Advanced Spaceborne Thermal Emission And Reflection Radiometer Data Research Articles

The use of ASTER and landsat remote sensing data for exploring hydrothermal mineral in Arabian-Nubian Shield

Multispectral remote sensing data provided valuable information for hydrothermal minerals exploration. In this article, Landsat-8 Operational Land Imager (OLI) and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were processed and analyzed using band ratio and principal component techniques to demarcate areas of hydrothermal mineral resources by highlighting the most influenced zone of hydrothermal alteration (high-grade) in Egypt. The area is a part of the Arabian-Nubian Shield, covering 1068 km2, and comprises wide varieties of Pre- and Pan-African rock assemblages. Preparing and integrating thematic layers consisting of the grade of hydrothermal intensity in a GIS technique supported generating a hydrothermal mineral predictive map (HMPM) using a multi-criteria decision-making technique. The plausible zone of very high mineral resources covers about 6.57 %. Analysis of ASTER data revealed that this zone is reliable with phyllic and argillic minerals and iron-oxides. The results of OLI data helped in demarcating potential areas of hydrothermal alteration areas (Al–OH) mixed with iron oxides. Field observations and investigations enabled the validation of the HMPM. The spatial distribution of hydrothermal zones throughout the study was reliable to that observed in the mine areas. Field and ore microscopic examination verified the existence of Au-sulfide minerals connected with hydrothermal alteration zones (HAZs) in different areas e.g., Abu-Marawat, Gidami, Semna, Abu-Gaharish, and Hamama mines. Overall, integrating multi-spectral data through overlay analysis techniques can provide valuable information about hydrothermal mineral resources in an accurate and cost-effective approach.

Evaluating ASTER data and field spectrometry for lithological discrimination in semi-arid region, Northeast Kohat Plateau, Pakistan

This study employs data of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor to delineate and map the distribution of sedimentary lithologies in the semi-arid region of Kohat Plateau, Pakistan. False color composites (FCC) and various image transformation and enhancement techniques including the principal component analysis (PCA), minimum noise fraction (MNF), and band rationing (BR) were used successfully to differentiate four lithological classes. These lithologies include chemically/biochemically formed beds of the marine environment and detrital sequences of marginal marine to the riverine environment. FCC from original reflectance data, PCA, and BR techniques displayed more prominent lithological variation. To map the lithology and show the potential of ASTER data, field spectrometry over the barren lithologies was carried out. The end-member spectra from field spectrometry shows strong agreement with the pixels spectra from ASTER scene. The Spectral Angle Mapper (SAM) mapping method was then used to produce a classified lithological map, where the image pixels spectra proved more suitable reference, in comparison to the end-member spectra. The accuracy of the classified lithological map was evaluated based on field-based point data, which resulted an overall accuracy of 70% and a Kappa coefficient value of 0.679. Carbonates and evaporites showed relatively higher user and producer accuracies which are attributed to their topographic behavior and weathered scree over the adjacent rock unit. The final lithological map provided a clearer picture of surface geology where the existing geological maps lacked lithological continuity.

Assessment of potential cobalt zones by modeling radiometric and ASTER data using the AHP method: A case of the Bou Azzer El Grara inlier, Central Anti-Atlas, Morocco

The Bou Azzer El Grara inlier belongs to the Central Anti-Atlas range, renowned for its mineral deposits. The study carried out on the inlier aims to identify potential areas of cobalt mineralization based on a multi-source methodology of radiometric and remote sensing data processing. The data used includes hydrothermal alteration minerals (argillic, phyllic, propylitic, iron oxides) mapped with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery using band ratio and principal component analysis (PCA) techniques, potassium enrichment zones through K/eTh and K/eU ratios calculated from radiometric data, as well as geological data (serpentinite outcrops and geological faults). The analytic hierarchy process (AHP) was used to combine the various thematic layers. Validation was carried out through the field studies in several anomalous zones. The mineral prospectivity map generated highlights nine major zones that appear the most favorable for mineralization, some of which coincide with zones in production and others requiring further exploration.

Lithological Discrimination And Mapping Using Aster Swir Data In The Tundi Area, Jharkhand

ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) multispectral data is utilized in mineralogical and lithological mapping along with alteration zone mapping. The present study is carried out using ASTER data, which has shown the effectiveness SWIR spectral bands of ASTER data in lithological mapping. Field validation is carried out in the areas where lithological updation was expected from the sspectral analysis. From the band ratios, argillic, ferric, phyllic and propylytic alteration zones were demarcated. Thus, ASTER SWIR bands coupled with the advanced image enhancement techniques are recommended as a rapid and cost effective tool for lithological discrimination and mapping in the hostile terrain of Dhanbad and other geological areas.

