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The aim of this study is to utilize the remotely sensed data and GIS techniques for instability assessment of terrain and prediction in the chosen study area, Rohtang to Baralacha, Lahaul Spiti District, Himanchal Pradesh, India. Several parameter maps were generated by the availability of different types of multi-temporal and various scales of remote sensing data. Inventory map was developed though the field work, past records and with the help of remote sensing data. Geological map, soil map, land use map, terrain map, slope map, aspect map, drainage density and snow melt parameter maps were generated or extracted from the existing ancillary data. In speeding up processing for hazard zonation and prediction assessment, GIS can be useful. To determine the degree of susceptibility to the landslides, information value method is used. The landslide hazard map is classified on the basis of its probability values into low, medium, high and very high risk zones. The results showed that over 3.09% of the area is liable to low landslide risk, 26.28% of the area is liable to medium landslide risk, 32.81% of the area is of high landslide risk and within this area about 37.81% has very high to severe risk. The landslide susceptibility map delineated in this paper can be used by various stakeholders, like state governments, researchers and citizens to develop and manage the developmental activities in the area in particular and in other similar areas in general. [ABSTRACT FROM AUTHOR]

This study has been done using Polarimetric Synthetic Aperture Radar (SAR) data to estimate the snow physical properties (snow wetness and snow density), inManali sub-basin of Himachal Pradesh, India. The SAR data used are of Radarsat-2 (RS2) and Environmental Satellite, Advanced Synthetic Aperture Radar (ASAR). SAR-based inversion models were implemented in Mathematica and MATLAB, and have been used for finding out wet and dry snow dielectric constant, snow wetness and snow density. The SAR-based inversion models were implemented separately for fully polarimetric RS2 and dual polarimetric ASAR Alternate Polarization System datasets. Masks for forest, built area, layover and shadow were considered in estimating snow parameters. The overall accuracy in terms of R 2 value comes out to be 0.86 for snow wetness and 0.84–0.72 for snow density based on the ground truth data for subset area of Manali sub-basin of Beas river up to Manali. [ABSTRACT FROM PUBLISHER]

More than two hundred thermal conductivity measurements for different snow densities and snow types were carried out in-situ at a field research station located in greater Himalayan range of India. These measurements were carried out using a commercially available portable thermal conductivity meter. Thermal conductivity measurements were carried out on the fresh snow, equi-temperature snow, and surface hoar and temperature-gradient snow. Average thermal conductivity of snow varied from 0.08 W/mK (Fresh snow of 120 kg/m³ density) to 0.32 W/m K (Equi-temperature snow of 420 kg/m³ density). Based on these measurements, a new density-based thermal conductivity equation is proposed. Using this proposed equation, modeled snowpack temperatures showed closer agreement with the observed data as compared to the predictions based on other well-known empirical and theoretical thermal conductivity equations for snow. This study highlights the advantages and limitations of empirical based thermal conductivity equations over the complex models based on snow microstructure. [ABSTRACT FROM AUTHOR]

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 [ABSTRACT FROM AUTHOR]