Advanced Spaceborne Thermal Emission And Reflection Research Articles

Comparison of different open‐source Digital Elevation Models for landslide susceptibility mapping

AbstractIn this study, the application of open‐source digital elevation model (DEM) is explored for regional landslide susceptibility mapping (LSM), and the potential impact of different DEM choices on the mapping accuracy is also examined. With the advancements in remote sensing technology, an increasing number of global open‐source DEMs have been available, with improvement in the accuracy. However, the latest released data are rarely evaluated in LSM research. In this paper, DEM‐based factors, including elevation, aspect, slope, plan curvature and profile curvature, were generated from seven open‐source DEMs, including Advanced Spaceborne Thermal Emission and Reflection (ASTER) V2, ASTERV3, ALOS World 3D‐30 m (AW3D30), Copernicus DEM 30 m (COP) Forest and Buildings removed Copernicus DEM (FABDEM), NASADEM, and Shuttle Radar Topography Mission (SRTM). DEM‐based factors were coupled with the distance to road, distance to river, land use, lithology, rain and normalized difference vegetation index (NDVI). The significant difference between DEMs is determined by comparing the area proportion. Slope, plane curvature and profile curvature are found to have a maximum difference of 15%–20%. Then, K‐Nearest Neighbours (KNN) and Random Forest (RF) were used to predict landslide susceptibility with two sampling methods, namely, 70% for training and 30% for testing (S1); 67% for training and 33% for testing (S2). For KNN with S1, the prediction rate is range from 0.8299 to 0.8701, with a difference of 0.0402. The difference of prediction rate is decreased to 0.0207 for S2 and 0.0258 for RF. COP has the highest prediction rate of 0.8701, 0.9254 and 0.9461 for KNN with S1 and RF with S1 and S2, respectively. ASTERV2 is the worst with prediction rate of 0.8897 and 0.8996 for KNN with S2 and RF with S1, respectively. The research result provides valuable insights for the selection of open‐source DEMs in future LSM.

A copula model of extracting DEM-based cross-sections for estimating ecological flow regimes in data-limited deltaic-branched river systems

For operational flood control and estimating ecological flow regimes in deltaic branched-river systems with limited surveyed cross-sections, accurate river stage and discharge estimation using public domain Digital Elevation Model (DEM)-extracted cross-sections are challenging. To estimate the spatiotemporal variability of streamflow and river stage in a deltaic river system using a hydrodynamic model, this study demonstrates a novel copula-based framework to obtain reliable river cross-sections from SRTM (Shuttle Radar Topographic Mission) and ASTER (Advanced Spaceborne Thermal Emission and Reflection) DEMs. Firstly, the accuracy of the CSRTM and CASTER models was assessed against the surveyed river cross-sections. Thereafter, the sensitivity of the copula-based river cross-sections was evaluated by simulating river stage and discharge using MIKE11-HD in a complex deltaic branched-river system (7000 km2) of Eastern India having a network of 19 distributaries. For this, three MIKE11-HD models were developed based on surveyed cross-sections and synthetic cross-sections (CSRTM and CASTER models). The results indicated that the developed Copula-SRTM (CSRTM) and Copula-ASTER (CASTER) models significantly reduce biases (NSE>0.8; IOA>0.9) in the DEM-derived cross-sections and hence, are capable of satisfactorily reproducing observed streamflow regimes and water levels using MIKE11-HD. The performance evaluation metrics and uncertainty analysis indicated that the MIKE11-HD model based on the surveyed cross-sections simulates with higher accuracies (streamflow regimes: NSE>0.81; water levels: NSE>0.70). The MIKE11-HD model based on the CSRTM and CASTER cross-sections, reasonably simulates streamflow regimes (CSRTM: NSE>0.74; CASTER: NSE>0.61) and water levels (CSRTM: NSE>0.54; CASTER: NSE>0.51). Conclusively, the proposed framework is a useful tool for the hydrologic community to derive synthetic river cross-sections from public domain DEMs, and simulate streamflow regimes and water levels under data-scarce conditions. This modelling framework can be easily replicated in other river systems of the world under varying topographic and hydro-climatic conditions.