Lithological mapping of northern Kohat Plateau’s limestone outcrops using integrated remote sensing and reflectance spectroscopy techniques

ABSTRACT Remote sensing (RS) has emerged as a valuable technology for precisely demarcating the extent of lithological units in rugged terrains. It offers valuable insights into the spatial distribution and structural dynamics of complex topographies. In this study, RS technology manifested by Sentinel-2A and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery is used to discriminate carbonaceous strata in various parts of the Kohat Plateau region, northwest Pakistan. Various spectral image transformation techniques, including principal component analysis (PCA), minimum noise fraction (MNF), band ratio (BR), and carbonate index (CI), were utilized after atmospheric correction modules. The resultant red-green-blue (RGB) color composites obtained from Sentinel-2A, incorporating PCA (PC1, PC2, PC4) and MNF (MNF1, MNF2, MNF3), demonstrated its applicability to accurately discriminate the target features. The derived RGB color composites from the BR on ASTER data (BR5/1, BR5/7, BR3/4 × 5/4) and CI B13/B14 effectively identified the limestone bodies, providing a better approach to differentiate limestone from other lithological units. Also, the integration of spectroscopic field data with satellite-based resultant images represented a meaningful contribution to rectifying previous geological maps. The derived maps from aforementioned techniques exhibited a notable attribute to record the target units and enhance limestone differentiation from other lithologies.

Space based Earth observation approach for exploring hydrocarbon prospects in an intracratonic rift basin – A study in north Cambay basin, Gujarat

In the present study, we carried out a conjugate analysis of Landsat-8 Operational Land Imager (OLI), Sentinel-2B Multi-Spectral Instrument (MSI), Advanced Land Observation Satellite Phased Array L-band SAR (ALOS PALSAR), and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data over a study area of northern Cambay Basin to delineate prospective areas for hydrocarbon exploration. In this regard, we utilized feature-oriented principal component (PC) images of Landsat OLI data, PC images of Sentinel-2B visible and near-infrared (VNIR)-short-wave infrared (SWIR) bands, thresholded Cartosat-1 Digital Elevation Model (DEM), and polarization image composite of ALOS PALSAR data to delineate geomorphic and lineament features in the study area. Most of the geomorphic features, such as scroll plain, gullies, elevated pediment, circular drainage anomalies, etc., indicate localized structural uplift. Geomorphic anomalies and lineaments, essential for identifying structural controls of near-surface hydrocarbon accumulation, were identified using enhanced satellite data. Geophysical anomalies derived from gravity datasets supplemented the presence and down-depth continuity of geological structures. Spectral profiles of calcrete-bearing soil samples were used to identify calcrete-rich zones in the optical remote sensing image data as a proxy owing to field evidence of close association of calcrete-rich soils with known subsurface hydrocarbon accumulations. Sub-pixel mapping algorithm Constrained Energy Minimization (CEM) applied to SWIR bands of ASTER data was used to delineate surface proxies of near-surface hydrocarbon seepage. Isopach maps of shallowest reservoir formations (Mid-/ Late Eocene) were used as indicators of favorable near-surface hydrocarbon accumulation.Earth observation-based geomorphic, lineament, and spectral anomalies were integrated with supplementary datasets, such as reservoir isopach and absorbed gas anomalies, as controls using the weighted summation method for quantitative deduction of the hydrocarbon potential map of the study area. The weights for input variables or parameters (geomorphic, spectral, geochemical, etc.) were inferred based on spatial associations of these anomalous units/variables with the existing oil & gas well locations. The identified potential map is in broad synergy with the field observations on localized uplift, geo-botanical anomalies, and calcrete-rich zones within the extent of high & moderate hydrocarbon potential areas.

