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Minerals
Special Issues
Remote Sensing-based Mineral Exploration
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Remote Sensing-based Mineral Exploration

A special issue of Minerals (ISSN 2075-163X). This special issue belongs to the section "Mineral Deposits".

Deadline for manuscript submissions: closed (16 July 2021) | Viewed by 11956

Special Issue Editors

Dr. Kati Laakso

E-Mail Website
Guest Editor
Harald Herlin Learning Centre, Aalto University, Otaniementie 9, 02150 Espoo, Finland
Interests: hyperspectral; mineral exploration; spectroscopy
Dr. Maarit Middleton

E-Mail Website
Guest Editor
Environmental Solutions, Geological Survey of Finland, Lähteentie 2, P.O.Box 77, 96101 Rovaniemi, Finland
Interests: biogeochemistry for mineral exploration; compositional data analysis; mineral prospectivity modeling
Special Issues, Collections and Topics in MDPI journals
Dr. Veronika Kopačková-Strandová

E-Mail Website
Guest Editor
Czech Geological Survey, 118 21 Prague, Czech Republic
Interests: imaging spectroscopy; mineral spectroscopy; environmental monitoring; optical and thermal remote sensing; raw materials
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Multispectral and hyperspectral remote sensing data have been used for mineral identification and exploration for decades. This development has been driven, on one hand, by a need to discover new ore deposits, and, on the other hand, by technological developments, such as the miniaturization of instruments. In recent years, unmanned aerial vehicle (UAV)-based remote sensing has gained momentum as an intermediate-scale and flexible solution that offers both high spatial resolution and the ability to cover large and inaccessible areas. Albeit limited to areas with no extensive vegetation or sediment cover, optical spectroscopy offers a non-destructive and potentially cost-effective means of optimizing mineral exploration strategies and identifying new ore deposits.

This Special Issue will give an overview of the latest trends in remote sensing-based mineral exploration. Papers from all perspectives relevant to the topic are welcome, including those that report new advances in laboratory-based hyperspectral imaging. In particular, papers that explore the use of recent and emerging technologies, such as deep learning algorithms and UAVs, are welcome.

Dr. Kati Laakso
Dr. Maarit Middleton
Dr. Veronika Kopačková-Strnadová
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 submissions that pass pre-check are 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. Minerals is an international peer-reviewed open access monthly 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
  • hyperspectral
  • multispectral
  • optical spectroscopy

Published Papers (2 papers)

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Research

23 pages, 136577 KiB  
Article
Lithium Potential Mapping Using Artificial Neural Networks: A Case Study from Central Portugal
by Martin Köhler, Delira Hanelli, Stefan Schaefer, Andreas Barth, Andreas Knobloch, Peggy Hielscher, Joana Cardoso-Fernandes, Alexandre Lima and Ana C. Teodoro
Minerals 2021, 11(10), 1046; https://0-doi-org.brum.beds.ac.uk/10.3390/min11101046 - 27 Sep 2021
Cited by 21 | Viewed by 5836
Abstract
The growing importance and demand of lithium (Li) for industrial applications, in particular rechargeable Li-ion batteries, have led to a significant increase in exploration efforts for Li-bearing minerals. To ensure and expand a stable Li supply to the global economy, extensive research and [...] Read more.
The growing importance and demand of lithium (Li) for industrial applications, in particular rechargeable Li-ion batteries, have led to a significant increase in exploration efforts for Li-bearing minerals. To ensure and expand a stable Li supply to the global economy, extensive research and exploration are necessary. Artificial neural networks (ANNs) provide powerful tools for exploration target identification. They can be cost-effectively applied in various geological settings. This article presents an integrated approach of Li exploration targeting using ANNs for data interpretation. Based on medium resolution geological maps (1:50,000) and stream sediment geochemical data (1 sample per 0.25 km2), the Li potential was calculated for an area of approximately 1200 km2 in the surroundings of Bajoca Mine (Northeast Portugal). Extensive knowledge about geological processes leading to Li mineralisation (such as weathering conditions and diverse Li minerals) proved to be a determining factor in the exploration model. Furthermore, Sentinel-2 satellite imagery was used in a separate ANN model to identify potential Li mine sites exposed on the ground surface by analysing the spectral signature of surface reflectance in well-known Li locations. Finally, the results were combined to design a final map of predicted Li mineralisation occurrences in the study area. The proposed approach reveals how remote sensing data in combination with geological and geochemical data can be used for delineating and ranking exploration targets of almost any deposit type. Full article
(This article belongs to the Special Issue Remote Sensing-based Mineral Exploration)
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16 pages, 12779 KiB  
Article
High-Resolution Hyperspectral Mineral Mapping: Case Studies in the Edwards Limestone, Texas, USA and Sulfide-Rich Quartz Veins from the Ladakh Batholith, Northern Pakistan
by Diana Krupnik and Shuhab D. Khan
Minerals 2020, 10(11), 967; https://0-doi-org.brum.beds.ac.uk/10.3390/min10110967 - 29 Oct 2020
Cited by 7 | Viewed by 4602
Abstract
The study of hand samples is a significant aspect of geoscience. This work showcases a technique for relatively quick and inexpensive mineral characterization, applied to a Cretaceous limestone formation and for sulfide-rich quartz vein samples from Northern Pakistan. Spectral feature parameters are derived [...] Read more.
The study of hand samples is a significant aspect of geoscience. This work showcases a technique for relatively quick and inexpensive mineral characterization, applied to a Cretaceous limestone formation and for sulfide-rich quartz vein samples from Northern Pakistan. Spectral feature parameters are derived from mineral mixtures of known abundance and are used for mineral mapping. Additionally, three well-known classification techniques—Spectral Angle Mapper (SAM), Support Vector Machine (SVM), and Neural Network—are compared. Point counting results from petrographic thin sections are used for validation the limestone samples, and QEMSCAN mineral maps for the sulfide samples. For classifying the carbonates, the SVM classifier produced results that are closest to the training set—with 84.4% accuracy and a kappa coefficient of 0.8. For classifying sulfides, SAM produced mineral abundances that were closest to the validation data, possibly due to the low reflectance of sulfides throughout the short-wave infrared spectrum with some differences in the overall spectral shape. Full article
(This article belongs to the Special Issue Remote Sensing-based Mineral Exploration)
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