Mapping of ferric (Fe3+) and ferrous (Fe2+) iron oxides distribution using ASTER and Landsat 8 OLI data, in Negash Lateritic iron deposit, Northern Ethiopia

ABSTRACT Iron plays an important role in industrial and engineering fields development of a country and as such there is an enormous demand for iron in Ethiopia. However, a search for this valuable primary mineral resource exploration remains challenging and costly. Therefore, this study aims to map iron oxide minerals using Landsat-8/operational land imager (OLI) and advanced space-borne thermal emission and reflection (ASTER) satellite imagery in Negash Lateritic iron deposit, Northern Ethiopia to ease the costs and reduce the time. Different image processing techniques such as band ratio, selective principal component analysis, linear spectral unmixing, and mixture-tuned matched filter were used to produce iron oxide maps. Minimum noise fraction (MNF), pixel purity index (PPI), and N-dimensional visualizer were also applied to extract endmembers in the automated spectral hourglass wizard. In addition to this, the enhanced image thresholding and scatter plot were used to map the potential areas. Ferric iron oxide band ratio of ASTER mapped maximum area of 62.1 km2 followed by a laterite band ratio of ASTER covering 57.8 km2. The result was validated using existing iron oxide polygons and the outcome obtained from selective PCA shows a strong match with the existing iron oxide polygons. The sub-pixel mapping techniques show poor accuracy in mapping goethite and hematite relative to the pixel level. Thus, it is evident from the results that ASTER mapped better than Landsat 8 OLI for band ratios of selective PCA, unmixing, MTMF, and mineralized areas while characterizing with limited fieldwork.

Modeling the Environmental Hazards of El-Kharga Oasis Sand Dunes, Western Desert of Egypt, using Remote Sensing and GIS Techniques

In this study, an integrated suite of Remote Sensing (RS) data and Geographic Information System (GIS) techniques supported by fieldwork is used to assess the sand dunes movement hazards at El-Kharga Oasis. Digital Elevation Model (DEM) data obtained from the Shuttle Radar Topography Mission (SRTM) and Advanced Spaceborne Thermal Emission and Reflection (ASTER) were integrated with GIS techniques to model the vulnerable locations and to study the terrain characteristics (slope angles and aspects) in the studied area. The risk assessment model output was verified with the field investigations using multi-temporal satellite images recorded between 1990 and 2019. Monthly wind roses showed that the sand drifts in the southeastern direction differed widely from one direction to another depending on the wind direction and velocity. The most important output of the spatial model’s, results was a geo-hazard map that classified the sand dunes hazard zones into low, slight, moderate, and high-risk zones. It is concluded that, the sand dunes pose a serious hazard because of their fast movement and accumulation near the monumental sites, over roads and invading the agricultural fields. The obtained results can serve as a basis for planners and decision-makers to take the necessary precautions and measures to minimize the sand dune hazard’s impact on the monumental sites (e.g., Hibis, El-Nadura, El-Ghueita and El-Zayyan), roads, and the agricultural fields at El-Kharga Oasis and lead to a sustainable development plan. Keywords Environmental Hazards; Sand Dunes Movement; Remote Sensing; Risk Assessment, Egypt

Assessing the effects of georeferencing error in a vertical comparison study of GDEMs on a local scale

