Special Issue "Mapping of Rocks and Minerals Using Hyperspectral Remote Sensing"

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

Deadline for manuscript submissions: 31 October 2022 | Viewed by 4378

Special Issue Editors

Prof. Dr. Shanjun Liu
E-Mail Website
Guest Editor
College of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
Interests: remote sensing rock mechanics; disaster remote sensing; hyperspectral remote sensing and mining applications
Dr. Nisha Bao
E-Mail Website
Guest Editor
College of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
Interests: environmental remote sensing; soil hyperspectral remote sensing; land reclamation in mining area
Dr. Lianhuan Wei
E-Mail Website
Guest Editor
College of Resources and Civil Engineering, Northeastern University, Shenyang 110819, China
Interests: radar interferometry; InSAR technology and application; microwave remote sensing and applications

Special Issue Information

Dear Colleagues,

In recent years, spectral identification of rocks and minerals together with remote sensing technology have been widely used in mineral resource exploration, geological mapping and mine geology, etc. However, due to the wide variety, complex structural composition, and diverse surface morphology of rocks and minerals, as well as the complicated influence of many factors on spectral measurements, there are still many challenges in hyperspectral remote sensing of rocks and minerals. This Special Issue provides a platform for researchers to discuss and exchange their ideas and results related to the above topics. Our Special Issue will cover a broad range of relevant topics of interest, such as:

  1. Spectral measurement of rock and mineral and data processing;
  2. Influencing factors and mechanism of rock and mineral spectrum;
  3. Construction of rock and mineral spectrum library;
  4. Hyperspectral image processing method of rock and ore;
  5. Rock spectral unmixing algorithm;
  6. Hyperspectral satellite data application in rock and mineral mapping;
  7. Ground-based hyperspectral imaging for mining applications;
  8. Airborne hyperspectral survey system and geological application;
  9. Spectral processing methods for geological remote sensing.

Prof. Dr. Shanjun Liu
Dr. Nisha Bao
Dr. Lianhuan Wei
Guest Editors

Manuscript Submission Information

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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 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.

Keywords

  • rock and mineral
  • spectral analysis
  • hyperspectral remote sensing
  • hyperspectral image processing
  • geological and mining application

Published Papers (5 papers)

