The identification of snow avalanche release areas is a very difficult task. The release mechanism of snow avalanches depends on many different terrain, meteorological, snowpack and triggering ...parameters and their interactions, which are very difficult to assess. In many alpine regions such as the Indian Himalaya, nearly no information on avalanche release areas exists mainly due to the very rough and poorly accessible terrain, the vast size of the region and the lack of avalanche records. However avalanche release information is urgently required for numerical simulation of avalanche events to plan mitigation measures, for hazard mapping and to secure important roads. The Rohtang tunnel access road near Manali, Himachal Pradesh, India, is such an example. By far the most reliable way to identify avalanche release areas is using historic avalanche records and field investigations accomplished by avalanche experts in the formation zones. But both methods are not feasible for this area due to the rough terrain, its vast extent and lack of time. Therefore, we develop an operational, easy-to-use automated potential release area (PRA) detection tool in Python/ArcGIS which uses high spatial resolution digital elevation models (DEMs) and forest cover information derived from airborne remote sensing instruments as input. Such instruments can acquire spatially continuous data even over inaccessible terrain and cover large areas. We validate our tool using a database of historic avalanches acquired over 56 yr in the neighborhood of Davos, Switzerland, and apply this method for the avalanche tracks along the Rohtang tunnel access road. This tool, used by avalanche experts, delivers valuable input to identify focus areas for more-detailed investigations on avalanche release areas in remote regions such as the Indian Himalaya and is a precondition for large-scale avalanche hazard mapping.
Meteorological data-trend analysis is widely used as statistical method to access long-term changes in time-series of meteorological variables like precipitation, temperature, and evaporation. ...Understanding meteorological pattern of natural hazards like landslides, avalanches, and tsunami, is tough. Statistical computing, modelling and forecasting meteorological data are key instruments for studying hidden patterns. Long-term time-series of meteorological observations in mountainous terrain are rare in Indian Himalayan. This study proposes a framework including data selection, pre-processing, and knowledge discovery for Manali region in Pir Panjal Range. Pre-processing ensures data quality along with descriptive and inferential statistical methods used to understand data. Both parametric and non-parametric models are developed to investigate variations in 18 parameters. Trend magnitude for these parameters are determined and compared for three field observatories: Bahang, Solang, and Dhundi. An increasing seasonal trend was found for temperature (maximum, dry and wet), total water equivalent from beginning of season, and snow penetration for station data. Across observatories, many variables show no significant trend due to less and fluctuating data. Study also investigate significant relationship between features and the most correlated meteorological variables. It is useful for understanding changing meteorological status in the region for data prediction and interpolation. Correlation results can help in building better statistical regional models.
Assessment of the snow cover state in the initiation zone of an avalanche is essential for the snow avalanche hazard mapping in the mountainous region. Seasonal snow cover is very dynamic in the ...Himalaya and changes its state from dry to moist or wet according to temperature during the progression of winter. In this study, a methodology based on decomposition techniques using polarimetric synthetic aperture radar (SAR) data in conjunction with field snow wetness profiles is adapted for characterization of snow cover into dry and moist or wet snow in the Himalayan region. C-band Radarsat-2 full-polarization SAR data is utilized for polarimetric descriptors generation by applying various in-coherent target decomposition techniques, viz., Yamaguchi 4-component, Freeman Durden 3-component and Wishart Unsupervised (H-α). The generated polarimetric descriptors, viz., surface, volume, double-bounce and helix scattering are utilized in the supervised classification and Entropy (H) and mean scattering angle (α) for understanding the underlying scattering process happening within the snowpack. These decomposition models although describes various physical scattering mechanisms but are inadequate to provide quantitative estimates of underlying physical properties (as in our case changes in snowpack wetness). A supervised classification based on support vector machine (SVM) is used to characterize snow cover with changing conditions of snow wetness from dry to moist or wet. Three data sets of snow-accumulation and snow-melt period of winter 2015 are processed and analyzed along with field snow wetness (% by volume) profiles. A total 47 Nos. of snow wetness