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Coastline change rate estimation on the southern coastal districts of Tamil Nadu, India using the multi temporal Google Earth images and GIS based statistical approach

  • Authors (legacy)
    Corresponding: Kumar
    Co-authors: Daswin Ebenezer M,
    Kumar M

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  • gnest_04482_published.pdf
  • Paper ID
    gnest_04482
  • Paper status
    Published
  • Date paper accepted
  • Date paper online
Graphical abstract
Abstract

Coastal erosion is the process of wearing away material from the coastal profile due to an imbalance in the supply and export of material from a particular section of the coast which cause property damage, and the loss of land. This study investigates the rate of erosion and accretion along the southern coast of Tamil Nadu which was exposed to a lot of tourism and development activities. The shorelines were delineated for the years 2000,2005,2010,2015 and 2020 on the high-resolution multi-temporal satellite images in the Google Earth Pro software by visual interpretation. The long-term and short-term changes were studied by three statistical parameters functionalized in DSAS v 5.0 tool in ArcGIS 10.6 software such as endpoint rate, net shoreline movement and linear regression rate (Cenci et al., 2018). The positions of the shoreline in 2030 and 2040 are forecasted using the Kalman filter-based forecast tool in DSAS and the changes were analysed. During the study period, the average rate of changes in Kanyakumari is -1.26 m/year, Tirunelveli is -1.01 m/year and Tuticorin is -1.09 m/year. the transects showed significant erosion along 19.54% of Kanyakumari, 3.79% of Tirunelveli, and 17.81% of Tuticorin coasts, and significant accretion is observed in 1.31% of Kanyakumari, 1.07% of Tirunelveli and 3.02% of Tuticorin coasts. The outcome of shows that erosion is prevailing on the Kanyakumari coast and erosion is prevailing in Tuticorin.

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Daswin, E. (2023) “Coastline change rate estimation on the southern coastal districts of Tamil Nadu, India using the multi temporal Google Earth images and GIS based statistical approach”, Global NEST Journal, 25(4). Available at: https://doi.org/10.30955/gnj.004482.