Advanced Spaceborne Thermal Emission And Reflection Radiometer Research Articles

Site Suitability Analysis Using GIS for a Renal Hospital in Urban Canacona, Goa, India

Site suitability analysis is a strategic approach for identifying an optimal location for a specific activity through an examination of the current site. To conduct a comprehensive site analysis, various factors, including administrative data, topographical characteristics, and thorough research on land use and land cover, may be taken into account. This study aims to propose the most fitting site for establishing a renal hospital in Canacona, where a notable increase in renal disorders has been observed in recent years. The study is prompted by the local population’s demand for healthcare services and the absence of adequate existing healthcare infrastructure. To compile the primary dataset, GPS survey and on-site observations were conducted to gather the geographical coordinates of existing hospitals and pharmacies. The secondary dataset includes satellite images obtained from Landsat 8 (OLI) to create a Land Use and Land Cover (LULC) map, as well as data from the Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) for generating a Digital Elevation Model (DEM). These datasets encompass various parameters such as DEM, LULC, road networks, existing hospitals, and pharmacies, all of which were utilized in the development of the site suitability model. The process involves assigning values to these parameters, and subsequently, a weighted overlay analysis was employed to determine suitable areas for the proposed renal hospital. The results were categorized into four classes: undesirable, less suitable, moderately suitable, and most suitable sites. After careful analysis, the optimal location for the renal hospital in Canacona was identified, spanning an area of approximately 1.55527 hectares.

Flood susceptibility modeling using geo-morphometric ranking approach in Jhelum River basin, Pakistan

This study is modeling flood susceptibility in Jhelum River Basin by employing Geo-Morphometric ranking approach. The study area is located in the Monsoon region and exposed to heavy rainfall events. Every year monsoon rainfall and melting of snow and glaciers together generate flash floods in the upper catchment and then riverine flood in the low-lying areas. Watershed modeling approach is used to delineate the Jhelum watershed and its sub-watershed. Advance Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Digital Elevation Model (GDEM) is utilized as input data in ArcMap software environment. Then Geo-Morphometric ranking model is applied to rank the susceptibility of each sub-watershed to flood. The analysis showed that geo-morphometric characteristics vary from basin to basin. There are 44 sub-basins which cover area less than 1000 ​km2. The area of sub-basins ranges from 47.68 ​km2 to 7667.77 ​km2. Based on our results, area of sub-basin directly impacts flood susceptibility. Geo-morphometric parameters such as gradient, relief and geology of basin such as type of bed rock govern the drainage pattern. The results of the study can assist line agencies and concerned government departments to design location specific flood risk reduction strategy.

Improving HJ-1B/IRS LST Retrieval of the Generalized Single-Channel Algorithm with Refined ERA5 Atmospheric Profile Database

Land surface temperature (LST) is a fundamental variable of environmental monitoring and surface equilibrium. Although the HJ-1B infrared scanner (IRS) has accumulated many observations, further application of HJ-1B/IRS is limited by the lack of LST products. This study refined the ERA5 atmospheric profile database, instead of the widely used traditional TIGR atmospheric profile database, and simulated the coefficients of the generalized single-channel (GSCs) algorithms to improve LST retrieval. GSCs can be divided into the GSCw and GSCwT algorithms, depending on whether the input is atmospheric water vapor content (WVC) or in situ near-surface air temperature and WVC. Land surface emissivity (LSE) was obtained from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) and vegetation/snow cover products. Then, the retrieved LSTs were evaluated using the LSTs from the RTE algorithm, TIGRw/TIGRwT profiles, and in situ near-surface air temperature from the HiWATER experiment in China from 2012 to 2014. The bias (root mean square error (RMSE)) values are displayed as ERA5wT < RTE < ERA5w < TIGRwT < TIGRw. The accuracy of ERA5wT, with a bias (RMSE) of 0.02 K (2.30 K), is higher than that of RTE, with a bias (RMSE) of 0.74 K (2.47 K). The accuracy of RTE is preferable to that of ERA5w, with a bias (RMSE) of 0.89 K (2.48 K), followed by TIGRwT, with a bias (RMSE) of −1.18 K (2.50 K), and then, TIGRw, with a bias (RMSE) of 1.60 K (2.77 K). In summary, the accuracy of LST obtained by GSC from the refined ERA5 atmospheric profiles is higher than that obtained from the TIGR profiles. The accuracy of LST obtained by GSCwT is greater than that obtained by GSCw. The accuracy of LST obtained using in situ near-surface air temperature is higher than that obtained using ERA5 air temperature. The accuracy of LSEASTER is slightly better than that of LSEMOD21. The aforementioned conclusions can provide scientific support to generate HJ-1B/IRS LST products.

