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Garg, Vaibhav

Integrated Approach for Snowmelt Run-Off Estimation Using Temperature Index Model, Remote Sensing and GIS

Abstract Views :242 | PDF Views:84

Authors

S. P. Aggarwal ¹, Praveen K. Thakur ¹, Bhaskar R. Nikam ¹, Vaibhav Garg ¹

Affiliations
1 Water Resources Department, Indian Institute of Remote Sensing Dehradun, 4-Kalidas Road, Dehradun 248 001, IN

Source

Current Science, Vol 106, No 3 (2014), Pagination: 397-407

Abstract

The snow and glacier melt run-off is one of the most important sources of freshwater for the perennial Himalayan rivers. The water from these rivers sustains billions of people in South Asia, especially during lean season. The study has been done to integrate temporal snow cover area (SCA) and digital elevation model (DEM) derived from satellite remote sensing data with Geographic Information System (GIS) and finally into temperature index-based snowmelt run-off estimation model. The study area for snowmelt run-off estimation is part of head reach sub-basins of Ganga river, i.e. Alakhnanda and Bhagirathi river basins up to Joshimath and Uttarkashi respectively. The temporal SCA (2002-07 for Bhagirathi river and 2000, 2008 for Alakhnanda river) was derived from remote sensing data and DEM was used to find elevation zones and aspect maps. Snowmelt run-off model (SRM) is a temperature index-based snowmelt run-off simulation model, which has been used in this study for simulating snowmelt run-off. The daily hydro meteorological data from India Meteorological Department and Central Water Commission were used for estimating snowmelt. Overall accuracy of SRM for Alakhnanda river in terms of coefficient of correlation (R²) is 0.84-0.90 for years 2000 and 2008, and 0.74-0.84 in Bhagirathi river for 2002-2007.

Keywords

Remote Sensing, Snowmelt Run-Off, Snow Cover Area, Temperature Index Model.

Full Text

Satellite-Based Mapping and Monitoring of Heavy Snowfall in North Western Himalaya and its Hydrologic Consequences

Abstract Views :201 | PDF Views:79

Authors

Bhaskar R. Nikam ¹, Vaibhav Garg ¹, Prasun K. Gupta ¹, Praveen K. Thakur ¹, A. Senthil Kumar ¹, Arpit Chouksey ¹, S. P. Aggarwal ¹, Pankaj Dhote ¹, Saurabh Purohit ¹

Affiliations
1 Indian Institute of Remote Sensing, 4, Kalidas Road, Dehradun 248 001, IN

Source

Current Science, Vol 113, No 12 (2017), Pagination: 2328-2334

Abstract

Snow cover is one of the most important land surface parameters in global water and energy cycle. Large area of North West Himalaya (NWH) receives precipitation mostly in the form of snow. The major share of discharge in rivers of NWH comes from snow and glacier melt. The hydrological models, used to quantify this runoff contribution, use snow-covered area (SCA) along with hydro-meteorological data as essential inputs. In this context, information about SCA is essential for water resource management in NWH region. Regular mapping and monitoring of snow cover by traditional means is difficult due to scarce snow gauges and inaccessible terrain. Remote sensing has proven its capability of mapping and monitoring snow cover and glacier extents in these area, with high spatial and temporal resolution. In this study, 8-day snow cover products from MODIS, and 15-daily snow cover fraction product from AWiFS were used to generate long-term SCA maps (2000–2017) for entire NWH region. Further, the long term variability of 8-daily SCA and its current status has been analysed. The SCA mapped has been validated using AWiFS derived SCA. The analysis of current status (2016–17) of SCA has indicated that the maximum extent of snow cover in NWH region in last 17 years. In 2nd week of February 2017, around 67% of NWH region was snow covered. The comparison of SCA during the 1st week of March and April in 2016–17 against 2015–16 indicates 7.3% and 6.5%, increased SCA in current year. The difference in SCA during 1st week of March 2017 and 1st week of April 2017 was observed to be 14%, which indicates that the 14% SCA has contributed to the snow melt during this period. The change in snow water equivalent retrieved using SCATSAT-1 data also validates this change in snow volume.

Keywords

AWiFS, MOD10A2, North Western Himalaya, Snow Cover Area, SCATSAT-1.

Full Text

References

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Aggarwal, S. P., Thakur, P. K., Nikam, B. R. and Garg, V., Integrated approach for snowmelt run-off estimation using temperature index model, remote sensing and GIS. Curr. Sci., 2014, 106(3), 397–407.

