Special Issue "Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing"

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: 30 October 2020.

Special Issue Editors

Mr. Yoshiki Ninomiya
E-Mail Website
Guest Editor
Geological Survey of Japan (GSJ), National Institute of Advanced Industrial Science and Technology (AIST)
Interests: spectral property of materials, especially rocks and minerals; remote sensing, especially of geology, based on the spectral properties of materials; tectonics and structural geology with the combined approach of the field and the remote sensing studies in global, regional, and local scales, especially for the Tibetan Plateau and the surrounding regions
Prof. Bihong Fu
E-Mail Website
Guest Editor
Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing, China
Interests: mapping; surface processes; tectonic geomorphology; world heritage; global change; earth observation

Special Issue Information

Dear Colleagues,

The utilization of remote sensing in geology originates from the photogeology applied on aerial photography. The primary purpose of the 1st LANDSAT launched in 1972 was the exploration of nonrenewable resources, and data analysis was mainly based on photogeology. With the benefit of technological progress, many subsequent sensors have been developed, with rapidly improved spatial, radiometric, and spectral resolutions, which enable various forms of advanced analysis of remote sensing data. For example, multispectral observation at increased numbers of bands enables us to analyze surface mineralogy based on the spectral properties of the materials, which is directly linked to the theme of this Special Issue. It also enables geomorphological analysis, applying the topographic data derived from the sensor itself with the capability of stereo vision. Advanced image data processing methods have been developed, and data fusion with GIS is evolved for more detailed mapping and analysis. Recently, ASTER sensor onboard Terra has been quite utilized in geological studies, especially for mineralogical and lithological mapping with spectral observation. Satellite-borne hyperspectral sensors (for example, Hyperion on EO-1) have been developed, and several similar ones are planned to be launched into orbit in the near future. New innovative sensors for UAV and other platforms are expected to be developed, which will be useful for the study of mineralogy and lithology.

We would like to invite you to submit articles about your recent research linked to the title of this Special Issue “Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing”, for example, concerning the following topics:

  • Spectral properties of the surface materials (especially, minerals and rocks) in various wavelength regions (i.e., ultraviolet (UV; ~0.4 mm), visible and near infrared (VNIR; 0.4 ~3 mm), thermal infrared (TIR; 3 ~100mm), microwaves (MW; 100mm ~));
  • Surface lithological/mineralogical mapping based on spectral properties of materials in UV, VNIR, TIR, MW or combinations thereof;
  • Lithology/mineralogy using computational data processing;
  • Lithology/mineralogy on the basis of geomorphological analysis;
  • Validation of mapping with existing and/or newly collected geological information;
  • Analysis of lithological/mineralogical mapping results related to structural geology;
  • Mineralogical relationship between the regolith and the underlying outcrop;
  • Deposition process of the surface rocks and minerals;
  • Studies in and around the glaciers;
  • Studies for the vegetated regions;
  • Mineral development process on the particular cases (e.g., meteor impact);
  • Relationship between the distribution of rocks/minerals and archeological/modern human activities;
  • Thermo-dynamistic approach;
  • Applications with SAR data;
  • Applications with a hyperspectral sensor;
  • Remote sensing of the Planets (e.g., moon and Mars);
  • Data fusion with GIS;
  • Mapping study in various scales (e.g., local, regional, and global);
  • Innovative sensor systems for UAV and other platforms applicable to geological studies.

Mr. Yoshiki Ninomiya
Guest Editor

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 2000 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.

Published Papers (4 papers)

