Advanced Spaceborne Thermal Emission And Reflection Radiometer Research Articles

Assessment of active tectonics from geomorphic indices and morphometric parameters in part of Ganga basin

Ganga river basins exposed to active erosional and deformational processes. The recurrence of landslides, floods, and seismic activities makes it more susceptible to deformational activities. The tectonic analysis using geomorphic indices and morphometric parameters will help in determining the hazard-prone area of the river basin. Geomorphic indices and morphometric parameters are calculated to investigate the role of neotectonic activities, as it acts as a controlling factor in the development of landforms in the tectonically active terrains. Neotectonic activities influence the terrain topography, which significantly affects the drainage system and geomorphological setup of the area. In this study, the assessment of active tectonics of study area was determined using Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) based on Geomorphic Indices (Stream Length Gradient index, Hypsometric integral, Asymmetry factor, Basin shape, Valley floor width to Valley height ratio, Mountain front sinuosity index) cumulatively with Linear, Areal and Relief morphometric parameters on 27 delineated basins of the study area. The combined classification of Relative Tectonic Activity Index (Iat) and morphometric parameters of 27 basins categorized all the zones into four different classes: Class 1 - Very High ( 2.21; 299 km2). The basins with tectonic activities have a consistent relationship with structural disturbances, basin geometry, and field studies. The tectonically active zonation of a part of Ganga basin using geomorphic indices and morphometric parameters suggest that it has significant influence of neotectonic activities in a part of Ganga basin.

Estimation of Soil Evaporation and Vegetation Transpiration Using Two Trapezoidal Models From MODIS Data

AbstractSoil evaporation (ETs) and vegetation transpiration (ETv) are essential in evaporative demand analyses and agricultural applications. This study estimated ETs and ETv using two trapezoidal models, that is, the conventional trapezoidal model and the end‐member‐based soil and vegetation energy partitioning (ESVEP) model, at three AmeriFlux tower sites. First, the land surface temperature of a Moderate Resolution Imaging Spectroradiometer (MODIS) pixel was decomposed into soil and vegetation temperatures (Ts and Tv, respectively) which involve important information related to ETs and ETv and were compared with corresponding temperatures from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Then ETs and ETv were parameterized, and the total evapotranspiration (ET) was validated with the eddy covariance system measurements after energy imbalance corrections via the residual energy and Bowen ratio methods. The results showed that the ESVEP model partitioned Ts, with a root‐mean‐square error (RMSE) of 1.56–2.70 K (4.98–10.16%), and Tv, with an RMSE of 2.54–2.80 K (9.73–11.11%), indicating a higher accuracy than that from the conventional trapezoidal model, where the RMSE was 2.26–3.64 K (7.31–13.40%) for Ts separation and 2.82–4.62 K (9.80–15.49%) for Tv separation. For the ET estimation, the ESVEP model still performed better. The estimation RMSE was 49.8–58.4 W/m2 (22.8–27.2%) for the ESVEP model and 72.0–89.5 W/m2 (29.8–41.1%) for the conventional trapezoidal model, compared to the RE corrected measurements; the RMSE was 42.6–53.3 W/m2 (24.4–28.1%) for the ESVEP model and 57.5–78.1 W/m2 (32.3–37.9%) for the conventional trapezoidal model, compared to the Bowen ratio corrected measurements.

Mapping Hydrothermal Zoning Pattern of Porphyry Cu Deposit Using Absorption Feature Parameters Calculated from ASTER Data

