Submit to Special Issue Submit Abstract to Special Issue Review for Remote Sensing Propose a Special Issue

Journal Menu

Journal Browser

New Trends on Remote Sensing Applications to Mineral Deposits-II

Print Special Issue Flyer
Special Issue Editors
Special Issue Information
Keywords
Benefits of Publishing in a Special Issue
Published Papers

A special issue of Remote Sensing (ISSN 2072-4292). This special issue belongs to the section "Remote Sensing in Geology, Geomorphology and Hydrology".

Deadline for manuscript submissions: 20 April 2025 | Viewed by 16761

Share This Special Issue

Special Issue Editors

Dr. Joana Cardoso-Fernandes

E-Mail Website
Guest Editor

Department of Geosciences, Environmental and Land Planning and Institute of Earth Sciences, Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
Interests: remote sensing; machine learning algorithms; geological exploration; mineral deposits; Li mineralizations; geochemistry
Special Issues, Collections and Topics in MDPI journals

Dr. Ana Cláudia Teodoro

E-Mail Website
Guest Editor

1. Faculty of Sciences, University of Porto, 4169-007 Porto, Portugal
2. Institute of Earth Sciences, University of Porto, 4169-007 Porto, Portugal
Interests: remote sensing; image processing; environmental applications; geologic applications; GIS
Special Issues, Collections and Topics in MDPI journals

Dr. Alexandre Lima

E-Mail Website
Guest Editor

Department of Geosciences, Environment and Land Planning, Faculty of Sciences, Institute of Earth Sciences (ICT), University of Porto, 4169-007 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 Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Satellite-based remote sensing has played an important 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 made it possible to target exploration areas all over the world. After the failure of ASTER’s shortwave infrared (SWIR) module, new approaches have been developed, with the commercial satellites WorldView-3 presenting a better compromise between spatial and spectral resolution. Despite lacking a thermal sensor, it has a similar spectral resolution to that of ASTER in the SWIR region. Recently, hyperspectral data have become increasingly used in mineral deposit studies, either in the exploration phase (e.g., using drone-borne sensors) or even during the exploitation phase, with hyperspectral imaging of drill cores (core scanners). Moreover, we are entering a new era of satellite hyperspectral imaging with the Environmental Mapping and Analysis Program (EnMAP) and Precursore Iperspettrale della Missione Applicativa (PRISMA) data. The Hyperspectral Imager Suite (HISUI) onboard the International Space Station (ISS) is another hyperspectral alternative. There can also be expected changes in the application of synthetic aperture radar (SAR) data to mineral deposits with the upcoming Phased Array type L-band Synthetic Aperture Radar-2 (PALSAR-2) data from the Advanced Land Observing Satellite-2 (ALOS-2) mission.

In recent years, we have observed increasing applications to non-traditional geological deposits such as diamond, bauxite, evaporite minerals, lithium, and rare earth elements (REEs), due to current paradigm shifts in global decarbonization and related technological advances that lead to a higher demand for critical raw materials. Simultaneously, we have seen a growing integration of geological/geophysical and remote sensing data, often using non-parametric methods such as machine- and deep-learning algorithms.

Therefore, in this Volume II Special Issue of Remote Sensing, we are looking for innovative remote sensing approaches that make use of new remote sensing data and/or machine- and deep-learning algorithms for non-traditional mineral deposits, welcoming works focusing on multi-data integration. Ultimately, the goal is to publish any research studies that can contribute to the current state of the art, and that may help to assess the challenges and potentials of new applications in the field of geological remote sensing.

We look forward to your contributions.

Dr. Joana Cardoso-Fernandes
Dr. Ana Cláudia Teodoro
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 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. 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 2700 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

Benefits of Publishing in a Special Issue

Ease of navigation: Grouping papers by topic helps scholars navigate broad scope journals more efficiently.
Greater discoverability: Special Issues support the reach and impact of scientific research. Articles in Special Issues are more discoverable and cited more frequently.
Expansion of research network: Special Issues facilitate connections among authors, fostering scientific collaborations.
External promotion: Articles in Special Issues are often promoted through the journal's social media, increasing their visibility.
e-Book format: Special Issues with more than 10 articles can be published as dedicated e-books, ensuring wide and rapid dissemination.

