Advanced Spaceborne Thermal Emission And Reflection Radiometer Images Research Articles

Spectral mixture analysis of intimate mixtures for lithological mapping

Spectral mixing is frequently encountered in remotely sensed images while imaging heterogeneous surfaces. Spectral mixing occurs at the macroscopic level due to the sensor’s low spatial resolution or multiple reflections from materials received by the sensor. Due to the mixing effect, retrieving the information of spectral endmembers becomes complicated. In this study, two different unmixing methods: (i) linear mixing models (LMM) and (ii) the Hapke nonlinear mixing model, were used to quantitatively estimate the fractional abundance of various rock types that will be helpful in remotely mapping the mineral prospects. Three types of constraints using the least square approach were applied independently to LMM and Hapke models, namely FCLS (sum to unity and nonnegativity constrained), NNLS (nonnegativity constrained), and UCLS (unconstrained). AVIRIS-NG (Airborne Visible and InfraRed Imaging Spectrometer-Next Generation) and ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) images from the Sittampundi anorthosite complex (SAC) were utilised for the spectral mixture analysis. Few studies are available on the anorthosite rocks of Sittampundi as an analogue to the Lunar anorthosite. The study area is known for its chromite deposits occurring in pyroxenite rocks. Therefore, in the present study, endmembers considered are anorthosite and pyroxenite/metagabbro.The moderately dense vegetation cover of the study area obscures the detection of rock outcrops on the surface. The study area exhibits nonlinear spectral mixing effects due to the vegetation cover. Hence apart from the two rock endmembers, vegetation is also considered as an endmembers in spectral mixture analysis. The results show a high fractional abundance of vegetation and anorthosite throughout the study region, with the pyroxenite/metagabbro mainly concentrated over the western and eastern regions. The Hapke model shows more accurate results than LMM on the AVIRIS-NG image. The Hapke model outperforms LMM in accurately retrieving the fractional abundance of endmembers due to the nonlinear spectral mixing effects caused by vegetation. Additionally, the constrained least squares methods, i.e., FCLS and NNLS, performed better than UCLS in linear and nonlinear mixing models, resulting in accurate and reliable abundance maps. AVIRIS-NG outperformed the ASTER images in fractional abundance mapping of endmembers, highlighting the importance of utilising higher spatial and spectral resolution data for more reliable land cover and lithology mapping. Overall, this study demonstrates the potential of spectral mixture analysis in identifying geologic outcrops in vegetation-covered regions. Hence, the methodology presented in this study benefits the lithological mapping of vegetation-covered regions and has wide applications in quantitatively assessing the lithology of mineral prospects.

Region Expansion of a Hyperspectral-Based Mineral Map Using Random Forest Classification with Multispectral Data

Observation images from hyperspectral (HS) sensors on satellites and aircraft can be used to map minerals in greater detail than those from multispectral (MS) sensors. However, the coverage of HS images is much less than that of MS images, so there are often cases where MS images cover the entire area of interest while HS images cover only a part of it. In this study, we propose a new method to more reasonably expand the mineral map of an HS image with an MS image in such cases. The method uses various mineral indices from the MS image and MS sensor’s band values as the input and HS image-based mineral classes as the output. Random forest (RF) two-class classification is then applied iteratively to determine the distribution of each mineral in turn, starting with the minerals that are most consistent with the HS image-based mineral map. The method also involves the correction of misalignment between HS and MS images and the selection of input variables by RF multiclass classification. The method was evaluated in comparison with other methods in the Cuprite area, Nevada, using the Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) and Hyperspectral Imager Suite (HISUI) as HS sensors and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) as MS sensors. As a result, all of the evaluated region-expansion methods with an HS–MS image pair, including the proposed method, showed better performance than the method using only an MS image. The proposed method had the highest performance, and the inter-mineral averages of the F1-scores for the overlap and non-overlap areas were 85.98% and 46.46% for the AVIRIS–ASTER image pair and 82.78% and 42.60% for the HISUI–ASTER image pair, respectively. Although the performance in the non-overlap region was lower than in the overlap region, the method showed high precision and high accuracy for almost all minerals, including minerals with only a few pixels. Misalignment between the HS–MS images is a factor that degrades accuracy and requires precise alignment, but the misalignment correction in the proposed method could suppress the effect of misalignment. Validation studies using different regions and different sensors will be carried out in the future.