Analysis of the Controlling Effect of Excess Topography on the Distribution of Coseismic Landslides during the Iburi Earthquake, Japan, on 6 September 2018

Coseismic landslides cause changes in the hillside material, and this erosion process plays an important role in the evolution of the topography. Previous studies seldom involved research on the influence of excess topography on the occurrences of coseismic landslides. The Iburi earthquake, which occurred in Japan on 6 September 2018 and triggered a large number of landslides, provided a research example to explore the relationship between coseismic landslides and excess topography. We used the average slope of the lithology as the threshold slope of the corresponding stratum to calculate the excess topography of the different lithological units. Based on the advanced spaceborne thermal emission and reflection radiometer (ASTER) digital elevation model (DEM) with a resolution of 30 m, a quantitative analysis was conducted on the excess topography in the study area. The results indicate that the excess topography in the study area was mainly distributed in the valleys on both sides of the river, and the thickness of the excess topography on the high and steep ridges was generally greater than that at the foot of the slope, which has a relatively flat topography or a low elevation. In the area affected by the earthquake, approximately 94.66% of the coseismic landslides (with an area of approximately 28.23 m2) developed in the excess topography area, indicating that the distribution of the excess topography had a strong controlling influence on the spatial distribution of the coseismic landslides. The Iburi earthquake mainly induced shallow landslides, but the thickness of the landslide body was much smaller than the excess topography height in the landslides-affected area. This may imply that the excess topography was not completely removed by the coseismic landslides, and the areas where the earthquake landslides occurred still have the possibility of producing landslides in the future.

Locating Potential Run-of-River Hydropower Sites by Developing Novel Parsimonious Multi-Dimensional Moving Window (PMMW) Algorithm with Digital Elevation Models

We developed a Parsimonious Multi-dimensional Moving Window (PMMW) algorithm that only requires Digital Elevation Model (DEM) data of a watershed to efficiently locate potentially optimal hydropower sites. The methodology requires only open source DEM data; therefore, it can be used even in remotest watersheds of the world where in situ measurements are scarce or not available at all. We used three parameters in this algorithm, and tested the method using the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) and the Shuttle Radar Topography Mission (SRTM) derived DEMs. Our case study on the Morony Watershed, Montana, USA shows that (1) along with 6 out of the 7 existing hydropower plants being successfully located, 12 new potential hydropower sites were also identified, using a clearance of 1 km, diversion of 90 m, and Hydropower Index (HI) threshold of 109 m as the criteria. For the 12 new potential hydropower sites, 737.86 Megawatts (MW) ± 84.56 MW untapped hydropower potential in the Morony Watershed was also derived; (2) SRTM DEM is more suitable for determining the potential hydropower sites; (3) although the ASTER and SRTM DEMs provide elevation data with high accuracy, micro-scale elevation differences between them at some locations may have a profound impact on the HI.

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

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

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

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

A long-term monthly surface water storage dataset for the Congo basin from 1992 to 2015