Identifying hydrothermal zoning pattern associated with porphyry copper deposit is important for indicating its economic potential. Traditional approaches like systematic sampling and conventional geological mapping are time-consuming and labor extensive, and with limitations for providing small scale information. Recent developments suggest that remote sensing is a powerful tool for mapping and interpreting the spatial pattern of porphyry Cu deposit. In this study, we integrated in situ spectral measurement taken at the Yudai copper deposit in the Kalatag district, northwestern China, information obtained by the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), as well as the spectra of samples (hand-specimen) measured using an Analytical Spectral Device (ASD) FieldSpec4 high-resolution spectrometer in laboratory, to map the hydrothermal zoning pattern of the copper deposit. Results proved that the common statistical approaches, such as relative band depth and Principle Component Analysis (PCA), were unable to identify the pattern accurately. To address the difficulty, we introduced a curve-fitting technique for ASTER shortwave infrared data to simulate Al(OH)-bearing, Fe/Mg(OH)-bearing, and carbonate minerals absorption features, respectively. The results indicate that the absorption feature parameters can effectively locate the ore body inside the research region, suggesting the absorption feature parameters have great potentials to delineate hydrothermal zoning pattern of porphyry Cu deposit. We foresee the method being widely used in the future.

Remote sensing-based geological mapping and petrogenesis of Wadi Khuda Precambrian rocks, South Eastern Desert of Egypt with emphasis on leucogranite

The exposed Precambrian rocks along Wadi Khuda area, South Eastern Desert of Egypt, comprise gneisses, amphibolites and migmatites associations (infrastructural rocks) which are intruded by diorite, tonalite, syenogranite and leucogranite (LG). LG forms homogeneous boss like bodies, devoid to xenoliths, except along the outer margins. Chemically and mineralogically they are dissimilar to the surrounding gneisses. In the current study, band ratio and Decorrelation Stretch image processing algorithms were proposed and applied on Landsat-8, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2 remote sensing data to discriminate the widely exposed lithological units in the mapped area. Geochemically, I-type classification and volcanic arc environment were revealed. LG is peraluminous (ASI average of 1.09) with normative corundum up to 1.38 for LG. The LG displays limited major and trace element variations (SiO2 = 74.5–76.2%, AI203 = 13.4–14.4%), MgO = 0.03–0.19%, Na2O = 3.6–4.6%), and Rb = 55–85 ppm) probably due to textural and mineralogical homogeneity. The contents of REE in the leucogranite are low (∑REE = 21–37), but the REE patterns are [(La/Yb)n = 2.45–9.03], [(La/Sm)n = 1.09–2.6], with negative Eu anomaly, (Eu/Eu* = 0.19–0.78). The negative Eu anomaly and the low Sr content (16–92 ppm) may be attributed to the plagioclase feldspars fractionation. The leucogranites represent residual melt of syenogranite which forms Um Itly pluton at the southern flank of Wadi Khuda entrance.

Comparison of ASTER and Sentinel-2 spaceborne datasets for geological mapping: a case study from North-East Greenland

Spaceborne remote sensing is a suitable tool for early mineral exploration and surveying large areas of high Arctic environment in a fast and cost-effective manner. While spaceborne data have been used widely to map geology in arid areas, similar approaches for remotely-sensed geological mapping of Arctic environments is yet to be developed. Freely available spaceborne optical data provides detailed information of high-quality that could potentially reduce resource exploration risk in remote regions. To this end, this study compares the use of two different multispectral spaceborne datasets (i.e. the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2) to map geological units in and around Wollaston Forland, North-East Greenland – an area rich in Jurassic and Cretaceous sedimentary rocks and important targets for offshore petroleum exploration. Multispectral image sensors simultaneously capture image data within multiple wavelength ranges (bands) across the electromagnetic spectrum. Each band is commonly described by the band number and the band wavelength centre position. Here, we identify the bands most suitable for geological mapping in an Arctic setting, using the Wollaston Forland area as an example. We compare the results obtained by processing spaceborne data with a published geological map for the area (Henriksen 2003).