Implementation of orthogonal subspace projection on ASTER data for exploring phosphorite -a study in Chhatarpur, Madhya Pradesh, India

In this study, we have identified surface exposures of phosphorite using visible near infrared (VNIR) and shortwave infrared (SWIR) bands of Advanced space borne thermal emission and reflection radiometer (ASTER) data. We analysed the spectral contrast of dolomite and phosphorite samples collected from the ground and also from the pixels of the ASTER image composite. We observed the consistent spectral contrast between the samples of dolomite and phosphorite and used the same for delineating phosphorite within the dolomite and associated carbonate rocks. In this regard, we first delineated dolomite and associated carbonate rocks using feature oriented principal component of the iron rich carbonate rocks. Subsequently, we implemented partial spectral unmixing method, known as orthogonal subspace projection on ASTER VNIR and SWIR bands to delineate phosphorite within dolomite. We could trace a few new occurrences of phosphorite in the study area beyond the known occurrence at Hirapur open cast mining area. We have validated the identified new areas during field survey using a rapid colorimetric test for phosphate to ascertain the exploration importance of these targets. We further estimated P2O5 grade content of the phosphorite samples in these sites and found few of them have very high P2O5 content.

Integration of Remote Sensing and Field Data in Ophiolite Investigations: A Case Study of Logar Ophiolite Complex, SE Afghanistan

Mafic–ultramafics complexes are crucial for their tectonic implication, upper mantle condition, and for hosting industrial minerals in a region. This study aims to highlight and characterize the mafic–ultramafic rocks of the Logar Ophiolite Complex using the integration of geospatial technology and field data. The spatial distribution of the ophiolitic complex was examined in this study using the mineralogical indices (MI), band ratio (BR), and spectral angle mapper (SAM) methods within the framework of geospatial technology using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. Additionally, several samples were collected from the identified complexes for validation, petrographic, and mineralogical analyses. Combining geospatial technology and conventional approaches, e.g., field sampling and geological data analysis yields efficient discrimination of mafic–ultramafic rocks with their associated hydrothermal altered minerals. The serpentinization and carbonate processes are predominantly seen along the eastern side of the active fault zone following the detection of ophiolites. Detailed mapping of the ophiolitic complex and associated rocks was achieved using refined mafic index (MI), band ratio 12/14 and 4/8 for rocks and SAM for highlighting the mafic–ultramafic altered minerals, and petrographic analysis of the collected samples. The field works verified the results of the ASTER data. The findings of this study can significantly contribute to detailed tectonic and geologic studies of the detected ophiolites in terms of their emplacement mechanism and ages.

Identifying Emeishan basalt by supervised learning with Landsat-5 and ASTER data

Multispectral-sensor images are advantageous in terms of discriminating major lithologies due to their high spatial resolution and intermediate spectral resolution, in addition to their low cost and high accessibility in comparison to hyperspectral images. In this study, Landsat-5 Thematic Mapper ™ and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data—which are the most widely used multispectral data for the discrimination of the mixed rock units—are utilized to identify basalts in our study area. Further, prior knowledge regarding basalt-distribution areas in our study region is obtained from the geological-survey results conducted by the Sichuan Geological Survey at 2005, which is used as the reference of correction to assess our identified results. Small portions of this prior area of basalt distribution were verified through field checks, which were then determined as sites for use as training data for remote-sensing imagery. Three supervised-classification algorithms within ENVI 5.3—k-nearest neighbors (KNN), maximum likelihood classification (MLC), and support vertical machine (SVM)—were utilized for model identification. As a result, six models were constructed, including the KNN prediction of basalts by ASTER images, SVM prediction by ASTER, MLC prediction by ASTER, KNN prediction by Landsat-5 images, SVM prediction by Landsat-5, and MLC prediction by Landsat-5. The performances of the six models, in terms of precision and accuracy, show that the optimum model is Landsat-5 by SVM, with a precision of 70.92% and accuracy of 99.97%, followed by the ASTER by SVM model, with a precision of 67.72% and accuracy of 99.89% and the Landsat-5 by KNN model, with a precision of 57.23% and accuracy of 99.85%.

Orographic Construction of a Numerical Weather Prediction Spectral Model Based on ASTER Data and Its Application to Simulation of the Henan 20·7 Extreme Rainfall Event

Numerical weather prediction (NWP) has become an important method of predicting extreme weather events, but orography is one of the key factors affecting the performance of NWPs. In this paper, based on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) elevation data, a method for constructing a global orographic dataset suitable for NWP spectral models is investigated. The Yin-He global spectrum model (YHGSM) is used to simulate the early and peak periods of the extreme rainfall event on 20 July 2021 in Henan Province, China, and the heavy rain in Beijing in order to verify the effectiveness and superiority of the proposed orographic construction method. It is demonstrated that in a few cases the direct two-dimensional filter can sometimes simulate more intense rainfall, but in general, the bidirectional one-dimensional filter is better than the direct two-dimensional filter in orographic processing, and the bidirectional one-dimensional filter can filter out more of the small-scale orographic information. The effect of the higher orographic resolution before conversion to spectral space is not very obvious, but it is demonstrated that the simulation results are better for the heavy-rainfall level. In conclusion, in most cases, the simulations conducted using the new global orographic dataset based on ASTER data are better than those obtained using the model’s original orography, especially for torrential and extreme rainfall. These conclusions provide a reference for future predictions of and research on extreme rainfall events.

