Comparative Analysis of Pixel and Object Based Classification Approach for Rapid Landslide Delineation with the Aid of Open Source Tools in Garhwal Himalaya
Authors
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School of Environmental Sciences, Jawaharlal Nehru University, New Delhi – 110 067
Harshita Asthana -
School of Environmental Sciences, Jawaharlal Nehru University, New Delhi – 110 067
Chandrashekhar A. Vishwakarma
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School of Environmental Sciences, Jawaharlal Nehru University, New Delhi – 110 067
Priyadarshini Singh
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School of Environmental Sciences, Jawaharlal Nehru University, New Delhi – 110 067
Pardeep Kumar
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School of Environmental Sciences, Jawaharlal Nehru University, New Delhi – 110 067
Vikas Rena
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School of Environmental Sciences, Jawaharlal Nehru University, New Delhi – 110 067
Saumitra Mukherjee
DOI:
https://doi.org/10.1007/s12594-020-1505-1Keywords:
No Keywords.Abstract
Landslides are the natural geomorphic processes that are essential for the landscape development. Loss of tremendous amount of life, natural resources as well as property has made this phenomenon a natural disaster. Number of researches have been done to understand this phenomenon and how to overcome this calamity with the aid of upcoming modern information services and technological advancements. Despite all these, there are still certain undocumented slope failure events due to inaccessibility and lack of a proper database. With the advancement of satellite remote sensing and geographical information system (GIS), it has certainly become easier to monitor and prepare landslide database especially in rough and rugged terrain of the Himalaya for delineation of risk zones. Free availability of high-resolution images and an open source efficient software have certainly been proved advantageous for this purpose. This paper aims at quick and accurate landslide inventory mapping, using high resolution Sentinel 2 data along with normalized difference vegetation index (NDVI), over which unsupervised and object-based image analysis (OBIA) was done to extract landslide features in an efficient manner. Both these processes were achieved with the aid of open source SAGA (System for Automated Geoscientific Analyses) software. The performances of these classifications were analyzed for their quantitative reliability in extracting the landslide features in two different areas (test area 1 and 2). In this, the object based image classification outstands, with the overall accuracy percentage difference of 17.11% higher than pixel-based classification approach in test area 1 and about 21% higher in test area 2.
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