Further information on MDPI's Special Issue polices can be found here.

Published Papers (7 papers)

Download All Papers

Order results

Result details

Show export options Show export options

Select all

Export citation of selected articles as:

Research

27 pages, 19798 KiB

Open AccessArticle

Discrimination of Fe-Ni-Laterites from Bauxites Using a Novel Support Vector Machines-Based Methodology on Sentinel-2 Data

by Alexandra Anifadi, Olga Sykioti, Konstantinos Koutroumbas, Emmanuel Vassilakis, Charalampos Vasilatos and Emil Georgiou

Remote Sens. 2024, 16(13), 2295; https://doi.org/10.3390/rs16132295 - 23 Jun 2024

Viewed by 1747

Abstract

Currently, the global shift towards green energy is at the forefront of efforts introducing a new era, thus rendering exploration for critical raw materials essential. To this purpose, the utilization of advanced machine learning methods in remote sensing has emerged as a rapid and cost-effective approach. This study proposes a new methodology, utilizing Sentinel-2 satellite data, to distinguish ferronickel (Fe-Ni-) laterite from bauxite across pre-mining, mining, and post-mining occurrences worldwide. Both ores contain mineral raw materials such as nickel, iron, cobalt, and alumina and their discrimination is generally macroscopically challenging, especially when their locations are often in geographical proximity. The proposed method is based on Support Vector Machines (SVM) classification using spectral signatures of known Fe-Ni-laterite and bauxite-bearing pixels in Greece, Cuba, and Jamaica. The highest classification accuracies are obtained by combining b12 with b6 or b7 spectral bands. Comparisons with specific ore mineralogies show that b6 and b7 are strongly linked to the ferric phase, while b12 is mainly associated with the argillic mineralogies, the latter probably being the key discriminating factor between the two ores. From laboratory chemical analyses, we also establish that b12 and b6 or b7 are strongly associated with Al₂O₃ and Fe₂O₃ content correspondingly. The proposed method is accurate, it has reduced prospection costs, and it can facilitate the initial screening of broad areas by automatically characterizing whether an ore is bauxite or Fe-Ni-laterite. This underscores the methodology’s significance in ore differentiation and exploration within the context of green energy endeavors. Full article

(This article belongs to the Special Issue New Trends on Remote Sensing Applications to Mineral Deposits-II)

► Show Figures

Figure 1

20 pages, 11612 KiB

Open AccessArticle

Convolutional Neural Networks Applied to Antimony Quantification via Soil Laboratory Reflectance Spectroscopy in Northern Portugal: Opportunities and Challenges

by Morgana Carvalho, Joana Cardoso-Fernandes, Alexandre Lima and Ana C. Teodoro

Remote Sens. 2024, 16(11), 1964; https://doi.org/10.3390/rs16111964 - 30 May 2024

Cited by 2 | Viewed by 1572

Abstract

Antimony (Sb) has gained significance as a critical raw material (CRM) within the European Union (EU) due to its strategic importance in various industrial sectors, particularly in the textile industry for flame retardants and as a component of Sb-based semiconductor materials. Moreover, Sb is emerging as a potential alternative for anodes used in lithium-ion batteries, a key element in the energy transition. This study explored the feasibility of identifying and quantifying Sb mineralisations through the spectral signature of soils using laboratory reflectance spectroscopy, a non-invasive remote sensing technique, and by employing convolutional neural networks (CNNs). Standard signal pre-processing techniques were applied to the spectral data, and the soils were analysed by inductively coupled plasma mass spectrometry (ICP-MS). Despite achieving high R-squared (0.7) values and an RMSE of 173 ppm for Sb, the study faces a significant challenge of generalisation of the model to new data. Despite the limitations, this study provides valuable insights into potential strategies for future research in this field. Full article

