MAPPING OF SITTAMPUNDI ANORTHOSITE COMPLEX (SAC) IN SOUTHERN GRANULITE TERRAIN (SGT), INDIA WITH ASTER AND SENTINEL-2A DATA

The Sittampundi Anorthosite Complex (SAC) is a well-exposed Archean layered anorthosite-gabbro- ultramafic rock complex in southern India. SAC well preserved white- and dark-anorthosite,gabbros, and ultramafic rocks. This study aims to discriminate, characterize, and separate from adjacent and surrounding rocks the anorthosite complex in sitampundi using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2A data. Methods such as band color composites (True color composite, False color composite and Pseudo color composite), Principal Component Analysis (PCA), Minimum Noise Fraction (MNF) and Spectral Angle Mapper (SAM), are applied to discriminate the anorthosite complex in SGT (Southern Granulite Terrain). Band composites enhance the litho units using visible and shortwave infrared bands and thus, assisted in mapping for the anorthosite complex. PCA and MNF have been applied to the ASTER and Sentinel- 2A bands in order to decrease the redundant information in highly correlated bands. PCA and MNF driven band combinations facilitate the validation and help in discriminating the various lithological units exposed in the study area. SAM classifier classification technique was utilized to characterize the selected surface mineral assemblages from Sitampundi Anorthosite Complex using spectral signatures. The above- mentioned digital image processing techniques have been proven resourceful in discrimination of anorthosite complex and associated rocks. The results obtained from ASTER and Sentinel-2A data processing were validated in field, followed by accuracy assessment

Delineation of Copper Mineralization Zones at Wadi Ham, Northern Oman Mountains, United Arab Emirates Using Multispectral Landsat 8 (OLI) Data

© Copyright © 2020 Howari, Ghrefat, Nazzal, Galmed, Abdelghany, Fowler, Sharma, AlAydaroos and Xavier. Copper deposits in the ultramafic rocks of the Semail ophiolite massifs is found to be enormous in the region of northern Oman Mountains, United Arab Emirates. For this study, samples of copper were gathered from 14 different sites in the investigation area and were analyzed in the laboratory using the X-ray diffraction, GER 3700 spectroradiometer, and Inductively Coupled Plasma-Mass Spectrometer. Detection and mapping of copper-bearing mineralized zones were carried out using different image processing approaches of minimum noise fraction, principal component analysis, decorrelation stretch, and band ratio which were applied on Landsat 8 (OLI) data. The spectra of malachite and azurite samples were characterized by broad absorption features in the visible and near infrared region (0.6–1.0 µm). The results obtained from the principal component analysis, minimum noise fraction, band ratio, decorrelation stretch, spectral reflectance analyses, and mineralogical and chemical analyses were found to be similar. Thus, it can be concluded that multispectral Landsat 8 data are useful in the detection iron ore deposits in arid and semi-arid regions

Towards better delineation of hydrothermal alterations via multi-sensor remote sensing and airborne geophysical data

Integrating various tools in targeting mineral deposits increases the chance of adequate detection and characterization of mineralization zones. Selecting a convenient dataset is a key for a precise geological and hydrothermal alteration mapping. Remote sensing and airborne geophysical data have proven their efficiency as tools for reliable mineral exploration. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced land imager (ALI), Landsat 8 (L8), and Sentinel 2 data are widely-used data among various types of remote sensing images in resolving lithological and hydrothermal alteration mapping over the last two decades. ASTER is a well-established satellite in geological remote sensing with detailed Short-wave infrared (SWIR) range compared to visible and near-infrared region (VNIR) that controls iron-associated alteration detection. On contrary, ALI has excellent coverage of the VNIR area (6 bands), but does not possess the potentiality of ASTER for the SWIR and thermal regions. Landsat 8 is widely used and highly recommended for lithological and hydrothermal alteration mapping. The higher spatial (up to 10 m) resolution of Sentinel 2 MSI has preserved its role in producing accurate geological mapping. Notwithstanding the foregoing, implementing the four datasets in a single study is time-consuming. Thus, an important question when commencing an exploration project for hydrothermal alterations-related mineralization (orogenic mineral deposits in the current research) is: which dataset should be adopted to fulfill proper and adequate outputs? Here the four widely recommended datasets (ASTER, ALI, L8, and sentinel 2) have been tested by applying the widely-accepted techniques (false color combinations, band ratios, directed principal component analysis, and constrained energy minimization) for geological and hydrothermal alteration mapping of Gabal El Rukham-Gabal Mueilha district, Egypt. The study area is covered mainly by Neoproterozoic heterogeneous collection of ophiolitic components, island arc assemblage, intruded by enormous granitic rocks. Additionally, airborne magnetic and radiometric data were applied and compared with the remote sensing investigations for deciphering the structural and hydrothermal alteration patterns within the study area. The results demonstrated a different extent from one sensor to another, highlighting their varied efficacy in detecting hydrothermal alterations (mainly hydroxyl-bearing alterations and iron oxides). Moreover, the analysis of airborne magnetic and radiometric data showed hydrothermal alteration zones that are consistent with the detected alteration pattern. The coincidence between high magnetic anomalies, high values of the K/eTh ratio, and the resultant alterations confirm the real alteration anomalies. Over and above that, the remote sensing results and airborne geophysical indications were verified with fieldwork and petrographic investigations, and strongly recommend combining ASTER and Sentinel 2 results in further investigations. Based on the outputs of the current research, we expect better hydrothermal alteration delineation by adopting the current findings as they sharply narrow the zones to be further investigated via costly geophysical and geochemical methods in mineral exploration projects