Analysis of Spatiotemporal Urban Temperature Characteristics by Urban Spatial Patterns in Changwon City, South Korea

Spatiotemporal air and land surface temperature (LST) characteristics were analyzed based on urban spatial patterns for Changwon City, South Korea. Twelve ASTER (Advanced spaceborne thermal emission and reflection radiometer) Thermal infrared radiance (TIR) images during the daytime and nighttime from June to September, 2012–2014 were used for LST analysis. Air temperature was measured at five meteorological stations. The landcover type, elevation, and location of the meteorological measurement stations were the spatial patterns. The differences among the mean LST for each landcover material were the maximum of 8 °C and 1 °C during the daytime and nighttime, respectively. The LST decreased with increasing built-up area ratio, most prominently in July, but less so with increasing forest area for the same area ratios. The changes of urban temperature according to the spatial pattern were found to be different in each period, and there were some differences from previous studies. This is because the thermal characteristics differ depending on the geographical location, climatic conditions, and building environment of the cities. Therefore, to mitigate the urban heat island continuously, it should be applied to urban planning considering the relationship between spatial patterns and urban temperature, and the urban environment should be considered rather than directly using the results of previous studies.

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.

Assessment of hotspots using sparse autoencoder in industrial zones.

Remote sensing satellite systems can be used to detect industrial zones by means of thermal infrared bands. There are several satellite systems loaded with thermal infrared sensors such as Landsat and Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). In this study, ASTER thermal infrared data were converted to land surface temperature (LST) in order to determine hotspots caused by industrial zones. High LST values surrounded by low LST values are called hotspots here. These hotspots can be determined by applying different methodologies. One of these methods of sparse autoencoder can be used to indicate hotspots using different sizes of hidden layers. The principle of sparse autoencoder depends on unlabeled data in unsupervised learning. It does not need any information about labeled data as in supervised learning. The autoencoder reproduces its output with the same dimensions as the input image by managing the size of the hidden layer. The reconstruction of the image depends on the minimization of a cost function. The size of the hidden layer sets the fitting degree of the function for the reproduced image. A low-order reproduced image is the main target for hotspot detection. In this study, the difference between the original image and the reproduced image was analyzed for hotspot detection. Sparse autoencoder was successfully applied to ASTER thermal band 10 for hotspot detection in 7 pre-defined sites of a region known for steel industry for the two different days.

Multispectral and Radar Data for the Setting of Gold Mineralization in the South Eastern Desert, Egypt

Satellite-based multi-sensor data coupled with field and microscopic investigations are used to unravel the setting and controls of gold mineralization in the Wadi Beitan–Wadi Rahaba area in the South Eastern Desert of Egypt. The satellite-based multispectral and Synthetic Aperture Radar (SAR) data promoted a vibrant litho-tectonic understanding and abetted in assessing the regional structural control of the scattered gold occurrences in the study area. The herein detailed approach includes band rationing, principal component and independent component analyses, directional filtering, and automated and semi-automated lineament extraction techniques to Landsat 8- Operational Land Imager (OLI), Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Phased Array L-band Synthetic Aperture Radar (PALSAR), and Sentinel-1B data. Results of optical and SAR data processed as grayscale raster images of band ratios, Relative Absorption Band Depth (RBD), and (mafic–carbonate–hydrous) mineralogical indices are used to extract the representative pixels (regions of interest). The extracted pixels are then converted to vector shape files and are finally imported into the ArcMap environment. Similarly, manually and automatically extracted lineaments are merged with the band ratios and mineralogical indices vector layers. The data fusion approach used herein reveals no particular spatial association between gold occurrences and certain lithological units, but shows a preferential distribution of gold–quartz veins in zones of chlorite–epidote alteration overlapping with high-density intersections of lineaments. Structural features including en-echelon arrays of quartz veins and intense recrystallization and sub-grain development textures are consistent with vein formation and gold deposition syn-kinematic with the host shear zones. The mineralized, central-shear quartz veins, and the associated strong stretching lineation affirm vein formation amid stress build-up and stress relaxation of an enduring oblique convergence (assigned as Najd-related sinistral transpression; ~640–610 Ma). As the main outcome of this research, we present a priority map with zones defined as high potential targets for undiscovered gold resources.

