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Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing

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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 (31 October 2021) | Viewed by 65867

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Special Issue Editors

Mr. Yoshiki Ninomiya

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Guest Editor

Geological Survey of Japan (GSJ), National Institute of Advanced Industrial Science and Technology (AIST), Tokyo, Japan
Interests: spectral property of materials, e.g., rocks and minerals; remote sensing of geology based on the spectral properties of materials; tectonics and structural geology with the combined approach of field and remote sensing studies at the global, regional, and local scale, especially for the Tibetan Plateau and surrounding regions

Prof. Dr. 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

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Published Papers (13 papers)

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Research

18 pages, 8451 KiB

Open AccessArticle

A Remote-Sensing-Based Alteration Zonation Model of the Duolong Porphyry Copper Ore District, Tibet

by Fojun Yao, Xingwang Xu, Jianmin Yang and Xinxia Geng

Remote Sens. 2021, 13(24), 5073; https://doi.org/10.3390/rs13245073 - 14 Dec 2021

Cited by 10 | Viewed by 5068

Abstract

Remote sensing (RS) of alteration zones and anomalies can provide information that is useful for geological prospecting and exploration. RS is an effective method for porphyry copper mineral exploration and prospecting prediction. More specifically, the Advanced Spaceborne Thermal Emission and Reflection radiometer (ASTER) data, which include 14 spectral channels from visible light to thermal infrared, are useful in such cases. This study uses visible-shortwave infrared and thermal infrared ASTER data together with surface material spectra from the Duolong porphyry copper ore district to construct an RS-based alteration zonation model of the deposit. In this study, an RS alteration zoning model is established based on ground-spectral alteration zoning results. The methods include PCA (Principal Component Analysis), Ratio, and Slope methods. The information obtained by each method is different. RS-based alteration zonation is developed based on the intersection of maps, resultant from the different methods for extracting information related to different minerals. The alteration zonation information extracted from ASTER RS data is consistent with geological observations. Using information from the RS-based model, we mapped the alteration minerals and zones of the Duolong ore district, thereby identifying prospecting target areas of the deposit. Full article

(This article belongs to the Special Issue Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing)

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16 pages, 4065 KiB

Open AccessArticle

Lithological Mapping Based on Fully Convolutional Network and Multi-Source Geological Data

by Ziye Wang, Renguang Zuo and Hao Liu

Remote Sens. 2021, 13(23), 4860; https://doi.org/10.3390/rs13234860 - 30 Nov 2021

Cited by 34 | Viewed by 4898

Abstract

Deep learning algorithms have found numerous applications in the field of geological mapping to assist in mineral exploration and benefit from capabilities such as high-dimensional feature learning and processing through multi-layer networks. However, there are two challenges associated with identifying geological features using deep learning methods. On the one hand, a single type of data resource cannot diagnose the characteristics of all geological units; on the other hand, deep learning models are commonly designed to output a certain class for the whole input rather than segmenting it into several parts, which is necessary for geological mapping tasks. To address such concerns, a framework that comprises a multi-source data fusion technology and a fully convolutional network (FCN) model is proposed in this study, aiming to improve the classification accuracy for geological mapping. Furthermore, multi-source data fusion technology is first applied to integrate geochemical, geophysical, and remote sensing data for comprehensive analysis. A semantic segmentation-based FCN model is then constructed to determine the lithological units per pixel by exploring the relationships among multi-source data. The FCN is trained end-to-end and performs dense pixel-wise prediction with an arbitrary input size, which is ideal for targeting geological features such as lithological units. The framework is finally proven by a comparative study in discriminating seven lithological units in the Cuonadong dome, Tibet, China. A total classification accuracy of 0.96 and a high mean intersection over union value of 0.9 were achieved, indicating that the proposed model would be an innovative alternative to traditional machine learning algorithms for geological feature mapping. Full article

(This article belongs to the Special Issue Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing)