profiles of the snowpack are collected using snow fork instrument in synchronous with satellite passes at well distributed, identifiable field locations and are used in the training (34 Nos.) and testing (13 Nos.) of the SVM model. Snow being heterogeneous material varies metamorphically within snowpack. Based on changing weather conditions and sun exposure duration, it results into different scattering behavior in microwave region of the electromagnetic spectrum. It has been qualitatively observed from target decomposition results that polarimetric descriptors generated using Yamaguchi 4-component scattering decomposition have better captured the increase in the surface scattering (during February and March imagery, due to wet snow conditions of snow cover) from volume scattering (during January imagery, due to dry or moist snow conditions) from seasonal snowpack over the acquisition period from January to March 2015. Further to understand and comprehend the scattering process in the snowpack metamorphism with time, Wishart unsupervised (H-α) classification has been applied on all Radarsat-2 datasets. It was observed in the Himalayan region that during 29 Jan 2015 under dry snow conditions, anisotropic and multiple scattering processes dominates and during period between 22 Feb 2015 and 18 Mar 2015 under moist or wet snow conditions the dipole and Bragg type scattering are dominating. A confusion matrix has been generated for the accuracy assessment of the SVM based classification results, which shows overall accuracy ∼ 79.6 % (for 29 Jan 2015), 72.4 % (for 22 Feb 2015) and 74.5 % (for 18 Mar 2015), respectively with the testing samples. The C-band polarimetric SAR data shown its capability for snow cover type characterization and can be applied for operational applications in the avalanche zones of the snow bound terrain.
Accuracy of the Digital Elevation Model (DEM) affects the accuracy of various geoscience and environmental modelling results. This study evaluates accuracies of the Advanced Spaceborne Thermal ...Emission and Reflection Radiometer (ASTER) Global DEM Version-2 (GDEM V2), the Shuttle Radar Topography Mission (SRTM) X-band DEM and the NRSC Cartosat-1 DEM V1 (CartoDEM). A high resolution (1 m) photogrammetric DEM (ADS80 DEM), having a high absolute accuracy 1.60 m linear error at 90 % confidence (LE90), resampled at 30 m cell size was used as reference. The overall root mean square error (RMSE) in vertical accuracy was 23, 73, and 166 m and the LE90 was 36, 75, and 256 m for ASTER GDEM V2, SRTM X-band DEM and CartoDEM, respectively. A detailed error analysis was performed for individual as well as combinations of different classes of aspect, slope, land-cover and elevation zones for the study area. For the ASTER GDEM V2, forest areas with North facing slopes (0°–5°) in the 4th elevation zone (3773–4369 m) showed minimum LE90 of 0.99 m, and barren with East facing slopes (>60°) falling under the 2nd elevation zone (2581–3177 m) showed maximum LE90 of 166 m. For the SRTM DEM, pixels with South-East facing slopes of 0°–5° in the 4th elevation zone covered with forest showed least LE90 of 0.33 m and maximum LE90 of 521 m was observed in the barren area with North-East facing slope (>60°) in the 4th elevation zone. In case of the CartoDEM, the snow pixels in the 2nd elevation zone with South-East facing slopes of 5°–15° showed least LE90 of 0.71 m and maximum LE90 of 1266 m was observed for the snow pixels in the 3rd elevation zone (3177–3773 m) within the South facing slope of 45°–60°. These results can be highly useful for the researchers using DEM products in various modelling exercises.
Remote sensing is an indispensable tool for Earth observation over large areal extent. Snow cover extent monitoring has been one such application where microwave sensors have been a popular choice ...due to their sensitivity toward the dielectric property. Snow is a dynamic matter since its dielectric state is dependent on climatic factors prevailing locally around it. In the literature, the polarization fraction (PF) has been used for landcover characterization. In this paper, we utilize the seasonal variation of the PF for mapping snow cover over the Himalayan terrain. Temporal variation of the strength in the polarized return due to change in landcover and season is the prime motivation behind this approach. In addition, the effect of SAR data acquisition time on mapping algorithms is considered in this work which is seldom discussed in the literature. Furthermore, a study is conducted to analyze the seasonal variation in the entropy (H) and the scattering type ( \alpha) parameters over the bare snow-covered ground and the forested areas. The seasonal response of the terrain in the H/\alpha plane corresponding to specific scattering characteristics is analyzed. The applicability of the approach is tested with RADARSAT-2 (FQ28) C-band full polarimetric image pairs over the Manali-Dhundi region, Himachal Pradesh, India, located in the western Hindu-Kush Himalayas. The snow cover map is explicitly validated with in situ observatory measurements and optical satellite imagery.
