Vellore Institute of Technology (VIT) is a private university located in&nbsp;Tamil Nadu, India. Founded in 1984, as Vellore Engineering College, the institution offers 20 undergraduate, 34 postgraduate, four integrated and four research programs. It has campuses in Vellore, Amravati, Bhopal and Chennai.

VIT is one of the top ranked private universities in India according to NIRF, THE and QS Rankings.&nbsp;Govt. of India has recognized&nbsp;VIT, Vellore as an&nbsp;Institution of Eminence. This has allowed VIT to take independent quality initiatives and move up in world ranking.

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VIT University

Snow cover area is a very important parameter for snowmelt runoff modeling and forecasting. Snow cover information is also useful for managing transportation and avalanche forecasting. Our study area includes Gangotri glacier region, Siachen glacier region and Beaskund glacier region in the North-West Himalayas of India. Our previous studies discuss the capability of several algorithms for optical sensor as well as SAR to map the snow cover area in Himalayan regions. In recent years, the SAR interferometry has provided number of attractive applications in landuse/landcover mapping. This study discusses the capability of both backscattering ratio techniques and InSAR coherence measurement techniques for snow cover mapping in Himalayan region with repeat passes data of ERS1/2 and ENVISAT-ASAR. By analyzing the several pairs of ENVISAT repeat passes ASAR images for the study area, we find that the coherence measurement from bare soil, bare rock and vegetation are high and snow covered area and glacier area have very low coherence except in one day difference image. © 2008 IEEE.

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Department of Communication Engineering

School of Electronics Engineering

Vellore Campus

International Geoscience and Remote Sensing Symposium (IGARSS)

The H/A/ALPHA polarimetric decomposition theorem and complex wishart distribution for snow cover monitoring

An endeavor is made for the study of movement of Himalayan glaciers using Spaceborne InSAR technique, which is based on preserving the coherence between two acquisitions of the same scene. Gangotn, Siachen, Bara Shign and Patsio are major glaciers in the Himalayan region, which are showing retreat, and their respective tributary glaciers completely disconnected from main body of glaciers. ERS-1/2 observations show high correlation on glacier area and hence movement of Siachen and Gangotn glacier are measured. Information about dynamism of glaciated terrain can be retrieved by counting differential interferograms. Displacement of Gangotri glacier in the radar look direction has been observed as 8.4 cm represented by 3 fringes. Siachen glacier exhibits a displacement of 22 cm represented by 8 fringes. ERS-1/2 tandem data over all these glaciers show highest correlation over glacier areas but ENVISAT ASAR data shows coherence loss over glacier area due to decollation. Coherence loss is usual phenomena in glaciated terrain as repeativity of sensor is high (35 days for ENVISAT). Among all these, Siachen glacier shows highest coherence and then Gangotri. respectively. A tandem pair of ERS-1&2 acquired on April 1 and 2, 1996 in descending pass over Siachen shows high coherence than the ascending pair acquired on May 2 and 3, 1996. It is due to change in climate between two acquisitions at glacier locations. A systematic monitoring of dynamism of Himalayan glaciers can be done using Permanent Scatterer Interferometnc SAR (PSInSAR) if we place a corner reflector on the glaciers. © 2008 IEEE.

Spaceborne insar technique for study of himalayan glaciers using envisat asar and ers data

The main objective of the study is to estimate snow wetness using ENVISAT ASAR data. Snow surface backscattering can be expressed as a function of permittivity of snow. Coding has been done for backscattering coefficient image generation using ENVISAT- Advanced Synthetic Aperture Radar (ASAR), single look complex (SLC) data with dual (HH and VV) polarization as well as single (HH) polarization data. Incidence angle images were extracted from the ASAR header data using interpolation method. These mages were multi-looked 5 times in azimuth and 1 time in range direction. ASAR backscattering coefficient images have been calibrated and processed into terrain corrected images in Universal Transverse Mercator (UTM), zone 43 north and WGS-84 datum map projection using ERDAS Imagine software. Corrected backscattering images are despeckled using Frost filter technique. For this study Integral equation method (IEM) for first order surface scattering based inversion model has been used. A Software has been developed using integral equation method (IEM) based inversion model to estimate snow permittivity, which can be further related to estimating snow wetness. A comparison was done between inversion model estimated snow wetness and field values of snow wetness in the study region. Comparison with field measurement showed that the correlation coefficient for snow wetness estimated from ASAR data was observed to be 0.94 at 95% confidence interval and standard error is observed as 1.28% by volume at 95% confidence interval. The comparison of ASAR derived snow wetness with ground measurements shows the average absolute error at 95% confidence interval as 2.8%. The snow wetness range varies from 0-15% by volume.

