Special Issue "New Trends on Remote Sensing Applications to Mineral Deposits"

A special issue of Remote Sensing (ISSN 2072-4292).

Deadline for manuscript submissions: 20 September 2022.

Special Issue Editors

Prof. Dr. Ana C. Teodoro
E-Mail Website1 Website2
Guest Editor
1.Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
2. Earth Sciences Institute (ICT), Pole of the FCUP, University of Porto, 4169-007 Porto, Portugal
Interests: image acquisition and processing for environmental; coastal; geological and health applications; machine learning algorithms; GIS
Special Issues, Collections and Topics in MDPI journals
Ms. Joana Cardoso-Fernandes
E-Mail Website
Guest Editor
1. Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, Porto, Portugal
2. Earth Sciences Institute (ICT), Faculty of Sciences, University of Porto, Porto, Portugal
Interests: remote sensing; macine learning algorithms; geological exploration; Li mineralizations
Dr. Alexandre Lima
E-Mail Website
Guest Editor
1. Department of Geosciences, Environment and Land Planning, Faculty of Sciences, University of Porto, Porto, Portugal
2. Earth Sciences Institute (ICT), Faculty of Sciences, University of Porto, Porto, Portugal
Interests: geological exploration; research in mineral resources; principally in the development of Au exploration and in the industrial rocks and minerals based in pegmatites as their possible metals: Li, Sn, Ta, Nb and W; Public understanding of Earth Science

Special Issue Information

Dear Colleagues,

Remote sensing data, in particular, satellite-acquired images, played a determinant role in the early stages of mineral exploration since the 1970s. For the last four decades, different product types and numerous image processing algorithms have allowed to target exploration areas all over the world. Among the most successful applications are the porphyry copper and gold deposits, often associated with hydrothermal alteration minerals that can be detected by following well-known procedures and algorithms. However, current paradigm shifts in the global markets and technological advances lead to high demand for other raw materials. Nevertheless, the possible contribution of remote sensing to target these mineral commodities is often not entirely assessed.

On the other hand, non-parametric methods such as machine and deep learning algorithms have gain popularity in several remote sensing-based applications during recent years. One example is their application in land-use/land-cover (LULC) problems. Similar results could and are being obtained in lithological mapping and mineral exploration, but the number of applications is still very small in comparison. Moreover, due to the inherently different nature of mineral exploration studies when compared to LULC applications, some difficulties should be expected when trying to apply machine and deep learning algorithms to real-life exploration problems.

Therefore, in this Special Issue of Remote Sensing, we are looking for new remote sensing approaches whether applied to non-traditional geological applications (such as diamond, bauxite, evaporite minerals, lithium, and rare earth elements (REE) exploration, etc.) or that make use of trending techniques such as machine and deep learning algorithms. Ultimately, the goal is to find any research study that can contribute to the current state of the art and that may help assess the challenges and potentials of new applications in the field of geological remote sensing.

We look forward to your contributions.

Prof. Dr. Ana Cláudia Teodoro
Ms. Joana Cardoso-Fernandes
Dr. Alexandre Lima
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Remote Sensing is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Mineral exploration
  • Multispectral and hyperspectral data
  • Machine learning
  • Deep learning
  • Satellite data

Published Papers (4 papers)

