Advanced Spaceborne Thermal Emission And Reflection Radiometer Thermal Infrared Research Articles

Geological mapping and mineral exploration in Francistown region, north east Botswana from integration of ASTER TIR and aeromagnetic data

To map the region's geology and mineral exploration, this study looked at how remote sensing satellite imaging from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Thermal Infrared (TIR) sensor and aircraft geophysics may be used. The Francistown region forms part of the Zimbabwe craton, northeast of Botswana. It comprises felsic rock types, including granite and dry river sand, mafic-ultramafic rocks, and gneiss country rock. Based on their spectral emissivity responses, mafic-ultramafic units and quartz-rich units were discovered. These units are important targets for the discovery of mineral resources. The research region can be described as convoluted, fractured, intruded, metamorphosed, and ultimately worn from a geological and geophysical standpoint. Several remote sensing and aeromagnetic approaches were employed. The false color composite (FCC) and mineral indices using ASTER TIR data provided helpful information for delineating the area's geology and concentration of major-rock-forming minerals. The aeromagnetic data extracted lineaments well, and the Center for Exploration Target (CET) grid analysis system was very helpful. The CET Porphyry Analysis technique was applied in mapping near-circular porphyry features, which are good indicators of possible sites with high mineralization potential. The analysis of the lineament map shows that mineral deposits are mostly along the NE-SW direction. The results from the ASTER TIR and aeromagnetic enhancements indicate the occurrence of mafic-ultramafic rocks and felsic-rich units, which have been documented to host mineralization in the region. Porphyry structures are also mapped successfully along the general strike of the geological structure in the study area. The combination of these methods proved their success, and they can be used further in other regions of high mineral potential and lithological mapping for their cost efficiency and reliability.

Detection of Subtle Thermal Anomalies: Deep Learning Applied to the ASTER Global Volcano Dataset

Twenty-one years of ASTER global thermal infrared (TIR) acquisitions provide a large amount of data for volcano monitoring. These data, with high spatial and spectral resolution, enable routine investigations of volcanoes in remote and inaccessible regions, including those with no ground-based monitoring. However, the dataset is too large to be manually analyzed on a global basis. Here, we systematically process the data over several volcanoes using a deep learning algorithm to automatically extract volcanic thermal anomalies. We explore the application of a Convolutional Neural Network (CNN), specifically UNET, to detect subtle to intense anomalies exploiting the spatial relationships of the volcanic features. We employ a supervised UNET network trained with the largest (1500) labeled dataset of ASTER TIR images from five different volcanoes, namely Etna (Italy), Popocatépetl (Mexico), Lascar (Chile), Fuego (Guatemala), and Kliuchevskoi (Russia). We show that our approach achieves high accuracy (93%) with excellent generalization capabilities. The effectiveness of our model for detecting the full range of thermal emission is shown for volcanoes with very different styles of activity and tested at Vulcano (Italy). The results demonstrate the potential applicability of the proposed approach to the development of automated thermal analysis systems at the global-scale using future TIR data such as the planned NASA SBG mission.

Detection of thermal features from space at Indonesian volcanoes from 2000 to 2020 using ASTER

Routine analysis and detection of volcanic thermal features (VTFs) are available globally for low spatial resolution (>1 km/pixel) thermal infrared (TIR) sensors. However, medium resolution (90 m/pixel) ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer) TIR data, which is sensitive to smaller and cooler VTFs, have yet to be systematically examined at all active Indonesian volcanoes. We manually examined cloud-free nighttime images at 136 potentially active volcanoes from 2000 to 2020 CE and found at least 71 volcanoes with VTFs, despite the low proportion (26.5%) of cloud-free images. Of these, 34 volcanoes have VTF detections from space published for the first time (to our knowledge) and at least 44 volcanoes have non-eruptive VTFs (fumaroles, heated volcanic lakes and open vent systems). The inclusion of hotspots detected by ASTER in this study increases the percentage of Indonesian volcanoes with recorded satellite-detectable thermal features from 27% to 52%, larger than in Latin America (27%) and the US (33%) which were surveyed in the same way and is possibly due to a higher frequency of eruptions in Indonesia. The distribution of VTFs across sub-regions in Indonesia is not uniform but may be correlated with the number of volcanoes with eruptions. This multi-decadal country-wide survey highlights the potential of TIR monitoring and detection of low-temperature volcanic features that may be eruption precursors, but also the limits of the available datasets since many volcanoes only have a few cloud-free nighttime images per year.