ABSTRACT Global digital elevation models (GDEMs) are one of the most important sources of elevation data. Like other spatial datasets, GDEMs are inevitably affected by various types of errors in both vertical and horizontal dimensions. One of the main sources of horizontal errors is georeferencing error which in steep areas intensifies the vertical error. Since horizontal and vertical errors are interwoven with one another, any error introduced in horizontal dimension influences its vertical counterpart. In this paper, for the first time, we intend to answer this question: to what extent the impact of georeferencing error is really important in vertical accuracy of GDEMs and their vertical comparison. Hence, the main purposes of this study are evaluation of georeferencing effects on (i) the vertical accuracy of GDEMs and (ii) vertical comparison between GDEMs. In this regard, Advanced Spaceborne Thermal Emission and Reflection (ASTER), Shuttle Radar Topography Mission (SRTM), and Advanced Land Observing Satellite (ALOS) World 3D-30 m (AW3D30) GDEMs over three different study areas (BumeHen, TazehAbad and Sainte-Maxime regions) were used and assessed. In order to assess the effects of georeferencing error on GDEMs accuracy, studies were conducted in two general categories, namely Local to Global (LG) and Global to Global (GG). In the first category, these effects were studied on vertical and horizontal differences of ASTER, AW3D30, and SRTM GDEMs with their corresponding reference DEMs (REFDEMs). In the second category, these effects were assessed on vertical and horizontal differences of ASTER and SRTM GDEMs with AW3D30 GDEM. To this end, prior to making GDEMs georeferencing consistent, they were vertically assessed. For the sake of consistency of GDEMs georeferencing, a registration process was performed and then, the registered GDEMs were vertically and horizontally assessed. After the registration process, in the first category, 4.4%, 13%, and 3.3% and in the second category 2.3%, 5%, and 1.2% improvement occurred on average root mean square error (RMSE) over all GDEMs for BumeHen, TazehAbad and Sainte-Maxime regions, respectively. These results refer to the fact that GDEM registration always increases their vertical accuracy, which is in fact originated from the improvement in GDEM georeferencing consistency. Experimental results also indicate that the improvement rate of vertical errors after the DEM registration process is directly related to terrain slope angle. From another perspective, we illustrated that in general the amounts of achieved improvements in GDEMs vertical accuracy are much smaller than their nominal accuracies. Finally, our results show that the effect of horizontal error on vertical comparison between GDEMs is unavoidable, but this amount of error can be ignored because the ratio of improvements occurred in the GDEMs with respect to their nominal accuracies is very low.

Monitoring Effect of Spatial Growth on Land Surface Temperature in Dhaka

Spatial urban growth and its impact on land surface temperature (LST) is a high priority environmental issue for urban policy. Although the impact of horizontal spatial growth of cities on LST is well studied, the impact of the vertical spatial distribution of buildings on LST is under-investigated. This is particularly true for cities in sub-tropical developing countries. In this study, TerraSAR-X add-on for Digital Elevation Measurement (TanDEM-XDEM), Advanced Spaceborne Thermal Emission and Reflection (ASTER)-Global Digital Elevation Model (GDEM), and ALOS World 3D-30m (AW3D30) based Digital Surface Model (DSM) data were used to investigate the vertical growth of the Dhaka Metropolitan Area (DMA) in Bangladesh. Thermal Infrared (TIR) data (10.6-11.2µm) of Landsat-8 were used to investigate the seasonal variations in LST. Thereafter, the impact of horizontal and vertical spatial growth on LST was studied. The result showed that: (a) TanDEM-X DSM derived building height had a higher accuracy as compared to other existing DSM that reveals mean building height of the Dhaka city is approximately 10 m, (b) built-up areas were estimated to cover approximately 94%, 88%, and 44% in Dhaka South City Corporation (DSCC), Dhaka North City Corporation (DNCC), and Fringe areas, respectively, of DMA using a Support Vector Machine (SVM) classification method, (c) the built-up showed a strong relationship with LST (Kendall tau coefficient of 0.625 in summer and 0.483 in winter) in comparison to vertical growth (Kendall tau coefficient of 0.156 in the summer and 0.059 in the winter), and (d) the ‘low height-high density’ areas showed high LST in both seasons. This study suggests that vertical development is better than horizontal development for providing enough open spaces, green spaces, and preserving natural features. This study provides city planners with a better understating of sustainable urban planning and can promote the formulation of action plans for appropriate urban development policies.

