Kanyakumari witnessing shoreline shift: A geospatial study

A few villages at the southern tip of the Indian subcontinent have shown high erosional rates (4 to 10 metres per year). This was revealed in a recent geospatial study conducted using multi-date satellite images.

Red marker showing the location of the shoreline shift (Basemap: Google Maps)

How was it detected?

Geospatial technology was utilised for this study. Satellite-based remotely sensed images, repeatedly acquired over the same area, can reveal the underlying changes in land and water regimes. This study used NASA’s Landsat-7 and ESA’s Sentinel-2 satellite datasets. Both the datasets are freely available. The study period was from 2009 to 2019.

The positional change of the boundary over time was analysed using Digital Shoreline Analysis System, computer software that works as an add-in to the ESRI ArcGIS computer application. End Point Rate statistics were calculated by dividing the distance of shoreline movement by the time elapsed between the two shorelines.

Perpendicular transacts from a baseline defined by the user (Source: USGS; Usage: Public domain)

What were the results?

High erosion rates were detected in Iraviputhanthurai, Inaiyam, Mandacaud, and Kodimunai villages of Vilavancode and Kalkulam taluks.

What are its implications?

Seventeen crore people live in the coastal regions of India. Their livelihood, in whole or part, is dependent on coastal activities. Changes in the coastal environment directly affect these activities. Coastal management is even more crucial now than ever due to alarming climate change scenarios being experienced worldwide. Studies like the one cited here help understand the priority areas for coastal management.

References

Study on focus:

Chrisben Sam, S. and Gurugnanam, B., 2022. End point rate analysis and estimation along the southwest coast of Kanyakumari, Tamil Nadu, using geospatial techniques. International Journal of Environmental Science and Technology, pp.1-14.

Other references:

Himmelstoss, E.A., Henderson, R.E., Kratzmann, M.G. and Farris, A.S., 2018. Digital shoreline analysis system (DSAS) version 5.0 user guide (No. 2018-1179). US Geological Survey.

Sutikno, S., Murakami, K., Handoyo, D.P. and Fauzi, M., 2015. Calibration of a numerical model for shoreline change prediction using satellite imagery data. Makara Journal of Technology, 19(3), p.3.