Snow Cover Area Detection using NDSI and Band Ratio Method
Priyanka Patel1, DippalIsrani2, Mrugendrasinh Rahevar3
1Priyanka Patel, U & P.U. Patel Department of Computer Engineering, CSPIT, CHARUSAT, Changa, India.
2DippalIsrani, Department of Information Technology, R.C. Technical Institute, Ahmedabad (Gujarat), India.
3Mrugendrasinh Rahevar, U & P.U. Patel Department of Computer Engineering, CSPIT, CHARUSAT, Changa.
Manuscript received on 26 April 2020 | Revised Manuscript received on 08 May 2020 | Manuscript Published on 22 May 2020 | PP: 66-70 | Volume-9 Issue-7S July 2020 | Retrieval Number: 100.1/ijitee.G10220597S20 | DOI: 10.35940/ijitee.G1022.0597S20
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© The Authors. Blue Eyes Intelligence Engineering and Sciences Publication (BEIESP). This is an open-access article under the CC-BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/)
Abstract: Glaciers are a main source of water during summer in Himalayan areas. Corresponding to the historical studies, glacier is directly affected by climate change. It is important to identify change in snow cover area (Glacier area) to identify change in glacier. Remote sensing and GIS technology are used to monitor Snow covered area. This paper focuses on Sentinel-2B data of trisul glacier which is a part of Indian Himalayas to identify glacier. These multispectral images were extracted from USGS Earth Explorer. The sentinel-2B data are processed using Semi automated Classification Plugin (SCP) of QGIS tool. Snow covered area is identified by using two automated methods: Normalized Difference Snow Index (NDSI) and Band Ratio. For NDSI reflectance of visible, shortwave band is used. For Band Ratio reflectance of near infrared, shortwave infrared band is used. It is challenging to detect snow covered area from the satellite as snow covered area and cloud area have same white colure i.e. same reflectance. In this paper, represents experiments on two methods for snow area extraction on satelliteimages.
Keywords: Glacier, Band Ratio, Geospatial, NDSI, QGIS.
Scope of the Article: Data Mining Methods, Techniques, and Tools