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(a) Inverted slope image of Stonewall Mountain located to the southeast of Cuprite; (b) Grayscale relief map (GRM) image of the same area. In (a), the valley trending northwest-southeast shown by the arrow is white and looks similar to the parallel white ridges in the ellipse. In (b), this valley is black and is easily distinguishable from the ridges in the ellipse.
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This paper proposes a method of combining and visualizing multiple lithological indices derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data and topographical information derived from digital elevation model (DEM) data in a single color image that can be easily interpreted from a geological point of view. For the...
Citations
... The daytime emissivity data were used to estimate the quartz content of the surface constituents. The abundance maps of clay, limestone, and sandstone were derived from the ASTER-SWIR bands [64] and the Landsat-8 Level-2 product, respectively. The clay and quartz abundance maps were used to calculate soil hydraulic parameters according to Appendix D. The thermal properties of rocks were mapped using Equations (17b)-(17e) and the values listed in Table 2 for thermal conductivity, mean density, and heat capacity. ...
... where D s (m 2 ·s −1 ) is the saturated soil water diffusivity, θ and θ s are as previously defined, b (-) is an empirical parameter related to the pore size distribution of the soil matrix, K s is the saturated hydraulic conductivity (m · s −1 ), ψ s is the soil water potential at air entry (m), 'sand' is the percentage of sand in the soil particles here taken equivalent to the quartz fraction, and 'clay' is the fraction of clays in the soil particles quantitatively derived from emissive and reflective multispectral data, respectively [64]. ...
A physically-based image processing approach, based on a single-source surface energy balance framework, is developed here to model the land surface temperature (LST) over complex/rugged geologic terrains at medium to high spatial resolution (<102 m). This approach combines atmospheric parameters with a bulk-layer soil model and remote-sensing-based parameterization schemes to simulate surface temperature over bare surfaces. The model’s inputs comprise a digital elevation model, surface temperature data, and a set of land surface parameters including albedo, emissivity, roughness length, thermal conductivity, soil porosity, and soil moisture content, which are adjusted for elevation, solar time, and moisture contents when necessary. High-quality weather data were acquired from a nearby weather station. By solving the energy balance, heat, and water flow equations per pixel and subsequently integrating the surface and subsurface energy fluxes over time, a model-simulated temperature map/dataset is generated. The resulting map can then be contrasted with concurrent remote sensing LST (typically nighttime) data aiming to remove the diurnal effects and constrain the contribution of the subsurface heating component. The model’s performance and sensitivity were assessed across two distinct test sites in China and Iran, using point-scale observational data and regional-scale ASTER imagery, respectively. The model, known as the Surface Kinetic Temperature Simulator (SkinTES), has direct applications in resource exploration and geological studies in arid to semi-arid regions of the world.
... The ASTER data contains 14 bands from the visible to the thermal infrared band range. Multiple channels contained in the short-wave infrared range (SWIR) can be used to identify types of minerals, such as Fe-related, carbonates, and hydroxides [40], that continue to give the ASTER data significant potential in geological applications [41,42]. ...
Geochemical data can reflect geological features, making it one of the basic types of geodata that have been widely used in mineral exploration, environmental assessment, resource potential analysis and other research. However, final decisions regarding activities are often limited by the spatial accuracy of geochemical data. Geochemical sampling is sometimes difficult to conduct because of harsh natural and geographic conditions (e.g., mountainous areas with high altitude and complex terrain), meaning that only medium/low-precision survey data could be obtained, which may not be adequate for regional geochemical mapping and exploration. Modern techniques such as remote sensing could be used to address this issue. In recent decades, the development of remote sensing technology has provided a huge amount of earth observation data with high spatial, temporal and spectral resolutions. The advantage of rapid acquisition of spatial and spectral information of large areas has promoted the broad use of remote sensing data in geoscientific research. Remote sensing data can help to differentiate various ground features by recording the electromagnetic response of the surface to solar radiation. Many problems that occur during the process of fusing remote sensing and geochemical data have been reported, such as the feasibility of existing fusion methods and low fusion accuracies that are less useful in practice. In this paper, a new strategy for integrating geochemical data and remote sensing data (referred to as ASTER data) is proposed; this strategy is achieved through linear regression as well as random forest and support vector regression algorithms. The results show that support vector regression can obtain better results for the available data sets and prove that the strategy currently proposed can effectively support the fusion of high-spatial-resolution remote sensing data (15 m) and low-spatial-resolution geochemical data (2000 m) in wide-range accurate geochemical applications (e.g., lithological identification and geochemical exploration).