Mapping Listvenite Occurrences in the Damage Zones of Northern Victoria Land, Antarctica Using ASTER Satellite Remote Sensing Data

Listvenites normally form during hydrothermal/metasomatic alteration of mafic and ultramafic rocks and represent a key indicator for the occurrence of ore mineralizations in orogenic systems. Hydrothermal/metasomatic alteration mineral assemblages are one of the significant indicators for ore mineralizations in the damage zones of major tectonic boundaries, which can be detected using multispectral satellite remote sensing data. In this research, Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) multispectral remote sensing data were used to detect listvenite occurrences and alteration mineral assemblages in the poorly exposed damage zones of the boundaries between the Wilson, Bowers and Robertson Bay terranes in Northern Victoria Land (NVL), Antarctica. Spectral information for detecting alteration mineral assemblages and listvenites were extracted at pixel and sub-pixel levels using the Principal Component Analysis (PCA)/Independent Component Analysis (ICA) fusion technique, Linear Spectral Unmixing (LSU) and Constrained Energy Minimization (CEM) algorithms. Mineralogical assemblages containing Fe2+, Fe3+, Fe-OH, Al-OH, Mg-OH and CO3 spectral absorption features were detected in the damage zones of the study area by implementing PCA/ICA fusion to visible and near infrared (VNIR) and shortwave infrared (SWIR) bands of ASTER. Silicate lithological groups were mapped and discriminated using PCA/ICA fusion to thermal infrared (TIR) bands of ASTER. Fraction images of prospective alteration minerals, including goethite, hematite, jarosite, biotite, kaolinite, muscovite, antigorite, serpentine, talc, actinolite, chlorite, epidote, calcite, dolomite and siderite and possible zones encompassing listvenite occurrences were produced using LSU and CEM algorithms to ASTER VNIR+SWIR spectral bands. Several potential zones for listvenite occurrences were identified, typically in association with mafic metavolcanic rocks (Glasgow Volcanics) in the Bowers Mountains. Comparison of the remote sensing results with geological investigations in the study area demonstrate invaluable implications of the remote sensing approach for mapping poorly exposed lithological units, detecting possible zones of listvenite occurrences and discriminating subpixel abundance of alteration mineral assemblages in the damage zones of the Wilson-Bowers and Bowers-Robertson Bay terrane boundaries and in intra-Bowers and Wilson terranes fault zones with high fluid flow. The satellite remote sensing approach developed in this research is explicitly pertinent to detecting key alteration mineral indicators for prospecting hydrothermal/metasomatic ore minerals in remote and inaccessible zones situated in other orogenic systems around the world.

Twenty Years of ASTER Contributions to Lithologic Mapping and Mineral Exploration

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.

LITHOLOGICAL MAPPING USING LANDSAT 8 OLI AND ASTER MULTISPECTRAL DATA IN IMINI-OUNILLA DISTRICT SOUTH HIGH ATLAS OF MARRAKECH

