Floodplain Mapping through Support Vector Machine and Optical/Infrared Images from Landsat 8 OLI/TIRS Sensors: Case Study from Varanasi

Ipsita Nandi, Prashant K. Srivastava and Kavita Shah ()
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Ipsita Nandi: Banaras Hindu University
Prashant K. Srivastava: Banaras Hindu University
Kavita Shah: Banaras Hindu University

Water Resources Management: An International Journal, Published for the European Water Resources Association (EWRA), 2017, vol. 31, issue 4, No 5, 1157-1171

Abstract: Abstract Floods are among the most destructive natural disasters causing huge loss to life and property. Any flood management strategy requires floodplain mapping through discrimination of the flood prone areas. The city of Varanasi or Benaras is believed to be the oldest continuously inhabited city of the world. This study aims to develop tools for mapping and discrimination of floodplain of river Ganga at Varanasi. During 2014 floods, the flooded areas were extracted through Normalized Difference Water Index (NDWI) and by Modified NDWI (MNDWI) using the NIR and SWIR bands separately from that of the Landsat 8 satellite imagery. The inundated areas were then identified through Support Vector Machines (SVMs) classification. The results reveal that the MNDWI images provide a better result for flood discrimination than the NDWI images. Ground based measurements for floodplain distance varied between 11 ± 5 m at Janki ghat (bank) and 80 ± 5 m at Asi ghat. The validation between measured and SVMs derived values indicate a strong positive correlation of 0.88 and a low value of Root Mean Square Error (RMSE) of 12.62. The t-test is suggestive of no significant difference between the observed and SVMs values at 95% confidence level, indicating a satisfactory performance of the SVMs for floodplain mapping using Landsat 8 imagery. Therefore, the methodology proposed in this study provides a novel and robust way for floodplain mapping and has potential applications in disaster management and mitigation in the flood affected regions.

Keywords: Floodplain; Indo-Gangetic Basin; Natural disaster; Satellite remote sensing; Support vector machines (search for similar items in EconPapers)
Date: 2017
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DOI: 10.1007/s11269-017-1568-y

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