Potential of ASTER’s VNIR, SWIR, and TIR bands for phosphate exploration in central west Tunisia

Owing to the inaccessibility of most of the mineral deposits as well as cost and time requirements for conventional ground-based methods, remote sensing techniques become very useful for the rapid detection, mapping, and multitemporal tracking of different mineralization surface states. In this context, a methodological approach based on hyperspectral spectroscopy and Advanced Spaceborne Thermal Emission and Reflection (ASTER) data is proposed for the phosphate exploration and mapping in a specific geological context in Tunisia. ASTER’s thermal infrared (TIR) portion has been particularly expedited in phosphate detection and mapping. Visible near-infrared (VNIR), shortwave infrared (SWIR), and TIR atmospherically and geometrically corrected data are thus processed using the matched filtering (MF) method and VNIR-SWIR reflectance spectra of the collected samples. Results revealed that different absorption features of carbonate minerals (calcite and dolomite) in the SWIR region have to be considered for an efficient identification of rock phosphate. Proposed band combinations using MF fraction maps have successfully discriminated phosphate mineralization and outcrops. Moreover, the surface temperature map derived from TIR bands using the emissivity normalization method reveals the usefulness of the proposed method for rock phosphate delineation. Both geological map and published prospects map as well as x-ray diffraction (XRD) analysis results are used for the validation purposes. A high kappa coefficient value of 0.92, 0.97, and 0.88, is thus calculated, respectively, for VNIR, SWIR, and TIR data-based maps. The high kappa coefficient values implicate the contribution of proposed methodological approach in exploration and mapping phosphates in the Chaketma mine site in central west Tunisia.

Potential Use of ASTER Derived Emissivity, Thermal Inertia and Albedo Image for Discriminating Different Rock Types of Aravalli Group of Rocks, Rajasthan

In the present study, broad band emissivity, apparent thermal inertia and albedo image products have been derived based on processing of Advanced Spaceborne Thermal Emission and reflection (ASTER) data and analyzed their potential for delineating different metasedimentary units of Aravalli Group in Rajasthan. The major focus of the work is to analyze the potential of emissivity bands with respect to conjugated image product of emissivity, albedo and inertia for discriminating different rock types. In this regard, it was observed that combined use of emissivity, thermal inertia and albedo was effective for delineating different rock types as the combined variation of thermo-physical and mineralogical properties were recorded in these parameters. On the other hand, broad band emissivity spectra of rocks of the study area had subtle contrast, which was not optimum for the discrimination of different rock types. This was evident in the ASTER derived emissivity composite in which spatial delineation of rocks of the study area was not possible. Therefore, it is concluded that the combined use of albedo, thermal inertia and emissivity was suitable to delineate different metasedimentary rocks. This was also evident from the three dimensional data plot of selected emissivity band, inertia and albedo which separated different rocks from one another.

Evaluation of Atmospheric Correction Methods for the ASTER Temperature and Emissivity Separation Algorithm Using Ground Observation Networks in the HiWATER Experiment

Land surface temperature and emissivity (LST&E) are key variables for a wide variety of surface–atmosphere studies, and it is very important to validate the accuracy of different LST&E retrieval algorithms. In this paper, the accuracy of the Advanced Spaceborne Thermal Emission and Reflection (ASTER) temperature emissivity separation (TES) algorithm with the water vapor scaling (WVS) method and the ASTER standard LSE&E products (without WVS) was validated using 12 ASTER scenes from May 2012 to September 2012 with concurrent ground LST and emissivity measurements collected in an arid area in northwest China during the Heihe Watershed Allied Telemetry Experimental Research experiment. Both the National Center for Environmental Prediction (NCEP) and MOD07 atmospheric profile products were employed to perform the atmospheric correction. The results showed that the WVSTES LSTs retrieved from the two profiles both demonstrate good accuracies, with average biases of 0.34 and 0.24 K and average root-mean-square errors (RMSEs) of 1.52 and 1.46 K for the NCEP and MOD07 profiles, respectively. When the WVS was not applied, we obtained an average bias of 1.26 K and an average RMSE of 2.05 K for the AST08 product. The emissivities from the WVSTES algorithm with the two profiles both showed good agreements with the ground CE312 measurements, with mean differences of the five ASTER bands that were less than 0.01. AST05 showed anomalous emissivity spectra and underestimated the emissivity values of graybody surfaces when compared with the ground data, with mean differences of greater than 0.015, which were more obvious for cases with high water vapor. This paper demonstrated that the WVS method is critical for retrieving ASTER TES LST with accuracies within 1.5 K and emissivity within 0.015 for a wide range of atmospheric conditions and land surface types.

