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Shekhar, Chander
- Snow and Glacier Investigations Using Hyperspectral Data in the Himalaya
Abstract Views :265 |
PDF Views:119
Authors
Affiliations
1 Snow and Avalanche Study Establishment, Him Parisar, Sector-37A, Chandigarh 160 036, IN
2 Space Applications Centre, ISRO, Ahmedabad 380 015, IN
1 Snow and Avalanche Study Establishment, Him Parisar, Sector-37A, Chandigarh 160 036, IN
2 Space Applications Centre, ISRO, Ahmedabad 380 015, IN
Source
Current Science, Vol 108, No 5 (2015), Pagination: 892-902Abstract
This article presents highlights of the research work done in hyperspectral remote sensing in the Himalayan cryosphere. Hyperspectral radiometric investigations conducted at different field locations of NW Himalaya and cold laboratory are discussed. Spectral signatures were collected for varying snow grain size, contamination, liquid water content, vegetation/soilmixed snow, glacier ice, moraines and other ambient objects. The important wavelengths for snow applications are found to be 440, 550, 590, 660, 860, 1050, 1240 and 1650 nm. Further, the retrieval of snow parameters such as grain size, spectral albedo and snow contamination using imaging data at the above wavelength channels is discussed. Wavelengths 550, 1240 and 1660 nm are found to be useful for discriminating different glacier features. Limitations in hyperspectral remote sensing such as availability of imaging data, rugged topography and further research issues such as multi-sensor mapping and data fusion, multiangle measurements, 3D adjacency effect and improved algorithms for quantitative retrieval of contaminants are identified.Keywords
Albedo, Hyperspectral, Hyperion, Reflectance, Snow Cover Monitoring, Spectroradiometer.Full Text
- Characterization and Retrieval of Snow and Urban Land Cover Parameters using Hyperspectral Imaging
Abstract Views :212 |
PDF Views:94
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
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: 1182-1195Abstract
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
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