Gulab Singh

Indian Institute of Technology Bombay

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G. Venkataraman

Indian Institute of Technology Bombay

Yoshio Yamaguchi

Niigata University

Debmita Bandyopadhyay

University of Cambridge

Shradha Mohanty

Deutsches Zentrum für Luft- und Raumfahrt e. V. (DLR)

Sang-Eun Park

Sejong University

Bala Raju Nela

Indian Institute of Technology Bombay

Anil V. Kulkarni

Indian Institute of Science Bangalore

Avik Bhattacharya

Indian Institute of Technology Bombay

Surendar Manickam

Vellore Institute of Technology University

Akshay Patil

Indian Institute of Technology Bombay

Cooperative Institutions

Indian Institute of Technology Bombay

Indian Institute of Technology Indore

Institute of Minerals and Materials Technology

Indian Space Research Organisation

Niigata University

Indian Agricultural Research Institute

Indian Institute of Remote Sensing

Birla Institute of Technology, Mesra

Banaras Hindu University

CSIR National Physical Laboratory of India

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Characterization of forests and deforestation in Cambodia using ALOS/PALSAR observation

Geocarto International (2011)

Ram Avtar Haruo Sawada Wataru Takeuchi Gulab Singh

In this study, we have demonstrated the capability of full polarimetric ALOS/Phased Array L-band Synthetic Aperture Radar data for the characterization of the forests and deforestation in Cambodia, to support climate change mitigation policies of Reducing Emission from Deforestation and Forest Degradation (REDD). We have observed mean backscattering coefficient (σ°), entropy (H), alpha angle (α), anisotropy (A), pedestal height (PH), Radar Vegetation Index (RVI) and Freeman–Durden three-component decomposition parameters. The observations show that the forest types and deforested area are showing variable polarimetric and backscattering properties because of the structural difference. Evergreen forest is characterized by a high value of σ° HV (−12.96 dB) as compared with the deforested area (σ° HV=−22.2 dB). The value of polarimetric parameters such as entropy (0.93), RVI (0.91), PH (0.41) and Freeman–Durden volume scattering (0.43) is high for evergreen forest, whereas deforested area is characterized by the low values of entropy (0.36) and RVI (0.17). Based on these parameters, it is found that σ° HV, entropy, RVI and PH provide best results among other parameters.

Deforestation

Evergreen forest

10.1080/10106049.2011.626081

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Citations (33)

Improved snow wetness estimation from fully polarimetric SAR image

IEEE Asia-Pacific Conference on Synthetic Aperture Radar (2013)

M. Surendar Gulab Singh Avik Bhattacharya G. Venkataraman P. A. Bharathi

Snow wetness is an important parameter for forecasting of snow avalanche and snow melt run off modeling in cragged areas specifically Himalayan region of India. In this paper a new snow wetness estimation approach is used for fully polarimetric Synthetic Aperture Radar (SAR) data. In this new methodology Freeman surface scattering and Cloude volume scattering components are introduced, which account for all independent relative polarimetric phase parameters of the coherency matrix. These parameters have been inverted into surface and volume dielectric constant of the snow respectively. A generalized spheroidal shape is considered as a snow particle structure for volume scattering model. The estimated snow wetness is validated using the field data, which was collected, synchronized with the satellite pass. The results were also compared with the Shi and Dozier [1] inversion model based snow wetness.

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InSAR Coherence Measurement Techniques for Snow Cover Mapping in Himalayan Region

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2008)

Gulab Singh G. Venkataraman Y. S. Rao Vijay Kumar Snehmani

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 ERSfrac12 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.

Snowmelt

Land Cover

10.1109/igarss.2008.4779913

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Surging Glacier Dynamics in Tarim Basin Using SAR Data

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2020)

Debmita Bandyopadhyay Gulab Singh Girjesh Dasaundhi Nela Bala Raju Akshay Patil

The glaciers of High Mountain Asia form one of the largest assemblies of ice-mass outside the Polar regions. The meltwater originating from these glaciers feed into innumerable river streams acting as an important perennial source of water. Within this region, we study one basin, Tarim which consists of glaciers that show surging activity. Of the 200 glaciers in the selected region of study, six glaciers show strong surge at the terminus. This has been explained with the high velocity observed for these glaciers. However, there are glaciers which are in the transient phase of their surge-cycle which tend to behave differently. Such events when compared to previously reported activities suggest that glaciers have surging cycles that are not the same throughout.

Meltwater

Glacier terminus

Tarim basin

Glacier morphology

Glacier ice accumulation

Accumulation zone

10.1109/igarss39084.2020.9323095

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Snow Cover Classification Using Polarimetric Data

G. Venkataraman Gulab Singh

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Snow density estimation using Polarimitric ASAR data

Gulab Singh G. Venkataraman

Remote sensing of radar polarimety has great potential to determine the extent and properties of snow cover. Availability of spaceborne sensor dual polarimetric C-band data of ENVISAT-ASAR can enhance the accuracy in measurement of snow physical parameters as compared to single fixed polarization data measurement. This study shows that the capability of C-band SAR data for estimating dry snow density over snow coverer rugged terrain in Himalayan region. The study area lies in Beas, Chandra and Bhaga catchments of Himachal state (India). For this investigation, the main assumptions are that the snow is dry and at C-band, total backscattering coefficient comes from snowpack and snow ground interface. An algorithm for estimating snow density has been developed based on snow volume scattering and snow-ground scattering components. Snow density estimation algorithm requires HH and VV polarization combination data. The radar backscattering coefficients of both HH and VV polarization and incidence angle are given as input to the developed algorithm. Finally, the algorithm gives the snow dielectric constant which can further be related to snow density using Looyenga's semi empirical formula. Comparison was done between algorithm estimated snow density and field value of snow density in the study region. The mean absolute error between estimated and measured snow density was 21.3 kg/m ³ .