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

EVALUATION OF DIGITAL ELEVATION MODELS FOR GEOMORPHOMETRIC ANALYSES ON DIFFERENT SCALES FOR NORTHERN CHILE

Abstract. The resolution and accuracy of digital elevation models (DEMs) have direct influence on further geoscientific computations like landform classifications and hydrologic modelling results. Thus, it is crucial to analyse the accuracy of DEMs to select the most suitable elevation model regarding aim, accuracy and scale of the study. Nowadays several worldwide DEMs are available, as well as DEMs covering regional or local extents. In this study a variety of globally available elevation models were evaluated for an area of about 190,000 km2. Data from Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) 30 m, Shuttle Radar Topography Mission (SRTM) 30 m and 90 m, Advanced Land Observing Satellite (ALOS) World 3D 30 m and TanDEM-X WorldDEM™ – 12 m and 90 m resolution were obtained. Additionally, several very high resolution DEM data were derived from stereo satellite imagery from SPOT 6/7 and Pléiades for smaller areas of about 100–400 km2 for each dataset. All datasets were evaluated with height points of the Geoscience Laser Altimeter System (GLAS) instrument aboard the NASA Ice, Cloud, and land Elevation (ICESat) satellite on a regional scale and with nine very high resolution elevation models from UAV-based photogrammetry on a very large scale. For all datasets the root mean square error (RMSE) and normalized median absolute deviation (NMAD) was calculated. Furthermore, the association of errors to specific terrain was conducted by assigning these errors to landforms from the topographic position index (TPI), topographic roughness index (TRI) and slope. For all datasets with a global availability the results show the highest overall accuracies for the TanDEM-X 12 m (RMSE: 2.3 m, NMAD: 0.8 m). The lowest accuracies were detected for the 30 m ASTER GDEM v3 (RMSE: 8.9 m, NMAD: 7.1 m). Depending on the landscape the accuracies are higher for all DEMs in flat landscapes and the errors rise significantly in rougher terrain. Local scale DEMs derived from stereo satellite imagery show a varying overall accuracy, mainly depending on the topography covered by the scene.

Comparative Study of Atmospheric Correction Methods of ASTER Data to Enhance the Delineation of Uranium Mineralized Zones

Multispectral imaging sensors are utilized to discover and identify various surface materials, topographical and geological features. Also it is a used to differentiate lithological units and to provide mineral maps. Although successful processing methodologies have been developed for Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) imagery for mineral exploration and geological mapping. Questions still arise regarding the optimal atmospheric correction for these applications. This article provides an outline and an evaluation of existing atmospheric compensation algorithms applied on the visible and shortwave infrared spectral region. This may assist in developing a uranium exploration program using ASTER imagery by delineation of the altered minerals associated with the uranium mineralization

Spectral Absorption Feature Analysis for Finding Ore: A Tutorial on Using the Method in Geological Remote Sensing

Geologists have been instrumental in shaping Earth observation satellite missions; likewise, geology has been the subject of many remote sensing studies [1]. Applications of optical remote sensing in geology date back to some early studies using the Earth Resources Technology Satellite-1 , the predecessor of the Landsat satellite program [2]. In the 1980s, the seventh channel in the short-wave infrared (SWIR) of the Landsat thematic mapper program was added, as a result of spectroscopic mineral studies by geologists [58]. A subsequent satellite-borne instrument, the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), launched in 1999, had specific bands in the SWIR and thermal infrared dedicated to mapping mineral groups [3].

Regional scale prospecting for non-sulphide zinc deposits using ASTER data and different spectral processing methods

ABSTRACT The Ravar-Kuhbanan-Bahabad belt (RKBB) in Central Iran contains several carbonate-hosted non-sulphide Zn (zinc)-Pb (lead) deposits. The Gujer Zn mine area located in the middle of the RKBB was selected as the case study. Due to its large extent, dolomitic envelope in carbonate host rocks can be considered as a more appropriate exploratory target than small Zn-rich gossans or blind karst filling ore. Based on previous studies, the occurrence of red sandstone as a candidate of supplying metal for mineralization and evaporate as sulphate source for mineralized liquids in the vicinity of carbonate rocks can be important exploratory key in the RKBB. Non-sulphide Zn deposits were formed through oxidation of primary Mississippi Valley-type (MVT) deposits in the study area. Remote sensing studies were undertaken using visible to near-infrared (VNIR) and shortwave infrared (SWIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) with the objective of lithological mapping. Five traverse lines containing a total of 81 samples were designed and followed with subsequent chemical analysis, thin section studies, and spectroscopy to verify the results. Two types of carbonates, namely, magnesian dolomite as host rock and surrounding calcitic limestone, were realized through using magnesium oxide (MgO) to calcium oxide (CaO) ratio. Based on spectroscopy studies, calcite and dolomite showed distinct absorption features at 2.35 µm and 2.32 µm, respectively, in ASTER band 8 while a shoulder at 2.25 µm was seen in ASTER band 7 for dolomite. Three image processing methods including spectral angle mapper (SAM), linear spectral unmixing (LSU), and mixture-tuned matched-filtering (MTMF) were applied to separate dolomite and limestone. The accuracy of image classification was numerically estimated using a confusion matrix. Limestone with the accuracy of 95.83% was more precisely enhanced using MTMF method compared to SAM and LSU methods. Highest accuracy of 75% for dolomite was obtained through using LSU method. Red sandstone and evaporate units were classified using MTMF and SAM/LSU methods, respectively. Rock units with the highest accuracy were selected and simply overlain on an image of ASTER in a GIS platform to create the potential map of the study area. Results showed that ASTER data can be successfully used to prepare a potential map for regional scale prospecting for carbonate-hosted non-sulphide Zn-Pb deposits in geological setting and climate condition similar to the RKBB.

