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


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

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

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Authors

S. K. Singh
Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, India
Gaurav Jain
Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, India
Asfa Siddiqui
Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
Smruti Naik
Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, India
B. P. Rathore
Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, India
Vaibhav Garg
Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
Snehmani
Snow and Avalanche Study Establishment, Chandigarh 160 036, India
Vinay Kumar
Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
I. M. Bahuguna
Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, India
S. A. Sharma
Space Applications Centre, Indian Space Research Organisation (ISRO), Ahmedabad 380 015, India
Chander Shekhar
Snow and Avalanche Study Establishment, Chandigarh 160 036, India
Praveen K. Thakur
Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
Kavach Mishra
Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
Pramod Kumar
Indian Institute of Remote Sensing, ISRO, Dehradun 248 001, India
T. H. Painter
University of California, Los Angeles, CA, United States
J. Dozier
University of California, Santa Barbara, CA, United States

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.

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DOI: https://doi.org/10.18520/cs%2Fv116%2Fi7%2F1182-1195