Digital shoreline analysis system-based change detection along the highly eroding Krishna–Godavari delta front

Kallepalli, A. , Kakani, N. R., James, D. B. and Richardson, M. A. (2017) Digital shoreline analysis system-based change detection along the highly eroding Krishna–Godavari delta front. Journal of Applied Remote Sensing, 11(3), 036018. (doi: 10.1117/1.JRS.11.036018)

Abstract

Coastal regions are highly vulnerable to rising sea levels due to global warming. Previous Intergovernmental Panel on Climate Change (2013) predictions of 26 to 82 cm global sea level rise are now considered conservative. Subsequent investigations predict much higher levels which would displace 10% of the world’s population living less than 10 m above sea level. Remote sensing and GIS technologies form the mainstay of models on coastal retreat and inundation to future sea-level rise. This study estimates the varying trends along the Krishna–Godavari (K–G) delta region. The rate of shoreline shift along the 330-km long K–G delta coast was estimated using satellite images between 1977 and 2008. With reference to a selected baseline from along an inland position, end point rate and net shoreline movement were calculated using a GIS-based digital shoreline analysis system. The results indicated a net loss of about 42.1  km2 area during this 31-year period, which is in agreement with previous literature. Considering the nature of landforms and EPR, the future hazard line (or coastline) is predicted for the area; the predication indicates a net erosion of about 57.6  km2 along the K–G delta coast by 2050 AD.

• Item Type: Articles
• Status: Published
• Refereed: Yes
• Glasgow Author(s) Enlighten ID: Kallepalli, Dr Akhil
• Authors: Kallepalli, A., Kakani, N. R., James, D. B., and Richardson, M. A.
• College/School: College of Science and Engineering > School of Physics and Astronomy
• Journal Name: Journal of Applied Remote Sensing
• Publisher: Society of Photo-optical Instrumentation Engineers
• ISSN: 1931-3195
• ISSN (Online): 1931-3195
• Published Online: 24 August 2017
• Copyright Holders: Copyright © 2017 SPIE
• First Published: First published in Journal of Applied Remote Sensing 11(3): 036018
• Publisher Policy: Reproduced in accordance with the publisher copyright policy