Partial sub-pixel and pixel-based alteration mapping of porphyry system using ASTER data: regional case study in western Yazd, Iran

ABSTRACT This study uses remote sensing image-processing to enhance recognition of areas with mineral abundance halo, which can be associated with porphyry mineralisation of the Urumieh-Dokhtar Magmatic Belt (UDMB) in western Yazd. Granitoid intrusions of Oligocene-Miocene age in UDMB hosts the most important Porphyry Copper Deposits (PCDs) of Iran such as Darezereshk and Aliabad PCDs. Diagnostic spectral absorption features of six shortwave infrared (SWIR) and three visible and near-infrared (VNIR) bands of the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) data were applied to determine the alteration minerals and their abundance. In this research reflectance data from various representative alteration types throughout the study area were measured by the FieldSpec-3 spectroradiometer and applied for mapping minerals. Different remote sensing analytical techniques, including the Principal Component Analysis (PCA), band ratio, relative absorption band depth, and MTMF, were applied to enhance mapping the alteration minerals. Field works including rock sampling, petrography and field spectroradiometer followed up the alteration mapping. Field observation implies that, the vein-type mineralisation above the cupola of this porphyry system is preserved in the western part, although it was eroded in the eastern area. Consequently, PCDs potential is expected to be higher in the western area.

Detecting daytime and nighttime land surface temperature anomalies using thermal infrared remote sensing in Dandong geothermal prospect

Traditional ground survey methods have limited the development and use of renewable energy from geothermal resources. In this paper, the geothermal potential area of Dandong, China is studied using the thermal infrared (TIR) remote sensing data from daytime Landsat 8 and nighttime Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER). Through pre-processing such as georeferencing, radiometric calibration, and atmospheric correction, the Landsat8 TIRS and ASTER data were used to invert the land surface temperature of the study area during the daytime and nighttime using the single-channel algorithm and temperature and emissivity separation algorithm. Furthermore, the land surface temperatures during daytime and nighttime of three natural land features—water, vegetation, and bare soil—were classified and analyzed. According to the results, vegetation and bare soil show relatively thermal anomalies during the day and relatively cold anomalies during the night. Conversely, water shows relatively cold anomalies during the day and relatively thermal anomalies during the night. Calculating the daytime and nighttime mean of land surface temperature (DNMLST) can eliminate the relatively cold/thermal anomalies exhibited by natural land features during the daytime and nighttime while highlighting the geothermal anomaly zone. Nine geothermal anomaly zones were identified using the threshold method. The auxiliary analysis of geological data excluded non-geothermal effects. Studies have shown that the distribution of the identified geothermal prospects is consistent with the development and distribution of faults. The fractures cut the land surface, causing groundwater to form structural fissure hot water through fracture structures and fissures after magma heating. The use of two higher-resolution TIR remote sensing data products to obtain the DNMLST for the detection of geothermal anomalies has proven cost-effective technical method.