Abstract. The spatio-temporal variation of surface water storage (SWS) in the Congo River basin (CRB), the second-largest watershed in the world, remains widely unknown. In this study, satellite-derived observations are combined to estimate SWS dynamics at the CRB and sub-basin scales over 1992–2015. Two methods are employed. The first one combines surface water extent (SWE) from the Global Inundation Extent from Multi-Satellite (GIEMS-2) dataset and the long-term satellite-derived surface water height from multi-mission radar altimetry. The second one, based on the hypsometric curve approach, combines SWE from GIEMS-2 with topographic data from four global digital elevation models (DEMs), namely the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced Land Observing Satellite (ALOS), Multi-Error-Removed Improved Terrain (MERIT), and Forest And Buildings removed Copernicus DEM (FABDEM). The results provide SWS variations at monthly time steps from 1992 to 2015 characterized by a strong seasonal and interannual variability with an annual mean amplitude of ∼101±23 km3. The Middle Congo sub-basin shows a higher mean annual amplitude (∼71±15 km3). The comparison of SWS derived from the two methods and four DEMs shows an overall fair agreement. The SWS estimates are assessed against satellite precipitation data and in situ river discharge and, in general, a relatively fair agreement is found between the three hydrological variables at the basin and sub-basin scales (linear correlation coefficient >0.5). We further characterize the spatial distribution of the major drought that occurred across the basin at the end of 2005 and in early 2006. The SWS estimates clearly reveal the widespread spatial distribution of this severe event (∼40 % deficit as compared to their long-term average), in accordance with the large negative anomaly observed in precipitation over that period. This new SWS long-term dataset over the Congo River basin is an unprecedented new source of information for improving our comprehension of hydrological and biogeochemical cycles in the basin. As the datasets used in our study are available globally, our study opens opportunities to further develop satellite-derived SWS estimates at the global scale. The dataset of the CRB's SWS and the related Python code to run the reproducibility of the hypsometric curve approach dataset of SWS are respectively available for download at https://doi.org/10.5281/zenodo.7299823 and https://doi.org/10.5281/zenodo.8011607 (Kitambo et al., 2022b, 2023).

Statistical retrieval of volcanic activity in long time series orbital data: Implications for forecasting future activity

Several high spatial resolution thermal infrared (TIR) missions are planned for the coming decade and their data will be crucial to constrain volcanic activity patterns throughout pre- and post-eruption phases. Foundational to these patterns is the subtle (1−2 K) thermal behavior, which is easily overlooked using lower spatial resolution data. In preparation for these new data, we conducted the first study using the entire twenty-two-year archive of higher spatial, lower temporal resolution TIR data from the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) sensor. This archive presents a unique opportunity to quantify low-magnitude temperature anomalies and small plumes over long time periods. We developed a new statistical algorithm to automatically detect the full range of thermal activity and applied it to >5000 ASTER scenes of five volcanoes with well-documented eruptions. Unique to this algorithm is its ability to use both day and night data, account for clouds, quantify accurate background temperatures, and dynamically scale depending on the anomaly size. Results improve upon those from the more commonly used lower spatial resolution data, despite the less frequent temporal coverage of ASTER, and show that high spatial resolution TIR data are equally as effective. Significantly, the smaller, subtle thermal detections served as precursory signals in ∼81% of eruptions, and the algorithm's results create a framework for classifying future eruptive styles.

Hydrocarbon Spectra Slope (HYSS): A Spectra Index for Quantifying and Characterizing Hydrocarbon oil on Different Substrates Using Spectra Data

Many sensors in Optical domain allow for detection of hydrocarbons in oil spills study. However, high resolution laboratory and airborne imaging spectrometers have shown potential for quantification and characterization of hydrocarbon. Available methods in literature for quantifying and characterizing hydrocarbons on these data relies mainly on shapes and positions of hydrocarbon key absorption features, mainly at 1.73 µm and 2.30 µm. Shapes formed by these absorption features are often influenced by spectral features of background substrates, thereby limiting the quality of results. Furthermore, multispectral sensors cannot resolve the shapes of key absorption features, a strong limitation for methods used in previous works. In this study, we present Hydrocarbon Spectra Slope (HYSS), a new spectra index that offers predictive quantification and characterization of common hydrocarbon oils. Slope values for the studied hydrocarbon oils enable clear discrimination for relative quantitative analysis of oil abundance classes and qualitative discrimination for common hydrocarbons on common background substrates. Data from ground-based spectrometers and Airborne Visible/Infrared Imaging Spectrometer (AVIRIS) are resampled to AVIRIS, Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and LANDSAT 7 Enhanced Thematic Mapper’s (ETM+) Full Width at Half Maximum (FWHM), in order to compute spectra slope values for hydrocarbon abundance /hydrocarbon-substrate characterization. Despite limitations of nonconformity of central wavelengths and/or band widths of multispectral sensors to key hydrocarbon band, statistical significance for both quantitative and qualitative analysis at 95% confidence level (P-value ˂0.01) suggests strong potential of the use of HYSS, multispectral and hyperspectral sensors as emergency response tools for hydrocarbon mapping.

