Lithological and Hydrothermal Alteration Mapping Using Terra ASTER and Landsat-8 OLI Multispectral Data in the North-Eastern Border of Kerdous Inlier, Western Anti-Atlasic Belt, Morocco

In this study, the satellite data of ASTER and Landsat 8 OLI were used for the discrimination of lithological units covering the Khyber range. Of the 24 tested band combinations, the most suitable include 632 and 468 of ASTER and 754 and 147 of OLI in the RGB sequence. The data were also tested with two conventional machine learning algorithms (MLAs), namely maximum likelihood classification (MLC) and support vector machine (SVM), for lithological mapping. Principal component analysis (PCA), minimum noise fraction (MNF), band ratios, and color composites in combination with available lithological maps and field data were utilized for training sample collection for the MLC and SVM models to classify the lithological units. The accuracy assessment of SVM and MLC was performed using a confusion matrix, which revealed a higher accuracy of 74.8419% and 72.1217% for ASTER and an accuracy of 58.4833% and 60.0257% for OLI, respectively. The results indicate that ASTER imagery is more suitable for lithological discrimination in the study area due to its high spectral resolution in the VNIR to SWIR range. The experiment revealed that the SVM classification offered the highest overall accuracy of nearly 75% and the kappa coefficient value of 0.7 on ASTER data. This demonstrates the effectiveness of SVM classification in exploring lithological mapping in dry to semi-arid regions.

Lithological studies and geological unit mappings are generally applicable to many fields of natural resource management. Relatively suitable aquifers have been formed in complex formations in northwest Shahrood due to the presence of carbonate rocks as well as erosion and tectonic forces in the region. This study aims to identify and separate the calcareous formations that can form karst aquifers in the study area. As a result of erosion and tectonic forces, the rocks of the region exhibit spectral fluctuations, making it difficult for mapping geological formations using multispectral images. Therefore, Landsat 8 satellite-based images were processed by adopting the minimum noise fraction (MNF), independent component analysis (ICA), and band ratio (BR). The indices of calcareous formations and shale formations were created by the BR through the spectral behavior of pure pixels. Moreover, the support vector machine (SVM) and maximum likelihood (ML) were employed for classification. The SVM classifier proved more capable of classification than the ML classifier, and the transform ICA outperformed the MNF in the separation of formations. The lithological maps were extracted using the SVM with an overall accuracy (OA) of 68.04%. Furthermore, a method decision tree (DT) was employed to improve the classification accuracy. The DT classifier was then utilized to reclassify lithological maps that were classified by SVM and ML through morphological characteristics and indices of formations The DT classifier improved the lithological map accuracy by 10%. The boundaries of calcareous formations were extracted from non-calcareous formations with an accuracy of 93%, and the regional constructions were separated with an accuracy of ∼80 % . Finally, the lithological map was developed with a kappa of 0.734 and an OA of 78.59%.

Fusing multi-source (remote sensing and geophysical) data and diverse approaches validation in targeting hydrothermal alteration and structural anomalies enhances the potential for accurately detecting and characterizing mineralization zones. Sentinel 2 data and ASTER were processed for lithological and hydrothermal alteration mapping in the rare metal-rich Umm Naggat area (Egypt). Different image processing techniques were implemented, including false color composites, minimum noise fraction, band rationing, band math, mineral indices, relative absorption band depth, and constrained energy minimization. The rare metal-bearing Umm Naggat younger granite (NYG) pluton was lithologically discriminated and intra-differentiated to mafic-rich biotite granites, mafic-poor alkali feldspar granites, and albitized granites. Extensive hydrothermal alterations, such as albitization, ferrugination, propylitization, argillization, and phyllitization, overprint the NYG pluton. Normalized standard deviation, automatic lineament extractions, and trend analysis highlighted the key structural directions (NW, NNW, NNE, and NE) and distinguished the NYG pluton as a moderate to high structural density zone. The high structural density and intensive alteration zones are spatially associated and more localized within the NYG pluton than the surrounding rocks. Spatial overlay analysis confirmed that the hydrothermal alterations and fluid circulation systems are structurally-controlled. Furthermore, the hydrothermal alteration mapping and structural analysis outcomes were verified by combining fieldwork, slab polishing, petrographic investigations, and mineral chemistry through semi-quantitative scanning electron microscopy with energy dispersive spectroscopy (SEM-EDS) and quantitative electron probe microanalysis (EPMA) analysis. As a result, the hydrothermal genesis of rare metal-bearing minerals (Nb-rutile, Nb-ilmenite, and columbite) close to or incorporated within alteration minerals (chlorite, muscovite, and hematite) is confirmed from the alteration zones (propylitic, phyllic, and ferruginated). In addition, biotite muscovitization and chloritization significantly contribute to the secondary rare metal enrichment. The current study emphasizes the extensive distribution of secondary rare metal-bearing minerals within the entire NYG pluton (not only limited to the northern albitized granite as depicted by previous studies), which might shed light on these hydrothermally-altered younger granites as a new potential source for Nb and Ta in Egypt.

