Coastal regions, especially river deltas are highly resourceful and hence densely populated; but these extremely low-lying lands are vulnerable to rising sea levels due to global warming threatening the life and property in these regions. Recent IPCC (2013) predictions of 26-82cm global sea level rise are now considered conservative as subsequent investigations such as by Met Office, UK indicated a vertical rise of about 190cm, which would displace 10% of the world’s population living within 10 meters above the sea level. Therefore, predictive models showing the hazard line are necessary for efficient coastal zone management. Remote sensing and GIS technologies form the mainstay of such predictive models on coastal retreat and inundation to future sea-level rise. This study is an attempt to estimate the varying trends along the Krishna–Godavari (K–G) delta region. Detailed maps showing various coastal landforms in the K-G delta region were prepared using the IRS-P6 LISS 3 images. The rate of shoreline shift during a 31-year period along different sectors of the 330km long K-G delta coast was estimated using Landsat-2 and IRS-P6 LISS 3 images between 1977 and 2008. With reference to a selected baseline from along an inland position, End Point Rate (EPR), Shoreline Change Envelope (SCE) and Net Shoreline Movement (NSM) were calculated, using a GIS–based Digital Shoreline Analysis System (DSAS). The results showed that the shoreline migrated landward up to a maximum distance of 3.13km resulting in a net loss of about 42.10km2 area during this 31-year period. Further, considering the nature of landforms and EPR, the future hazard line is predicted for the area, which also indicated a net erosion of about 57.68km2 along the K-G delta coast by 2050 AD.
Akhil Kallepalli, Nageswara Rao Kakani, and David B. James, "Remote sensing and GIS analysis for demarcation of coastal hazard line along the highly eroding Krishna-Godavari delta front," Proc. SPIE 10005, Earth Resources and Environmental Remote Sensing/GIS Applications VII, 1000507 (Presented at SPIE Remote Sensing: September 27, 2016; Published: 18 October 2016); https://doi.org/10.1117/12.2239485.
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