Analysis of ash emissions from the 2020 Nishinoshima eruption using ASTER thermal infrared orbital data

The 2020 eruption of the Nishinoshima volcano, Japan, emplaced new lava flows as well as numerous ash plumes from explosive eruptions. Several of these plumes were imaged by the Advanced Spaceborne Thermal Emission and reflection Radiometer (ASTER) as part of the Urgent Request Protocol (URP) during July 2020. Linear spectral deconvolution was used to model ASTER thermal infrared (TIR) data using the ASTER Volcanic Ash Library (AVAL), a spectral library containing volcanic ash compositional and particle size range end members. The airborne ash plume best matched the andesite library end member suites having a significant fine (<25 μm) fraction proximal to the vent, however particles >63 μm were also detected downwind, and distally around the cloud edges, assumed due to sampling of lower levels of the plume containing these larger particles even as the optical depth remains high. These results demonstrate that both composition and particle size variations within a plume can be estimated from the high spatial and spectral resolution ASTER TIR data using this approach, provided that representative spectral end members are present in AVAL and where the plume remains opaque to upwelling radiance.

Retrieval of precise land surface temperature from ASTER night-time thermal infrared data by split window algorithm for improved coal fire detection in Jharia Coalfield, India

The study proposed a methodology for the retrieval of precise Land Surface Temperature (LST) in Jharia Coalfield from night-time ASTER multispectral thermal infrared (TIR) data by split-window algorithm (SWA) using atmospheric transmittance and band-specific Land Surface Emissivity (LSE). For deriving night-time atmospheric transmittance, water vapor content was retrieved from night-time ASTER TIR data by modified split-window covariance and variance ratio approach. Improved LSE was retrieved by the proposed modified LSE model by integrating refined thermal emission-vegetation cover model, modified normalized difference water index and bandwidth-weighted red band reflectivity model. The retrieved SWA LST was compared with LST obtained by single-channel algorithm (SCA) across three coal fire test sites to demonstrate significant improvement in temperature contrast between coal fire and background pixels. Besides, SWA LST based coal fire thermal anomalies are significantly comparable (including substantially reduced false alarms) with in-situ observations than that of SCA LST.

Lunar Calibration for ASTER VNIR and TIR with Observations of the Moon in 2003 and 2017

The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), which is a multiband pushbroom sensor suite onboard Terra, has successfully provided valuable multiband images for approximately 20 years since Terra’s launch in 1999. Since the launch, sensitivity degradations in ASTER’s visible and near infrared (VNIR) and thermal infrared (TIR) bands have been monitored and corrected with various calibration methods. However, a unignorable discrepancy between different calibration methods has been confirmed for the VNIR bands that should be assessed with another reliable calibration method. In April 2003 and August 2017, ASTER observed the Moon (and deepspace) for conducting a radiometric calibration (called as lunar calibration), which can measure the temporal variation in the sensor sensitivity of the VNIR bands enough accurately (better than 1%). From the lunar calibration, 3–6% sensitivity degradations were confirmed in the VNIR bands from 2003 to 2017. Since the measured degradations from the other methods showed different trends from the lunar calibration, the lunar calibration suggests a further improvement is needed for the VNIR calibration. Sensitivity degradations in the TIR bands were also confirmed by monitoring the variation in the number of saturated pixels, which were qualitatively consistent with the onboard and vicarious calibrations.

Evaluation of ASTER TIR data-based lithological indices in Malanjkhand Copper Mines of Madhya Pradesh, India

ABSTRACTThis study focuses on the Malanjkhand Copper Mines area of Madhya Pradesh, India and compares different types of quartz, feldspar and mafic remote sensing indices used for the detection of quartz, feldspar and mafic minerals using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) thermal infrared (TIR) bands. Our data indicate that these lithological indices are useful for delineating quartz, feldspar or mafic minerals. Correlation matrices generated for various lithological indices indicate that both Guha's (GMI) and Ninomiya's (NMI) mafic indices yield nearly identical results. In addition, Guha's quartz index (GQI) yields significantly better results than Ninomiya's quartz index (NQI) during the identification of the quartz content. This study also shows that GQI is comparable with the Rockwall and Hofstra's quartz index (RHQI) for the identification of the quartz content. Hence, ASTER TIR data-based remote sensing indices play a key role in lithological mapping.

