Potential of multispectral reflectance for assessment of snow geophysical parameters in Solang valley in the lower Indian Himalayas

ABSTRACT

Snow geophysical parameters such as wetness, density and permittivity are a significant input in hydrological models and water resource management. In this paper, we utilize the triangle method based on a feature space developed with the near-infrared (NIR) reflectance and the Normalized Differenced Snow Index (NDSI) for the estimation of surface snow wetness, permittivity and density. The triangular feature space based on NIR reflectance and NDSI is parameterized to yield a linear relationship between the snow wetness and the NIR reflectance. Snow density and permittivity are derived based on the least squares solution of empirical relations based on the observations of surface snow wetness. The proposed methodology was evaluated using Sentinel-2 data, and the modeled snow geophysical parameters were validated with respect to field measurements. Based on the results, it was inferred that the NIR reflectance varies linearly with the liquid water content in the snow. A good agreement was determined between the modeled and measured parameters for wet snow conditions as observed by the coefficient of determination of 0.968, 0.521 and 0.969 for the snow wetness, density and permittivity (real part), respectively. The proposed approach can be significantly utilized with unmanned aerial sensors for monitoring of physical properties of fresh or wet snow and is thus expected to contribute considerably in hydrological applications and avalanche studies.     

Dry/wet snow mapping based on the synergistic use of dual polarimetric SAR and multispectral data

We propose a multi-sensor multi-spectral and bi-temporal dual-polarimetric Synthetic Aperture Radar(SAR) data integration scheme for dry/wet snow mapping using Sentinel-2 and Sentinel-1 data which are freely available to the research community. The integration is carried out by incorporating the information retrieved from ratio images of the conventional method for wet snow mapping and the multispectral data in two different frameworks. Firstly, a simple differencing scheme is employed for dry/wet snow mapping, where the snow cover area is derived using the Normalized Differenced Snow Index(NDSI). In the second framework, the ratio images are stacked with the multispectral bands and this stack is used for supervised and unsupervised classification using support vector machines for dry/wet snow mapping. We also investigate the potential of a state of the art backscatter model for the identification of dry/wet snow using Sentinel-1 data. The results are validated using a reference map derived from RADARSAT-2 full polarimetric SAR data. A good agreement was observed between the results and the reference data with an overall accuracy greater than 0.78 for the different blending techniques examined. For all the proposed frameworks, the wet snow was better identified. The coefficient of determination between the snow wetness derived from the backscatter model and the reference based on RADARSAT-2 data was observed to be 0.58 with a significantly higher root mean square error of 1.03 % by volume.     

Paragenesis of Cr-rich muscovite and chlorite in green-mica quartzites of Saigaon–Palasgaon area,Western Bastar Craton,India

Journal of Earth System Science - Green mica (fuchsite or chromian-muscovite) is reported worldwide in the Archaean metasedimentary rocks, especially quartzites. They are generally associated with...     

Potential of Landsat-8 and Sentinel-2A composite for land use land cover analysis

Abstract

This study proposes the development of a multi-sensor, multi-spectral composite from Landsat-8 and Sentinel-2A imagery referred to as ‘LSC’ for land use land cover (LULC) characterisation and compared with respect to the hyperspectral imagery of the EO1: Hyperion sensor. A three-stage evaluation was implemented based on the similarity observed in the spectral response, supervised classification results and endmember abundance information obtained using linear spectral unmixing. The study was conducted for two areas located around Dhundi and Rohtak in Himachal Pradesh and Haryana, respectively. According to the analysis of the spectral reflectance curves, the spectral response of the LSC is capable of identifying major LULC classes. The kappa accuracy of 0.85 and 0.66 was observed for the classification results from LSC and Hyperion data for Dhundi and Rohtak datasets, respectively. The coefficient of determination was found to be above 0.9 for the LULC classes in both the datasets as compared to Hyperion, indicating a good agreement. Thus, these three-stage results indicated the significant potential of a composite derived from freely available multi-sensor multi-spectral imagery as an alternative to hyperspectral imagery for LULC studies.     

Influence of size of granulated rubber and tyre chips on the shear strength characteristics of sand–rubber mix

Use of scrap tyres in isolation systems for seismic damping, requires a knowledge of the engineering properties of sand–rubber mixtures (SRM). The primary objective of this study is to assess the influence of granulated rubber and tyre chips size and the gradation of sand on the strength behaviour of SRM by carrying out large-scale direct shear tests. A large direct shear test has been carried out on SRM considering different granulated rubber and tyre chip sizes and compositions. The following properties were investigated to know the effect of granulated rubber on dry sand; peak shear stress, cohesion, friction angle, secant modulus and volumetric strain. From the experiments, it was determined that the major factors influencing the above-mentioned properties were granulated rubber and tyre chip sizes, percentage of rubber in SRM and the normal stress applied. It was observed that the peak strength was significantly increased with increasing granulated rubber size up to rubber size VI (passing 12.5 mm and retained on 9.5 mm), and by adding granulated rubber up to 30%. This study shows that granulated rubber size VI gives maximum shear strength values at 30% rubber content. It was also found that more uniformly graded sand gives an improved value of shear strength with the inclusion of granulated rubber when compared to poorly graded sand.