EventsThe 6th International Electronic Conference on Remote Sensing
Published
This submission belongs to the session S6. Remote Sensing of the Environment of the event The 6th International Electronic Conference on Remote Sensing
Published date
14 Oct, 2026
Academic Editor
author-avatarMagaly Koch
Citation
Rishitha K. P., SHORELINE CHANGE ANALYSIS OF THE CHENNAI–MAHABALIPURAM–MARAKKANAM COAST USING REMOTE SENSING, GIS AND MACHINE LEARNING TECHNIQUES, in Proceedings of The 6th International Electronic Conference on Remote Sensing, 19 October–21 October 2026, MDPI: Basel, Switzerland
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SHORELINE CHANGE ANALYSIS OF THE CHENNAI–MAHABALIPURAM–MARAKKANAM COAST USING REMOTE SENSING, GIS AND MACHINE LEARNING TECHNIQUES

1. Department of Geoinformatics Engineering, College of Engineering Guindy (CEG), Anna University, Chennai – 600025, Tamil Nadu, India
Abstract

The shoreline is continuously modified by coastal processes such as waves, tides, currents, sediment transport, sea-level fluctuations, and anthropogenic activities and represents the boundary between land and sea, and it helps in monitoring coastal dynamics and supporting sustainable coastal management. The present study investigates shoreline changes along the Mahabalipuram coast from Odiyur Lake and Marakkanam in the south to the northern coastal region near Chennai using multi-temporal Landsat 5 TM and Landsat 8/9 OLI datasets acquired in 1990, 2010, and 2025 over a 35-year period. Shorelines were extracted using the Modified Normalized Difference Water Index (MNDWI). The extracted shorelines were analyzed with the help of transect-based analysis using the QSCAT (QGIS Shoreline Change Analysis Tool) plugin to quantify erosion and accretion trends through parameters such as Net Shoreline Movement (NSM), End Point Rate (EPR), Shoreline Change Envelope (SCE), Linear Regression Rate (LRR), and Weighted Linear Regression (WLR). The NSM was classified into Very High Erosion (< -25,000 m), High Erosion (-25,000 to -15,000 m), Moderate Erosion (-15,000 to -5,000 m), and Low Erosion/Stable (> -5,000 m) and the result was useful in predicting the spatial variability in shoreline dynamics. Net Shoreline Movement (NSM) values ranged from −33,930 m to −2,653 m, with the highest shoreline retreat observed in the northern coastal sector. Hotspot analysis was subsequently carried out to identify erosion-prone sectors and the result showed that 57.1% of the coastline falls under severe erosion conditions, 28.6% under moderate erosion, and only 14.3% under stable conditions, and future shoreline positions for 2030 and 2040 were predicted using WLR (Weighted Linear Regression) trends using Python. The results demonstrate that shoreline change maps; erosion hotspot zones; statistical assessments, which are integrated with GIS; and remote sensing help in coastal protection and management strategies for the Chennai coastline.

Keywords
Shoreline Change Analysis
MNDWI
QSCAT
DSAS
NSM,EPR,SCE,LRR,WLR
Hotspot Analysis.
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