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

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

Towards better delineation of hydrothermal alterations via multi-sensor remote sensing and airborne geophysical data

Integrating various tools in targeting mineral deposits increases the chance of adequate detection and characterization of mineralization zones. Selecting a convenient dataset is a key for a precise geological and hydrothermal alteration mapping. Remote sensing and airborne geophysical data have proven their efficiency as tools for reliable mineral exploration. Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER), Advanced land imager (ALI), Landsat 8 (L8), and Sentinel 2 data are widely-used data among various types of remote sensing images in resolving lithological and hydrothermal alteration mapping over the last two decades. ASTER is a well-established satellite in geological remote sensing with detailed Short-wave infrared (SWIR) range compared to visible and near-infrared region (VNIR) that controls iron-associated alteration detection. On contrary, ALI has excellent coverage of the VNIR area (6 bands), but does not possess the potentiality of ASTER for the SWIR and thermal regions. Landsat 8 is widely used and highly recommended for lithological and hydrothermal alteration mapping. The higher spatial (up to 10 m) resolution of Sentinel 2 MSI has preserved its role in producing accurate geological mapping. Notwithstanding the foregoing, implementing the four datasets in a single study is time-consuming. Thus, an important question when commencing an exploration project for hydrothermal alterations-related mineralization (orogenic mineral deposits in the current research) is: which dataset should be adopted to fulfill proper and adequate outputs? Here the four widely recommended datasets (ASTER, ALI, L8, and sentinel 2) have been tested by applying the widely-accepted techniques (false color combinations, band ratios, directed principal component analysis, and constrained energy minimization) for geological and hydrothermal alteration mapping of Gabal El Rukham-Gabal Mueilha district, Egypt. The study area is covered mainly by Neoproterozoic heterogeneous collection of ophiolitic components, island arc assemblage, intruded by enormous granitic rocks. Additionally, airborne magnetic and radiometric data were applied and compared with the remote sensing investigations for deciphering the structural and hydrothermal alteration patterns within the study area. The results demonstrated a different extent from one sensor to another, highlighting their varied efficacy in detecting hydrothermal alterations (mainly hydroxyl-bearing alterations and iron oxides). Moreover, the analysis of airborne magnetic and radiometric data showed hydrothermal alteration zones that are consistent with the detected alteration pattern. The coincidence between high magnetic anomalies, high values of the K/eTh ratio, and the resultant alterations confirm the real alteration anomalies. Over and above that, the remote sensing results and airborne geophysical indications were verified with fieldwork and petrographic investigations, and strongly recommend combining ASTER and Sentinel 2 results in further investigations. Based on the outputs of the current research, we expect better hydrothermal alteration delineation by adopting the current findings as they sharply narrow the zones to be further investigated via costly geophysical and geochemical methods in mineral exploration projects.

Slight Mass Loss in Glaciers over the Ulugh Muztagh Mountains during the Period from 2000 to 2020

Knowledge about changes in the glacier mass balance and climate fluctuation in the East Kunlun Mountains is still incomplete and heterogeneous. To understand the changes in the glacier mass in the Ulugh Muztagh Mountains in the East Kunlun Mountains due to global warming, a time series of satellite stereo-images from the Terra Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) were derived from 2000 to 2020. Digital elevation models (DEMs) of the glaciers were generated and used to assess the changes in these glacier masses from 2000 to 2020. The results show that the surface elevation of glaciers in the Ulugh Muztagh region changed by −0.17 ± 10.74 m from 2000 to 2020, corresponding to a mass change of −0.14 ± 9.13 m w.e. The glacier mass balance increased by 0.64 ± 9.22 m w.e. in 2000–2011 and then decreased by 0.78 ± 9.04 m w.e. in 2011–2020. The annual mass balance of the glaciers was −0.0072 ± 0.46 m w.e./yr from 2000 to 2020, showing glacial stability. The equilibrium line altitude (ELA) of the glacier was 5514 m a.s.l. from 2000 to 2020. In addition, we also found that the glacier mass losses in the west and north slopes were more significant than those in the east and south slopes. There was a phenomenon of glacier surges in the Yulinchuan glacier from 2007 to 2011. Overall, the glaciers were relatively stable with respect to the total glacier thickness in the Ulugh Muztagh Mountains.

