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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: closed (20 September 2022) | Viewed by 41459

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Special Issue Editors

Dr. Ana Cláudia Teodoro

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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
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Dr. Joana Cardoso-Fernandes

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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
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Dr. Alexandre Lima

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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,

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

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Keywords

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

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Published Papers (9 papers)

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Research

25 pages, 7421 KiB

Open AccessArticle

Application and Evaluation of Deep Neural Networks for Airborne Hyperspectral Remote Sensing Mineral Mapping: A Case Study of the Baiyanghe Uranium Deposit in Northwestern Xinjiang, China

by Chuan Zhang, Min Yi, Fawang Ye, Qingjun Xu, Xinchun Li and Qingqing Gan

Remote Sens. 2022, 14(20), 5122; https://doi.org/10.3390/rs14205122 - 13 Oct 2022

Cited by 16 | Viewed by 2725

Abstract

Deep learning is a popular topic in machine learning and artificial intelligence research and has achieved remarkable results in various fields. In geological remote sensing, mineral mapping is an appealing application of hyperspectral remote sensing for geological surveyors. Whether deep learning can improve the mineral identification ability in hyperspectral remote sensing images, especially for the discrimination of spectrally similar and intimately mixed minerals, needs to be evaluated. In this study, shortwave airborne spectrographic imager (SASI) hyperspectral images of the Baiyanghe uranium deposit in Northwestern Xinjiang, China, were used as experimental data. Three deep neural network (DNN) models were designed: a fully connected neural network (FCNN), a one-dimensional convolutional neural network (1D CNN), and a one-dimensional and two-dimensional convolutional neural network (1D and 2D CNN). A sample dataset containing five minerals was constructed for model training and validation, which was divided into training, validation and test sets at a ratio of 6:2:2. The final test accuracies of the FCNN, 1D CNN, and 1D and 2D CNN were 91.24%, 93.67% and 94.77%, respectively. The three DNNs were used for mineral identification and mapping of SASI hyperspectral images of the Baiyanghe uranium mining area. The mapping results were compared with the mapping results of the support vector machine (SVM) and the mixture-tuned matched filtering (MTMF) method. Combined with the ground spectral data obtained by the spectrometer, spectral verification and interpretation were carried out on sections that the two kinds of methods identified differently. The verification results show that the mapping results of the 1D and 2D CNN were more accurate than those of the other methods. More importantly, for minerals with similar spectral characteristics, such as short-wavelength white mica and medium-wavelength white mica, the 1D and 2D CNN model had a more accurate discrimination effect than the other DNN models, indicating that the introduction of spatial information can improve the mineral identification ability in hyperspectral remote sensing images. In general, CNNs have good application prospects in geological mapping of hyperspectral remote sensing images and are worthy of further development in future work. Full article

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

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20 pages, 3616 KiB

Open AccessEditor’s ChoiceArticle

Hyperspectral Reconnaissance: Joint Characterization of the Spectral Mixture Residual Delineates Geologic Unit Boundaries in the White Mountains, CA

by Francis J. Sousa and Daniel J. Sousa

Remote Sens. 2022, 14(19), 4914; https://doi.org/10.3390/rs14194914 - 1 Oct 2022

Cited by 9 | Viewed by 3128

Abstract

We use a classic locale for geology education in the White Mountains, CA, to demonstrate a novel approach for using imaging spectroscopy (hyperspectral imaging) to generate base maps for the purpose of geologic mapping. The base maps produced in this fashion are complementary to, but distinct from, maps of mineral abundance. The approach synthesizes two concepts in imaging spectroscopy data analysis: the spectral mixture residual and joint characterization. First, the mixture residual uses a linear, generalizable, and physically based continuum removal model to mitigate the confounding effects of terrain and vegetation. Then, joint characterization distinguishes spectrally distinct geologic units by isolating residual, absorption-driven spectral features as nonlinear manifolds. Compared to most traditional classifiers, important strengths of this approach include physical basis, transparency, and near-uniqueness of result. Field validation confirms that this approach can identify regions of interest that contribute significant complementary information to PCA alone when attempting to accurately map spatial boundaries between lithologic units. For a geologist, this new type of base map can complement existing algorithms in exploiting the coming availability of global hyperspectral data for pre-field reconnaissance and geologic unit delineation. Full article