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Research

Article
Hyperspectral and Geochemical Analysis of Chlorites at the Gongchangling High-Grade Magnetite Deposit, NE China: Implications for Alteration Processes
Minerals 2022, 12(5), 629; https://doi.org/10.3390/min12050629 - 15 May 2022
Viewed by 511
Abstract
The Gongchangling deposit is a representative banded iron formation (BIF) in China, in which developed several high-grade magnetite ores. The surrounding alteration rocks recorded the genesis information of the high-grade ores. However, the study related to alteration processes remains poor. In this study, [...] Read more.
The Gongchangling deposit is a representative banded iron formation (BIF) in China, in which developed several high-grade magnetite ores. The surrounding alteration rocks recorded the genesis information of the high-grade ores. However, the study related to alteration processes remains poor. In this study, we investigate the sub-types and formation temperature of chlorite using hyperspectral imaging and electronic probe microanalysis (EPMA), and deciphered the elemental migration trend during alteration processes by whole-rock geochemistry. The chlorites in the alteration rocks were divided into three sub-types according to the spectral features of the Fe-OH band near 2250 nm. The range of wavelength position is approximately 2250–2255 nm for chlorite-I, 2255–2260 nm for chlorite-II, and 2260–2265 nm for chlorite-III. The variation in Mg# is 0.32–0.44 in chlorite-I, 0.20–0.34 in chlorite-II, and 0.15–0.23 in chlorite-III, which is consistent with the range of wavelength position. The hydrothermal alteration resulted in the enrichment of iron and the depletion of silicon. The results shed new light on the recognition of chlorite sub-types and deciphered the hydrothermal alteration processes of high-grade magnetite ores, which proposed an effective method for mineralogical mapping. Full article
(This article belongs to the Special Issue Mapping of Rocks and Minerals Using Hyperspectral Remote Sensing)
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Article
Thermal-Infrared Spectral Feature Analysis and Spectral Identification of Monzonite Using Feature-Oriented Principal Component Analysis
Minerals 2022, 12(5), 508; https://doi.org/10.3390/min12050508 - 20 Apr 2022
Viewed by 537
Abstract
Rock spectral analysis is an important research field in hyperspectral remote sensing information processing. Compared with the spectra in the short-wave infrared and visible–near-infrared regions, the emittance spectrum of rocks in the thermal infrared (TIR) region is highly significant for identifying some major [...] Read more.
Rock spectral analysis is an important research field in hyperspectral remote sensing information processing. Compared with the spectra in the short-wave infrared and visible–near-infrared regions, the emittance spectrum of rocks in the thermal infrared (TIR) region is highly significant for identifying some major rock-forming minerals, including feldspar, biotite, pyroxene and hornblende. Even for the same rock type, slight differences in mineral composition generally result in varying spectral signatures, undoubtedly increasing the difficulty in discriminating rock types on the Earth’s surface via TIR spectroscopy. In this study, amounts of monzonite samples from different regions were collected in the central part of Hunan Province, China, and emission spectra at 8–14 μm were measured using a portable thermal infrared spectrometer. The experimental result illustrates 13 remarkable feature positions for all the monzonite samples from different geological environments. Furthermore, by combining the extracted features with the principal component analysis (PCA) method, feature-oriented PCA was applied to establish a model for identifying monzonite accurately and quickly without performing spectral library matching and spectral deconvolution. This study provides an important method for rock type identification in the TIR region that is helpful for the rock spectral analysis, geological mapping and pixel unmixing of remote sensing images. Full article
(This article belongs to the Special Issue Mapping of Rocks and Minerals Using Hyperspectral Remote Sensing)
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Article
Iron Ore Tailing Composition Estimation Using Fused Visible–Near Infrared and Thermal Infrared Spectra by Outer Product Analysis
Minerals 2022, 12(3), 382; https://doi.org/10.3390/min12030382 - 19 Mar 2022
Viewed by 624
Abstract
Iron ore tailings are mainly composed of SiO2 and iron, whose content determines the potential reuse strategy of the tailings. Compared with the traditional wet chemistry approach, spectroscopy has proven its superior effectiveness in characterizing and predicting minerals, such as iron oxides, [...] Read more.
Iron ore tailings are mainly composed of SiO2 and iron, whose content determines the potential reuse strategy of the tailings. Compared with the traditional wet chemistry approach, spectroscopy has proven its superior effectiveness in characterizing and predicting minerals, such as iron oxides, clay, and SiO2. This study aims to estimate the content of SiO2 and TFe in iron ore tailings based on visible–near infrared (VIS–NIR, 350–2500 nm) and thermal infrared (TIR, 8–14 μm) spectroscopy. The outer product analysis (OPA) method is used to combine VIS–NIR and TIR spectral domains, from which an outer product matrix of fusion data can be generated. The study area is the iron ore tailing dam from Waitoushan, which is one of the super-large iron deposits in the Anshan–Benxi iron cluster of northeastern China. The spectral analysis results demonstrated the following: (1) The reflectance feature at 1163–2499 nm in the VIS–NIR range correlates with TFe and the emissivity feature at 8–9.4 and 10.7–12 μm in the TIR range correlates with SiO2. (2) Compared