Improving the accuracy of hydrothermal alteration mapping based on image fusion of ASTER and Sentinel-2A data: a case study of Pulang Cu deposit, Southwest China

Mapping hydrothermal alteration is an important means of mineral exploration, therefore, effective improvement of the accuracy of identification of hydrothermal alteration minerals is a hot topic. In this study, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2A data were selected for the Pulang copper deposit, to improve the accuracy of hydrothermal alteration mineral mapping. Three data fusion methods of Principal Component Analysis (PCA), Gram–Schmidt (GS) and High-Pass Filtering (HPF) were employed, and method of spectral matching was introduced to obtain high spatial resolution and high spectral fidelity fused data. The comparison showed that the HPF fusion method had the best effect. The fused data by the HPF fusion method were then implemented to map hydrothermal alteration minerals by using a multifractal-based method of PCA + Spectrum–Area. Through the verification of a field investigation, the results exhibited a similar hydrothermal alteration distribution, compared with the results obtained from the original ASTER data, but with higher identification accuracy. Therefore, the case study strongly suggests that the image fusion method with spectral matching is an effective tool to increase spatial resolution, while maintaining high spectral fidelity. Thus, it is a valuable and economic method for improving the identification accuracy of hydrothermal alteration minerals. HIGHLIGHTS The spectral matching method was introduced to unify the surface reflectance of ASTER and Sentinel-2A data. Principal Component Analysis (PCA), Gram-Schmidt (GS) and High-Pass Filtering (HPF) methods were employed to fuse AST ER and Sentinel-2A data. The results show that the extraction accuracy of hydrothermal alteration minerals based on AST ER - Sentinel-2A fused data has 6.98% higher than that of original ASTER data.

ASTER and GF-5 Satellite Data for Mapping Hydrothermal Alteration Minerals in the Longtoushan Pb-Zn Deposit, SW China

Hydrothermal alteration minerals are an effective prospecting indicator. Advanced spaceborne thermal emission and reflection radiometer (ASTER) satellite data are some of the most commonly adopted multispectral data for the mapping of hydrothermal alteration minerals. Compared to multispectral data, hyperspectral data have stronger ground object recognition ability. Chinese Gaofen-5 (GF-5) is the first hyperspectral satellite independently developed by China that has the advantages of both wide-width and high-spectral-resolution technology. However, the mapping ability of GF5 data for hydrothermal alteration minerals requires further study. In this study, ASTER and GF-5 satellite data were implemented to map hydrothermal alteration minerals in the Longtoushan Pb-Zn deposit, SW China. Selective principal component analysis (SPCA) technology was employed to map iron oxide/hydroxides, argillic, quartz, and carbonate minerals at the pixel level using ASTER data, and the mixture tuned matched filtering (MTMF) method was implemented for the extracted hematite, kaolinite, calcite, and dolomite at the sub-pixel level using GF-5 data. When mapping the hydrothermal alteration minerals, the distribution features of the hydrothermal alteration minerals from the Longtoushan Pb-Zn deposit were systematically revealed. A comprehensive field investigation and petrographic study were conducted to verify the extraction accuracy of the hydrothermal alteration minerals. The results showed that the overall accuracies for the ASTER and GF-5 data were 82.6 and 92.9 and that the kappa coefficients were 0.78 and 0.90, respectively. This indicates that the GF-5 data are able to map hydrothermal alteration minerals well and that they can be promoted as a hyperspectral data source for mapping systematic hydrothermal alteration minerals in the future.