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.

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%.

Processing of Multispectral ASTER Data to Delineate Bauxite Abundant Zones and its Geochemical Characterisation Deposited over Deccan Traps of Central India in Mainpat Plateau, Surguja District, Chhattisgarh

Abstract The current study deals with the application of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral data to delineate bauxite deposits over Mainpat plateau, Surguja district, Chhattisgarh, India. Application of specific band combinations help to delineate bauxite in the (SWIR) region (2.16-2.33 μm) and visible near infrared (VNIR) band (0.4–1.0 μm) of multispectral ASTER data. Various image processing techniques like atmospheric correction, minimum noise fraction, pixel purity index, n-dimensional visualiser, and spectral angle mapper were applied to ASTER image to delineate bauxite rich zones. The pure pixels were differentiated from the mixed pixels using pixel purity index which were further subjected to n-dimensional visualiser to identify the target end-members. The presence of bauxite was confirmed by comparing the spectral signatures of end-members with in-built spectral plots of United States Geological Survey spectral libraries and categorized as bauxite and ferruginous bauxite. Finally, the end-members were categorised as bauxite and ferruginous bauxite and mapped using spectral angle mapper. The result obtained was further compared with the lithological map provided by Geological Survey of India for accuracy. Then XRF analysis was carried out for the collected samples to confirm the presence of bauxite and ferruginous bauxite. Geochemical analysis also categorized the samples as bauxite and ferruginous bauxite that added accuracy to the results obtained from remote sensing technique. This study might be considered as a time saving eco-friendly exploitation of natural resources and finally the potential of multispectral ASTER data for mapping mineral resources.

Mapping of mafic-ultramafic rocks in SMUC-SGT, India using ASTER & Sentinel-2A satellite images

The Salem Mafic–Ultramafic Complex (SMUC) is a major complex in southern Granulite Terrane (SGT), in the northern part of the Cauvery Suture Zone (CSZ), India, also known as the Chalk Hills of Salem. The present study aims to review the efficiency of satellite image processing and related techniques to map the Mafic-Ultramafic rocks in Salem Mafic-Ultramafic Complex (SMUC). This mapping is done using atmospherically corrected Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2A satellite image data. The processes employed were image enhancement techniques like Band Combinations (BC), Principal Component Analysis (PCA), and Minimum Noise Fraction (MNF) to map the various rock types. Band combinations constructed from the output of PCA and MNF in RGB format showed promising results in discriminating various rock units. The Spectral Angle Mapper (SAM) and Support Vector Machine (SVM) classifications techniques were used to classify ASTER and Sentinel-2A satellite images for the study area. The classified lithologies showed strong match with the lithology map published by the Geological Survey of India (GSI), other researches, and field investigation. Particularly, SVM classification of Sentinel-2A MNF + DEM showed 90% overall accuracy when compared with field investigation. The study proved that the processing of VNIR and SWIR bands of ASTER and Sentinel-2A Satellite images coupled with DEM and ground investigation has excellent potential in mapping Mafic-Ultramafic rocks in SMUC SGT.

Detection of geothermal anomalies in the Northern Lake Abaya geothermal field, Main Ethiopian Rift