HYPERSPECTRAL ANALYSIS OF ASTER AND LANDSAT 8 DATA TO CHARACTERIZE EL-MISSIKAT URANIUM BEARING SILICEOUS VEINS AND THEIR ANALOGY TO EL-ERADIYA AND GATTAR GRANITES, EASTERN DESERT, EGYPT

This work aims to apply spectral image processing techniques that have been used often to processhyperspectral data to analyze multispectral ASTER (Advanced Spaceborne Thermal Emission andReflection Radiometer) and Landsat 8 data for mapping uranium-bearing siliceous rocks within El-Missikat granite. These techniques succeeded effectively in recognition of the same features at El-Eradiyaand Gattar granites. Siliceous materials (siliceous sinter), as used here, is a spectral group of SiO2 mineralsincluding opal, chalcedony and cristobalite derived from hydrothermal fluids and/or the decomposition ofgranite. The spectral identification of such minerals depend mainly on the uses a hydration absorptionfeature that characterize some hydrated silica at wavelength region of 2.2- to 2.4-μm.The effectiveness of the hyperspectral analysis techniques used to compare a pixel spectrum with thespectra of known pure materials, extracted from the spectral end member selection procedures, includingminimum noise fraction (MNF), pixel purity index (PPI) and n-dimensional visualization. Among thoseof the spectral analysis algorithms employed, spectral angle mapping (SAM) and matched filtering (MF)produced accurate classifications that were close to the ground reference data. The hyperspectralanalysis of an ASTER and Landsat 8 dataset covering the studied areas, has successfully effective indetecting lithological units than traditional multispectral analysis procedures.Field validation and laboratory investigation were carried out to prove and support the resultsobtained from ASTER and Landsat 8 processing. These results succeeded to delineate additional sitesof siliceous materials that could be a promising areas to host the uranium mineralization.

Mapping Lithologic Components of Ophiolitic Mélanges Based on ASTER Spectral Analysis: A Case Study from the Bangong-Nujiang Suture Zone (Tibet, China)

ASTER (Advanced Spaceborne Thermal Emission and Reflection) satellite imagery is useful in assisting lithologic mapping and, however, its effectiveness is yet to be evaluated for lithologic complex such as tectonic mélange. The Mugagangri Group (MG), the signature unit of the Bangong-Nujiang suture zone (BNSZ), Tibet and consisting of ophiolitic mélanges, was previously mapped as a single unit due to its poorly-described internal structures and an informative map with refined lithologic subdivision is needed for future petrologic and tectonic studies. In this paper, based on a combination of field work and ASTER data analysis, the MG is mapped as five subunits according to our newly-proposed lithologic subdivision scheme. In particular, we apply a data-processing sequence to first analyze the TIR band ratios to reveal approximate distribution of carbonates and silicate-dominated lithologies and then the VNIR/SWIR band ratios and false color images to differentiate the lithologic units and delineate their boundaries. The generalized procedures of ASTER data processing and lithologic mapping are applicable for future studies in not only the BNSZ but also other Tibetan ranges. Moreover, the mapping result is consistent with that the MG represents an accretionary complex accreted to the south Qiangtang margin as a result of northward-subduction of the Bangong-Nujiang oceanic crust.