Mapping hydrothermally altered minerals with AST_07XT, AST_05 and Hyperion datasets using a voting-based extreme learning machine algorithm

Detailed hydrothermally altered mineral mapping is important for mineral exploration. ASTER data are commonly combined with Hyperion data to classify the hydrothermally altered minerals. However, when machine learning algorithms are applied to the shortwave infrared (SWIR) bands of an ASTER reflectance image (AST_07XT), misclassification of Mg-OH group minerals is the major source of errors. In this study, an ASTER emissivity image (AST_05) and the AST_07XT SWIR bands are integrated to map minerals in the Duolong area, Tibetan Plateau. The results show that ASTER thermal infrared (TIR) bands can successfully identify Mg-OH group minerals. To improve the performance of classification, a novel voting-based extreme learning machine (V-ELM) algorithm is introduced to map hydrothermally altered minerals. The classification based on the ASTER SWIR-TIR data gets good identification of Mg-OH group minerals, which is better than those acquired using SWIR and TIR data alone. Moreover, these results also show that the AST_05 TIR bands cannot discriminate Al-OH group minerals. Compared with the classification applied to AST_07XT SWIR data, the classification applied to the ASTER SWIR-TIR data can achieve a higher overall accuracy (99.01%). The ASTER data results are spatially consistent with those of the Hyperion data. In accordance with the image processing results, a new deposit associated with felsic intrusions has been validated by field investigations. These schemes are promising for mineral mapping in the Tibetan Plateau.

Regional mineral mapping of island arc terranes in southeastern Mongolia using multi-spectral remote sensing data

Over the last few decades, many porphyry Cu deposits have been discovered in southeastern (SE) Mongolia. In particular, the Gurvansayhan and Mandalovoo island arc terranes, have high potential for ore deposits. Regional mineral mapping of these terranes using data from the Landsat Enhanced Thematic Mapper Plus (ETM+) and the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) have provided new geological information on the metallogenic province, revealing potential mineral deposits for future explorations. Iron oxides/hydroxides with high ETM+ band 1/3 ratio values occur extensively in Quaternary deposits located at the boundary between the Gurvansayhan and Mandalovoo island arcs terranes and basins. The high average band ratios (>1.35) registered for the porphyry Cu and Au deposits and their occurrences are associated with iron oxidation which originates from the erosion and weathering of ore zones. Regional mapping of the island arc terranes using the ASTER shortwave infrared (SWIR) logical operators showed circular or semicircular alteration patterns which usually cluster along faults. The mineral assemblages mapped by the ASTER SWIR analysis in five major porphyry Cu districts can be divided into two types. The first type includes argillic, phyllic, and propylitic mineral assemblages, as exemplified by the Shuteen, Ikh Shankhai, and Oyu Tolgoi deposits. The second type of assemblage includes phyllic and propylitic mineral assemblages, as exemplified by the Kharmagtai and Tsagaan Suvarga deposits. Within the deposits of the first type, quartz was also mapped using the ASTER thermal infrared (TIR) mineral index. The difference between the two types may be related to the presence of a lithocap. The results of a field survey including spectral reflectance measurements in the Ikh Shankhai porphyry Cu district, showed a good correlation with the image analysis results.

Application of spectral analysis to determine geothermal anomalies in the Tuzla region, NW Turkey

We used remote sensing data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite to identify the mineral properties and geothermal anomalies related to hot springs in the Tuzla area, including the fault system with NW-SE trend, which is located southwest of Canakkale, NW Turkey. In the study area, the lithological units of the Tuzla geothermal field and the surrounding area consist of Miocene volcanic (trachyandesite, trachyte, and ignimbrites) and Pliocene sedimentary (conglomerate, sandstone, and mudstone) rocks with siliceous, argillaceous, and ferrous alteration linked to the geothermal fluid. ASTER visible/near-infrared (VNIR), short-wave infrared (SWIR), and TIR bands were analyzed by different approaches in order to highlight hot springs in the study area. From these approaches, band ratios were constructed from ASTER VNIR, SWIR, and TIR bands for obtaining geological properties of the region. The geothermal areas were defined by the minimum noise fraction (MNF) and principal component analysis (PCA) methods that was extracted from 5 thermal infrared (TIR) bands as well. Land surface temperatures (LST) support the results from MNF and PCA that were estimated for 5 TIR bands using the inversion of Planck function method. Four days of data including daytime and nighttime satellite images from ASTER were used for the analysis. The used procedure displayed a good match with the ground reality based on field observations in the Tuzla Region.