(This article belongs to the Special Issue New Trends on Remote Sensing Applications to Mineral Deposits-II)

► Show Figures

Graphical abstract

30 pages, 31593 KiB

Open AccessArticle

Satellite Advanced Spaceborne Thermal Emission and Reflection Radiometer Mineral Maps of Australia Unmixed of Their Green and Dry Vegetation Components: Implications for Mapping (Paleo) Sediment Erosion–Transport–Deposition Processes

by Tom Cudahy and Liam Cudahy

Remote Sens. 2024, 16(10), 1740; https://doi.org/10.3390/rs16101740 - 14 May 2024

Viewed by 1256

Abstract

The 2012 satellite ASTER geoscience maps of Australia were designed to provide public, web-accessible, and spatially comprehensive surface mineralogy for improved mapping and solutions to geoscience challenges. However, a number of the 2012 products were clearly compromised by variable green and/or dry vegetation cover. Here, we show a strategy to first estimate and then unmix the contributions of both these vegetation components to leave, as residual, the target surface mineralogy. The success of this unmixing process is validated by (i) visual suppression/removal of the regional climate and/or local fire-scar vegetation patterns; and (ii) pixel values more closely matching field sample data. In this process, we also found that the 2012 spectral indices used to gauge the AlOH content, AlOH composition, and water content can be improved. The updated (new indices and vegetation unmixed) maps reveal new geoscience information, including: (i) regional “wet” and “dry” zones that appear to express “deep” geological characters often expressed through thick regolith cover, with one zone over the Yilgarn Craton spatially anti-correlated with Archaean gold deposits; (ii) a ~1000 km wide circular feature over the Lake Eyre region defined by a rim of abundant “muscovite” that appears to coincide with opal deposits; (iii) a N–S zonation across the western half of the continent defined by abundant muscovite in the south and kaolinite in the north, which appears to reflect opposing E ↔ W aeolian sediment transport directions across the high-pressure belt; (iv) various paleo-drainage networks, including those over aeolian sand covered the “lowlands” of the Canning Basin, which are characterized by low AlOH content, as well as those over eroding “uplands”, such as the Yilgarn Craton, which have complicated compositional patterns; and (v) a chronological history of Miocene barrier shorelines, back-beach lagoons, and alluvial fans across the Eucla Basin, which, to date, had proved elusive to map using other techniques, with potential implications for heavy mineral sand exploration. Here, we explore the latter three issues. Full article

(This article belongs to the Special Issue New Trends on Remote Sensing Applications to Mineral Deposits-II)

► Show Figures

Figure 1

27 pages, 22610 KiB

Open AccessArticle

Mapping Geothermal Indicator Minerals Using Fusion of Target Detection Algorithms

by Mahmut Cavur, Yu-Ting Yu, Ebubekir Demir and Sebnem Duzgun

Remote Sens. 2024, 16(7), 1223; https://doi.org/10.3390/rs16071223 - 30 Mar 2024

Cited by 1 | Viewed by 1326

Abstract

Mineral mapping from satellite images provides valuable insights into subsurface mineral alteration for geothermal exploration. In previous studies, eight fundamental algorithms were used for mineral mapping utilizing USGS spectra, a collection of reflectance spectra containing samples of minerals, rocks, and soils created by the USGS. We used an ASD FieldSpec 4 Hi-RES NG portable spectrometer to collect spectra for analyzing ASTER images of the Coso Geothermal Field. Then, we established the ground-truth information and the spectral library by analyzing 97 samples. Samples collected from the field were analyzed using the CSIRO TSG (The Spectral Geologist of the Commonwealth Scientific and Industrial Research Organization). Based on the mineralogy study, multiple high-purity spectra of geothermal alteration minerals were selected from collected data, including alunite, chalcedony, hematite, kaolinite, and opal. Eight mineral spectral target detection algorithms were applied to the preprocessed satellite data with a proposed local spectral library. We measured the highest overall accuracy of 87% for alunite, 95% for opal, 83% for chalcedony, 60% for hematite, and 96% for kaolinite out of these eight algorithms. Three, four, five, and eight algorithms were fused to extract mineral alteration with the obtained target detection results. The results prove that the fusion of algorithms gives better results than using individual ones. In conclusion, this paper discusses the significance of evaluating different mapping algorithms. It proposes a robust fusion approach to extract mineral maps as an indicator for geothermal exploration. Full article