Detection of magnetite in the Roossenekal area of the Eastern Bushveld Complex, South Africa, using multispectral remote sensing data

Multispectral sensors, along with common and advanced algorithms, have become efficient tools for routine lithological discrimination and mineral potential mapping. It is with this paradigm in mind that this paper sought to evaluate and discuss the detection and mapping of magnetite on the Eastern Limb of the Bushveld Complex, using high spectral resolution multispectral remote sensing imagery and GIS techniques. Despite the wide distribution of magnetite, its economic importance, and its potential as an indicator of many important geological processes, not many studies had looked at the detection and exploration of magnetite using remote sensing in this region. The Maximum Likelihood and Support Vector Machine classification algorithms were assessed for their respective ability to detect and map magnetite using the PlanetScope Analytic data. A K-fold cross-validation analysis was used to measure the performance of the training as well as the test data. For each classification algorithm, a thematic landcover map was created and an error matrix, depicting the user’s and producer’s accuracies as well as kappa statistics, was derived. A pairwise comparison test of the image classification algorithms was conducted to determine whether the two classification algorithms were significantly different from each other. The Maximum Likelihood Classifier significantly outperformed the Support Vector Machine algorithm, achieving an overall classification accuracy of 84.58% and an overall kappa value of 0.79. Magnetite was accurately discriminated from the other thematic landcover classes with a user’s accuracy of 76.41% and a producer’s accuracy of 88.66%. The overall results of this study illustrated that remote sensing techniques are effective instruments for geological mapping and mineral investigation, especially iron oxide mineralization in the Eastern Limb of the Bushveld Complex.http://sajg.geoscienceworld.orgam2021Geography, Geoinformatics and MeteorologyGeolog

Machine Learning for Gully Feature Extraction Based on a Pan-Sharpened Multispectral Image: Multiclass vs. Binary Approach

K

Big geochemical data through remote sensing for dynamic mineral resource monitoring in tailing storage facilities

Evolution in geoscientific data provides the mineral industry with new opportunities. A direction of geochemical data generation evolution is towards big data to meet the demands of data-driven usage scenarios that rely on data velocity. This direction is more significant where traditional geochemical data are not ideal, which is the case for evaluating unconventional resources, such as tailing storage facilities (TSFs), because they are not static due to sedimentation, compaction and changes associated with hydrospheric and lithospheric processes (e.g., erosion, saltation and mobility of chemical constituents). In this paper, we generate big secondary geochemical data derived from Sentinel-2 satellite-remote sensing data to showcase the benefits of big geochemical data using TSFs from the Witwatersrand Basin (South Africa). Using spatially fused remote sensing and legacy geochemical data on the Dump 20 TSF, we trained a machine learning model to predict in-situ gold grades. Subsequently, we deployed the model to the Lindum TSF, which is 3 km away, over a period of a few years (2015-2019). We were able to visualize and analyze the temporal variation in the spatial distributions of the gold grade of the Lindum TSF. Additionally, we were able to infer extraction sequencing (to the resolution of the data), acid mine drainage formation and seasonal migration. These findings suggest that dynamic mineral resource models and live geochemical monitoring (e.g., of elemental mobility and structural changes) are possible without additional physical sampling

DEVELOPING INNOVATIVE SPECTRAL AND MACHINE LEARNING METHODS FOR MINERAL AND LITHOLOGICAL CLASSIFICATION USING MULTI-SENSOR DATASETS

