Mapping the Mine: Combining Portable X-ray Fluorescence, Spectroradiometry, UAV, and Sentinel-2 Images to Identify Contaminated Soils—Application to the Mostardeira Mine (Portugal)

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Abstract

Old and abandoned mines are testimonials of ancient industrial activities, and as such, they are able to convey environmental concerns. A multidisciplinary approach combining ground measurements—i.e., portable X-ray fluorescence and optical spectroradiometry—with airborne multispectral images—i.e., Sentinel-2 and unmanned aerial vehicles—was conducted to define a baseline for the characterization of areas that constitute environmental burdens. The Mostardeira mine, an old copper mine located in the Portuguese Ossa-Morena Zone, was selected as a case study. The results reveal that the soils have toxic metals—e.g., As (mean = 1239 ppm) and Cu (mean = 435 ppm)—above the defined health thresholds. The spectroradiometry provided insights into the soil characterization using data from the vis-NIR spectral region, allowing us to distinguish agricultural soil, mine waste, and bare soils. The spectra obtained are comparable with the USGS soil spectra standards, namely Clinozoisite Epidote HS299, Hematitic Alt. Tuff CU91-223, and Sand DWO-3-DEL2ar1 no oil. The airborne images considered through the lens of principal components analysis and supervised and unsupervised machine learning techniques (random forest and K-means) are found to be effective tools in creating cartographic representations of the contaminated soils. The collected data are used to construct a baseline for characterizing these environmentally challenging areas, whereas the methodological approach is revealed to be successful for tackling the posed environmental problems, allowing us to map the old mine environment passives.

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Nogueira, P., Silva, M., Roseiro, J., Potes, M., & Rodrigues, G. (2023). Mapping the Mine: Combining Portable X-ray Fluorescence, Spectroradiometry, UAV, and Sentinel-2 Images to Identify Contaminated Soils—Application to the Mostardeira Mine (Portugal). Remote Sensing, 15(22). https://doi.org/10.3390/rs15225295

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