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18 pages, 2834 KiB

Open AccessArticle

Evaluation of the Performance of Time-Series Sentinel-1 Data for Discriminating Rock Units

by Yi Lu, Changbao Yang and Qigang Jiang

Remote Sens. 2021, 13(23), 4824; https://doi.org/10.3390/rs13234824 - 27 Nov 2021

Cited by 6 | Viewed by 2999

Abstract

The potential use of time-series Sentinel-1 synthetic aperture radar (SAR) data for rock unit discrimination has never been explored in previous studies. Here, we employed time-series Sentinel-1 data to discriminate Dananhu formation, Xinjiang group, Granite, Wusu group, Xishanyao formation, and Diorite in Xinjiang, China. Firstly, the temporal variation of the backscatter metrics (backscatter coefficient and coherence) from April to October derived from Sentinel-1, was analyzed. Then, the significant differences of the time-series SAR metrics among different rock units were checked using the Kruskal–Wallis rank sum test and Tukey’s honest significant difference test. Finally, random forest models were used to discriminate rock units. As for the input features, there were four groups: (1) time-series backscatter metrics, (2) single-date backscatter metrics, (3) time-series backscatter metrics at VV, and (4) VH channel. In each feature group, there were three sub-groups: backscatter coefficient, coherence, and combined use of backscatter coefficient and coherence. Our results showed that time-series Sentinel-1 data could improve the discrimination accuracy by roughly 9% (from 55.4% to 64.4%), compared to single-date Sentinel-1 data. Both VV and VH polarization provided comparable results. Coherence complements the backscatter coefficient when discriminating rock units. Among the six rock units, the Granite and Xinjiang group can be better differentiated than the other four rock units. Though the result still leaves space for improvement, this study further demonstrates the great potential of time-series Sentinel-1 data for rock unit discrimination. Full article

(This article belongs to the Special Issue Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing)

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19 pages, 28193 KiB

Open AccessArticle

Quantitative Remote Sensing of Metallic Elements for the Qishitan Gold Polymetallic Mining Area, NW China

by Gong Cheng, Huikun Huang, Huan Li, Xiaoqing Deng, Rehan Khan, Landry SohTamehe, Asad Atta, Xuechong Lang and Xiaodong Guo

Remote Sens. 2021, 13(13), 2519; https://doi.org/10.3390/rs13132519 - 28 Jun 2021

Cited by 11 | Viewed by 3827

Abstract

The recent development in remote sensing imagery and the use of remote sensing detection feature spectrum information together with the geochemical data is very useful for the surface element quantitative remote sensing inversion study. This aim of this article is to select appropriate methods that would make it possible to have rapid economic prospecting. The Qishitan gold polymetallic deposit in the Xinjiang Uygur Autonomous Region, Northwest China has been selected for this study. This paper establishes inversion maps based on the contents of metallic elements by integrating geochemical exploration data with ASTER and WorldView-2 remote sensing data. Inversion modelling maps for As, Cu, Hg, Mo, Pb, and Zn are consistent with the corresponding geochemical anomaly maps, which provide a reference for metallic ore prospecting in the study area. ASTER spectrum covers short-wave infrared and has better accuracy than WorldView-2 data for the inversion of some elements (e.g., Au, Hg, Pb, and As). However, the high spatial resolution of WorldView-2 drives the final content inversion map to be more precise and to better localize the anomaly centers of the inversion results. After scale conversion by re-sampling and kriging interpolation, the modeled and predicted accuracy of the models with square interpolation is much closer compare with the ground resolution of the used remote sensing data. This means our results are much satisfactory as compared to other interpolation methods. This study proves that quantitative remote sensing has great potential in ore prospecting and can be applied to replace traditional geochemical exploration to some extent. Full article

(This article belongs to the Special Issue Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing)