(This article belongs to the Special Issue New Trends on Remote Sensing Applications to Mineral Deposits-II)

► Show Figures

Figure 1

20 pages, 80951 KiB

Open AccessArticle

Lithium-Rich Pegmatite Detection Integrating High-Resolution and Hyperspectral Satellite Data in Zhawulong Area, Western Sichuan, China

by Wenqing Ding, Lin Ding, Qingting Li, Jinxiang Li and Liyun Zhang

Remote Sens. 2023, 15(16), 3969; https://doi.org/10.3390/rs15163969 - 10 Aug 2023

Cited by 5 | Viewed by 4651

Abstract

Lithium (Li) has grown to be a strategic key metal due to the enormous demand for the development of new energy industries over the world. As one of the most significant sources of Li resources, pegmatite-type Li deposits hold a large share of the mining market. In recent years, several large and super-large spodumene (Spd)-rich pegmatite deposits have been discovered successively in the Hoh-Xil–Songpan-Garzê (HXSG) orogenic belt of the northern Tibetan Plateau, indicative of the great Li prospecting potential of this belt. Hyperspectral remote sensing (HRS), as a rapidly developing exploration technology, is especially sensitive to the identification of alteration minerals, and has made important breakthroughs in porphyry copper deposit exploration. However, due to the small width of the pegmatite dykes and the lack of typical alteration zones, the ability of HRS in the exploration of Li-rich pegmatite deposits remains to be explored. In this study, Li-rich pegmatite anomalies were directly extracted from ZY1-02D hyperspectral imagery in the Zhawulong (ZWL) area of western Sichuan, China, using target detection techniques including Adaptive Cosine Estimator (ACE), Constrained Energy Minimization (CEM), Spectral Angle Mapper (SAM), and SAM with BandMax (SAMBM). Further, the Li-rich anomalies were superimposed with the distribution of pegmatite dykes delineated based on GF-2 high-resolution imagery. Our final results accurately identified the known range of Spd pegmatite dykes and further predicted two new exploration target areas. The approaches used in this study could be easily extended to other potential mineralization areas to discover new rare metal pegmatite deposits on the Tibetan Plateau. Full article

(This article belongs to the Special Issue New Trends on Remote Sensing Applications to Mineral Deposits-II)

► Show Figures

Graphical abstract

21 pages, 68892 KiB

Open AccessArticle

Nickel Grade Inversion of Lateritic Nickel Ore Using WorldView-3 Data Incorporating Geospatial Location Information: A Case Study of North Konawe, Indonesia

by Geng Zhang, Qi Chen, Zhifang Zhao, Xinle Zhang, Jiangqin Chao, Dingyi Zhou, Wang Chai, Haiying Yang, Zhibin Lai and Yangyidan He

Remote Sens. 2023, 15(14), 3660; https://doi.org/10.3390/rs15143660 - 22 Jul 2023