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Research

Open AccessArticle
Detecting the Sources of Methane Emission from Oil Shale Mining and Processing Using Airborne Hyperspectral Data
Remote Sens. 2020, 12(3), 537; https://doi.org/10.3390/rs12030537 - 06 Feb 2020
Abstract
Methane (CH4) is one of important greenhouse gases that affects the global radiative balance after carbon dioxide (CO2). Previous studies have demonstrated the detection of known sources of CH4 emission using the hyperspectral technology based on in situ [...] Read more.
Methane (CH4) is one of important greenhouse gases that affects the global radiative balance after carbon dioxide (CO2). Previous studies have demonstrated the detection of known sources of CH4 emission using the hyperspectral technology based on in situ vertical CH4 profile or ground CH4 emissions data. However, those approaches have not yet to detect the unknown terrestrial sources of CH4 emission at local-scale or regional-scale. In this paper, the Shortwave Airborne Spectrographic Imager (SASI) was employed to detect concentrated sources of CH4 emissions based on the absorption of CH4 in the shortwave infrared (SWIR) region. As a result, a band ratio (namely RCH4, RCH4 = Band91/Band78) determined through wavelet transform singularity detection has proposed for detection of the terrestrial CH4 emissions sources using SASI hyperspectral radiance image data, and elevated CH4 locations in the oil shale retorting plants were identified. Additionally, SASI surface reflectance data and multiple reference spectra in the spectral angle mapper (SAM) were used to classify surface sources of CH4 release. High-resolution Google Earth imagery and thermal imaging camera (FLIR GF320) had also verified that the CH4 releasing sources are mainly the oil shale mining field and the retorting plant. Therefore, the high-resolution imaging hyperspectral spectrometer can provide a powerful tool for detecting terrestrial CH4 release sources at local-scale to reduce the greenhouse gas emissions related to hydrocarbon development. Full article
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Open AccessArticle
Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers
Remote Sens. 2020, 12(1), 177; https://doi.org/10.3390/rs12010177 - 03 Jan 2020
Abstract
Most available studies in lithological mapping using spaceborne multispectral and hyperspectral remote sensing images employ different classification and spectral matching algorithms for performing this task; however, our experiment reveals that no single algorithm renders satisfactory results. Therefore, a new approach based on an [...] Read more.
Most available studies in lithological mapping using spaceborne multispectral and hyperspectral remote sensing images employ different classification and spectral matching algorithms for performing this task; however, our experiment reveals that no single algorithm renders satisfactory results. Therefore, a new approach based on an ensemble of classifiers is presented for lithological mapping using remote sensing images in this paper, which returns enhanced accuracy. The proposed method uses a weighted pooling approach for lithological mapping at each pixel level using the agreement of the class accuracy, overall accuracy and kappa coefficient from the multi-classifiers of an image. The technique is implemented in four steps; (1) classification images are generated using a variety of classifiers; (2) accuracy assessments are performed for each class, overall classification and estimation of kappa coefficient for every classifier; (3) an overall within-class accuracy index is estimated by weighting class accuracy, overall accuracy and kappa coefficient for each class and every classifier; (4) finally each pixel is assigned to a class for which it has the highest overall within-class accuracy index amongst all classes in all classifiers. To demonstrate the strength of the developed approach, four supervised classifiers (minimum distance (MD), spectral angle mapper (SAM), spectral information divergence (SID), support vector machine (SVM)) are used on one hyperspectral image (Hyperion) and two multispectral images (ASTER, Landsat 8-OLI) for mapping lithological units of the Udaipur area, Rajasthan, western India. The method is found significantly effective in increasing the accuracy in lithological mapping. Full article
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Open AccessArticle
Identification of Hydrothermal Alteration Minerals for Exploring Gold Deposits Based on SVM and PCA Using ASTER Data: A Case Study of Gulong
Remote Sens. 2019, 11(24), 3003; https://doi.org/10.3390/rs11243003 - 13 Dec 2019
Abstract
Dayaoshan, as an important metal ore-producing area in China, is faced with the dilemma of resource depletion due to long-term exploitation. In this paper, remote sensing methods are used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, [...] Read more.
Dayaoshan, as an important metal ore-producing area in China, is faced with the dilemma of resource depletion due to long-term exploitation. In this paper, remote sensing methods are used to circle the favorable metallogenic areas and find new ore points for Gulong. Firstly, vegetation interference was removed by using mixed pixel decomposition method with hyperplane and genetic algorithm (GA) optimization; then, altered mineral distribution information was extracted based on principal component analysis (PCA) and support vector machine (SVM) methods; thirdly, the favorable areas of gold mining in Gulong was delineated by using the ant colony algorithm (ACA) optimization SVM model to remove false altered minerals; and lastly, field surveys verified that the extracted alteration mineralization information is correct and effective. The results show that the mineral alteration extraction method proposed in this paper has certain guiding significance for metallogenic prediction by remote sensing. Full article
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Open AccessArticle
Towards Multiscale and Multisource Remote Sensing Mineral Exploration Using RPAS: A Case Study in the Lofdal Carbonatite-Hosted REE Deposit, Namibia
Remote Sens. 2019, 11(21), 2500; https://doi.org/10.3390/rs11212500 - 25 Oct 2019
Abstract
Traditional exploration techniques usually rely on extensive field work supported by geophysical ground surveying. However, this approach can be limited by several factors such as field accessibility, financial cost, area size, climate, and public disapproval. We recommend the use of multiscale hyperspectral remote [...] Read more.
Traditional exploration techniques usually rely on extensive field work supported by geophysical ground surveying. However, this approach can be limited by several factors such as field accessibility, financial cost, area size, climate, and public disapproval. We recommend the use of multiscale hyperspectral remote sensing to mitigate the disadvantages of traditional exploration techniques. The proposed workflow analyzes a possible target at different levels of spatial detail. This method is particularly beneficial in inaccessible and remote areas with little infrastructure, because it allows for a systematic, dense and generally noninvasive surveying. After a satellite regional reconnaissance, a target is characterized in more detail by plane-based hyperspectral mapping. Subsequently, Remotely Piloted Aircraft System (RPAS)-mounted hyperspectral sensors are deployed on selected regions of interest to provide a higher level of spatial detail. All hyperspectral data are corrected for radiometric and geometric distortions. End-member modeling and classification techniques are used for rapid and accurate lithological mapping. Validation is performed via field spectroscopy and portable XRF as well as laboratory geochemical and spectral analyses. The resulting spectral data products quickly provide relevant information on outcropping lithologies for the field teams. We show that the multiscale approach allows defining the promising areas that are further refined using RPAS-based hyperspectral imaging. We further argue that the addition of RPAS-based hyperspectral data can improve the detail of field mapping in mineral exploration, by bridging the resolution gap between airplane- and ground-based data. RPAS-based measurements can supplement and direct geological observation rapidly in the field and therefore allow better integration with in situ ground investigations. We demonstrate the efficiency of the proposed approach at the Lofdal Carbonatite Complex in Namibia, which has been previously subjected to rare earth elements exploration. The deposit is located in a remote environment and characterized by difficult terrain which limits ground surveys. Full article
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