Kulkarni, A. V., Singh, S. K., Mathur, P. and Mishra, V. D., Algorithm to monitor snow cover using AWiFS data of Resourcesat-1 for the Himalayan region. Int. J. Remote Sens., 2006, 27, 2449–2457.

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Hall, D. K., Riggs, G. A. and Roman, M. O., VIIRS Snow Cover Algorithm Theoretical Basis Document (ATBD), Version 1.0, NASA Goddard Space Flight Center, Greenbelt, Maryland, 2015, p. 38.

NSIDC, National Snow and Ice Data Centre website: Snow Cover Products; https://nsidc.org/data/MOD10A2 (accessed on 13 February 2017).

Hall, D. K. and Riggs, G. A., MODIS/Terra Snow Cover 8-Day L3 Global 500 m Grid, Version 6. Boulder, Colorado USA. NASA National Snow and Ice Data Center Distributed Active Archive Center, 2016; doi: http://dx.doi.org/10.5067/MODIS/MOD-10A2.006 (accessed on 13 February 2017).

Nikam, B. R., Garg, V., Gupta, P. K., Thakur, P. K., Aggarwal, S. P. and Kumar, A. S., A Preliminary Assessment Report on Assessment of Long-Term and Current Status (2016-17) of Snow Cover Area in North Western Himalayan River Basins using Remote Sensing. Technical Report, IIRS/WRD/Technical Report/ 2017/212, IIRS Dehradun, 2017, p. 26.

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Thakur, P. K., Aggarwal, S. P., Arun, G., Sood, S., Kumar, A. S., Snehmani and Dobhal, D. P., Estimation of snow cover area, snow physical properties and glacier classification in parts of Western Himalayas using C-band SAR data. J. Indian Soc. Remote Sens., 2016; doi:10.1007/s12524-016-0609-y.

Thakur, P. K., Garg, P. K., Aggarwal, S. P., Garg, R. D. and Snehmani, Snow cover area mapping using synthetic aperture radar in Manali watershed of Beas River in the Northwest Himalayas. J. Indian Soc. Remote Sens., 2013; doi:10.1007/s12524-012-0236-1.

Water Quality Assessment of River Ganga and Chilika Lagoon using AVIRIS-NG Hyperspectral Data

Abstract Views :215 | PDF Views:86

Authors

S. Chander ¹, Ashwin Gujrati ¹, K. Abdul Hakeem ², Vaibhav Garg ³, Annie Maria Issac ², Pankaj R. Dhote ³, Vinay Kumar ³, Arvind Sahay ¹

Affiliations
1 Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, IN
2 National Remote Sensing Centre, ISRO, Hyderabad 500 037, IN
3 Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, IN

Source

Current Science, Vol 116, No 7 (2019), Pagination: 1172-1181

Abstract

Remote sensing is a vital tool to assess water quality parameters in water bodies like rivers, lakes, estuaries and lagoons. All these fall under the category of optically complex waters (case 2), where water-leaving radiance is affected by optically active water constituents and bottom substrate. The present study estimates water quality parameters, viz. turbidity, suspended sediment concentration and chlorophyll in River Ganga in Buxar (Bihar), and Howrah (West Bengal) and Chilika lagoon (Odisha) using hyperspectral reflectance data of AVIRIS-NG. Concurrent ground-truth data of water samples were collected and simultaneous spectro-radiometer measurements were made in synchronous with the AVIRIS-NG flight over the study area. Semi-analytical simulation modelling followed by inversion and contextual image analysis-based methods were used for estimating the water quality parameters. Water turbidity maps were generated for both the study sites. Over Ganga river, water was relatively clear in Buxar (6.87–20 NTU, TSS 42–154 mg/l), while it was extremely turbid in Howrah (50–175 NTU, TSS 75–450 mg/l). In Chilika lagoon, water was more turbid in the northern sector, which may be due to the river input and resuspension from shallow bathymetry. The results suggest that the small-scale changes in turbidity due to point sources like river tributaries or sewerage discharges can be identified using hyperspectral data. The imaging spectroscopy data over water are a key source to find out potential locations of water contamination.

Keywords

Hyperspectral Data, Remote Sensing Reflectance, Semi-Analytical Algorithms, Spectroradiometer.