Snowpack

10.1109/igarss.2009.5418165

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Estimating Dynamic Parameters of Bara Shigri Glacier and Derivation of Mass Balance from Velocity

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2020)

Bala Raju Nela Gulab Singh Debmita Bandyopadhyay Akshay Patil Shradha Mohanty

Glaciers are the most important component in the cryosphere and are a reliable indicator of climate change. Thus, observation of glacier dynamics helps understand glacier health and monitor climate change. Glacier velocity, thickness and mass balance are the few important parameters to consider regarding glacier dynamics. The main aim of this study is to estimate these three dynamic parameters of a glacier and deriving mass balance form the velocity. DEM differencing method is the most familiar recent remote sensing technique to estimate mass balance. But for the first time, we are using velocity to derive the mass balance and radar interferometry technique used to estimate the velocity. The potential of Differential Interferometric Synthetic Aperture Radar (DInSAR), a SAR technique, to measure surface movement with millimeter level accuracy is utilized to observe the glacier movement. Laminar flow law used here to derive the thickness from velocity. In this study, we used 15 years' time difference data (1999 and 2015 datasets) to check the health of the largest glacier in Himachal Pradesh, Bara Shigri glacier through dynamic parameters. Bara Shigri glacier velocity and thickness both were decreased in the 2015 year compared with 1999. We used these two thickness maps (1999 and 2015) to estimate ice thickness change and we mainly observed more thinning in the ablation region but in accumulation region, ice thickness was increased from 1999 to 2015 year. Thickness change (2015-1999) used to calculate the mass balance of Bara Shigri glacier, observed this as negative and the yearly thinning rate is 1.18 m. This further used to estimate the specific mass balance using density and area of the glacier.

10.1109/igarss39084.2020.9323152

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Drift Glacier/ice/snow

Encyclopedia of earth sciences series/Encyclopedia of earth sciences (2011)

Gulab Singh Farjana Birajdar

Glacier ice accumulation

Glacier morphology

Glacier mass balance

10.1007/978-90-481-2642-2_121

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A Novel Approach for The Retrieval Of Snow Water Equivalent Using SAR Data

IGARSS 2022 - 2022 IEEE International Geoscience and Remote Sensing Symposium (2019)

Akshay Patil Gulab Singh Christoph Rüdiger

Over the last decade, many snowpack parameter retrieval algorithms utilizing C- and X-band Synthetic Aperture Radar (SAR) data have been proposed. These algorithms are capable of retrieving snow density and snow wetness accurately, but not Snow Water Equivalent (SWE). In this paper, a novel approach of SWE estimation is proposed based on TerraSAR-X full-polarimetric data. A unique relationship between the extinction coefficient of the snowpack and snow wetness is built and verified with field data. This relationship enables the retrieval of Snow Depth (SD) and SWE using existing snow density algorithm and volume scattering power. The validation of SD and SWE is carried out with near-real-time ground truth measurements. The accuracy assessment for SD showed Mean Absolute (MAE) and Root Mean Squared (RMSE) Errors of 15 cm and 16 cm, respectively. On the other hand, SWE retrievals resulted in MAE and RMSE of 22.7 mm and 30 mm, respectively.

Snowpack

Ground truth

Water equivalent

10.1109/igarss.2019.8900565

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Retrieval of Snow Depth and Snow Water Equivalent Using Dual Polarization SAR Data

Remote Sensing (2020)

Akshay Patil Gulab Singh Christoph Rüdiger

This paper deals with the retrieval of snow depth (SD) and snow water equivalent (SWE) using dual-polarization (HH-VV) synthetic aperture radar (SAR) data. The effect of different snowpack conditions on the SD and SWE inversion accuracy was demonstrated by using three TerraSAR-X acquisitions. The algorithm is based on the relationship between the SD, the co-polar phase difference (CPD), and particle anisotropy. The Dhundi observatory in the Indian Himalaya was selected as a validation test site where a field campaign was conducted for ground truth measurements in January 2016. Using the field measured values of the snow parameters, the particle anisotropy has been optimized and provided as an input to the SD retrieval algorithm. A spatially variable snow density ( ρ s ) was used for the estimation of the SWE, and a temporal resolution of 90 m was achieved in the inversion process. When the retrieval accuracy was tested for different snowpack conditions, it was found that the proposed algorithm shows good accuracy for recrystallized dry snowpack without distinct layering and low wetness (w). The statistical indices, namely, the root mean square error (RMSE), the mean absolute difference (MAD), and percentage error (PE), were used for the accuracy assessment. The algorithm was able to retrieve SD with an average MAE and RMSE of 6.83 cm and 7.88 cm, respectively. The average MAE and RMSE values for SWE were 17.32 mm and 21.41 mm, respectively. The best case PE in the SD and the SWE retrieval were 8.22 cm and 18.85 mm, respectively.

Snowpack

Dual-polarization interferometry

10.3390/rs12071183

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Citations (33)