The detection of geothermal anomalies using Thermal Infrared (TIR) remote sensing data is challenging because of how sensor specifications (such as the infrared wavelength used for the measurement, spectral dependence of the emissivity, angle at which the measurement is made, state of the surface and height of the sensor above the surface) and physical parameters (such as solar radiation, topography, albedo, soil compaction and coherence of rocks) affect Land Surface Temperature (LST) retrieval and analysis. This work tests whether TIR remote sensing measurements with thorough spatial and temporal sampling can improve LST retrievals. Multi-temporal TIR data from 2000 through 2019 from Landsat 7 and 8 TIR instruments and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were used to detect geothermal areas in the geologically active region of the southern Main Ethiopian Rift. In addition, field-based temperature data from 19 sites were evaluated for comparison to the remotely detected geothermal anomaly areas. We have used the single-channel algorithm and Normalized Difference Vegetation Index (NDVI) method of emissivity retrieval to derive LST for each year. The result shows that the mean LST is highest in 2003 (320.1 K) and lowest in 2019 (303.1 K). The change in mean LST was between −9 K to 13 K. These LST results from ASTER images were validated with MODIS LST products and showed a correlation coefficient >0.6. LST of the year 2003 has been much closer to the actual temperature value from field data. Fifteen sites (79%) fit with the identified geothermal anomaly areas. LST values in known geothermal activity sites show no correlation (< 0.5) with time attesting. That is, even though LST varies with time (e.g., day and night and seasonal changes), the LST of areas with geothermal potential remain more or less constant on yearly basis.

Application of ASTER images for identification and localization of iron minerals in red mud dumps of the Ural aluminum plant

Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) was used to identify and mapping of industrially and environmentally important iron oxides and hydroxides in red mud dumps. In paper we were focused on specified region of interests - one of the largest boxite processing manufactory in Russia Ural Aluminum Plan (UAP). Initially we use ASTER L1T images. The graph of processing includes band relationship, principal component analysis and spectral angle mapper procedures for identifying spatial distribution of targeted minerals. Several minerals were mapped within red mud dumps No 2 and No 3: Hematite, Goetite, Jarosite and Magnetite. At the next stage the Scanning Electronic Microscopy (SEM) of red mud mineral samples was carried out. The results show a good correlation between remote sensing techniques and standard mineralogical analysis (SEM) method. In both cases the principal iron oxides and hydroxides were observed. Combination of remote sensing and traditional mineralogical analysis methods allows to increase the reliability of assessing ore potential and efficiency of planning utilization technologies for wastes in technogenic deposits. In addition, processing of several ASTER images from different time gap (2004 and 2015) allows to trace in dynamics the transformation of mineral composition in red muds.

Thermal remote sensing over heterogeneous urban and suburban landscapes using sensor-driven super-resolution.

Thermal remote sensing is an important tool for monitoring regional climate and environment, including urban heat islands. However, it suffers from a relatively lower spatial resolution compared to optical remote sensing. To improve the spatial resolution, various "data-driven" image processing techniques (pan-sharpening, kernel-driven methods, and machine learning) have been developed in the previous decades. Such empirical super-resolution methods create visually appealing thermal images; however, they may sacrifice radiometric consistency because they are not necessarily sensitive to specific sensor features. In this paper, we evaluated a "sensor-driven" super-resolution approach that explicitly considers the sensor blurring process, to ensure radiometric consistency with the original thermal image during high-resolution thermal image retrieval. The sensor-driven algorithm was applied to a cloud-free Moderate Resolution Imaging Spectroradiometer (MODIS) scene of heterogeneous urban and suburban landscape that included built-up areas, low mountains with a forest, a lake, croplands, and river channels. Validation against the reference high-resolution thermal image obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) shows that the sensor-driven algorithm can downscale the MODIS image to 250-m resolution, while maintaining a high statistical consistency with the original MODIS and ASTER images. Part of our algorithm, such as radiometric offset correction based on the Mahalanobis distance, may be integrated with other existing approaches in the future.

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.

