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http://krishi.icar.gov.in/jspui/handle/123456789/34305
Title: | Geospatially extracting snow and ice cover distribution in the cold arid zone of India. |
Other Titles: | Not Available |
Authors: | Gaur, M. K., Goyal, R.K., Raghuvanshi, M. S., Bhatt, R. K., Pandian, M. and Sheikh, A. |
ICAR Data Use Licennce: | http://krishi.icar.gov.in/PDF/ICAR_Data_Use_Licence.pdf |
Author's Affiliated institute: | National Bureau of Soil Survey and Land Use Planing |
Published/ Complete Date: | 2019-09-11 |
Project Code: | Not Available |
Keywords: | Snow cover, slope, shortwave infrared, Normalized Difference Snow Index, Normalized Difference Snow Thermal Index.. |
Publisher: | Not Available |
Citation: | Gaur, M. K., Goyal, R.K., Raghuvanshi, M. S., Bhatt, R. K., Pandian, M. and Sheikh, A. (2019). Geospatially extracting snow and ice cover distribution in the cold arid zone of India. International Journal of System Assurance Engineering and Management (2019). https://doi.org/10.1007/s13198-019-00883-w (issue 13198). |
Series/Report no.: | Not Available; |
Abstract/Description: | 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. |
Description: | Not Available |
ISSN: | Not Available |
Type(s) of content: | Research Paper |
Sponsors: | Not Available |
Language: | English |
Name of Journal: | International Journal of System Assurance Engineering and Management |
NAAS Rating: | Not Available |
Volume No.: | Not Available |
Page Number: | Not Available |
Name of the Division/Regional Station: | Not Available |
Source, DOI or any other URL: | https://doi.org/10.1007/s13198-019-00883-w |
URI: | http://krishi.icar.gov.in/jspui/handle/123456789/34305 |
Appears in Collections: | NRM-NBSSLUP-Publication |
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