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17 pages, 3453 KiB

Open AccessArticle

Lithology Discrimination Using Sentinel-1 Dual-Pol Data and SRTM Data

by Yi Lu, Changbao Yang and Zhiguo Meng

Remote Sens. 2021, 13(7), 1280; https://doi.org/10.3390/rs13071280 - 27 Mar 2021

Cited by 23 | Viewed by 4652

Abstract

Compared to various optical remote sensing data, studies on the performance of dual-pol Synthetic aperture radar (SAR) on lithology discrimination are scarce. This study aimed at using Sentinel-1 data to distinguish dolomite, andesite, limestone, sandstone, and granite rock types. The backscatter coefficients VV and VH, the ratio VV–VH; the decomposition parameters Entropy, Anisotropy, and Alpha were firstly derived and the Kruskal–Wallis rank sum test was then applied to these polarimetric derived matrices to assess the significance of statistical differences among different rocks. Further, the corresponding gray-level co-occurrence matrices (GLCM) features were calculated. To reduce the redundancy and data dimension, the principal component analysis (PCA) was carried out on the GLCM features. Due to the limited rock samples, before the lithology discrimination, the input variables were selected. Several classifiers were then used for lithology discrimination. The discrimination models were evaluated by overall accuracy, confusion matrices, and the area under the curve-receiver operating characteristics (AUC-ROC). Results show that (1) the statistical differences of the polarimetric derived matrices (backscatter coefficients, ratio, and decomposition parameters) among different rocks was insignificant; (2) texture information derived from Sentinel-1 had great potential for lithology discrimination; (3) partial least square discrimination analysis (PLSDA) had the highest overall accuracy (0.444) among the classification models; (4) though the overall accuracy is unsatisfactory, according to the AUC-ROC and confusion matrices, the predictive ability of PLSDA model for limestone is high with an AUC value of 0.8017, followed by dolomite with an AUC value of 0.7204. From the results, we suggest that the dual-pol Sentinel-1 data are able to correctly distinguish specific rocks and has the potential to capture the variation of different rocks. Full article

(This article belongs to the Special Issue Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing)

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24 pages, 37182 KiB

Open AccessArticle

Unraveling the Morphological Constraints on Roman Gold Mining Hydraulic Infrastructure in NW Spain. A UAV-Derived Photogrammetric and Multispectral Approach

by Javier Fernández-Lozano and Enoc Sanz-Ablanedo

Remote Sens. 2021, 13(2), 291; https://doi.org/10.3390/rs13020291 - 15 Jan 2021

Cited by 9 | Viewed by 4977

Abstract

The province of León preserves a unique hydraulic infrastructure 1200 km-long, used for the exploitation of auriferous deposits in Roman times. It represents the most extensive waterworks in Europe and is one of the best-preserved examples of mining heritage in Antiquity. In this work, three mining exploitation sectors (upper, middle, and lower) characterized by channels and leats developed in different geological materials were examined, using Unmanned Aerial Vehicles (UAVs). A multi-approach based on a comparison of photogrammetric and multispectral data improved the identification and description of the hydraulic network. Comparison with traditional orthoimages and LiDAR data suggests that UAV-derived multispectral images are of great interest in areas where these sets of data have low resolution or areas that are densely covered by vegetation. The results showed that the size of the channel box and its width were factors that do not depend exclusively on the available water resources, as previously suggested, but also on the geological and hydraulic conditioning factors that intervene in each sector. Additionally, the detailed study allowed the establishment of a water sheet maximum height that was much lower than previously thought. All in all, these inferences might help researchers develop new strategies for mapping the Roman mining infrastructure and establishing the importance of geological inheritance on the construction of the hydraulic system that led the Romans to the accomplishment of the largest mining infrastructure ever known in Europe. Full article

(This article belongs to the Special Issue Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing)