Mapping hydrocarbon microseepage prospect areas by integrated studies of ASTER processing, geochemistry and geophysical surveys in Assam-Arakan Fold Belt, NE India

Long-term microseepage of hydrocarbons creates a reducing environment in the near-surface soil and sediment cover inducing an array of diagenetic alterations such as bleaching of red beds and increasing abundance of ferrous, clay and carbonate minerals. Spectral enhancements of satellite images have effectively been used in microseepage-induced mineral mapping in arid regions. However, the present study aims to delineate microseepage-induced mineral alteration areas in Assam-Arakan Fold Belt (AAFB) in the north-eastern part of India, dominated by humid climate and heavy precipitation. The study uses an integrated approach consisting of advanced spaceborne thermal emission and reflection radiometer (ASTER) image processing, geochemistry, gravity and magnetic data. ASTER image processing involves specialized band ratios (BRs), false colour composite (FCC) and spectral angle mapping (SAM). Integration of remote sensing, geochemistry, gravity and magnetic anomaly data successfully delineates microseepage prospect areas in the basin. The study would provide important advantages in hydrocarbon exploration strategy in similar basins around the world.

An Approach to Accuracy Assessment of ASTER Derived Mineral Maps

An accuracy assessment of a classified thematic map is critical for the success of a remote sensing project. The identification and quantification of accuracy sources for classified mineral maps derived from satellite images enable confident decisions to be made for further exploration operations. Nineteen rock samples were collected from the different lithological units of the hydrothermally altered Kuh Panj Cu porphyry occurrence within the south-eastern part of Iran. SPECIM hyperspectral imaging was applied to the rock samples, followed by X-ray diffraction (XRD) analysis to compare the SPECIM mineral maps. The SPECIM results were then interpreted for mineralogy and compositional mapping extracted from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) images. The spectral angle mapper (SAM) and real value−area (RV−A) fractal methods were applied on SPECIM and ASTER images to interpret the mineralogy and derived classified map products. Two methods, including confusion matrix and one minus standard deviation over mean, were used to assess the accuracy of the classified SPECIM and ASTER derived mineral maps. Performing sensitivity studies were investigated, including the effect of spatially displacing ASTER mineral mapping and changing the SAM-derived threshold values on the accuracy of the ASTER mineral map. The sensitivity analyses demonstrated that selecting an optimum SAM-derived threshold value is more important than spatial displacement. Finally, accuracy sources were summarized in an accuracy budget table. The results demonstrated 89 and 88% accuracy for SPECIM and 88 and 92% accuracy for ASTER mineral maps via the confusion matrix and one minus standard deviation over mean methods, respectively. The accuracy budget helped to evaluate and compare all sources of accuracy with their accuracy values.

Mapping hydrothermal alteration zones using ASTER images in the Arabian–Nubian Shield: A case study of the northwestern Allaqi District, South Eastern Desert, Egypt

In order to map and differentiate ore-bearing hydrothermal alteration zones at the northwestern part of Allaqi-Heiani, we use a combined method of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image processing, field relationship, mineralogical features, and rock chemical compositions. Hydrothermal alteration zones are identified on the basis of Advanced Spaceborne Thermal Emission and Reflection Radiometer data. Propylitic, phyllic, argillic, and silicification zones are recorded in different types of igneous and metamorphic rocks typically with chromite, goethite, ilmenite, galena and gold. Crosta and CEN techniques using ASTER are successfully applied to highlight the hydrothermal alteration zones and the structural elements represented by lithologic contacts and faults/fracture zones. Combining data of lineaments, alteration zones and ore mineralogy studied after obtaining a consequence of each map allowed predicting and mapping areas of probable high mineral resources. According to petrographic and remote sensing studies, types of alteration include sericitization, chloritization, epidotization, kaolinitization, carbonatization and silicification. The results of remote sensing in the study area are confirmed by X-ray diffraction (XRD), petrography, and SEM. In the studied district, there are ophiolitic ultramafic rocks,island-arc assemblage of metasediments, volcaniclastics and metagabbro-diorite, syntectonic graniteand dykes. Accordingly, to the present study can identify the most potential areas of mineralization taking us into consideration the overlapping of high lineament density areas with abundant alterations.