... The remote-sensing approach affords significant tools for characterizing and delineating geological, structural, and lithological features that have helped identify areas of mineralization for many decades [3][4][5][6]. The substantial progress in processing remotely-sensed images has allowed for identifying rocks and minerals based on their spectral properties using multispectral and/or hyperspectral sensors in the visible-near-infrared (VNIR) and the shortwave infrared (SWIR) regions of the electromagnetic spectrum (EMS) [1][2][3][4][5][6][7][8][9][10][11][12][13]. Therefore, the use of remote-sensing has been extended to mineral exploration by careful characterization of fault/fracture zones and/or hydrothermal alteration minerals [1,8,9,[14][15][16][17] containing Al-OH, Fe-OH, Mg-OH, Si-OH, and -CO3 radicals [1,18,19]. ...
... However, using mineral indices OHI, kaolinite index (KAI) [21,22], and (B4 × 3)/ (B5 + B6 + B7) (cf. [13]) of ASTER data clearly depicts areas rich in Al-OH-bearing minerals in white (Figure 5c). ...
... × 3)/(B5 + B6 + B7) of ASTER data clearly depicts areas rich in Al-OH-bearing minerals in white(Figure 5c). Using SWIR depth = (B4 × 3)/(B5 + B6 + B7) of ASTER data (cf.[13]) enhances the appearance of hydrothermal alteration areas. ...
The Arabian Nubian Shield (ANS) contains a variety of gold deposits in the form of veins and veinlets formed by hydrothermal fluids. Characterizing potential areas of hydrothermal alteration zones therefore provides a significant tool for prospecting for hydrothermal gold deposits. In this study, we develop a model of exploration for hydrothermal mineral resources in an area located in the ANS, Egypt, using multiple criteria derived from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Landsat-Operational Land Imager (OLI), and Sentinel-2 data and field work through GIS-based fuzzy logic approach. The hydrothermal alteration zones (HAZs) map extracted from combining mineral indices, spectral bands, and ratios is consistent with observed argillic alteration zones around the mineralized veins. Combining HAZs and lineament density led to identification of six zones based on their mineralization potential, and provides a tool for successful reconnaissance prospecting for future hydrothermal mineral deposits. The detected zones are labeled as excellent, very high, high, moderate, low, and very low, based on their potential for Au production, and the predictive excellent and very high zones cover about 1.6% of the study area. This model also shows that target prospective zones are quartz veins controlled by NNW-SSE trending fracture/fault zones all crosscutting Precambrian rocks of the ANS. Field observations and petrographic and X-ray diffraction analyses were performed to validate the mineral prospective map and revealed that quartz veins consist of gold–sulfide mineralization (e.g., gold, pyrite, chalcopyrite, and sphalerite). Consistency between the high potential hydrothermal alterations zones (HAZs) and the location of gold mineralization is achieved.
... The majority of geological studies based on TIR remote sensing data have focused on multispectral data. Some typical sensors, such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) [12][13][14] and Thermal Infrared Multispectral Scanner (TIMS) [9,11], have been successfully used to identify lithologic units based on spectral indices of quartz, carbonate and so on [12,14]. With the development of technology, thermal infrared hyperspectral data can be obtained from airborne platforms, especially Thermal Airborne Spectrographic Imagery (TASI). ...