Comparison of Landsat OLI, ASTER, and Sentinel 2A data in lithological mapping : A Case study of Rich area (Central High Atlas, Morocco)

Tree species’ composition of forests is essential in forest management and nature conservation. We aimed to identify the tree species structure of a floodplain forest area using a hyperspectral image. We proposed an efficient novel strategy including the testing of three dimension reduction (DR) methods: Principal Component Analysis, Minimum Noise Fraction (MNF) and Indipendent Component Analysis with five machine learning (ML) algorithms (Maximum Likelihood Classifier, Support Vector Classification, Support Vector Machine, Random Forest and Artificial Neural Network) to find the most accurate outcome; altogether 300 models were calculated. Post-classification was applied by combining the multiresolution segmentation and filtering. MNF was the most efficient DR technique, and at least 7 components were needed to gain an overall accuracy (OA) of > 75%. Forty-five models had > 80% OAs; MNF was 43, and the Maximum Likelihood was 19 times among these models. Best classification belonged to MNF with 10 components and Maximum Likelihood classifier with the OA of 83.3%. Post-classification increased the OA to 86.1%. We quantified the differences among the possible DR and ML methods, and found that even > 10% worse model can be found using popular standard procedures related to the best results. Our workflow calls the attention of careful model selection to gain accurate maps.

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.

DOI:10.17014/ijog.9.1.45-60Several researchers through geochemical analysis have proven the presence of gold mineralization in Kokap, Kulon Progo, as a result of hydrothermal alteration. Alteration mapping with optical remote sensing images in tropical areas is very difficult due to atmospheric conditions, dense vegetation cover, and rapid weathering. This study aims to assess the ability of Landsat 8 images in the mapping of hydrothermal alteration in Kokap, Kulon Progo, with the Principles Component Analysis (PCA) method. Three conventional machine learning methods, including artificial neural network (ANN), maximum likelihood classification (MLC), and support vector machine (SVM) were compared to find an optimal classifier for hydrothermal alteration mapping. The experiment revealed that the MLC method offered the highest overall accuracy. Two alteration zones were mapped, i.e. argillic zone and propylitic zone. The comparison results showed that the MLC classification of band ratio images of 5:2 and 6:7 yielded a classification accuracy of 56.4% and kappa coefficient of 0.36, which was higher than those of other machine learning methods and band combinations. The combination of Landsat 8 with DEM succeeded in increasing accuracy to 59.5% with kappa coefficient of 0.4.

Remote sensing data has been widely applied to classify the land cover more frequently and on a near real-time basis for updating as it is more economic, less time consuming compared to ground based survey. Accurate classification of the land use/cover classes such as water body, cropland, built-up area, scrub land, fallow land, forest etc., is one of the biggest challenges in natural resource inventory, management and monitoring. As accuracy of remote sensing data classification is affected by many parameters which include type of data, presence of heterogeneous landscapes in study area, classification approaches etc., as satellite imagery is complex in nature. Many classifiers have been developed and tested on remotely sensed data for better classification. Classification of remote sensing data is mainly divided into two categories such as supervised and unsupervised. In supervised classification, the decision boundaries in feature space are determined by training the samples. Two supervised classifiers namely maximum likelihood (ML) classifier, which is a parametric classifier that assumes data to be normally distributed, and support vector machine classifier (SVM) which is a non-parametric classifier are used in classification. In the present study, the accuracy of these two classifiers is studied on five different data sets of Sentinel-2 satellite image of different years and sessions to accommodate intra and inter annual variations of the datasets. Sentinel-2 satellite images covering part of Nagpur, located in Maharashtra, India were used for the classification. Classifier accuracy has been calculated using overall accuracy and kappa statistics based on ground truth information. The result obtained were carefully examined by comparing classification accuracies and then by visual analysis. The result shows that SVM classifiers gives better overall accuracy and kappa coefficients and its average value for intra and inter annual classification outputs were 91.78% and 0.89 in that order which is far better than ML classifier which gave 87.07% and 0.83 respectively. The experimental results obtained from the present study, it is clear that SVM classifier produced better accuracy than ML classifier in classifying Sentinel-2 optical image and have significant potential in classifying various land cover classes in the heterogeneous land use/land cover conditions of the tropical regime.