Satellite-based hydrocarbon exploration employing ASTER and fuzzy logic

Abstract Despite the known practical applications of remote sensing in a wide range of industries and situations, it has not been used extensively in petroleum exploration, which has relied mostly on geological and / or geophysical surveys. With the advances made in sensing equipment since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) became operational, extraction of potential indicators such as: surface emissivity, surface kinetic temperature, brightness temperature and surface radiance need to be re-evaluated in the context of petroleum exploration. Land Surface Temperature (LST) anomalies associated with geothermal activity was used, to determine if petroleum in place, can be the source of thermal anomalies detected at surface, by ASTER. A study area, in onshore Iran, consisting of 53 existing petroleum-rich areas was selected. Twenty (20) ASTER scenes (Granules) covering the study area were provided by National American Space Agency (NASA). In order to highlight subsurface contributors to geothermal anomalies detected at surface, assumed to be due to underlying hydrocarbons, ASTER images were processed to minimize temperature variations caused by any sources other than the underlying hydrocarbons. In order to assign weight to the role-playing variables according to their degree of influence over the final LST, fuzzy logic was employed, as the main processing approach. In the resulting processed maps, following the removal of all the contributors to LST, the remaining thermal anomalies, which were reduced up to 80% in some areas of the study area, could then be linked to the underlying hydrocarbons. The final results indicate that, even after the influence of all the contributors to LST has been removed, still 67.9% (36 out of 53 petroleum containing areas) of the pixels within the buffer zones of petroleum reservoirs of the study area are considered to be thermally-anomalous. Continuity of thermal anomalies, in different directions, was also investigated within the buffer zones of each of the petroleum reservoirs, by Variograms. The results of the Variogram analysis indicate that even if the detected thermal anomalies within the buffer zones of some of the reservoir of the study area were not high, the direction of thermal anomalies strongly follow the NW-SE direction, which is the direction along the trend line of elongation axes of petroleum reservoirs. Details of the spatial analysis post LST processing will be reported. By employing GIS and fuzzy logic, a dynamic model was developed, with variable input data from geology and ASTER, which could be customised and changed according to target area's thermal specifications and characteristics.

Comparison of emissivity retrieval methods from ASTER data using Fourier-Transform Infrared Spectroscopy

Land surface emissivity retrieval is important for the remote identification of natural materials and can be used to identify the presence of silicate minerals. However, its estimation from passive sensors involves an undetermined function related to radiance data, which is influenced by the atmosphere. We tested three methods for temperature emissivity retrieval in a dune field composed of 99.53% quartz (SiO2) using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery. The tested methods were the reference channel method (RCM), emissivity normalization method (ENM), and temperature emissivity separation (TES) method. An average quartz reference spectrum for the dune samples was calculated from an emissivity database based on temperature and used to evaluate the emissivity products of four ASTER images. In general, the three tested methods had a good approximation when analysed the emissivity reference curve, especially for longer wavelengths that ranged between 2 and 4% of emissivity. The RCM and ENM produced very similar results with the coefficients of determination (R2) as 0.9960 (RMSE 0.0184) and 0.9959 (RMSE 0.0185), respectively. RCM method presented superior results (R2: 0.9960, RMSE: 0.0184), compared to the TES method (R2: 0.9947, RMSE: 0.0197). The TES method showed good results only for shorter wavelengths and, hence, to identify specific targets using ASTER data, such as silicate minerals, it is better to use the RCM method. The emissivity value selected at the saturation point of the spectral library based on temperature is fundamental in acquiring more reliable data.