with the original absorbance spectra, the correlation coefficients of fusion spectra improve from 0.66 to 0.87 for TFe and from 0.64 to 0.84 for SiO2. (3) The partial least squares regression, random forest (RF), and extreme learning machine exploiting particle swarm optimization modeling methods are established for SiO2 and TFe estimation. The prediction accuracy results indicate that the prediction model with OPA-fused spectra performs significantly better than with individual VIS–NIR and TIR spectra. The RF model with input-fused spectra provides the highest accuracy with the coefficients of determination of 0.95 and 0.91, root mean square errors of 0.97% and 0.96%, and ratios of performance to interquartile distance of 6.49 and 2.31 for SiO2 and TFe content estimation, respectively. These outcomes provide a theoretical basis and technical support for tailing composition estimation using spectroscopy. Full article
(This article belongs to the Special Issue Mapping of Rocks and Minerals Using Hyperspectral Remote Sensing)
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Article
An Open Integrated Rock Spectral Library (RockSL) for a Global Sharing and Matching Service
Minerals 2022, 12(2), 118; https://doi.org/10.3390/min12020118 - 20 Jan 2022
Cited by 2 | Viewed by 814
Abstract
Minerals and rocks are important natural resources that are formed over a long period of geological history. Spectroscopy is the basis of the identification and characterisation of rocks and minerals via proximal sensing in the field or remote sensing systems with multi- and [...] Read more.
Minerals and rocks are important natural resources that are formed over a long period of geological history. Spectroscopy is the basis of the identification and characterisation of rocks and minerals via proximal sensing in the field or remote sensing systems with multi- and hyper-spectral capabilities. However, spectral data is scattered around different institutions worldwide and stored in various formats, resulting in poor data usability and an unnecessary waste of time and information. To improve the usability and performance of mineral spectral data, we developed an integrated open mineral spectral library (Rock Spectral Library, RockSL). Shared spectral data and related information were collected worldwide, and data cleaning measures were performed to retain the qualified spectra and merge all qualified data (raster, vector, and text formats) in a common framework to establish a reliable and comprehensive digital data set for an easy sharing and matching service. A software system was developed for the RockSL to manage, analyse, and apply the spectral data of minerals and rocks. We demonstrate how the information encoded in RockSL can determine the species of unknown rocks and describe specific mineral compositions. We also provide a reference scheme of the work chain and present key technologies for building different spectral libraries in diverse fields using RockSL. New contributions to RockSL are encouraged for this work to be improved to provide a better service and extend the applications of geo-sciences. This article introduces the characteristics of RockSL and demonstrates an experimental application. Full article
(This article belongs to the Special Issue Mapping of Rocks and Minerals Using Hyperspectral Remote Sensing)
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Article
Salinity Monitoring at Saline Sites with Visible–Near-Infrared Spectral Data
Minerals 2021, 11(10), 1086; https://doi.org/10.3390/min11101086 - 02 Oct 2021
Cited by 1 | Viewed by 740
Abstract
To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research [...] Read more.
To address the global phenomenon of the salinisation of large land areas, a quantitative inversion model of the salinity of saline soils and soil visible–near-infrared (NIR) spectral data was developed by considering saline soils in Zhenlai County, Jilin Province, China as the research object. The original spectral data were first subjected to Savitzky–Golay (SG) smoothing, multiplicative scattering correction (MSC) pre-processing, and a combined transformation technique. The pre-processed spectral data were then analysed to construct the difference index (DI), ratio index (RI), and normalised difference index (NDI), and the Spearman rank correlation coefficient (r) between these three spectral indices and the salt content in the samples was calculated, while a combined spectral index (r > 0.8) was eventually selected as a sensitive spectral index. Finally, a quantitative inversion model for the salinity of saline soils was developed, and the model’s accuracy was evaluated based on partial least squares regression (PLSR), the random forest (RF) algorithm, and the radial basis function (RBF) neural network algorithm. The results indicated that the inversion of soil salt content using the selected combination of spectral indices based on the RBF neural network algorithm was the most effective, with the prediction model yielding an R2 value of 0.950, a root mean square error (RMSE) of 1.014, and a relative percentage deviation (RPD) of 4.479, which suggested a good prediction effect. Full article
(This article belongs to the Special Issue Mapping of Rocks and Minerals Using Hyperspectral Remote Sensing)
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Planned Papers

The below list represents only planned manuscripts. Some of these manuscripts have not been received by the Editorial Office yet. Papers submitted to MDPI journals are subject to peer-review.

 

Title: Iron Ore Tailings Estimation using Fused Visible-Near Infrared Spectra and Thermal Infrared Spectra by Outer Product Analysis

Authors: Nisha Bao*, Haimei Lei1, Yue Cao, Shanjun Liu
Institute for Geo-informatics & Digital Mine Research, Northeastern University, Shenyang 110819, China

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