Detection of ground thermal anomaly under dense vegetation based on ASTER TIR images

Ground thermal anomalies associated with geothermal surface manifestations such as hot springs, fumaroles, altered surfaces, and steaming grounds serve as crucial indicator for geothermal explorations. In order to highlight the ground thermal anomalies, this study was raised to use the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data to estimate surface temperature under condition of dense vegetation. This study was focused to exploit the ASTER Thermal Infrared Radiometer (TIR) under lab and field scale conditions. The ASTER TIR bands were used to extract the Land Surface Temperature (Ts) from radiances by excluding surface emissivity (εs). The ε is a surface parameter dependent to the surface materials. In this paper, we demonstrated the performance of the Normalized Emissivity Method (NEM) to calculate the Ts by estimating the ε under condition of dense vegetation. The Patuha and Wayang Windu Geothermal field in West Java (Indonesia) were selected as study site due to existence of surface manifestations under canopy vegetation and open area. According to the method, we identified that the ground thermal anomalies are located at surface manifestations. Due to the different spatial scales of satellite and surface measurements and the lack of homogeneous areas, which are representative for low resolution pixels and ground measurements, ground-validation is necessary. The consistency between ASTER data and location of geothermal manifestations indicated that thermal remote sensing data integrated with a spatial-based model, provides an effective means for identifying geothermal potential.

Application of WorldView-3 imagery and ASTER TIR data to map alteration minerals associated with the Rodalquilar gold deposits, southeast Spain

The Rodalquilar epithermal quartz-alunite gold deposits that occur within the Rodalquilar caldera complex in southeast Spain, are associated with a pronounced hydrothermal alteration of the country rocks. The hydrothermal alteration zones that are exposed on the surface consist of the vuggy silica zone, the advanced argillic alteration zone, the intermediate argillic alteration zone, the propylitic alteration zone, and a second stage supergene acid sulfate alteration. High spatial resolution multispectral imagery recorded by the WorldView-3 satellite was used in this study to map the spatial distribution of the main alteration minerals in the Rodalquilar caldera complex. Thermal infrared (TIR) data of the ASTER satellite were used to detect the quartz-rich zones. The analysis of the Rodalquilar WorldView-3 data was based on the Adaptive Coherence Estimator (ACE), a partial unmixing algorithm. The ACE processing accurately mapped the spatial distribution of alunite, kaolinite, illite and goethite. Alunite is abundant in the vuggy silica and advanced argillic alteration zones, and in the second stage supergene acid sulfate alteration. Kaolinite is predominant in the intermediate argillic alteration zone. Illite is abundant in the outer parts of the intermediate argillic alteration zone. Goethite image maps gossans that mainly occur in the vuggy silica and advanced argillic alteration zones, and in the areas characterized by the second stage supergene acid sulfate alteration. The detection of quartz-rich zones from the ASTER TIR data complemented the WorldView-3 mapping results. The study shows the efficiency of high spatial resolution multispectral remote sensing imagery recorded by the WorldView-3 satellite for district-level mineral exploration studies.

Lithological Mapping using Landsat 8 OLI and ASTER TIR Multispectral Data- A Comparative Study