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

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23 pages, 8649 KiB

Open AccessArticle

Integration of Hyperspectral and Magnetic Data for Geological Characterization of the Niaqornarssuit Ultramafic Complex in West-Greenland

by Agnieszka Kuras, Björn H. Heincke, Sara Salehi, Christian Mielke, Nicole Köllner, Christian Rogass, Uwe Altenberger and Ingunn Burud

Remote Sens. 2022, 14(19), 4877; https://doi.org/10.3390/rs14194877 - 29 Sep 2022

Cited by 5 | Viewed by 3081

Abstract

The integration of imaging spectroscopy and aeromagnetics provides a cost-effective and promising way to extend the initial analysis of a mineral deposit. While imaging spectroscopy retrieves surface spectral information, magnetic responses are used to determine magnetization at both shallower and greater depths using 2D and 3D modeling. Integration of imaging spectroscopy and magnetics improves upon knowledge concerning lithology with magnetic properties, enhances understanding of the geological origin of magnetic anomalies, and is a promising approach for analyzing a prospective area for minerals having a high iron-bearing content. To combine iron diagnostic information from airborne hyperspectral and magnetic data, we (a) used an iron absorption feature ratio to model pseudo-magnetic responses and compare them with the measured magnetic data and (b) estimated the apparent susceptibility along the surface by some equivalent source modeling, and compared them with iron ratios along the surface. For this analysis, a Modified Iron Feature Depth index was developed and compared to the surface geochemistry of the rock samples in order to validate the spectral information of iron. The comparison revealed a linear increase in iron absorption feature depths with iron content. The analysis was performed by empirically modeling the statistical relationship between the diagnostic absorption features of hyperspectral (HS) image spectra of selected rock samples and their corresponding geochemistry. Our results clearly show a link between the spectral absorption features and the magnetic response from iron-bearing ultra/-mafic rocks. The iron absorption feature ratio of Fe³⁺/Fe²⁺ integrated with aeromagnetic data (residual magnetic anomaly) allowed us to distinguish main rock types based on physical properties. This separation matches the lithology of the Niaqornarssuit complex, our study area in West Greenland. Full article

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

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27 pages, 28374 KiB

Open AccessEditor’s ChoiceArticle

Spectral Analysis to Improve Inputs to Random Forest and Other Boosted Ensemble Tree-Based Algorithms for Detecting NYF Pegmatites in Tysfjord, Norway

by Douglas Santos, Joana Cardoso-Fernandes, Alexandre Lima, Axel Müller, Marco Brönner and Ana Cláudia Teodoro

Remote Sens. 2022, 14(15), 3532; https://doi.org/10.3390/rs14153532 - 23 Jul 2022

Cited by 36 | Viewed by 5329

Abstract

As an important source of lithium and rare earth elements (REE) and other critical elements, pegmatites are of great strategic economic interest for present and future technological development. Identifying new pegmatite deposits is a strategy adopted by the European Union (EU) to decrease its import dependence on non-European countries for these raw materials. It is in this context that the GREENPEG project was established, an EU project whose main objective is to identify new deposits of pegmatites in Europe in an environmentally friendly way. Remote sensing is a non-contact exploration tool that allows for identifying areas of interest for exploration at the early stage of exploration campaigns. Several RS methods have been developed to identify Li-Cs-Ta (LCT) pegmatites, but in this study, a new methodology was developed to detect Nb-Y-F (NYF) pegmatites in the Tysfjord area in Norway. This methodology is based on spectral analysis to select bands of the Sentinel 2 satellite and adapt RS methods, such as Band Ratios and Principal Component Analysis (PCA), to be used as input in the Random Forest (RF) and other tree-based ensemble algorithms to improve the classification accuracy. The results obtained are encouraging, and the algorithm was able to successfully identify the pegmatite areas already known and new locations of interest for exploration were also defined. Full article