Full Text

References

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Characterization and Retrieval of Snow and Urban Land Cover Parameters using Hyperspectral Imaging

Abstract Views :208 | PDF Views:91

Authors

S. K. Singh ¹, Gaurav Jain ¹, Asfa Siddiqui ², Smruti Naik ¹, B. P. Rathore ¹, Vaibhav Garg ², Snehmani ³, Vinay Kumar ², I. M. Bahuguna ¹, S. A. Sharma ¹, Chander Shekhar ³, Praveen K. Thakur ², Kavach Mishra ², Pramod Kumar ², T. H. Painter ⁴, J. Dozier ⁵

Affiliations
1 Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, IN
2 Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, IN
3 Snow and Avalanche Study Establishment, Chandigarh 160 036, IN
4 University of California, Los Angeles, CA, US
5 University of California, Santa Barbara, CA, US

Source

Current Science, Vol 116, No 7 (2019), Pagination: 1182-1195

Abstract

Snow and urban land cover are important due to their role in hydrological management and utility, climate response, social aspects and economic viability, along with influencing the Earth’s environment at local, regional and global scale. Hyperspectral data enable identification, characterization and retrieval of these land-cover features based on physical and chemical properties of compositional materials. AVIRISNG hyperspectral airborne data, with synchronous ground observations using field spectroradiometer and collateral instruments, were collected over two widely varied land-cover types, viz. a relatively homogenous area covered by snow in the extreme cold environment of the Himalaya (Bhaga sub-basin, Himachal Pradesh), and a completely heterogeneous urban area of a metropolitan city (Ahmedabad, Gujarat).

AVIRIS-NG airborne data were analysed to understand the effect of terrain parameters such as slope and aspect on snow reflectance. Snow grain index using visible and near-infrared (VNIR) bands and absorption peak in the near-infrared (NIR) were used to retrieve grain size in parts of the Himalayan region. A radiative transfer model was used to understand the grain size variability and its effect on absorption peak in NIR. Continuum removal was performed for snow spectral observations obtained from airborne, modelled and field platforms to estimate band depth at 1030 nm. Grain size was observed to vary with altitude from 100 to 500 μm using AVIRIS-NG image. In the urban area, the data also separated pervious and impervious surface cover using spectral unmixing technique, identified several urban features over multispectral data such as buildings with red tiled roofs, metallic surfaces and tarpaulin sheets using the material spectral profiles. Two single-frame superresolution methods namely sparse regression and natural prior (SRP), and gradient profile prior (GPP) were applied on AVIRIS-NG data for the mixed environment around Kankaria Lake in the city of Ahmedabad, which revealed that SRP method was better than GPP, and affirmed by eight indices. Preliminary analysis of AVIRIS-NG imaging over snow-covered areas and densely populated cities indicated utility of future spaceborne hyperspectral missions, particularly for hydrological and climatological applications in such diverse environments.

Keywords

AVIRIS-NG, Hyperspectral Imaging, Snow Reflectance, Super-Resolution Method, Terrain Parameters, Urban Land Cover.

Full Text

References

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Performance Assessment of a Bathymetry System in Open Inland Waterbodies

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Authors

Satyendra S. Raghuwanshi ¹, Vaibhav Garg ², Bhaskar R. Nikam ², G. Varaprasad Babu ¹, S. Muralikrishnan ¹

Affiliations
1 National Remote Sensing Centre, Indian Space Research Organisation, Hyderabad 500 037, IN
2 Indian Institute of Remote Sensing, Indian Space Research Organisation, Dehradun 248 001, IN

Source

Current Science, Vol 124, No 5 (2023), Pagination: 585-590

Abstract

Bathymetry of an open waterbody can be estimated remotely using airborne and space-borne sensors with wide coverage. However, unmanned aerial vehicle (UAV)- borne bathymetric systems are current trends for applications with limited depth subjected to the quality of water. Estimation of accurate bathymetry using surface-based sensors is essential for validating the remote sensing-derived results. To cater to the requirements of the in situ measurement system, especially for supporting the airborne (aircraft/UAVs) remote sensing-based bathymetry systems, a customized and compact, immersion-type bathymetry system using single-frequency (typ. 500 kHz) transducer was developed in-house at the National Remote Sensing Centre (NRSC), ISRO, Hyderabad. In the present study, we assess the performance of the developed system in the field against physical measurements and a reference acoustic transducer for shallow and deep inland open waterbodies. Performance testing was carried out in the Asan Lake, a shallow waterbody, with a depth of up to 4 m and in the Tehri reservoir for deep bathymetry with a depth of more than 150 m. The results show that the estimated TVU for the developed system during shallow bathymetry assessment was 0.272 m which complies with the IHO order 1. The observed performance of the developed system was consistent with the system specifications, which advocate its utility for hydrology and water resource management applications along with its intended use to support remote sensing-based bathymetric systems

Keywords

Acoustic Transducer, Bathymetry, Echo Sounder, Waterbodies, Water-Depth Measurement.

Full Text

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