A Comparative Study of Convolutional Neural Networks and Conventional Machine Learning Models for Lithological Mapping Using Remote Sensing Data

Lithological mapping is a critical aspect of geological mapping that can be useful in studying the mineralization potential of a region and has implications for mineral prospectivity mapping. This is a challenging task if performed manually, particularly in highly remote areas that require a large number of participants and resources. The combination of machine learning (ML) methods and remote sensing data can provide a quick, low-cost, and accurate approach for mapping lithological units. This study used deep learning via convolutional neural networks and conventional ML methods involving support vector machines and multilayer perceptron to map lithological units of a mineral-rich area in the southeast of Iran. Moreover, we used and compared the efficiency of three different types of multispectral remote-sensing data, including Landsat 8 operational land imager (OLI), advanced spaceborne thermal emission and reflection radiometer (ASTER), and Sentinel-2. The results show that CNNs and conventional ML methods effectively use the respective remote-sensing data in generating an accurate lithological map of the study area. However, the combination of CNNs and ASTER data provides the best performance and the highest accuracy and adaptability with field observations and laboratory analysis results so that almost all the test data are predicted correctly. The framework proposed in this study can be helpful for exploration geologists to create accurate lithological maps in other regions by using various remote-sensing data at a low cost.

Application of ASTER Data for Differentiating Carbonate Minerals and Evaluating MgO Content of Magnesite in the Jiao-Liao-Ji Belt, North China Craton

Numerous reports have successfully detected or differentiated carbonate minerals such as calcite and dolomite by using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). However, there is a need to determine whether existing methods can differentiate magnesite from other carbonate minerals. This study proposes optimal band ratio combinations and new thresholds to distinguish magnesite, dolomite, and calcite using ASTER shortwave-infrared (SWIR) data. These were determined based on the spectral and chemical analysis of rock samples collected from Liaoning, China and Danchon, North Korea and the reflectance values from ASTER images. The results demonstrated that the simultaneous use of thresholds 2.13 and 2.015 for relative absorption band depths (RBDs) of (6 + 8)/7 and (7 + 9)/8, respectively, was the most effective for magnesite differentiation. The use of RBDs and band ratios to discriminate between dolomite and calcite was sufficiently effective. However, talc, tremolite, clay, and their mixtures with dolomite and calcite, which are commonly found in the study area, hampered the classification. The assessment of the ASTER band ratios for magnesite grade according to magnesium oxide content indicated that a band ratio of 5/6 was the most effective for this purpose. Therefore, this study proved that ASTER SWIR data can be effectively utilized for the identification and grade assessment of magnesite on a regional scale.

Hybrid Fuzzy-Analytic Hierarchy Process (AHP) Model for Porphyry Copper Prospecting in Simorgh Area, Eastern Lut Block of Iran

The eastern Lut block of Iran has a high potential for porphyry copper mineralization due to the subduction tectonic regime. It is located in an inaccessible region and has harsh arid conditions for traditional mineral exploration campaigns. The objective of this study is to use Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data for porphyry copper exploration in Simorgh Area, eastern Lut block of Iran. Hydrothermal alteration zones such as argillic, phyllic and propylitic zones associated with porphyry copper systems in the study were identified using false color composition (FCC), band ratio (BR), principal component analysis (PCA) and minimal noise fraction (MNF). The thematic alteration layers extracted from FCC, BR, PCA and MNF were integrated using hybrid Fuzzy-AHP model to generate a porphyry copper potential map for the study area. Four high potential zones were identified in the central, western, eastern and northeastern of the study area. Fieldwork was used to validate the approach used in this study. This investigation exhibits that the use of hybrid Fuzzy-AHP model for the identification of hydrothermal alteration zones associated with porphyry copper systems that is typically applicable to ASTER data and can be used for porphyry copper potential mapping in many analogous metallogenic provinces.

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

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.

Implementation of Robust Satellite Techniques for Volcanoes on ASTER Data under the Google Earth Engine Platform

The RST (Robust Satellite Techniques) approach is a multi-temporal scheme of satellite data analysis widely used to investigate and monitor thermal volcanic activity from space through high temporal resolution data from sensors such as the Moderate Resolution Imaging Spectroradiometer (MODIS), and the Spinning Enhanced Visible and Infrared Imager (SEVIRI). In this work, we present the results of the preliminary RST algorithm implementation to thermal infrared (TIR) data, at 90 m spatial resolution, from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Results achieved under the Google Earth Engine (GEE) environment, by analyzing 20 years of satellite observations over three active volcanoes (i.e., Etna, Shishaldin and Shinmoedake) located in different geographic areas, show that the RST-based system, hereafter named RASTer, detected a higher (around 25% more) number of thermal anomalies than the well-established ASTER Volcano Archive (AVA). Despite the availability of a less populated dataset than other sensors, the RST implementation on ASTER data guarantees an efficient identification and mapping of volcanic thermal features even of a low intensity level. To improve the temporal continuity of the active volcanoes monitoring, the possibility of exploiting RASTer is here addressed, in the perspective of an operational multi-satellite observing system. The latter could include mid-high spatial resolution satellite data (e.g., Sentinel-2/MSI, Landsat-8/OLI), as well as those at higher-temporal (lower-spatial) resolution (e.g., EOS/MODIS, Suomi-NPP/VIIRS, Sentinel-3/SLSTR), for which RASTer could provide useful algorithm’s validation and training dataset.