... The majority of geological studies based on TIR remote sensing data have focused on multispectral data. Some typical sensors, such as the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) [12][13][14] and Thermal Infrared Multispectral Scanner (TIMS) [9,11], have been successfully used to identify lithologic units based on spectral indices of quartz, carbonate and so on [12,14]. With the development of technology, thermal infrared hyperspectral data can be obtained from airborne platforms, especially Thermal Airborne Spectrographic Imagery (TASI). ...
In recent decades, lithological mapping techniques using hyperspectral remotely sensed imagery have developed rapidly. The processing chains using visible-near infrared (VNIR) and shortwave infrared (SWIR) hyperspectral data are proven to be available in practice. The thermal infrared (TIR) portion of the electromagnetic spectrum has considerable potential for mineral and lithology mapping. In particular, the abovementioned rocks at wavelengths of 8–12 μm were found to be discriminative, which can be seen as a characteristic to apply to lithology classification. Moreover, it was found that most of the lithology mapping and classification for hyperspectral thermal infrared data are still carried out by traditional spectral matching methods, which are not very reliable due to the complex diversity of geological lithology. In recent years, deep learning has made great achievements in hyperspectral imagery classification feature extraction. It usually captures abstract features through a multilayer network, especially convolutional neural networks (CNNs), which have received more attention due to their unique advantages. Hence, in this paper, lithology classification with CNNs was tested on thermal infrared hyperspectral data using a Thermal Airborne Spectrographic Imager (TASI) at three small sites in Liuyuan, Gansu Province, China. Three different CNN algorithms, including one-dimensional CNN (1-D CNN), two-dimensional CNN (2-D CNN) and three-dimensional CNN (3-D CNN), were implemented and compared to the six relevant state-of-the-art methods. At the three sites, the maximum overall accuracy (OA) based on CNNs was 94.70%, 96.47% and 98.56%, representing improvements of 22.58%, 25.93% and 16.88% over the worst OA. Meanwhile, the average accuracy of all classes (AA) and kappa coefficient (kappa) value were consistent with the OA, which confirmed that the focal method effectively improved accuracy and outperformed other methods.
... 2018 [11] used of Aster data and geochemical analysis for the exploration of gold at Samut (Samut) area, South Eastern Desert, Egypt. The concept of the band rationing technique was widely used in different areas (Rowan et [12][13][14][15][16][17][18][19][20]. Ratio images are. ...
... Landsat data were successfully used for mapping alteration minerals (Banerjee et al., 2019;Eldosouky et al., 2017;Liu et al., 2017), Li-bearing pegmatites (Cardoso-Fernandes et al., 2019), iron ore deposits (Ghrefat et al., 2018), gold exploration (Yousefi et al., 2018), ZnePb sulfide mineralization (Beiranvand Pour et al., 2018), and so on. The advanced multispectral dataset like Advanced Spaceborne Thermal Emission and Reflection Radiometer also successfully delineate the occurrences of gold sulfide mineralizations (Rani et al., 2019), alteration minerals (Fereydooni and Mojeddifar, 2017), silicate and carbonate rocks (Kurata and Yamaguchi, 2019), mafic and ultramafic rock units (Rejith and Sundararajan, 2018b), limestone deposits (Basavarajappa et al., 2019), and sillimanite mineralization (Amer and El-Desoky, 2017). Increased advancements in satellite sensor technology have paved the way for production of remote sensing datasets with high spatial, spectral, and temporal resolutions that made a significant breakthrough in the field of mineral exploration. ...
The coastal tracts of India consist of beach sands showing good concentration of strategic heavy minerals. In the present study, the mineralogy of important coastal placer deposits in India like Chavara deposits in Kerala and Manavalakurichi deposits in Tamilnadu were studied using Landsat multispectral satellite data for deriving potential targets of these strategic minerals. The ilmenite mineral predominantly exists in these areas, followed by monazite, sillimanite, rutile, zircon, garnet, leucoxene, and kyanite. The distribution of minerals was studied using advanced hyperspectral techniques like minimum noise fraction transformation, pixel purity index, and n-dimensional visualizer followed by spectral angle mapper (SAM) classification. Two end members of ilmenite mineral and quartz were identified from the Landsat imagery and successfully mapped using SAM algorithms. Detailed investigation of the mineralogy of beach sediments using advanced hyperspectral remote sensing techniques helps to derive potential targets for the ecofriendly exploration of minerals.