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Research

Article
Interpretation of the Reflectance Spectra of Lithium (Li) Minerals and Pegmatites: A Case Study for Mineralogical and Lithological Identification in the Fregeneda-Almendra Area
Remote Sens. 2021, 13(18), 3688; https://doi.org/10.3390/rs13183688 - 15 Sep 2021
Viewed by 687
Abstract
Reflectance spectroscopy has been used to identify several deposit types. However, applications concerning lithium (Li)-pegmatites are still scarce. Reflectance spectroscopic studies complemented by microscopic and geochemical studies were employed in the Fregeneda–Almendra (Spain–Portugal) pegmatite field to analyze the spectral behavior of Li-minerals and [...] Read more.
Reflectance spectroscopy has been used to identify several deposit types. However, applications concerning lithium (Li)-pegmatites are still scarce. Reflectance spectroscopic studies complemented by microscopic and geochemical studies were employed in the Fregeneda–Almendra (Spain–Portugal) pegmatite field to analyze the spectral behavior of Li-minerals and field lithologies. The spectral similarity of the target class (Li-pegmatites) with other elements was also evaluated. Lepidolite was discriminated from other white micas and the remaining Li-minerals. No diagnostic feature of petalite and spodumene was identified, since their spectral curves are dominated by clays. Their presence was corroborated (by complementary techniques) in petalite relics and completely replaced crystals, although the clay-related absorption depths decrease with Li content. This implies that clays can be used as pathfinders only in areas where argillic alteration is not prevalent. All sampled lithologies present similar water and/or hydroxide features. The overall mineral assemblage is very distinct, with lepidolite, cookeite, and orthoclase exclusively identified in Li-pegmatite (being these minerals crucial targets for Li-pegmatite discrimination in real-life applications), while chlorite and biotite can occur in the remaining lithologies. Satellite data can be used to discriminate Li-pegmatites due to distinct reflectance magnitude and mineral assemblages, higher absorptions depths, and distinct Al–OH wavelength position. The potential use of multi- and hyperspectral data was evaluated; the main limitations and advantages were discussed. These new insights on the spectral behavior of Li-minerals and pegmatites may aid in new Li-pegmatite discoveries around the world. Full article
(This article belongs to the Special Issue New Trends on Remote Sensing Applications to Mineral Deposits)
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Article
New Insights into the Pulang Porphyry Copper Deposit in Southwest China: Indication of Alteration Minerals Detected Using ASTER and WorldView-3 Data
Remote Sens. 2021, 13(14), 2798; https://doi.org/10.3390/rs13142798 - 16 Jul 2021
Viewed by 625
Abstract
The Pulang porphyry copper deposit (PCD), one of the main potential areas for copper resource exploration in China, exhibits typical porphyry alteration zoning. However, further investigation of the indicative significance of alteration minerals, additional insight into metallogenic characteristics, and prospecting guidelines continue to [...] Read more.
The Pulang porphyry copper deposit (PCD), one of the main potential areas for copper resource exploration in China, exhibits typical porphyry alteration zoning. However, further investigation of the indicative significance of alteration minerals, additional insight into metallogenic characteristics, and prospecting guidelines continue to be challenging. In this study, ASTER and WorldView-3 data were used to map hydrothermal alteration minerals by employing band ratios, principal component analysis, and spectrum-area techniques; and subsequently, the indication significance of alteration minerals was studied in-depth. The following new insights into the metallogenic structure and spatial distribution of alteration zoning in Pulang PCD were obtained and verified. (1) A new NE trending normal fault, passing through the northeast of Pulang PCD, was discovered. (2) Two mineralization alteration centers, exhibiting alteration zoning characteristics of potassic-silicified, phyllic, and propylitic zones from the inside to the outside, were observed on both sides of the fault. (3) At the junction of the redivided potassic-silicification and phyllic zones, favorable prospecting potential areas were delineated. This study shows that the spectral/multi-sensor satellite data are valuable and cost-effective tools for the preliminary stages of porphyry copper exploration in inaccessible and remote areas around the world. Full article
(This article belongs to the Special Issue New Trends on Remote Sensing Applications to Mineral Deposits)
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Article
Mapping of Aluminum Concentration in Bauxite Mining Residues Using Sentinel-2 Imagery
Remote Sens. 2021, 13(8), 1517; https://doi.org/10.3390/rs13081517 - 14 Apr 2021
Cited by 2 | Viewed by 622
Abstract
There is a growing interest in the characterization of mining residues, both for environmental assessments and critical raw materials recovery. The lack of sufficient in situ samples hampers an effective geostatistical modelling of material concentrations variability. This paper proposes a method to characterize [...] Read more.
There is a growing interest in the characterization of mining residues, both for environmental assessments and critical raw materials recovery. The lack of sufficient in situ samples hampers an effective geostatistical modelling of material concentrations variability. This paper proposes a method to characterize the aluminum spatial variability in a mine residue from remote sensing data and imprecise information from daily dumping procedures. The method is proposed for the mapping of aluminum within a Greek bauxite residue, using Sentinel-2 imagery. The spatial correlation between metal concentrations and remote sensing indicators (e.g., spectral band ratios) is the premise for mapping aluminum varieties. The proposed method is based on Conditional Gaussian Co-Simulation, where Sentinel-2 images can be used as auxiliary variables. Simulation results are compared with the Co-kriging estimation method. To perform the Co-kriging estimation, the same conditions as simulation are used (same inputs, models, and neighborhoods). Simulation results quantified the metals variability in mining residues, presenting the metal concentration of piled materials in two time periods. For results validation and selecting the best map, fourteen validation samples were used. For the best representative maps of aluminum concentration, a correlation coefficient of about 0.7 between the validation data and obtained aluminum concentration map was obtained. Full article
(This article belongs to the Special Issue New Trends on Remote Sensing Applications to Mineral Deposits)
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Article
Improving Boundary Constraint of Probability Integral Method in SBAS-InSAR for Deformation Monitoring in Mining Areas
Remote Sens. 2021, 13(8), 1497; https://doi.org/10.3390/rs13081497 - 13 Apr 2021
Cited by 1 | Viewed by 673
Abstract
Coal-mining subsidence causes ground fissures and destroys surface structures, which may lead to severe casualties and economic losses. Time series interferometric synthetic aperture radar (TS-InSAR) plays an important role in surface deformation detection and monitoring without the restriction of weather and sunlight conditions. [...] Read more.
Coal-mining subsidence causes ground fissures and destroys surface structures, which may lead to severe casualties and economic losses. Time series interferometric synthetic aperture radar (TS-InSAR) plays an important role in surface deformation detection and monitoring without the restriction of weather and sunlight conditions. In addition, the probability integral method (PIM) is a surface movement model that is widely used in the field of mining subsidence. In recent years, the integration of TS-InSAR and the PIM has been extensively studied. In this paper, we propose a new method to estimate mining subsidence with the PIM based on TS-InSAR results. This study focuses on the improvement of a boundary constraint and dynamic parameter estimation in the PIM through the inversion of the line-of-sight (LOS) time series deformation derived by TS-InSAR. In addition, 45 Sentinel-1A images from 17 June 2015 to 27 December 2017 of a coal mine in Jiaozuo are utilized to acquire the surface displacement. We apply a time series deformation analysis using small baseline subsets (SBAS) and place the results into an improved PIM to estimate the mining parameters. The simulated mining subsidence is highly consistent with the leveling data, exhibiting an RMSE of 0.0025 m. Compared with the conventional method, the proposed method is more accurate in discovering displacement in mining areas. In the final section of this paper, some sources of error that affect the experiment are discussed. Full article
(This article belongs to the Special Issue New Trends on Remote Sensing Applications to Mineral Deposits)
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