Using reflectance spectroscopy and Advanced Spaceborne Thermal Emission and Reflection Radiometer data to identify bauxite deposits in vicinity of Az Zabirah, northern Saudi Arabia

Bauxite samples from deposits in northern Saudi Arabia were identified in the laboratory using spectral reflectance measurements (at 0.3–2.5 μm) and X-ray diffraction (XRD). The XRD results revealed that the bauxite deposits are composed mainly of goethite, gibbsite, and boehmite with small amounts of kaolinite, hematite, and quartz. The bauxite spectra revealed the presence of significant iron oxides (at 0.5 and 0.87 μm) accompanied by water (at 1.4 and 1.9 μm) and aluminum hydroxide (at 2.2 μm). The convolved Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) spectra of the bauxite samples were characterized by an aluminum hydroxide feature in ASTER band 6 (at 2.2 μm). The results demonstrated that principal components analysis band PC2 is the best component for delineating the bauxite deposits. Fractional abundances of bauxite were derived by using a matched-filtering method. This study demonstrates the applicability of reflectance spectroscopy and ASTER data to provide spectral information for distinguishing economically important minerals from visible and near-infrared and shortwave-infrared spectra in arid and semiarid environments such as in Saudi Arabia.

Implementation of the NHI (Normalized Hot Spot Indices) Algorithm on Infrared ASTER Data: Results and Future Perspectives.

The Normalized Hotspot Indices (NHI) tool is a Google Earth Engine (GEE)-App developed to investigate and map worldwide volcanic thermal anomalies in daylight conditions, using shortwave infrared (SWIR) and near infrared (NIR) data from the Multispectral Instrument (MSI) and the Operational Land Imager (OLI), respectively, onboard the Sentinel 2 and Landsat 8 satellites. The NHI tool offers the possibility of ingesting data from other sensors. In this direction, we tested the NHI algorithm for the first time on Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. In this study, we show the results of this preliminary implementation, achieved investigating the Kilauea (Hawaii, USA), Klyuchevskoy (Kamchatka; Russia), Shishaldin (Alaska; USA), and Telica (Nicaragua) thermal activities of March 2000–2008. We assessed the NHI detections through comparison with the ASTER Volcano Archive (AVA), the manual inspection of satellite imagery, and the information from volcanological reports. Results show that NHI integrated the AVA observations, with a percentage of unique thermal anomaly detections ranging between 8.8% (at Kilauea) and 100% (at Shishaldin). These results demonstrate the successful NHI exportability to ASTER data acquired before the failure of SWIR subsystem. The full ingestion of the ASTER data collection, available in GEE, within the NHI tool allows us to develop a suite of multi-platform satellite observations, including thermal anomaly products from Landsat Thematic Mapper (TM) and Enhanced Thematic Mapper Plus (ETM+), which could support the investigation of active volcanoes from space, complementing information from other systems.

Mapping of Ophiolitic Complex in Logar and Surrounding Areas (SE Afghanistan) With ASTER Data

Ophiolites are the distinctive assemblage of mafic to ultramafic rocks and represent the abducted remnant of oceanic crust that was overthrust onto continental crust. Such types of rocks are rare around the world. One of the largest mafic–ultramafic complexes with an apparent area of about 2000 km2 is in the southeast of Afghanistan between two active sinistral strike-slip faults within Logar and surrounding provinces. This study aims to discriminate, characterize, and separate the ophiolitic complex within Logar and adjacent regions in Afghanistan using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data. In this study, four different methods; Mineral Spectral Indices (SI), Band Ratios (BR), Relative Absorption Band Depth (RBD), and Spectral Angle Mapper (SAM), are applied to discriminate and separate the ophiolitic complex. SAM classifier is an efficient way to distinguish the selected surface mineral assemblage using USGS spectral library. The combination of SAM and refined MI yields efficient discrimination of mafic–ultramafic rocks with their alteration minerals around. Detailed mapping of the ophiolitic complex and associated rocks was achieved using refined MI, Band Ration 4/8, and relative absorption band depth (RBD). The field works verified the results from ASTER data.