Lithological and lineament mapping using remote sensing is a fundamental step in various geological studies, as it forms the basis for the interpretation and validation of the results obtained. There were two objectives for this study, applied in the Imini-Ounilla-Asfalou district, South High Atlas of Marrakech region: first, lithological mapping by satellite image processing techniques such as ASTER L1B (hight spectral and spatial resolution), namely Principal Component Analysis (PCA), Minimum Noise Fraction (MNF), as well as the application of three types of supervised classification, namely Spectral Angle Mapper (SAM), Maximum Likelihood (ML) and Minimum Distance (MD), on the visible/near-infrared (VNIR) and short-wave infrared (SWIR) spectral bands of our ASTER image; second, an analysis of the distribution of lineaments by automatic extraction using a Global Digital Elevation Model (GDEM) and the PC1 image derived from the PCA transformation applied to the satellite image. The best results are highlighted by the delineation of new facies in relation to the existing map; after confirmation in the field, all of these facies, which include Eocene, Triassic and Jurassic formations, are represented on the new map. The results of lineaments showed that each of them systematically shows a similarity in terms of concentration and orientation, with four preferential oriented systems: NE-SW, E-W, NNE-SSW and NW-SE. The lineaments mainly follow those of the major fault zones, with high concentrations in the northeast and southwest parts of the study area.

Artificial intelligence (AI)-based multispectral remote sensing has been the best supporting tool using limited resources to enhance the lithological mapping abilities with accuracy, supported by ground truthing through traditional mapping techniques. The availability of the dataset, choice of algorithm, cost, accuracy, computational time, data labeling, and terrain features are some crucial considerations that researchers continue to explore. In this research, support vector machine (SVM) and artificial neural network (ANN) were applied to the Sentinel-2 MSI dataset for classifying lithologies having subtle compositional differences in the Kohat Basin’s remote, inaccessible regions within Pakistan. First, we used principal component analysis (PCA), minimum noise fraction (MNF), and available maps for reliable data annotation for training SVM and (ANN) models for mapping ten classes (nine lithological units + water). The ANN and SVM results were compared with the previously conducted studies in the area and ground truth survey to evaluate their accuracy. SVM mapped ten classes with an overall accuracy (OA) of 95.78% and kappa coefficient of 0.95, compared to 95.73% and 0.95 by ANN classification. The SVM algorithm was more efficient concerning computational efficiency, accuracy, and ease due to available features within Google Earth Engine (GEE). Contrarily, ANN required time-consuming data transformation from GEE to Google Cloud before application in Google Colab.

This study evaluated and compared six image classifiers, including minimum distance (MD), Mahalanobis distance (MAHD), maximum likelihood (ML), spectral angle mapper (SAM), mixture tuned matched filtering (MTMF) and support vector machine (SVM), for detecting and mapping giant reed (Arundo donax L.), an invasive weed that presents a severe threat to agroecosystems throughout the southern US and northern Mexico. Airborne hyperspectral imagery was collected from a giant reed-infested site along the US-Mexican portion of the Rio Grande in 2009 and 2010. The imagery was transformed with minimum noise fraction (MFN) and the six classifiers were applied to the 30-band MNF imagery for each year. Accuracy assessment showed that SVM and ML generally performed better than the other four classifiers for overall classification and for distinguishing giant reed in both years. These results indicate that airborne hyperspectral imagery in conjunction with SVM and ML classification techniques is effective for detecting giant reed.

Hyperspectral remote sensing data contains a large number of spectral bands along with a large a number of features. So for classification purposes, it becomes imperative to reduce the number of spectral bands as well as to reduce the number of features to achieve higher performance in terms of accuracy and computational complexity. Principal component analysis (PCA) and minimum noise fraction (MNF) were used for dimension reduction of spectral bands, on the other hand correlation based feature selection (CFS) technique was employed for selection of features. Parametric classifier like maximum likelihood classifier (MLC), advanced non parametric classifiers like support vector machine (SVM), Multilayer Perceptron (MLP) and ensemble random forest (RF) classifier were used for investigation in two test datasets of different land cover characteristics of Hyperion sensor. It was observed that all of the classifiers have behaved differently with the dimension reduced dataset produced either in spectral bands or feature level. Most of the classifiers have achieved higher performance with dimension reduced dataset produced by MNF as compared to PCA. On the other hand, dataset with selected features could not give noticeably better classification performance than the datasets with entire features. However, computational complexity of the classifiers has been reduced greatly while they were classified with selected features.