... The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), for example, has five spectral bands in the region of thermal infrared. Its importance for soil mineralogy and lithological evaluations has been highlighted in several works (Abrams and Yamaguchi, 2019;Bhadra et al., 2013;Cudahy et al., 2016;Kurata and Yamaguchi, 2019;Laukamp et al., 2012;Vicente and de Souza Filho, 2011;Mulder et al., 2013;Ninomiya and Fu, 2019). Besides that, other spectral ranges in the mid-IR region can also provide valuable information for soil mineralogical studies and need to be explored. ...
... Considered as useful tools for geological mapping and minerals explorations (Abrams and Yamaguchi, 2019;Bhadra et al., 2013;Cudahy et al., 2016;Fu et al., 2019;Kurata and Yamaguchi, 2019), simulated TIR bands have been successfully tested to discriminate contrasting soils in this study (Fig. 9). However, the use of remote sensing images is limited by the influence of several atmospheric factors (Ben-Dor et al., 2009), which sometimes limit the definition of spectral regions useful for mineralogical studies. ...
The soil mineralogical constitution directly influences its chemical, physical and hydraulic characteristics.Although very important, it is still rarely used for decision-making in agriculture, mainly due to the complexityand cost of standard analyzes. In this sense, the middle infrared spectroscopy (mid-IR, 4000 to 400 cm−1) hasgreat potential to obtain soil mineralogical information quickly and accurately. Nevertheless, some soil con-stituents can severely influence the spectra and produce misinterpretations. In this research, we aim to detectchanges in the mid-IR spectra caused by water, iron forms and organic matter (OM), and to relate soil attributesto laboratory spectra and remote sensing simulated spectral bands. The research area is located in São PauloState, Brazil, where seventeen soil samples were collected. The reflectance intensities, shapes and absorptionfeatures of the mid-IR spectra before and after the removal of OM and iron forms and the addition of water weredescribed. Soil attributes, such as kaolinite, gibbsite, 2:1 minerals among others were correlated with the mid-IRspectra and simulated ASTER spectral bands by Pearson's analysis, to verify its potential on mineralogicalevaluation. The description of mid-IR revealed that the removal of the OM from the soil samples decreased thereflectance intensities between 4000 and 2000 cm−1. Iron forms mainly influence the 3250–1200 cm−1spectral range and mask the spectral features of other minerals as well. The addition of water masked severalabsorption features and decreased the reflectance intensities from 3700 to 2700 cm−1. High correlation coef-ficients were obtained between soil attributes and ASTER simulated spectral bands, which allowed the selectionof potential spectral regions for future satellite sensors: 2760–2500 cm−1(3600–4000 nm), 2150–1875 cm−1(4600–5300 nm), and 840–740 cm−1(11900–3500 nm)
... The visible and near-infrared (VNIR), shortwave infrared (SWIR) and thermal infrared (TIR) bands of multispectral remote sensing data contain unprecedented spectral and spatial capabilities for detecting hydrothermal alteration minerals and lithological units associated with a variety of ore mineralization [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20][21][22]. Numerous investigations successfully used Landsat data series, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), and the Advanced Land Imager (ALI) multispectral data with moderate spatial resolution for the reconnaissance stages of mineral exploration around the world [23][24][25][26][27][28][29]. ...
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 Remote Sens. 2019, 11, 2430 2 of 40 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.
... This is excellent, considering the presence of vegetation contamination and cover. A final example, published in this 20th Anniversary ASTER Special Issue, by Kurata and Yamaguchi [76] proposed a method of combining and visualizing multiple lithological indices derived from ASTER data, and topographical information derived from digital elevation model data, in a single color image that can be easily interpreted from a geological point of view. Indices highlighted silicate rocks, carbonate rocks, and amounts and types of clay minerals. ...
