## 1 Introduction
[7], optical images are analyzed using multiple approaches such as supervised and unsupervised classification, spectral indices, time-series analysis, and several machine learning algorithms, including k-Nearest Neighbors (kNN), artificial neural networks (ANNs), decision trees (DTs), support vector machines (SVMs), random forests (RFs), and classification and regression trees. According to [PERSON] and [PERSON] (2007), the results of soil surface mapping are influenced not only by the adequacy of the images but also by the correct choice of processing and classification methods. Based on the results of an extensive literature review, [PERSON] et al. [22] presented progress in RS monitoring research regarding land use and land cover changes in mining areas. The authors focused on the application and perspectives of RS techniques in the context of biodiversity ecological environment monitoring, highlighting aspects related to landscape structure, vegetation changes, soil environment, surface conditions, and atmospheric environment in mining areas. RS technologies are widely used to identify natural features or physical objects on the Earth's surface, utilizing various spatial, temporal, spectral, and resolution datasets [23; 24; 25; 26], serving as effective sources for identifying the environmental impacts of gold mining, such as water contamination and soil degradation [21]. These technologies offer significant advantages for detecting, mapping, and monitoring gold mining activities and their effects [27; 28; 29]. To overcome the limitations of spatiotemporal frequency in land use and land cover surveys, the availability of optical images from PlanetScope Planet Labs' constellation of nanosatellites emerged in 2016. The PlanetScope constellation, consisting of more than 180 CubeSats in sun-synchronized orbits, capable of capturing multispectral images with a resolution ranging from 3.7 to 4.1 m depending on altitude [30], has a daily revisit, making it essential for the immediate detection of land changes as well as monitoring the expansion or maintenance of existing activities. This network has the unique capability of daily capturing free images of the entire planet, achieving an impressive coverage of up to 200,000,000 km\({}^{2}\)[30]. The Normalized Difference Vegetation Index (NDVI) [31] and the Normalized Difference Water Index (NDWI) [32] can, respectively, provide information on vegetation and water presence in mining areas. Several studies over the past five years have demonstrated the potential of the NDVI and NDWI spectral indices for mapping and monitoring land use and land cover in mining areas. [PERSON] et al. [33] used high-resolution multispectral images acquired with an Unmanned Aerial System (UAV) and Soil Adjusted Vegetation Index (SAVI), Modified Soil Adjusted Vegetation Index (MSAVI), NDVI, and NDWI indices to evaluate vegetation development in a restored limestone quarry. [PERSON] et al. [34] developed a systematic image analysis approach based on geographic objects (GEOBIA) to map revegetated areas and quantify land use and land cover changes in open-pit mines in the Carajas/Amazon region/Brazil from high spatial resolution satellite images (Geo-Eye, WorldView-3, and IKONOS) from 2011 to 2015 and the NDVI and NDWI spectral indices. [PERSON] et al. [35] used Landsat 8 and Sentinel-2 data to monitor the Soca River area in Slovenia using SAM (Spectral Angle Mapper) and fuzzy SSMA (Spectral Signal Mixture Analysis) classification methods, additionally introducing NDVI, NDWI, and other complementary indices into the classification algorithm. [PERSON] et al. [36] presented an extensive RS literature review, focusing on the ecological rehabilitation of mining sites. RS data are used to observe three main aspects related to gold mining: deforestation or changes in land cover, water pollution from mining near rivers, detection of turbidity levels in river channels, and estimating the presence of mercury [37; 38; 39]. [PERSON] et al.