Abstract. This study exploited the multispectral Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Landsat 8 Operational Land Imager (OLI) data in order to map lithological units and structural map in the south High Atlas of Marrakech. The method of analysis was used by principal component analysis (PCA), band ratios (BR), Minimum noise fraction (MNF) transformation. We performed a Support Vector Machine (SVM) classification method to allow the joint use of geomorphic features, textures and multispectral data of the Advanced Space-borne Thermal Emission and Reflection radiometer (ASTER) satellite. SVM based on ground truth in addition to the results of PCA and BR show an excellent correlation with the existing geological map of the study area. Consequently, the methodology proposed demonstrates a high potential of ASTER and Landsat 8 OLI data in lithological units discrimination. The application of the SVM methods on ASTER and Landsat satellite data show that these can be used as a powerful tool to explore and improve lithological mapping in mountainous semi-arid, the overall classification accuracy of Landsat8 OLI data is 97.28% and the Kappa Coefficient is 0.97. The overall classification accuracy of ASTER using nine bands (VNIR-SWIR) is 74.88% and the Kappa Coefficient is 0.71.

The gold mineralization located in the southern Eastern Desert of Egypt mostly occurs in characteristic geologic and structural settings. The gold-bearing quartz veins and the alteration zones are confined to the ductile shear zones between the highly deformed ophiolitic blocks, sheared metavolcanics, and gabbro-diorite rocks. The present study attempts to integrate multisensor remotely sensed data, structural analysis, and field investigation in unraveling the geologic and structural controls of gold mineralization in the Gabal Gerf area. Multispectral optical sensors of Landsat-8 OLI/TIRS (L8) and Sentinel-2B (S2B) were processed to map the lithologic rock units in the study area. Image processing algorithms including false color composite (FCC), band ratio (BR), principal component analysis (PCA), minimum noise fraction (MNF), and Maximum Likelihood Classifier (MLC) were effective in producing a comprehensive geologic map of the area. The mafic index (MI) = (B13-0.9147) × (B10-1.4366) of ASTER (A) thermal bands and a combined band ratio of S2B and ASTER of (S2B3+A9)/(S2B12+A8) were dramatically successful in discriminating the ophiolitic assemblage, that are considered the favorable lithology for the gold mineralization. Three alteration zones of argillic, phyllic and propylitic were spatially recognized using the mineral indices and constrained energy minimization (CEM) approach to ASTER data. The datasets of ALSO PALSAR and Sentinel-1B were subjected to PCA and filtering to extract the lineaments and their spatial densities in the area. Furthermore, the structural analysis revealed that the area has been subjected to three main phases of deformation; (i) NE-SW convergence and sinistral transpression (D2); (ii) ~E-W far field compressional regime (D3), and (iii) extensional tectonics and terrane exhumation (D4). The gold-bearing quartz veins in several occurrences are controlled by D2 and D3 shear zones that cut heterogeneously deformed serpentinites, sheared metavolcanic rocks and gabbro-diorite intrusions. The information extracted from remotely sensed data, structural interpretation and fieldwork were used to produce a gold mineralization potential zones map which was verified by reference and field observations. The present study demonstrates the remote sensing capabilities for the identification of alteration zones and structural controls of the gold mineralization in highly deformed ophiolitic regions.

Considering the form diversity of tree species composition in the Bagong Mountain National Forest Park of China, we mapped tree species utilizing Machine Learning Algorithms (support vector machines (SVM) and random forest (RF) classifiers) based on the OHS-2 hyperspectral satellite image by different datasets which combined spectral information and hyperspectral-derived vegetation indices (VIs) for improving tree species classification and explored the best performance of them. To verify the improvement, the results of physically-based spectral classifiers (spectral angle mapper (SAM) and maximum likelihood (ML) classifiers) were applied to compare with the results of machine learning algorithms. The results indicated an overall accuracy of 94.01%, 96.08%, 82.9% and 79.3% for SVM, RF, SAM and ML classifiers of the best performance using different datasets. Highest accuracies resulted from two machine learning algorithms classifiers; SVM and RF compared to SAM and ML classifiers. Although SVM outperformed RF when using all hyperspectral bands and VIs, the overall accuracy of the RF classifier is higher when compared to the SVM classifier using VIs combined selected features. Meanwhile, the RF classifier performed better than SVM after removing the redundancy of spectral data in training samples. Moreover, the machine learning algorithms successfully classified a small number of tree species (Cedrus deodara and Pterocarya stenoptera C. DC.) in the study area, but the physical spectroscopy-based method failed to classify these species. Such integration strategy improved the effectiveness of enhancing the accuracy of tree species classification and mapping their distribution on broad spatial and temporal scales using machine learning algorithms and hyperspectral imagery.