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Abstract

Mine wastes, including tailings (the by-products of mineral processing), are subject to weathering, leading to environmental issues. During the last decades, the traditional, cost-effective, and time-consuming field methods are replaced by remote sensing (RS), which is based on multispectral and hyperspectral data for mining monitoring. In this case study, we investigate the waste material of an inactive bauxite mine in Greece. We select satellite data with different spatial and spectral resolutions to map mining wastes. The goal of this study is to classify mine waste based on mineral indicators using the Spectral Angle Mapper (SAM) with hyperspectral and multispectral data. Moreover, spectral signatures of minerals from two different spectral libraries are used, namely the United States Geological Survey (USGS) Spectral Library and the Jet Propulsion Laboratory (JPL) Spectral Library. The spectral signatures related to the objective of this study are resampled to the Environmental Mapping and Analysis Program (EnMap), Sentinel-2, and World View 3 (WV3) spectral bands. We present the results of all datasets. We also describe each satellite sensor's capability to map and discriminate the specified mineral indicators and refer to their detected differences. This study demonstrates that RS exhibits varying levels of effectiveness based on data spatial and spectral resolution to identify and map mineral indicators.