The seasonal snow cover contributes significantly to the water resource in the Indian Himalayas, and snow density is one of the vital parameters in the determination of the hydrological potential of ...snow. The application of conventional methods for snow density retrieval using fully polarimetric SAR data is constrained by the properties of snow primarily due to the melt and frost cycles, as compared to fresh dry snow. The surface component of backscatter is significant in case of melt and frost. In the conventional decomposition based methods, the surface component is not considered for the inversion of permittivity, as proposed in this study. In this paper, we also propose a methodology for the estimation of snow density using bi-temporal fully polarimetric C-band RADARSAT-2 synthetic aperture radar (SAR) data. We utilize the relation between the differential modified Mueller matrix components, the attenuation constants, and the Fresnel transmission coefficients. The permittivity of snow is derived using the inversion of the Fresnel transmission coefficients which is used to determine the snow density using a state of the art empirical relation. The snow density estimates from the proposed method are compared with other methods based on coherency matrix decomposition and evaluated against in-situ measurements collected during a field campaign carried out in Dhundi in the state of Himachal Pradesh in India. The snow density estimates using the proposed method are observed to correlate with the in-situ measurements and were also found to be better than the decomposition based methods.
•C-band microwave signal sensitivity to the mountainous snowpack•New method for snow density using bi-temporal RADARSAT-2 data•Inversion of snow density with partial information from Quad-PolSAR data•Comparison of existing PolSAR decomposition based snow density retrieval methods•Inversion of snow density without a prior knowledge of the study region
The paper concerns ice dams and glacial lake outburst floods (GLOFs) in the Karakoram. Some 146 events are identified, including 30 major disasters. Large downstream populations and major ...infrastructure are threatened. Risk factors differ from recent reports of other Himalayan GLOFs associated with glacier recession and global warming. Ice dams are largely or entirely active ice, put in place by advancing glaciers. Climate change is a factor, but the ice cover in the Karakoram has been sustained, and even some increase in mass. Surge-type glaciers comprise or affect~70% of our inventory. The most frequent, large GLOFs come from local clusters of glaciers in five sub-basins, given special attention here. In four there were new ice dams formed since 2008 and two generated dangerous GLOFs. An urgent need arises to track short-term ice and lake behavior and how surge dynamics may be involved. Satellite images and DEMs are employed in cross-correlation feature tracking and elevation change respectively. The glaciers of interest all exhibit irregular movement, including recent advances, but with great variability and no clear relation to climatic fluctuations.
•Study updates inventory of ice dams and related GLOFs in the Karakoram.•Study reports recent evidence of surface displacement and elevation changes of glaciers involved in ice dam generation.•Surge-type glaciers comprise or affect ~70% of inventory of ice dams and related GLOFs.•Four glaciers formed new ice dams since 2008, two (Kyagar, Khurdopin) generating dangerous GLOFs.•The studied glaciers exhibit irregular movement, but with great variability and no clear relation to climatic fluctuations.