Knickpoints along the upper Indus River, Pakistan: an exploratory survey of geomorphic processes

This article summarizes an exploratory study carried out to investigate the significance of various geomorphic features on the formation of observed knickpoints along the upper Indus River in northern Pakistan. These features include bedrock lithology, active faults, sediment flux from tributary channels, and landslide dams which have blocked the main channel. The knickpoints and their related geomorphic parameters (channel profile, concavity, drainage area and normalized steepness index, etc.) were extracted from Advanced Spaceborne Thermal Emission and Reflection (ASTER) Global Digital Elevation Models (GDEMs) with 30 m resolution using ArcGIS, River Tools, and Matlab software. A total of 251 major and minor knickpoints were extracted from the longitudinal profile along a ~ 750 km reach upstream of Tarbela Reservoir. The identified knickpoints and their respective normalized steepness index (ksn values) were compared with bedrock lithology, mapped faults, regional landslide/rockslide inventory, and the locations of historic landslide dams. The analyses revealed that the knickpoints do not correlate with the bedrock lithology except where major unit boundaries coexist with mapped faults, especially in reaches criss-crossed by active thrust faults in the Nanga Parbat Haramosh (NPHM) region. Neither did the river’s major confluences exhibit any notable knickpoints, but the correlations between knickpoints, mapped landslides, and historic rockslide avalanche dams accounted for approximately 75% of the observed knickpoints, a surprising finding. These observations suggest that more detailed studies aided by high resolution data should be undertaken to further explore the characteristics of knickpoints triggered by tectonic uplift, local fault offset, bedrock erodibility, and landslide/rockslide dams.

Temporal variations in snow albedo at glaciated upper elevation zone of an Eastern Himalayan river basin

AbstractSnow albedo is an important climate parameter as it governs the amount of solar energy absorbed by the snow and can be considered a major contributor to the surface radiation budget. The present study deals with the estimation of temporal variation of snow albedo at the upper elevation zone of glaciated Mago Basin of Arunachal Pradesh in eastern Himalaya. Moderate Resolution Imaging Spectroradiometer (MODIS) Daily Snow Products (MOD10A1 and MYD10A1) at 500 m spatial resolution were used. Both the MODIS data for ten years (2003–13) and the Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) of the study area were downloaded from NASA DAAC of NSIDC. The percentage area under different snow types (dry snow, wet snow, firn and ice) was determined by masking the upper elevation zone of the DEM into the albedo images. The average monthly slopes show a decreasing trend in area (%) of dry snow and wet snow and an increasing trend for firn and ice. Dry snow and wet snow cover percentages were observed to be decreasing, whereas firn and ice cover showed an increasing trend for most of the months. Firn dominated the type of snow, followed by ice then wet snow; the smallest area (%) was that of dry snow for the study period.

Application of ASTER and Landsat 8 imagery data and mathematical evaluation method in detecting iron minerals contamination in the Chadormalu iron mine area, central Iran

Satellite images are widely used to map geological and environmental features at different map scales. The ability of visible to near-infrared (VNIR) scanner systems to map gossans, rich in iron and associated with weathered sulfide occurrences, as well as to characterize regoliths, is perhaps one of the most important current applications of this technology. Initial results of this study show that advanced space-borne thermal emission and reflection (ASTER), VNIR, and short-wave infrared radiometer scanner systems can be used successfully to map iron ores. By applying internal average relative reflectance, false color composite, minimum noise fraction transform, and mathematical evaluation method (MEM) techniques, iron contaminations were successfully detected in the Chadormalu iron mine area of central Iran. An attempt was also made to discriminate between the geogenic and anthropogenic iron contaminations in the vicinity of the Chadormalu iron deposit. This research compares ASTER and Landsat 8 data images and the MEM with the band ratio method in a full scope view scale and demonstrates ASTER image data capability in detecting iron contaminations in the Chadormalu area. This indicates that ASTER bands 3, 2, and 1 have a higher spatial (15 m) resolution compared with sensors used in previous works. In addition, the capability of the MEM in detecting Fe-contaminants, unlike the color judgments of the band ratio method, can discriminate between iron pollution in an alluvial plain and the Fe-contents of the host and country rocks in the study area. This study proved that Landsat 8 data illustrate exaggeration both in the MEM and band ratio final results (outputs) and cannot display iron contamination in detail.