Cited by 2 | Viewed by 2627

Abstract

The North Konawe region in Indonesia, known for its lateritic nickel (Ni) deposits, holds significant potential for obtaining Ni resources. However, the complex topographic conditions of this area pose challenges. Exploring the application of remote sensing technology to reveal the spectral response mechanism of Ni grade from high-precision multispectral data and inversion of Ni grade represents a novel direction in future Ni resource exploration. Traditional remote sensing inversion methods solely consider the spectral characteristics of sample data and ignore vital geospatial location information. As a result, efficiently obtaining regional details of target substance content over large areas has become challenging. The introduction of the geographically weighted regression (GWR) method offers an opportunity for fine-grained Ni grade inversion based on remote sensing. This study focused on the E and K blocks within the lateritic Ni mining area in North Konawe. Through utilizing the WordView-3 multispectral data which exhibits immense potential in quantitative remote sensing inversion studies, GWR was employed to integrate spectral features and spatial information. The goal was to reveal the correlation between multispectral remote sensing data and Ni grade. The obtained results were then compared and analyzed with multiple linear regression (MLR) and back propagation neural network (BPNN) models. The findings revealed that GWR achieved the highest coefficient of determination R² of 0.96, surpassing MLR and BPNN values of 0.05 and 0.17, respectively. Additionally, GWR exhibited the lowest root mean square error of 0.04, which was lower than those of MLR and BPNN with the values of 0.25 and 0.23, respectively. These results confirmed the enhanced stability and accuracy of the GWR method compared to MLR and BPNN. Furthermore, GWR effectively mapped the spatial distribution trends of Ni grades in the study area, providing evidence of the method’s effectiveness in Ni grade inversion. The study also delved into the inversion effect of the GWR method in areas with varying weathering crust thickness and vegetation cover. The research revealed that higher values of weathering crust thickness negatively impacted the inversion effect. However, the influence mechanism of vegetation cover on Ni grade inversion necessitated further investigation. These results served as a significant demonstration of the remote sensing inversion of mineral resource grades in similar areas. They provided valuable insights for future exploration and decision-making processes. Full article

(This article belongs to the Special Issue New Trends on Remote Sensing Applications to Mineral Deposits-II)

► Show Figures

Figure 1

21 pages, 15060 KiB

Open AccessArticle

Estimation of the Multielement Content in Rocks Based on a Combination of Visible–Near-Infrared Reflectance Spectroscopy and Band Index Analysis

by Guo Jiang, Xi Chen, Jinlin Wang, Shanshan Wang, Shuguang Zhou, Yong Bai, Tao Liao, He Yang, Kai Ma and Xianglian Fan

Remote Sens. 2023, 15(14), 3591; https://doi.org/10.3390/rs15143591 - 18 Jul 2023

Cited by 2 | Viewed by 1419

Abstract

Rock geochemical methods are effective for geological surveys, but typical sampling and laboratory-based analytical methods are time-consuming and costly. However, using visible–near-infrared spectroscopy to estimate the metal element content of rock is an alternative method. This study discussed the potential of hyperspectral estimation of Cu and its significant associated elemental content. Ninety-five rock samples were collected from the Kalatage Yudai copper–nickel deposit in Hami, Xinjiang. The effects of different spectral resolutions, spectral preprocessing, band indices, and characteristic band selection on the estimation of the element contents of Fe, Cu, Co, and Ti were investigated. The results show that when the spectral resolution is 5 nm, good results are obtained for all four metal elements, Fe, Cu, Co, and Ti, with the coefficients of determination R² reaching 0.54, 0.59, 0.41, and 0.78, respectively. The best results are obtained for all transformed spectra with continuum removal, inverse transformation, continuum removal, and logarithmic transformation, respectively. In addition, the accuracy of the estimation models constructed by combining band indices and feature band selection was superior compared with full-band spectra for Fe (R² = 0.654, MAE = 1.27%, and RPD = 1.498), Cu (R² = 0.694, MAE = 20.509, and RPD = 1.711), Co (R² = 0.805, MAE = 2.573, and RPD = 2.199), and Ti (R² = 0.501, MAE = 0.04%, and RPD = 1.412). The results indicate that using band indices can provide a more accurate estimation of metal element content, providing a new technical method for the efficient acquisition of regional mineralization indicator element content distribution. Full article

(This article belongs to the Special Issue New Trends on Remote Sensing Applications to Mineral Deposits-II)

► Show Figures

Journal Menu

Journal Browser

New Trends on Remote Sensing Applications to Mineral Deposits-II

Share This Special Issue

Special Issue Editors

Special Issue Information

Keywords

Benefits of Publishing in a Special Issue

Published Papers (7 papers)

Research

Further Information

Guidelines

MDPI Initiatives

Follow MDPI