The Advanced Spaceborne Thermal Emission and Reflection Radiometer is one of five instruments operating on the National Aeronautics and Space Administration (NASA) Terra platform. Launched in 1999, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) has been acquiring optical data for 20 years. ASTER is a joint project between Japan’s Ministry of Economy, Trade and Industry; and U.S. National Aeronautics and Space Administration. Numerous reports of geologic mapping and mineral exploration applications of ASTER data attest to the unique capabilities of the instrument. Until 2000, Landsat was the instrument of choice to provide surface composition information. Its scanners had two broadband short wave infrared (SWIR) bands and a single thermal infrared band. A single SWIR band amalgamated all diagnostic absorption features in the 2–2.5 micron wavelength region into a single band, providing no information on mineral composition. Clays, carbonates, and sulfates could only be detected as a single group. The single thermal infrared (TIR) band provided no information on silicate composition (felsic vs. mafic igneous rocks; quartz content of sedimentary rocks). Since 2000, all of these mineralogical distinctions, and more, could be accomplished due to ASTER’s unique, high spatial resolution multispectral bands: six in the SWIR and five in the TIR. The data have sufficient information to provide good results using the simplest techniques, like band ratios, or more sophisticated analyses, like machine learning. A robust archive of images facilitated use of the data for global exploration and mapping.
... ASTER measures reflected radiation in three bands in the 0.52-to 0.86 µm (the visible and near-infrared (VNIR)), six bands in the 1.6-to 2.43 µm (the shortwave infrared (SWIR)), and five bands of emitted radiation in the 8.125-to 11.65 µm (the thermal infrared (TIR)) with 15, 30, and 90 meter resolution, respectively [18,19]. Hydrothermal alteration zones associated with various ore deposits such as porphyry copper, orogenic gold, epithermal gold, massive sulfide, iron, and chromite deposits have been successfully detected and mapped using ASTER imagery in metallogenic provinces around the world [20][21][22][23][24][25][26][27]. Specifically, some studies used ASTER data for the exploration of polymetallic vein-type ore deposits. ...
Polymetallic vein-type ores are important sources of precious metal and a principal type
of orebody for various base-metals. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) remote sensing data were used for mapping hydrothermal alteration zones associated with epithermal polymetallic vein-type mineralization in the Toroud–Chahshirin Magmatic Belt (TCMB), North of Iran. The TCMB is the largest known goldfield and base metals province in the central-north of Iran. Propylitic, phyllic, argillic, and advanced argillic alteration and silicification zones are typically associated with Au-Cu, Ag, and/or Pb-Zn mineralization in the TCMB. Specialized image processing techniques, namely Selective Principal Component Analysis (SPCA), Band Ratio Matrix Transformation (BRMT), Spectral Angle Mapper (SAM) and Mixture Tuned Matched Filtering (MTMF) were implemented and compared to map hydrothermal
alteration minerals at the pixel and sub-pixel levels. Subtle differences between altered and non-altered rocks and hydrothermal alteration mineral assemblages were detected and mapped in the study area. The SPCA and BRMT spectral transformation algorithms discriminated the propylitic, phyllic, argillic and advanced argillic alteration and silicification zones as well as lithological units.
The SAM and MTMF spectral mapping algorithms detected spectrally dominated mineral groups such as muscovite/montmorillonite/illite, hematite/jarosite, and chlorite/epidote/calcite mineral assemblages, systematically. Comprehensive fieldwork and laboratory analysis, including X-ray diffraction (XRD), petrographic study, and spectroscopy were conducted in the study area for
verifying the remote sensing outputs. Results indicate several high potential zones of epithermal polymetallic vein-type mineralization in the northeastern and southwestern parts of the study area, which can be considered for future systematic exploration programs. The approach used in this research has great implications for the exploration of epithermal polymetallic vein-type mineralization in other base metals provinces in Iran and semi-arid regions around the world.
