.M.R.-A., U.J.d.S.J., S.M.d.S., F.D.R.F. and C.A.P.M.G. ; Data curation, A.d.P.P., J.A.S.d.N., A.M.R.-A., U.J.d.S.J., S.M.d.S., F.D.R.F. and C.A.P.M.G. ; Formal analysis, A.d.P.P., J.A.S.d.N., A.M.R.-A., U.J.d.S.J., L.M.M.d.O., S.M.d.S., F.D.R.F. and C.A.P.M.G. ; investigation, A.d.P.P., J.A.S.d.N., A.M.R.-A., U.J.d.S.J., L.M.M.d.O., S.M.d.S., F.D.R.F. and C.A.P.M.G. ; Methodology, A.d.P.P., J.A.S.d.N., A.M.R.-A., U.J.d.S.J., L.M.M.d.O., S.M.d.S., F.D.R.F. and C.A.P.M.G.