Proceedings of SPIE - The International Society for Optical Engineering

Investigating Snow wetness using dual polarization advanced synthetic aperture radar imagery

The measurement of snow parameters is important for hydrological modeling. Spatial and temporal changes in snow grain size can help us to characterize the thermal state of snow pack and to estimate the timing and spatial distribution of snowmelt. This paper discusses the methodology of Advanced Synthetic Aperture Radar (ASAR) data analysis for estimating snow grain size. In this investigation, we have used ENVISAT-ASAR image mode SLC data in HHpolarization with incidence angle range 39.1° - 42.8 °of 31st January 2006. Survey of India (SOI) topographical sheet (52H3) in 1:50,000 scale is used for preparation of digital elevation model (DEM) and for the registration of satellite data. Field data were measured synchronous with satellite pass. Envisat-advanced synthetic aperture radar single polarized, single look complex (SLC) data have been processed for backscattering coefficient image generation. Incidence angle image was extracted from the ASAR header data using interpolation method. These images were multi-looked 5 times in azimuth and 1 time in range direction. ASAR Backscattering coefficient images have been calibrated. The scattering and absorption efficiencies of an ice particle are only weakly dependent on the shape of the particle. A Snowflake, although non-spherical in shape, may be treated using the Rayleigh expression for a spherical particle of the same mass provided the Rayleigh condition applies. This study has been done using Rayleigh scattering condition based model. The effect of snow grain size on backscattering coefficient is studied in detail. The comparison of ASAR C-band estimated value with field grain size measurement shows an absolute error of 0.045 mm and relative error 9.6%. Backscattering coefficient increases as the grain size increases with elevation.

Snow grain size estimation in Himalayan snow covered region using advanced synthetic aperture radar data

This paper discusses the methodology of Synthetic Aperture Radar (SAR) data analysis for studying various aspects of snow characteristics viz snow dielectric constant, snow wetness and snow density. ENVISAT- Advanced Synthetic Aperture Radar (ASAR), single look complex (SLC) data have been processed for backscattering coefficient image generation. ASAR Backscattering coefficient images have been calibrated and processed into terrain corrected images. Corrected backscattering images are despeckled using Frost filter technique. The estimation of snow pack characteristics is optimal at different incidence angles. The relation between snow characteristics like wetness, and snow density and radar backscatter has been studied and the importance of radar backscatter to infer various snow characteristics has been emphasized. This investigation shows the backscattering coefficient is inversely correlated to snow wetness and density. The correlation between the backscattering coefficients and snow wetness and snow density were observed as 0.8 and 0.92 respectively. 14.74 % and 13.31% part of the study area was found affected by layover and low or grazing local incidence respectively in ENVISAT-ASAR IS6 image. In this study, the wetness range was found to vary from 0.05% to 10.28% by volume and mean absolute error was found to be 0.64% by volume and snow density range varies from < 0.1 to 0.48 gm/cc and mean absolute error for density was found 0.032 gm/cc. At higher elevation to moderate elevation estimated snow wetness was observed to be 0.05-4% by volume, increasing to 4-10.28 % by volume at moderate to lower elevation.

ENVISAT-ASAR single polarized SLC data analysis for the study of snow pack characteristics

This paper discusses the methodology of Synthetic Aperture Radar (SAR) data analysis for studying snow porosity and its effect on electric property of snow. ENVISAT-Advanced Synthetic Aperture Radar (ASAR) single polarized, single look complex (SLC) data have been processed for backscattering coefficient image generation. Incidence angle image has been extracted from the ASAR header data using interpolation method. These images were multi-looked 5 times in azimuth and 1 time in range direction. ASAR Backscattering coefficient images have been calibrated. The estimation of snow porosity is optimal at different incidence angles. The effect of snow porosity on snow wetness, snow density and backscattering coefficient is studied in detail. The correlation coefficient between estimated and measured porosity is observed to be 0.88 and absolute error was 0.045. © 2007 Society of Photo-Optical Instrumentation Engineers.

Journal of Applied Remote Sensing

Snow porosity estimation using advanced synthetic aperture radar single look complex data analysis and its effects on backscattering coefficient

We have made an endeavor to investigate the snow water equivalent (SWE) variations in Himalayan mountain region which is the most difficult terrain to access during winter seasons. The area also covers the large glaciers such as Siachen and Gangotri. A time series multi scale of SWE L3 product derived from Aqua Advanced Microwave Scanning Radiometer (AMSR-E) data have been analysed for three consecutive winters during 2002-2005 for Himalayan Snow cover region. A major emphasis is on the study of SWE trend in the two glacier areas viz. Siachen and Gangotri. It has been observed through AMSRE 5-day product that snow cover area (SCA) over whole Himalayan region is very dynamic. AMSR-E derived 5-day SWE is analysed at the two test sites viz. Patsio (Lat 32° 45′ 17.89″ N and Lon 770, 15′, 43.13″E) and Dhundi (Lat 320, 22′ 05″N and Lon 770 15′'E) concurrently with the available in-situ data. The result indicates that in the peak winter days only we found reasonable co-relation. At the Patsio, minimum and maximum SWE observed are 26mm and 108mm respectively using in situ data.