Multi-source remotely sensed data fusion for improving land cover classification

Although many advances have been made in past decades, land cover classification of fine-resolution remotely sensed (RS) data integrating multiple temporal, angular, and spectral features remains limited, and the contribution of different RS features to land cover classification accuracy remains uncertain. We proposed to improve land cover classification accuracy by integrating multi-source RS features through data fusion. We further investigated the effect of different RS features on classification performance. The results of fusing Landsat-8 Operational Land Imager (OLI) data with Moderate Resolution Imaging Spectroradiometer (MODIS), China Environment 1A series (HJ-1A), and Advanced Spaceborne Thermal Emission and Reflection (ASTER) digital elevation model (DEM) data, showed that the fused data integrating temporal, spectral, angular, and topographic features achieved better land cover classification accuracy than the original RS data. Compared with the topographic feature, the temporal and angular features extracted from the fused data played more important roles in classification performance, especially those temporal features containing abundant vegetation growth information, which markedly increased the overall classification accuracy. In addition, the multispectral and hyperspectral fusion successfully discriminated detailed forest types. Our study provides a straightforward strategy for hierarchical land cover classification by making full use of available RS data. All of these methods and findings could be useful for land cover classification at both regional and global scales.

Digital image processing of multispectral ASTER imagery for delineation of alteration and related clay minerals in Sakoli belt: Maharashtra – A case study

The extraction of spectral signature through remote sensing data in geological studies is very limited in Indian terrain. The present research is an attempt to delineate alteration and associated minerals using high resolution multispectral advanced space borne thermal emission and reflection (ASTER) data in Sakoli volcano-sedimentary sequence. The alteration zones are generally associated with certain types of mineral and mineral aggregates and are localized in the ground that can be mapped remotely using spectral mapping techniques. The band ratio and selective PCT were applied on calibrated ASTER image to extract spectral signature of index mineral. The results showed that high abundance of these minerals within pixels is corresponding to the alteration zones. The field observation, petrography and XRD analysis of the alteration zones confirmed the presence of dominant mineral phases that were identified remotely.

Integration between morphometric parameters, hydrologic model, and geo-informatics techniques for estimating WADI runoff (case study WADI HALYAH—Saudi Arabia)

In arid and semiarid areas, the only surface and groundwater recharge source is the runoff generated through flash floods. Lack of hydrological data in such areas makes runoff estimation extremely complicated. Flash floods are considered catastrophic phenomena posing a major hazardous threat to cities, villages, and their infrastructures. The objective of this study is to assess the flash flood hazard and runoff in Wadi Halyah and its sub-basins. Integration of morphometric parameters, geo-informatics, and hydrological models has been done to overcome the challenge of scarcity of data. Advanced Spaceborne Thermal Emission and Reflection (ASTER) data was used to prepare a digital elevation model (DEM) with 30-m resolution, and geographical information system (GIS) was used in the evaluation of network, geometry, texture, and relief features of the morphometric parameters. Thirty-eight morphometric parameters were estimated and have been linked together for producing nine effective parameters for evaluation of the flash flood hazard in the study basin. Flash flood hazard in Wadi Halyah and its sub-basins was identified and grouped into three classes depending on nine effective parameters directly influencing the flood prone areas. Calculated runoff volume of Wadi Halyah ranges from 26.7 × 106 to 111.4 × 106 m3 with an inundation area of 15 and 27 km2 at return periods of 5 and 100 years, respectively. Mathematical relationships among rainfall depth, runoff volume, infiltration losses, and rainfall excess demonstrate a strong directly proportional relationships with correlation coefficient of about 0.99.