Supraglacial debris significantly hampers the mapping of glaciers using remote sensing data. A semi-automated approach for the mapping of debris-covered glacier was applied, which combined the inputs ...from thermal and optical remote sensing data and the Digital Elevation Model (DEM) derived morphometric parameters. A thermal mask that delineates the supraglacial debris extent was generated by the thresholding of surface temperature layer obtained from Landsat TM/ETM+ thermal band satellite data. The extent of clean glacier ice was identified by band ratioing and thresholding of TM/ETM+ 4 and TM/ETM+ 5 bands. Morphometric parameters like slope, plan curvature and profile curvature were rearranged in similar surface groups using the technique of cluster analysis. All these masks were vectorized and final classification maps were generated using geographic information system (GIS) overlay operations. The areal extent of semi-automated outlines of Hamtah and Patsio Glaciers derived from cluster analysis varied from manually derived outline using pan-sharpened Landsat ETM+ September 2000 image by −1.3% and −1.6%, respectively. Year 2011 classification map for Patsio Glacier was compared with the field observations and a high correlation and overall accuracy (~91%) were observed. The same classification methodology was adopted for images of years 2000 and 1989 for Patsio Glacier to observe the effects of varying snow cover patterns on adopted methodology. Also the methodology was adopted and verified for Hamtah Glacier, with different geometry and terrain conditions as compared to Patsio Glacier. Although the spatial resolution limitation of ASTER GDEM and Landsat TM/ETM+ thermal band limits the automated mapping of small debris-covered glaciers, the outcomes are still favorable enough to apply such methodologies for mapping different types of debris-covered glaciers in the future.
•Methodology for mapping debris covered glacier using free remote sensing data•Field based validation and problem area identification•Effects of seasonal snow & shadow on classification are highlighted.
Avalanches are behind the majority of fatalities and heavy damage to property in snow-covered mountainous terrain like Himalaya. Recognizing avalanche susceptible areas and publication of avalanche ...susceptibility maps assist decision-makers and planners to execute suitable measures to reduce the avalanche risk. The present study is an attempt to prepare an avalanche susceptibility map of the Nubra valley region using multi-criteria decision analysis-analytical hierarchy process model in GIS environment. The most prominent avalanche occurrence factors used in this model are slope, aspect, curvature, elevation, terrain roughness and ground cover. ASTER GDEM V2 and Landsat 8 satellite imagery were used to generate considered factors. For validation of the results, prediction rate/accuracy is calculated using the avalanche inventory map of documented avalanche locations. To calculate the prediction accuracy, area under the ROC curve (ROC-AUC) method has been used. The prediction accuracy of the validation results using ROC-AUC shows 91%.
Synthetic Aperture Radar (SAR) remote sensing is a state-of-the-art tool for snow monitoring and snow parameters estimation. SAR remote sensing-based techniques, such as interferometric SAR (InSAR) ...and Polarimetric SAR (PolSAR) have already proven useful in the estimation of geophysical parameters of snow. InSAR-based techniques utilize interferometric phase information from repeat-pass datasets for snow parameters retrieval. During the monitoring of snow, the large temporal gap between the repeat passes results in the temporal decorrelation in the snowpack, which leads to the loss of interferometric coherence. Hence, there is a need for a technique for snow parameters estimation, which can work with zero temporal baseline datasets. This study works on the development of a Polarimetric SAR Interferometry (PolInSAR) based modelling approach for snow-depth estimation using TerraSAR-X/TanDEM-X datasets acquired in the pursuit monostatic mode (temporal baseline = 10 seconds). The study area of this work is the Manali region of Himachal Pradesh situated in the Beas basin. Multi-temporal analysis of the snow-depth variation is executed utilizing the two pursuit-monostatic TanDEM-X interferometric quad-pol dataset pairs of the dates 21 January 2015 and 22 January 2015. In this study, the PolInSAR-based Coherence Amplitude Inversion modelling approach is used for the snow-depth retrieval. The magnitude of the complex interferometric coherence is used during the modelling implementation. The snow extinction coefficient is estimated and used as an input during PolInSAR modelling. Further, a comparison of the calculated volume coherence magnitude and the observed volume coherence magnitude is done during model implementation for the snow-depth estimation. The snow depth is estimated at a resolution of 15 m × 15 m in range and azimuth directions respectively. The estimated snow depth for both the dates shows a precise correlation with the ground datasets. The rise in model retrieved snow-depth value from 0.84 m to 1.24 m is observed during the period. The retrieved results were validated using the ground data of snow depth from the Automatic weather station (AWS) of Snow and Avalanche Study Establishment (SASE), Defence Research and Development Organization (DRDO), and Indian Institute of Remote Sensing (IIRS) installed in the Dhundi region of the study area for same dates.
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