Quantifying shoreline dynamics in the Indian Sundarban delta with Google Earth Engine (GEE)-based automatic extraction approach

Shoreline detection and estimation of changes is a well-established concept in the field of coastal zone management. Recent technological advancements in the form of machine learning (ML) have transformed shoreline detection methodologies. However, due to the constantly changing nature of coasts, identification of the boundary between land and ocean has become an intricate process. In this particular investigation, long-term changes in shoreline along the lower segment of the Indian Sundarbans Delta (ISD) are estimated by employing Landsat sensor's optical imageries spanning 28 years from 1995 to 2023. A fully automated approach involving support vector machine (SVM) classification algorithm with a 99.5% accuracy and zero-crossing edge detection algorithm for shoreline extraction from optical imagery has been proposed. The implementation stage of shoreline extraction utilizes Google Earth Engine's cloud platform. In contrast, subsequent analysis to calculate shoreline changes conforms to the Digital Shoreline Analysis System (DSAS v.5), an extension of ArcGIS Desktop's functionality. This research article examines the long-term shoreline changes in the Indian Sundarban Delta. The study uses three statistical measures: end point rate (EPR), linear regression rate (LRR), and net shoreline movement (NSM). EPR analysis shows significant erosion on Kanak Island and Bhangaduni Island up to 88.21 m. LRR statistics reveal negative trends on the eastern side. NSM analysis highlights maximum accretion in the northwestern part of the delta. This study offers valuable insights into dynamic coastal processes in the area.