Lithological mapping is a fundamental step in various mineral prospecting studies because it forms the basis of the interpretation and validation of retrieved results. This study utilizes the Advanced Space borne Thermal Emission and Reflection Radiometer (ASTER) and Landsat 8 Operational Land Imager (OLI) multispectral data to map lithological units in the Ananthapur District of Andhra Pradesh. Geologically, the area is made up Archaean rocks consisting of Peninsular gneiss, Dharwarsupracrustal rocks, Proterozoic meta sedimentary basin (consisting of quartzite, slate and dolomites) and younger granitoids, mafic dike and kimberlite. The band ratios, also known as Mineral indices, specially catered to discriminate mafic, and felsic mineral such as, Mafic Index (MI), Quartz index (QIand feldspar Rock Index (FRI) were applied to ASTER Thermal Infrared (TIR) bands and Band ratiocolour composite were generated. MNF transformation was applied over the Landsat 8 OLI image and MNF component color composite was generated. Geological mapping was carried out by visual interpretation and onscreen digitization. Geological map of a Ramgiri schist belt prepared from the ASTER TIR mineral indices colour composite and Landsat 8 MNF colour composites were compared with the existing Geological map prepared by Geological Survey of India. The results show an excellent correlation with the existing geological map of the study area. ASTER TIR band ratio colour composite could effectively demarcate silica rich rocks such as Quartz veins, quartzites, granites and granitic gneisses from and Mafic rocks such as dolerite/gabbro dykes, amphibolites and carbonate rocks (including Calcite and dolomite). The Landsat8 OLI MNF colour composite generated using MNF bands 523 were found to discriminate lithology effectively. However, TIR based geological mapping was found superior to the MNF based mapping. The structural details were enhanced in Landsat images compared to ASTER TIR data, owing to its higher radiometric and spatial resolution. Geological map prepared by combination of ASTER TIR and Landsat OLI will help in updating the existing geological map. The work demonstrates the utility of ASTER TIR and Landsat 8 OLI data in preparing/updating the geological map of an area. Keywords Anantapur district; ASTER TIR; Band Ratio; Geological mapping; Landsat 8 OLI; MNF DOI: https://doi.org/10.23953/cloud.ijarsg.369

Downscaling of ASTER Thermal Images Based on Geographically Weighted Regression Kriging

The lower spatial resolution of thermal infrared (TIR) satellite images and derived land surface temperature (LST) is one of the biggest challenges in mapping temperature at a detailed map scale. An extensive range of scientific and environmental applications depend on the availability of fine spatial resolution temperature data. All satellite-based sensor systems that are equipped with a TIR detector depict a spatial resolution that is coarser than most of the multispectral bands of the same system. Certain studies may therefore be not feasible if applied in areas that depict a high spatial variation in temperature at small spatial scales, such as urban centers and flooded pristine areas. To solve this problem, this study applied an image downscaling method to enhance the spatial resolution of LST data by combining TIR, multispectral images, and derived data, such as Normalized Difference Vegetation Index (NDVI), according to the geographically weighted regression (GWRK) and area-to-point kriging of regressed residuals. The resulting LST images of the natural and anthropogenic urban areas of the Brazilian Pantanal are very highly correlated to the reference LST images. The approach, combining ASTER TIR with ASTER visible/infrared (VNIR) and Sentinel-2 images according to the GWRK method, performed better than all of the remaining state-of-the-art downscaling methods.

Comparative Analysis of Potentials of ASTER Thermal Infrared Band Derived Emissivity Composite, Radiance Composite and Emissivity–Temperature Composite in Geological Mapping of Proterozoic Rocks in Parts of Banswara, Rajasthan

In this study, we have compared the potential utility of relative emissivity bands derived from Level 1B (at sensor georectified radiance) and Level 2 (multiband emissivity product provided by ASTER service provider) data products of Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) sensor for delineating different geological units of volcano-sedimentary provinces in parts Banswara, Rajasthan. Reference channel method was used for deriving emissivity from Level 1B data whereas temperature emissivity separation algorithm was used to derive Level 2 emissivity data products. In the false colour composite image of emissivity bands of two different data products, quartzite was enhanced but other rocks were not discernible. On the other hand, other rocks of the study area were detectable (basalt, gneiss, dolomitic marble) in Level 1B radiance data. In radiance image, variations in surface temperature and emissivity were recorded and this was helpful for delineating different rocks. Surface temperature is sensitive to land cover, moisture and it helped in detecting rocks like dolomite which was covered with moisture rich soil and vegetation. Similarly, emissivity–temperature composite was also proved useful in delineating basalt, quartzite (chloritic), dolomite/dolomitic marble and associated gneiss/migmatites as it brought out the contrast of composition and the differences in the thermophysical parameters resulted temperature variation. An image composite prepared using emissivity band 13, ratio band derived using emissivity band 12 and band 11 and radiant temperature band also could delineate all the major rock types. Further, scatter plots of emissivity bands and temperature used for clustering of different rock units could delineate these rocks from each other. Therefore, conjugate composite images of emissivity and radiant temperature was suitable in delineating all the rocks than the emissivity composite images of different types.

