Geospatially extracting snow and ice cover distribution in the cold arid zone of India

Mahesh Kumar Gaur (), R. K. Goyal, M. S. Raghuvanshi, R. K. Bhatt, M. Pandian, Ashish Mishra and Suraj Ismail Sheikh
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Mahesh Kumar Gaur: ICAR-Central Arid Zone Research Institute
R. K. Goyal: ICAR-Central Arid Zone Research Institute
M. S. Raghuvanshi: ICAR-Central Arid Zone Research Institute
R. K. Bhatt: ICAR-Central Arid Zone Research Institute
M. Pandian: ICAR-Central Arid Zone Research Institute
Ashish Mishra: ICAR-Central Arid Zone Research Institute
Suraj Ismail Sheikh: ICAR-Central Arid Zone Research Institute

International Journal of System Assurance Engineering and Management, 2020, vol. 11, issue 1, No 8, 84-99

Abstract: Abstract The snow cover is greatly diverse in distribution due to landscape, slope, duration, wind, etc. However, the snow build-up and spatial patterns play an important role in the hydrological cycle. These characteristics can be determined through a number of weather station which widely represents the entire glacier and hilly region of Leh-Ladakh to support understanding of the spatial and temporal distribution of snow cover. Remotely sensed data overcome these natural and other anthropogenic limitations that hinder data collection. Snow and ice cover has a distinct spectral reflectance from the land surfaces, therefore, shortwave infrared (SWIR-1) bands were used to discriminate these. Snow was extracted by applying Normalized Difference Snow Index and Normalized Difference Snow Thermal Index. In this study, snow and ice of different classes like fresh snow, dirty snow, and blue ice from the optical images were interpreted and Landsat 8 OLI and Sentinel-2 images were used to extract both spatial and temporal aspects. Temporal changes of snow and ice in the year of 2015–2017 shows a decline in snow cover area. The accuracy assessment of supervised classification using maximum likelihood and support vector machine accuracy with the Sentinel-2 optical image was compared and it was 94.40%. The landsat-8 image depicted 80.88% accuracy of snow and ice.

Keywords: Remote sensing; NDSI; NDSTI; Extraction; Snow distribution; Ice and avalanche (search for similar items in EconPapers)
Date: 2020
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DOI: 10.1007/s13198-019-00883-w

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