Emittance Spectroscopy and Broadband Thermal Remote Sensing Applied to Phosphorite and Its Utility in Geoexploration: A Study in the Parts of Rajasthan, India

The contrast in the emissivity spectra of phosphorite and associated carbonate rock can be used as a guide to delineate phosphorite within dolomite. The thermal emissivity spectrum of phosphorite is characterized by a strong doublet emissivity feature with their absorption minima at 9 µm and 9.5 µm; whereas, host rock dolomite has relatively subdued emissivity minima at ~9 µm. Using the contrast in the emissivity spectra of phosphorite and dolomite, data obtained by the thermal bands of Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor were processed to delineate phosphorite within dolomite. A decorrelation stretched ASTER radiance composite could not enhance phosphorite rich zones within the dolomite host rock. However, a decorrelation stretched image composite of selected emissivity bands derived using the emissivity normalization method was suitable to enhance large surface exposures of phosphorite. We have found that the depth of the emissivity minima of phosphorite gradually has increased from dolomite to high-grade phosphorite, while low-grade phosphate has an intermediate emissivity value and the emissivity feature can be studied using three thermal bands of ASTER. In this context, we also propose a relative band depth (RBD) image using selected emissivity bands (bands 11, 12, and 13) to delineate phosphorite from the host rock. We also propose that the RBD image can be used as a proxy to estimate the relative grades of phosphorites, provided the surface exposures of phosphorite are large enough to subdue the role of intrapixel spectral mixing, which can also influence the depth of the diagnostic feature along with the grade. We have validated the phosphorite pixels of the RBD image in the field by carrying out colorimetric analysis to confirm the presence of phosphorite. The result of the study indicates the utility of the proposed relative band depth image derived using ASTER TIR bands for delineating Proterozoic carbonate-hosted phosphorite.

Assessment of Landslide Susceptibility Using Statistical- and Artificial Intelligence-Based FR–RF Integrated Model and Multiresolution DEMs

Landslide is one of the most important geomorphological hazards that cause significant ecological and economic losses and results in billions of dollars in financial losses and thousands of casualties per year. The occurrence of landslide in northern Iran (Alborz Mountain Belt) is often due to the geological and climatic conditions and tectonic and human activities. To reduce or control the damage caused by landslides, landslide susceptibility mapping (LSM) and landslide risk assessment are necessary. In this study, the efficiency and integration of frequency ratio (FR) and random forest (RF) in statistical- and artificial intelligence-based models and different digital elevation models (DEMs) with various spatial resolutions were assessed in the field of LSM. The experiment was performed in Sangtarashan watershed, Mazandran Province, Iran. The study area, which extends to 1072.28 km2, is severely affected by landslides, which cause severe economic and ecological losses. An inventory of 129 landslides that occurred in the study area was prepared using various resources, such as historical landslide records, the interpretation of aerial photos and Google Earth images, and extensive field surveys. The inventory was split into training and test sets, which include 70 and 30% of the landslide locations, respectively. Subsequently, 15 topographic, hydrologic, geologic, and environmental landslide conditioning factors were selected as predictor variables of landslide occurrence on the basis of literature review, field works and multicollinearity analysis. Phased array type L-band synthetic aperture radar (PALSAR), ASTER (Advanced Spaceborne Thermal Emission and Reflection Radiometer), and SRTM (Shuttle Radar Topography Mission) DEMs were used to extract topographic and hydrologic attributes. The RF model showed that land use/land cover (16.95), normalised difference vegetation index (16.44), distance to road (15.32) and elevation (13.6) were the most important controlling variables. Assessment of model performance by calculating the area under the receiving operating characteristic curve parameter showed that FR–RF integrated model (0.917) achieved higher predictive accuracy than the individual FR (0.865) and RF (0.840) models. Comparison of PALSAR, ASTER, and SRTM DEMs with 12.5, 30 and 90 m spatial resolution, respectively, with the FR–RF integrated model showed that the prediction accuracy of FR–RF–PALSAR (0.917) was higher than FR–RF–ASTER (0.865) and FR–RF–SRTM (0.863). The results of this study could be used by local planners and decision makers for planning development projects and landslide hazard mitigation measures.