Lithological discrimination of accretionary complex (Sivas, northern Turkey) using novel hybrid color composites and field data

One of the most appropriate approaches to better understand and interpret geologic evolution of an accretionary complex is to make a detailed geologic map. The fact that ophiolite sequences consist of various rock types may require a unique image processing method to map each ophiolite body. The accretionary complex in the study area is composed mainly of ophiolitic and metamorphic rocks along with epi-ophiolitic sedimentary rocks. This paper attempts to map the Late Cretaceous accretionary complex in detail in northern Sivas (within İzmir-Ankara-Erzincan Suture Zone in Turkey) by the analysis of all of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) bands and field study. The new two hybrid color composite images yield satisfactory results in delineating peridotite, gabbro, basalt, and epi-ophiolitic sedimentary rocks of the accretionary complex in the study area. While the first hybrid color composite image consists of one principle component (PC) and two band ratios (PC1, 3/4, 4/6 in the RGB), the PC5, the original ASTER band 4 and the 3/4 band ratio images were assigned to the RGB colors to generate the second hybrid color composite image. In addition to that, the spectral indices derived from the ASTER thermal infrared (TIR) bands discriminate clearly ultramafic, siliceous, and carbonate rocks from adjacent lithologies at a regional scale. Peridotites with varying degrees of serpentinization illustrated as a single color were best identified in the spectral indices map. Furthermore, the boundaries of ophiolitic rocks based on fieldwork were outlined in detail in some parts of the study area by superimposing the resultant maps of ASTER maps on Google Earth images of finer spatial resolution. Eventually, the encouraging geologic map generated by the image analysis of ASTER data strongly correlates with lithological boundaries from a field survey.

Wide area lithologic mapping with ASTER thermal infrared data: Case studies for the regions in/around the Pamir Mountains and the Tarim basin

After the authors have proposed the mineralogical indices, e.g., Quartz Index (QI), Carbonate Index (CI), Mafic Index (MI) for ASTER thermal infrared (TIR) data, many articles have been applied the indices for the geological case studies and proved to be robust in extracting geological information at the local scale. The authors also have developed a system for producing the regional map with the indices, which needs mosaicking of many scenes considering the relatively narrow spatial coverage of each ASTER scene. The system executes the procedures very efficiently to find ASTER data covering a wide target area in the vast and expanding ASTER data archive. Then the searched ASTER data are conditioned, prioritized, and the indices are calculated before finally mosaicking the imagery. Here in this paper, we will present two case studies of the regional lithologic and mineralogic mapping of the indices covering very wide regions in and around the Pamir Mountains and the Tarim basin. The characteristic features of the indices related to geology are analysed, interpreted and discussed.

Regional Lithological Mapping Using ASTER-TIR Data: Case Study for the Tibetan Plateau and the Surrounding Area

The mineralogical indices the Quartz Index (QI), Carbonate Index (CI) and Mafic Index (MI) for ASTER multispectral thermal infrared (TIR) data were applied to various geological materials for regional lithological mapping on the Tibetan Plateau. Many lithological and structural features are not currently well understood in the central Tibetan Plateau, including the distribution of mafic-ultramafic rocks related to the suture zones, the quartzose and carbonate sedimentary rocks accreted to the Eurasian continent, and sulfate layers related to the Tethys and neo-Tethys geological setting. These rock types can now be mapped with the interpretation of the processed ASTER TIR images described in this paper. A methodology is described for the processing of ASTER TIR data applied to a very wide region of the Tibetan Plateau. The geometrical and radiometric performance of the processed images is discussed, and the advantages of using ortho-rectified data are shown. The challenges of using ASTER data with a small footprint in addition to selecting an appropriate subset of scenes are also examined. ASTER scenes possess a narrow swath width when compared to LANDSAT data (60 km vs. 185 km, respectively). Furthermore, the ASTER data archive is vast, consisting of approximately three million images. These details can present an added level of complexity during an image processing workflow. Finally, geological interpretations made on the maps of the indices are compared with prior geological field studies. The results from the investigations suggest that the indices perform well in the classification of quartzose rocks based on the carbonate and mafic mineral content, in addition to the granitic rocks based on the feldspar content.