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20 pages, 11455 KiB

Open AccessArticle

Mapping the Lithological Features and Ore-Controlling Structures Related to Ni–Cu Mineralization in the Eastern Tian Shan, NW China from ASTER Data

by Shuo Zheng, Yanfei An, Pilong Shi and Tian Zhao

Remote Sens. 2021, 13(2), 206; https://doi.org/10.3390/rs13020206 - 8 Jan 2021

Cited by 6 | Viewed by 2820

Abstract

The study of lithological features and tectonic evolution related to mineralization in the eastern Tian Shan is crucial for understanding the ore-controlling mechanism. In this paper, the lithological features and ore-controlling structure of the Huangshan Ni–Cu ore belt in the eastern Tian Shan are documented using advanced spaceborne thermal emission and reflection radiometer (ASTER) multispectral data based on spectral image processing algorithms, mineral indices and directional filter technology. Our results show that the algorithms of b2/b1, b6/b7 and b4/b8 from ASTER visible and near-infrared (VNIR)- shortwave infrared (SWIR) bands and of mafic index (MI), carbonate index (CI) and silica index (SI) from thermal infrared (TIR) bands are helpful to extract regional pyroxenite, external foliated gabbro bearing Ni–Cu ore bodies as well as the country rocks in the study area. The detailed interpretations and analyses of the geometrical feature of fault system and intrusive facies suggest that the Ni–Cu metallogenic belts are related to Carboniferous arc intrusive rocks and Permian wrench tectonics locating at the intersection of EW- and NEE-striking dextral strike-slip fault system, and the emplacement at the releasing bends in the southern margin of Kanggur Fault obviously controlled by secondary faults orthogonal or oblique to the Kanggur Fault in the post-collision extensional environment. Therefore, the ASTER data-based approach to map lithological features and ore-controlling structures related to the Ni–Cu mineralization are well performed. Moreover, a 3D geodynamic sketch map proposes that the strike-slip movement of Kanggur Fault in Huangshan-Kanggur Shear Zone (HKSZ) during early Permian controlled the migration and emplacement of three mafic/ultramafic intrusions bearing Ni–Cu derived from partial mantle melting and also favored CO₂-rich fluids leaking to the participation of metallogenic processes. Full article

(This article belongs to the Special Issue Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing)

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19 pages, 5307 KiB

Open AccessArticle

Application of Lithological Mapping Based on Advanced Hyperspectral Imager (AHSI) Imagery Onboard Gaofen-5 (GF-5) Satellite

by Bei Ye, Shufang Tian, Qiuming Cheng and Yunzhao Ge

Remote Sens. 2020, 12(23), 3990; https://doi.org/10.3390/rs12233990 - 6 Dec 2020

Cited by 53 | Viewed by 5644

Abstract

The Advanced Hyperspectral Imager (AHSI), carried by the Gaofen-5 (GF-5) satellite, is the first hyperspectral sensor that simultaneously offers broad coverage and a broad spectrum. Meanwhile, deep-learning-based approaches are emerging to manage the growing volume of data produced by satellites. However, the application potential of GF-5 AHSI imagery in lithological mapping using deep-learning-based methods is currently unknown. This paper assessed GF-5 AHSI imagery for lithological mapping in comparison with Shortwave Infrared Airborne Spectrographic Imager (SASI) data. A multi-scale 3D deep convolutional neural network (M3D-DCNN), a hybrid spectral CNN (HybridSN), and a spectral–spatial unified network (SSUN) were selected to verify the applicability and stability of deep-learning-based methods through comparison with support vector machine (SVM) based on six datasets constructed by GF-5 AHSI, Sentinel-2A, and SASI imagery. The results show that all methods produce classification results with accuracy greater than 90% on all datasets, and M3D-DCNN is both more accurate and more stable. It can produce especially encouraging results by just using the short-wave infrared wavelength subset (SWIR bands) of GF-5 AHSI data. Accordingly, GF-5 AHSI imagery could provide impressive results and its SWIR bands have a high signal-to-noise ratio (SNR), which meets the requirements of large-scale and large-area lithological mapping. And M3D-DCNN method is recommended for use in lithological mapping based on GF-5 AHSI hyperspectral data. Full article

(This article belongs to the Special Issue Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing)