Earth observation approach for targeting stratiform deposit of manganese in central India

The stratiform manganese deposits, hosted within the Sausar Group of rocks, in Madhya Pradesh state of India, has undergone polyphase deformation and metamorphism. The region is an enterprising geosite to understand the lithological and tectonic control on manganese mineralization. We have carried out a conjugate remote sensing based analysis using Visible, Near Infrared and Short Wave Infrared bands (VIS-NIR-SWIR) of optical imagery from multiple sensors supplemented with analysis of L-band microwave data over the Balaghat district of Madhya Pradesh to delineate prospective areas for manganese exploration, supported by the lab and field-based evidence.The enhanced image products (i.e.band ratios, principal components) and spectral anomaly map derived from Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) and Sentinel-2B multispectral bands are used to delineate the lithological contrast of the host rock and indicate key locales of mineralization. The geological structures interpreted using the merged product of Advanced Land Observation Satellite Phased Array L-band SAR (ALOS PALSAR −1), Landsat 8 PC bands (2 & 3) and the processed Horizontal Gradient Magnitude (HGM) from advanced satellite gravity model (EIGEN-6C4), illustrates the structural control on the mineralization. The petrological details derived from the host rock samples shows microstructures like micro-folding, foliations, quartz grain elongation etc., thus representing the microscale deformations within the rock units occurring in the area.The lab-based spectral signatures of manganese-bearing host rock was resampled to image spectra and spectral anomaly map using ConstrainedEnergyMinimization(CEM), which shows good correlation with the existing mine locations.The information from remote sensing and lab analysis has been integrated thematically using the knowledge-based Weighted Sum Method (WSM) with existing mine locations as the training dataset. The derived mineral prospect map indicates several new areas of manganese prospectivity which can serve as a guide for detailed future explorations in the region .

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

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

Determination of alteration zones applying fractal modeling and Spectral Feature Fitting (SFF) method in Saryazd porphyry copper system, central Iran

The target of this research is recognition of the alteration zones utilizing concentration-area fractal methodology according to the reflection of the main minerals of each alteration zone that enhanced by Spectral Feature Fitting (SFF) method due to Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) satellite images in Saryazd porphyry system, central Iran. The alterations and mineralization developed in Eocene volcanics. Remote sensing results achieved by the SFF method and Concentration -Area (C - A) fractal modeling represent different parts of propylitic, argillic and phyllic alteration zones due to their intensity and pixel values. In addition, the results reveal that there is a ring-shaped structure in the alteration zones, which are correlated with results, derived from X-Ray Diffraction (XRD) analyses and field observations.

Comparison between the ASTER and ECOSTRESS global emissivity datasets

High spatial resolution global land surface emissivity datasets are valuable for various applications. The Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Dataset (GED) is the only accessible global emissivity data with a high spatial resolution (∼100 m). Since the launch of the ECOsystem Spaceborne Thermal Radiometer Experiment on Space Station (ECOSTRESS) in June 2018, another high spatial resolution GED can be generated. A comprehensive comparison is needed to investigate the consistency between the ASTER and ECOSTRESS GEDs before subsequent applications and the release of the ECOSTRESS GED. We conducted a first-ever comparison between the ASTER and ECOSTRESS GEDs in two ways: 1) global spatial comparison and 2) validation using emissivity measurements at 9 sand dune and 2 vegetated sites. The results reveal that the ECOSTRESS emissivities in bands 2 and 4 are in good agreement with ASTER GED, with mean absolute biases (MAB) of ∼ 1 % averaged over the globe. The emissivity difference is the largest for snow and ice in these two bands. For band 5, the discrepancy is relatively larger with a MAB of 1.7 %. The largest emissivity difference is found over evergreen broadleaf forests in humid regions. Comparison with measurements reveals an average root-mean-square error (RMSE) of ECOSTRESS emissivity of ∼ 3 % among all the nine sand sites for all the 3 bands. At the two vegetated sites, the average RMSE is ∼ 1.5 %. The absolute biases are below 3 % in all the bands. The average RMSE between ASTER and ECOSTRESS GEDs among all sand dune sites is 1.29 %. The evaluation results demonstrate a good agreement between these two emissivity retrievals overall and high accuracy of the ECOSTRESS GED.