New ASTER derived thermal indices to delineate mineralogy of different granitoids of an Archaean Craton and analysis of their potentials with reference to Ninomiya's indices for delineating quartz and mafic minerals of granitoids—An analysis in Dharwar Craton, India

We processed five thermal infrared (TIR) bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) “at-sensor radiance” (Level 1B) data to derive few new indices to delineate variation in quartz, feldspar and mafic minerals in three different variants of granitoid; commonly occurring quartz bearing intrusive rock group. In this regard, three indices named as quartz-bearing rock index (QRI), feldspar-bearing rock index (FRI) and mafic-bearing rock index(MRI) were proposed.QRI index was derived using band 10, band 12 and band 13((band 10/band 12)∗(band 13/band 12)) of ASTER radiance image. MRI index was derived using band 12, band 13 and band 14 ((band 12/band 13)∗(band 13/band 14)) of ASTER radiance bands while band 10, band 11 were combined to derive FRI ((band 10/band 11) index. Three indices were combined in false colour composite image (FCC) and three-dimensional scatter plot to delineate granite, alkali granite and mafic rich granodioritic gneiss from each other as these granitoids had variable abundances of quartz, feldspar and mafic minerals. QRI and MRI were compared with the corresponding quartz and mafic indices proposed by Ninomiya (2005). It was observed from the respective ratio images and their regression plots that MRI and Ninomiya's mafic index (NMI) were complementary to each other. On the other hand, QRI image was better in enhancing quartz enrichment in alkali granites than Ninomiya's quartz index (NQI).However, QRI index was comparable with the quartz index proposed by Rockwall and Hofstra (2008) in terms of delineating quartz enrichment in alkali granite. Mutual exclusive nature of mafic minerals and quartz in granitoids was also evident from the negative correlation between MRI and QRI indices of the granitoids. On the other hand, FRI and QRI were negatively correlated with low regression value. This was resulted due to the combined effect of inverse relation of abundance of two dominant feldspars with quartz in different granitoids. In granitoids, abundance of plagioclase is known to increase with decreasing quartz content in granodiorite and tonalite although alkali feldspar bearing granites are characterised with high silica content. Results of discrimination of granitoids using proposed indices were validated based on deriving emissivity spectra of rocks and comparing them with ASTER TIR band resampled laboratory spectra of respective granitoids in addition to use geological map of the study area. Emissivity spectra of granitoids were derived from emissivity image (derived using emissivity normalisation method) after geospatially tagging it with QRI–FRI–MRI image composite; which was used to delineate exposures of granitoids. Further, we also found that the QRI, MRI and FRI indices had poor temperature dependence; when these indices were compared with relative surface temperature image derived from radiance bands using emissivity normalisation algorithm. Therefore, proposed indices can be implemented for delineating mineralogical variations of granitoids irrespective of surface temperature condition. Hence, proposed indices may be used successfully to delineate different granitic intrusions and relating their mineralogical variations with metallogeny.

Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 2, Validation and Sensitivity Analysis

The self-adaptive gradient-based thresholding (SAGBT) method is a simple non-interactive coal fire detection approach involving segmentation and a threshold identification algorithm that adapts to the spatial distribution of thermal features over a landscape. SAGBT detects coal fire using multispectral thermal images acquired by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. The method was detailed by our previous work “Self-Adaptive Gradient-Based Thresholding Method for Coal Fire Detection Based on ASTER Data—Part 1, Methodology”. The current study evaluates the performance of SAGBT and validates its results by using ASTER thermal infrared (TIR) images and ground temperature data collected at the Wuda coalfield (China) during satellite overpass. We further analyzed algorithm performance by using nighttime TIR images and images from different seasons. SAGBT-derived fires matched fire spots measured in the field with an average offset of 32.44 m and a matching rate of 70%–85%. Coal fire areas from TIR images generally agreed with coal-related anomalies from visible-near infrared (VNIR) images. Further, high-temperature pixels in the ASTER image matched observed coal fire areas, including the major extreme high-temperature regions derived from field samples. Finally, coal fires detected by daytime and by nighttime images were found to have similar spatial distributions, although fires differ in shape and size. Results included the stratification of our study site into two temperature groups (high and low temperature), using a fire boundary. We conclude that SAGBT can be successfully used for coal fire detection and analysis at our study site.