Decadal Region-Wide and Glacier-Wide Mass Balances Derived from Multi-Temporal ASTER Satellite Digital Elevation Models. Validation over the Mont-Blanc Area

Since 2000, a vast archive of stereo-images has been built by the Advanced Spaceborne Thermal Emission and Reflection (ASTER) satellite. Several studies already extracted glacier mass balances from multi-temporal ASTER digital elevation models (DEMs) but they lacked accurate independent data for validation. Here, we apply a linear regression to a time series of 3D-coregistered ASTER DEMs to estimate the rate of surface elevation changes (dh/dtASTER) and geodetic mass balances of Mont-Blanc glaciers (155 km²) between 2000 and 2014. Validation using field and spaceborne geodetic measurements reveals large errors at the individual pixel level (> 1 m a-1) and an accuracy of 0.2-0.3 m a-1 for dh/dtASTER averaged over areas larger than 1 km². For all Mont-Blanc glaciers, the ASTER region-wide mass balance (-1.05±0.37 m water equivalent (w.e.) a-1) agrees remarkably with the one measured using Spot5 and Pleiades DEMs (-1.06±0.23 m w.e. a-1) over their common 2003-2012 period. This multi-temporal ASTER DEM strategy leads to smaller errors than the simple differencing of two ASTER DEMs. By extrapolating dh/dtASTER to mid-February 2000, we infer a mean penetration depth of about 9±3 m for the C-band Shuttle Radar Topographic Mission (SRTM) radar signal, with a strong altitudinal dependency (range 0-12 m). This methodology thus reveals the regional pattern of glacier surface elevation changes and improves our knowledge of the penetration of the radar signal into snow and ice.

Mapping of Coal Fire in Jharia Coalfield, India: a Remote Sensing Based Approach

In India, Jharia Coalfield (JCF) has one of the densest congregations of surface-subsurface coal fires known worldwide. Systematic investigation and quantification of actual scenario of coal fires in JCF is always necessary to plan sustainable mining, industrial growth and environmental remediation on a long term basis. The present approach involves evaluation and mapping of coal fire using ASTER (Advanced Spaceborne Thermal Emission and Reflection) data. Mapping reveals that the area located around western, eastern and south-eastern parts of JCF covering territories of Shatabdi opencast, Barora; Sijua opencast; Godhar colliery; Kusunda; Bokapahari; Kujama and Lodna are under intense fire with cumulative coverage of 6.23 km2. The ASTER derived Land Surface Temperature (LST) of the anomalous areas have been subsequently validated by the field observations, carried out in JCF in February, 2010. The methodology adopted in the present study would provide precise evaluation and monitoring of coal fire in Jharia.

Integrated remote sensing and GIS approach for delineation of groundwater potential zones using aquifer parameters in Devak and Rui watershed of Jammu and Kashmir, India

Remote sensing and geographic information system (GIS) have become one of the leading tools in the field of hydrogeological science, which helps in assessing, monitoring and conserving groundwater resources. In this paper, integrated remote sensing, GIS and fieldwork techniques were combined to delineate the groundwater potential zones map of Devak and Rui watersheds of Jammu and Kashmir. Remote sensing data were used to prepare hydrogeomorphology, drainage density and land use/land cover maps. The advanced space borne thermal emission and reflection (ASTER) digital elevation model (DEM) data was used for preparing the slope map. Pumping test data were used to prepare discharge, specific capacity, static water level, transmissivity and hydraulic conductivity maps. Well inventory data were collected to prepare the pre-monsoon water table, post-monsoon water table, static water level and fluctuation maps. All the thematic maps pertaining to chosen parameters were converted to raster data. The raster maps of these parameters were assigned an appropriate theme weight and class score. All thematic layers were integrated using the weighted index overlay analysis (WIOA) method in the GIS environment to determine groundwater potential map. The ground water prospects zones were depicted in five categories excellent, good, moderate, low and runoff zone. The results revealed that excellent zone covers 13.5 %, good zone 22.7 %, moderate zone 15.8 %, low zone 18.5 %, and runoff zone covers 29.5 % of the total study area. The result depicts the groundwater potential zones found to be helpful in better planning and management of groundwater resources in the study area.