Heterogeneous mass balance of selected Glaciers in the Hindu Kush, Karakoram, and Himalaya between 2000 and 2018

ABSTRACT Glaciers in the Hindu Kush-Karakoram-Himalayas (HKH) are a major source of freshwater in the agriculture-dependent economy of Pakistan. In recent decades, mountain glaciers have been threatened by global warming. In this study, we estimated the Equilibrium Line Altitude (ELA) and geodetic mass balance of fifteen representative glaciers from Hunza (Karakoram), Chitral (Hindu Kush), and Astore (Himalaya) using Landsat satellite images and Advanced Space-borne Thermal Emission and Reflection Radiometer (ASTER) DEMs between 2000 and 2018. The climatic trends (temperature and precipitation) in the 19 years (1995–2013) time series of the three regions were assessed using the non-parametric Mann-Kendall test and Sen’s Slope. The ELA of the observed glaciers except for two Atrak (Chitral) and Gulkin (Hunza) is shifted upward, while the mass balance indicates a heterogeneous pattern ranging from −0.23 ± 0.05 to −0.01 ± 0.015 m w.e a−1, −0.13 ± 0.05 to+0.17 ± 0.11 m w.e a−1 and −0.03 ± 0.02 to+0.23 ± 0.09 m w.e a−1 in Chitral, Hunza, and Astore basin, respectively. A significant or slight increasing or decreasing trend was found in the mean annual temperature, mean total precipitation, and mean discharge of the studied basins. This study will be a good contribution to understanding snow and glacier dynamics in HKH, the factors that influence them, and their interaction with the environment.

Application of Satellite Remote Sensing, UAV-Geological Mapping, and Machine Learning Methods in the Exploration of Podiform Chromite Deposits

The irregular and sporadic occurrence of chromite pods in podiform chromite deposits (PCD), especially in mountainous terranes with rough topography, necessitates finding innovative methods for reconnaissance and prospecting. This research combines several remote sensing methods to discriminate the highly serpentinized peridotites hosting chromite pods from the other barren ultramafic and mafic cumulates. The case study is the area of the Sabzevar Ophiolite (NE Iran), which hosts several known chromite and other mineral deposits. The integration of satellite images [e.g., Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) satellite sensor, Landsat series, and Sentinel-2] coupled with change detection, band rationing, and target detection algorithms [including the Spectral Angle Mapper (SAM)] were used to distinguish potential lithological units hosting chromites. Results have been verified by an initial on-field checking and compared with the high-resolution (GSD ~6 cm) orthomosaic images obtained by the processing of photographs taken from an Unmanned Aerial Vehicle (UAV) at a promising area of 35 km2. The combination of visual interpretation and supervised classification by machine learning methods [Support Vector Machine (SVM)] yielded the production of a geological map, in which the lithological units and structures are outlined, including the crust-mantle transition zone units, mafic cumulates, crosscutting dykes, and mantle sequences. The validation of the results was performed through a second phase, made up of field mapping, sampling, chemical analysis, and microscopic studies, leading to the discovery of new chromite occurrences and mineralized zones. All ultramafic units were classified into four groups based on the degree of serpentinization, represented by the intensity of their average spectral reflectance. Based on their presumed protolith, the highly serpentinized ultramafics and serpentinites were classified into two main categories (dunite or harzburgite). The serpentinite with probable dunitic protolith, discriminated for a peculiar Fe-rich Ni-bearing lateritic crust, is more productive for chromite prospecting. This is particularly true at the contact with mafic dykes, akin to some worldwide chromite deposits. The results of our work highlight the potential of multi-scale satellite and UAV-based remote sensing to find footprints of some chromite mineral deposits.

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

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