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

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22 pages, 6915 KiB

Open AccessArticle

Framework for Remote Sensing and Modelling of Lithium-Brine Deposit Formation

by Cristian Rossi, Luke Bateson, Maral Bayaraa, Andrew Butcher, Jonathan Ford and Andrew Hughes

Remote Sens. 2022, 14(6), 1383; https://doi.org/10.3390/rs14061383 - 12 Mar 2022

Cited by 10 | Viewed by 6700

Abstract

The demand for “green” metals such as lithium is increasing as the world works to reduce its reliance on fossil fuels. More than half of the world’s lithium resources are contained in lithium-brine deposits, including the salt flats, or “salars”, of the Andean region of South America, also known as the Lithium Triangle. The genesis of lithium-brine deposits is largely driven by the leaching of lithium from source rocks in watersheds, transport via groundwater systems to salars, and evaporative concentration in salars. The goal of this research is to create a consistent and seamless methodology for tracking lithium mass from its source in the watershed to its greatest concentration in the nucleus. The area of interest is in and around Bolivia’s Salar de Uyuni, the world’s largest salt flat. We explore how Li-brine deposits form, where the water and solute come from, how the brines are formed, and how abstraction affects the mass balance inside the salar. To support the entire system, open-source Earth observation (EO) data are analysed. We found that by constructing a flexible and repeatable workflow, the question of how lithium reaches the Salar de Uyuni can be addressed. The work demonstrated the importance of groundwater flow to the river network and highlighted the need for flow data for the main river supplying the salar with both water inflow and lithium mass. Full article

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

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33 pages, 32186 KiB

Open AccessArticle

Interpretation of the Reflectance Spectra of Lithium (Li) Minerals and Pegmatites: A Case Study for Mineralogical and Lithological Identification in the Fregeneda-Almendra Area

by Joana Cardoso-Fernandes, João Silva, Mônica M. Perrotta, Alexandre Lima, Ana C. Teodoro, Maria Anjos Ribeiro, Filipa Dias, Odile Barrès, Jean Cauzid and Encarnación Roda-Robles

Remote Sens. 2021, 13(18), 3688; https://doi.org/10.3390/rs13183688 - 15 Sep 2021

Cited by 27 | Viewed by 6864

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 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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20 pages, 19123 KiB

Open AccessArticle

New Insights into the Pulang Porphyry Copper Deposit in Southwest China: Indication of Alteration Minerals Detected Using ASTER and WorldView-3 Data

by Qi Chen, Zhifang Zhao, Jiaxi Zhou, Min Zeng, Jisheng Xia, Tao Sun and Xin Zhao

Remote Sens. 2021, 13(14), 2798; https://doi.org/10.3390/rs13142798 - 16 Jul 2021

Cited by 13 | Viewed by 3415

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 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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18 pages, 25271 KiB

Open AccessArticle

Mapping of Aluminum Concentration in Bauxite Mining Residues Using Sentinel-2 Imagery

by Sara Kasmaeeyazdi, Emanuele Mandanici, Efthymios Balomenos, Francesco Tinti, Stefano Bonduà and Roberto Bruno

Remote Sens. 2021, 13(8), 1517; https://doi.org/10.3390/rs13081517 - 14 Apr 2021

Cited by 11 | Viewed by 3660

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 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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18 pages, 12986 KiB

Open AccessArticle

Improving Boundary Constraint of Probability Integral Method in SBAS-InSAR for Deformation Monitoring in Mining Areas

by Mengyao Shi, Honglei Yang, Baocun Wang, Junhuan Peng, Zhouzheng Gao and Bin Zhang

Remote Sens. 2021, 13(8), 1497; https://doi.org/10.3390/rs13081497 - 13 Apr 2021

Cited by 14 | Viewed by 3177

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. 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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