Detection of Changes in Land Cover in a Mining Area of Mexico Using Remote Sensing and Machine Learning

Mining generates various alterations to the environment, affecting flora, fauna, morphology, and soil. To contribute to solving this problem, this study measured land cover (LC) changes induced by open-pit mining in Zacatecas, Mexico. Remote sensing techniques were applied using multitemporal Landsat 5 and 8 satellite imagery, along with supervised classification, to detect land cover variations. Tests performed on eleven classification algorithms showed that the Spectral Angle Mapper (SAM) obtained the best results, with an accuracy of 85.16% and a Kappa coefficient of 0.79. Measurements of changes in land cover revealed an increase in the surface area of ​​water bodies of +556.83 Ha, in mining cover of +1729.35 Ha, in infrastructure of +2.61 Ha, and of bare soil of +1488.15 Ha, and a loss of soil of -2372.49 Ha, of scrubland of -1444.59 Ha, and of vegetation of -9.45 Ha. The use of supervised classification for multi-temporal satellite imagery allowed for the measurement of alterations to land cover. These alterations highlight the need for sustainable management strategies, environmental restoration, and the importance of continued monitoring for informed decision-making. It is recommended to explore new categories and techniques, such as deep learning, to improve the accuracy of land cover classification.