Please use this identifier to cite or link to this item: http://hdl.handle.net/10174/36797

Title: Mapping the Mine: Combining Portable X-ray Fluorescence, Spectroradiometry, UAV, and Sentinel-2 Images to Identify Contaminated Soils—Application to the Mostardeira Mine (Portugal)
Authors: Nogueira, Pedro
Silva, Marcelo
Roseiro, José
Potes, Miguel
Rodrigues, Gonçalo
Keywords: principal components analysis
random forest
contaminated soil
portable X-ray fluorescence
unmanned aerial vehicle
Sentinel-2
portable spectroradiometry
K-means
Issue Date: 9-Nov-2023
Publisher: MDPI
Citation: Nogueira, P.; Silva, M.; Roseiro, J.; Potes, M.; Rodrigues, G. 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 Sens. 2023, 15, 5295. https://doi.org/10.3390/rs15225295.
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.
URI: https://doi.org/10.3390/rs15225295
http://hdl.handle.net/10174/36797
Type: article
Appears in Collections:ICT - Publicações - Artigos em Revistas Internacionais Com Arbitragem Científica

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