Geospatial assessment of ultramafic rocks and ore minerals of Salem, India

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) image interpretation methods such as colour composite images (false colour composite, true colour composite) were adapted to capture the image for interpreting the visible and shortwave infrared raw bands and thus generating the mapping for the ultramafic terrain. ASTER colour composite image generated from shortwave infrared (SWIR) bands 8 and 4 and NIR band 3 shows contrast signature for the presence of rock types in the ultramafic terrain. The low and high silica percentages to be interpreted through the absorption features of the spectral range from 8.1 to 12 μm. The combinations used to map silica index are of bands 10, 11 and 7 as RGB. PCA was applied to SWIR and visible and near-infrared spectral bands and from PCA output PC 6, 2 and 1 used in the generation of RGB colour composite. Hence, the results of PCA processed image highlights the magnesite mining area by dark red colour. The minimum noise fraction transform (B1, B2, B4) in RGB applied to ASTER image to focus on the overlapped rock types inclusive of magnesite and the same achieved as sharp image output. ASTER band ratios (R3/1, G4/5 and B6/8) in RGB calculated in which the numerator represents shoulders of absorption, and the denominator represents nearest absorption band. Thus, this combination indicates clearly the magnesite mining area in blue colour. The relative band depth in thermal infrared (TIR) bands (B13 and B14) correlated to find the carbonate index is high, medium and low through the pixel ratio output of the ultramafic terrain. Preprocessing of ASTER data involved atmospheric correction using FLAASH algorithm. The minimum noise fraction transform reduces the spectral dimensionality of data in a linear combination of bands. Spectral angle mapper and support vector machines are supervised classification technique adopted that are key learning models with the aid of analyse and categorization of data. The classification defends user, producer, overall accuracy and kappa coefficient. The magnesite mining area and adjust rock samples were field observed, and the mapping of the ultramafic terrain supports the output achieved through chosen ASTER band colour composite, band ratios, relative band depth, silica index, PCA, minimum noise fraction and pixel classification. Thus, a more informative lithological map for the ultramafic terrain generated to discriminate magnesite-mining region.

Spectral characteristics of talc and mineral abundance mapping in the Jahazpur Belt of Rajasthan, India using AVIRIS-NG data

ABSTRACT Spaceborne data with Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Short Wave Infrared (SWIR) bands have been used worldwide in discriminating different clay minerals. However, due to wide bandwidth of ASTER sensors, it is difficult to distinguish between pure and impure variety of talc mineral. The present study highlights the efficacy of airborne hyperspectral data Airborne Visible/Infrared Imaging Spectrometer – Next Generation (AVIRIS-NG) for spectral analysis in talc mineral in the Jahazpur Belt of Bhilwara district in Rajasthan, India. Due to its better spectral and spatial resolution, AVIRIS-NG is found to be extremely useful in identifying talc mineral in SWIR region. Talc is characterized by doublet shaped spectral pattern in AVIRIS-NG image with strong absorption dips at 2289.75 nm and 2314.80 nm which could not be picked up in ASTER image. Spectral matching of talc pixels is verified with the field spectra of samples and available spectral library of United States Geological Survey, Jet Propulsion Laboratory, and International Geological Correlation Programme. Variation in reflectance percentage of different talc pixels is attributed to variation in chemical composition of talc. However, a large number of mixed pixels with spectral pattern of both talc and kaolinite minerals have been identified in AVIRIS-NG image which are found to be an impure variety of talc. Whole-pixel to sub-pixel classification techniques with Spectral Angle Mapper, Spectral Feature Fitting, Mixture Tuned Matched Filtering, and Linear Spectral Unmixing (LSU) techniques have been applied for talc abundance mapping in Jahazpur area. LSU is found to be the most useful and accurate technique in classifying spectrally pure to impure variety of talc.

ASTER Cloud Coverage Assessment and Mission Operations Analysis Using Terra/MODIS Cloud Mask Products

Since the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) instrument cannot detect clouds accurately for snow-covered or nighttime images due to a lack of spectral bands, Terra/MODIS cloud mask (MOD35) products have been alternatively used in cloud assessment for all ASTER images. In this study, we evaluated ASTER cloud mask images generated from MOD35 products and used them to analyze the mission operations of ASTER. In the evaluation, ASTER cloud mask images from different MOD35 versions (Collections 5, 6, and 6.1) showed a large discrepancy in low- or high-latitude areas, and the rate of ASTER scenes with a high uncertain-pixel rate (≥30%) showed to be 2.2% in daytime and 12.0% in nighttime. In the visual evaluation with ASTER browse images, about 2% of cloud mask images showed some problems such as mislabeling and artifacts. In the mission operations analysis, the cloud avoidance function implemented in the ASTER observation scheduler showed a decrease in the mean cloud coverage (MCC) and an increase in the rate of clear scenes by 10% to 15% in each. Although 19-year-old time-series of MCC in five areas showed weather-related fluctuations such as the El Niño Southern Oscillation (ENSO), they indicated a small percent reduction in MCC by enhancement of the cloud avoidance function in April 2012. The global means of the number of clear ASTER scenes were 15.7 and 6.6 scenes in daytime and nighttime, respectively, and those of the success rate were 33.3% and 40.4% in daytime and nighttime, respectively. These results are expected to contribute not only to the ASTER Project but also to other optical sensor projects.

Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer, and WorldView-3 Multispectral Satellite Imagery for Prospecting Copper-Gold Mineralization in the Northeastern Inglefield Mobile Belt (IMB), Northwest Greenland

Several regions in the High Arctic still lingered poorly explored for a variety of mineralization types because of harsh climate environments and remoteness. Inglefield Land is an ice-free region in northwest Greenland that contains copper-gold mineralization associated with hydrothermal alteration mineral assemblages. In this study, Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and WorldView-3 multispectral remote sensing data were used for hydrothermal alteration mapping and mineral prospecting in the Inglefield Land at regional, local, and district scales. Directed principal components analysis (DPCA) technique was applied to map iron oxide/hydroxide, Al/Fe-OH, Mg-Fe-OH minerals, silicification (Si-OH), and SiO2 mineral groups using specialized band ratios of the multispectral datasets. For extracting reference spectra directly from the Landsat-8, ASTER, and WorldView-3 (WV-3) images to generate fraction images of end-member minerals, the automated spectral hourglass (ASH) approach was implemented. Linear spectral unmixing (LSU) algorithm was thereafter used to produce a mineral map of fractional images. Furthermore, adaptive coherence estimator (ACE) algorithm was applied to visible and near-infrared and shortwave infrared (VINR + SWIR) bands of ASTER using laboratory reflectance spectra extracted from the USGS spectral library for verifying the presence of mineral spectral signatures. Results indicate that the boundaries between the Franklinian sedimentary successions and the Etah metamorphic and meta-igneous complex, the orthogneiss in the northeastern part of the Cu-Au mineralization belt adjacent to Dallas Bugt, and the southern part of the Cu-Au mineralization belt nearby Marshall Bugt show high content of iron oxides/hydroxides and Si-OH/SiO2 mineral groups, which warrant high potential for Cu-Au prospecting. A high spatial distribution of hematite/jarosite, chalcedony/opal, and chlorite/epidote/biotite were identified with the documented Cu-Au occurrences in central and southwestern sectors of the Cu-Au mineralization belt. The calculation of confusion matrix and Kappa Coefficient proved appropriate overall accuracy and good rate of agreement for alteration mineral mapping. This investigation accomplished the application of multispectral/multi-sensor satellite imagery as a valuable and economical tool for reconnaissance stages of systematic mineral exploration projects in remote and inaccessible metallogenic provinces around the world, particularly in the High Arctic regions.

Lithological mapping in Sangan region in Northeast Iran using ASTER satellite data and image processing methods

ABSTRACTLithological mapping using satellite images, particularly the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data help in effectively defining the best initial targets for regional exploration. ASTER data allow for the discrimination of rock units in the broader region. This research work is focused on the use of remote sensing techniques for geological mapping using ASTER satellite image and generating a geological map of Sangan region. The study area is located in southeast of Khorasan-e-Razavi province and at the eastern edge of the Khaf-Kashmar-Bardskan Volcano-Plutonic Metallogenic Belt in northeast Iran. Band ratio (BR), spectral angle mapper (SAM) and support vector machines (SVMs) methods were used for classifying the main lithologic units in Sangan region. The results of BR, SAM and SVM techniques were quantitatively compared with geological boundaries mapped in the field showing an accuracy of nearly 79 %. SVMs method, in comparison to conventional methods of classification, was known to provide superior results. As the final result of this research, integration of remote sensing and field investigations led to generating high accuracy geological map in the Sangan region. Application of the methods has invaluable implications for geological mapping and mineral exploration in inaccessible regions.