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27 pages, 16287 KiB

Open AccessArticle

High Accuracy Geochemical Map Generation Method by a Spatial Autocorrelation-Based Mixture Interpolation Using Remote Sensing Data

by Chenhui Huang and Akinobu Shibuya

Remote Sens. 2020, 12(12), 1991; https://doi.org/10.3390/rs12121991 - 21 Jun 2020

Cited by 2 | Viewed by 3032

Abstract

Generating a high-resolution whole-pixel geochemical contents map from a map with sparse distribution is a regression problem. Currently, multivariate prediction models like machine learning (ML) are constructed to raise the geoscience mapping resolution. Methods coupling the spatial autocorrelation into the ML model have been proposed for raising ML prediction accuracy. Previously proposed methods are needed for complicated modification in ML models. In this research, we propose a new algorithm called spatial autocorrelation-based mixture interpolation (SABAMIN), with which it is easier to merge spatial autocorrelation into a ML model only using a data augmentation strategy. To test the feasibility of this concept, remote sensing data including those from the advanced spaceborne thermal emission and reflection radiometer (ASTER), digital elevation model (DEM), and geophysics (geomagnetic) data were used for the feasibility study, along with copper geochemical and copper mine data from Arizona, USA. We explained why spatial information can be coupled into an ML model only by data augmentation, and introduced how to operate data augmentation in our case. Four tests—(i) cross-validation of measured data, (ii) the blind test, (iii) the temporal stability test, and (iv) the predictor importance test—were conducted to evaluate the model. As the results, the model’s accuracy was improved compared with a traditional ML model, and the reliability of the algorithm was confirmed. In summary, combining the univariate interpolation method with multivariate prediction with data augmentation proved effective for geological studies. Full article

(This article belongs to the Special Issue Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing)

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16 pages, 13500 KiB

Open AccessArticle

Detecting the Sources of Methane Emission from Oil Shale Mining and Processing Using Airborne Hyperspectral Data

by Chunlei Xiao, Bihong Fu, Hanqing Shui, Zhaocheng Guo and Jurui Zhu

Remote Sens. 2020, 12(3), 537; https://doi.org/10.3390/rs12030537 - 6 Feb 2020

Cited by 12 | Viewed by 5454

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 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 R_CH4, R_CH4 = 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

(This article belongs to the Special Issue Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing)

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30 pages, 7186 KiB

Open AccessArticle

Optimized Lithological Mapping from Multispectral and Hyperspectral Remote Sensing Images Using Fused Multi-Classifiers

by Mahendra Pal, Thorkild Rasmussen and Alok Porwal

Remote Sens. 2020, 12(1), 177; https://doi.org/10.3390/rs12010177 - 3 Jan 2020

Cited by 52 | Viewed by 6114

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 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

(This article belongs to the Special Issue Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing)

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22 pages, 9217 KiB

Open AccessArticle

Identification of Hydrothermal Alteration Minerals for Exploring Gold Deposits Based on SVM and PCA Using ASTER Data: A Case Study of Gulong

by Kai Xu, Xiaofeng Wang, Chunfang Kong, Ruyi Feng, Gang Liu and Chonglong Wu

Remote Sens. 2019, 11(24), 3003; https://doi.org/10.3390/rs11243003 - 13 Dec 2019

Cited by 17 | Viewed by 5854

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, 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

(This article belongs to the Special Issue Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing)

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28 pages, 18491 KiB

Open AccessArticle

Towards Multiscale and Multisource Remote Sensing Mineral Exploration Using RPAS: A Case Study in the Lofdal Carbonatite-Hosted REE Deposit, Namibia

by René Booysen, Robert Zimmermann, Sandra Lorenz, Richard Gloaguen, Paul A. M. Nex, Louis Andreani and Robert Möckel

Remote Sens. 2019, 11(21), 2500; https://doi.org/10.3390/rs11212500 - 25 Oct 2019

Cited by 18 | Viewed by 7548

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 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

(This article belongs to the Special Issue Surface Mineral Allocation and Lithological Mapping Based on Remote Sensing)

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