The sustainable exploration of mineral resources plays a significant role in the economic development of any nation. The lithological maps and surface mineral distribution can be vital baseline data to narrow down the geochemical and geophysical analysis potential areas. This study developed innovative spectral and Machine Learning (ML) methods for mineral and lithological classification. Multi-sensor datasets such as Airborne Visible/Infrared Imaging Spectrometer-Next Generation (AVIRIS-NG), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing (ALOS) Phased Array type L-band Synthetic Aperture Radar (PALSAR), Sentinel-1, and Digital Elevation Model (DEM) were utilized. The study mapped the hydrothermal alteration minerals derived from Spectral Mapping Methods (SMMs), including Spectral Angle Mapper (SAM), Spectral Information Divergence (SID), and SIDSAMtan using high-resolution AVIRIS-NG hyperspectral data in the Hutti-Maski area (India). The SIDSAMtan outperforms SID and SAM in mineral mapping. A spectral similarity matrix of target and non-target classes based optimum threshold selection was developed to implement the SMMs successfully. Three new effective SMMs such as Dice Spectral Similarity Coefficient (DSSC), Kumar-Johnson Spectral Similarity Coefficient (KJSSC), and their hybrid, i.e., KJDSSCtan has been proposed, which outperforms the existing SMMs (i.e., SAM, SID, and SIDSAMtan) in spectral discrimination of spectrally similar minerals. The developed optimum threshold selection and proposed SMMs are recommended for accurate mineral mapping using hyperspectral data. An integrated spectral enhancement and ML methods have been developed to perform automated lithological classification using AVIRIS-NG hyperspectral data. The Support Vector Machine (SVM) outperforms the Random Forest (RF) and Linear Discriminant Analysis (LDA) in lithological classification. The performance of SVM also shows the least sensitivity to the number and uncertainty of training datasets. This study proposed a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks for accurate lithological classification using ML models. Different input features, such as (a) spectral, (b) spectral and transformed spectral, (c) spectral and morphological, (d) spectral and textural, and (e) optimum hybrid, were evaluated for lithological classification. The developed approach has been assessed in the Chattarpur area (India) consists of similar spectral characteristics and poorly exposed rocks, weathered, and partially vegetated terrain. The optimal hybrid input features outperform other input features to accurately classify different rock types using the SVM and RF models, which is ~15% higher than as obtained using spectral input features alone. The developed integrated approach of spectral enhancement and ML algorithms, and a multi-sensor datasets-based optimal integration of spectral, morphological, and textural characteristics of rocks, are recommended for accurate lithological classification. The developed methods can be effectively utilized in other remote sensing applications, such as vegetation/forest mapping and soil classification

Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China

As a source of data continuity between Landsat and SPOT, Sentinel-2 is an Earth observation mission developed by the European Space Agency (ESA), which acquires 13 bands in the visible and near-infrared (VNIR) to shortwave infrared (SWIR) range. In this study, a Sentinel-2A imager was utilized to assess its ability to perform lithological classification in the Shibanjing ophiolite complex in Inner Mongolia, China. Five conventional machine learning methods, including artificial neural network (ANN), k-nearest neighbor (k-NN), maximum likelihood classification (MLC), random forest classifier (RFC), and support vector machine (SVM), were compared in order to find an optimal classifier for lithological mapping. The experiment revealed that the MLC method offered the highest overall accuracy. After that, Sentinel-2A image was compared with common multispectral data ASTER and Landsat-8 OLI (operational land imager) for lithological mapping using the MLC method. The comparison results showed that the Sentinel-2A imagery yielded a classification accuracy of 74.5%, which was 2.5% and 5.08% higher than those of the ASTER and OLI imagery, respectively, indicating that Sentinel-2A imagery is adequate for lithological discrimination, due to its high spectral resolution in the VNIR to SWIR range. Moreover, different data combinations of Sentinel-2A + ASTER + DEM (digital elevation model) and OLI + ASTER + DEM data were tested on lithological mapping using the MLC method. The best mapping result was obtained from Sentinel-2A + ASTER + DEM dataset, demonstrating that OLI can be replaced by Sentinel-2A, which, when combined with ASTER, can achieve sufficient bandpasses for lithological classification

Lithological Classification Using Sentinel-2A Data in the Shibanjing Ophiolite Complex in Inner Mongolia, China

As a source of data continuity between Landsat and SPOT, Sentinel-2 is an Earth observation mission developed by the European Space Agency (ESA), which acquires 13 bands in the visible and near-infrared (VNIR) to shortwave infrared (SWIR) range. In this study, a Sentinel-2A imager was utilized to assess its ability to perform lithological classification in the Shibanjing ophiolite complex in Inner Mongolia, China. Five conventional machine learning methods, including artificial neural network (ANN), k-nearest neighbor (k-NN), maximum likelihood classification (MLC), random forest classifier (RFC), and support vector machine (SVM), were compared in order to find an optimal classifier for lithological mapping. The experiment revealed that the MLC method offered the highest overall accuracy. After that, Sentinel-2A image was compared with common multispectral data ASTER and Landsat-8 OLI (operational land imager) for lithological mapping using the MLC method. The comparison results showed that the Sentinel-2A imagery yielded a classification accuracy of 74.5%, which was 2.5% and 5.08% higher than those of the ASTER and OLI imagery, respectively, indicating that Sentinel-2A imagery is adequate for lithological discrimination, due to its high spectral resolution in the VNIR to SWIR range. Moreover, different data combinations of Sentinel-2A + ASTER + DEM (digital elevation model) and OLI + ASTER + DEM data were tested on lithological mapping using the MLC method. The best mapping result was obtained from Sentinel-2A + ASTER + DEM dataset, demonstrating that OLI can be replaced by Sentinel-2A, which, when combined with ASTER, can achieve sufficient bandpasses for lithological classification