Assessment of coastal geomorphological changes using multi-temporal Satellite-Derived Bathymetry. (16th December 2020)

• Record Type: Journal Article
• Authors: Misra, Ankita; Ramakrishnan, Balaji
• Is Part Of: Continental shelf research. Volume 207(2020)
• Publication Date: 2020-12-16
• Subjects: Multi-temporal -- Geomorphology -- Nearshore bathymetry -- Support vector machines -- Landsat 8 OLI -- Optical remote sensing; Continental shelf -- Periodicals; Submarine geology -- Periodicals; 551.41
• DOI: 10.1016/j.csr.2020.104213
• Languages: English
• ISSNs: 0278-4343

Abstract

Abstract: The present study demonstrates the usability of Satellite-Derived Bathymetry (SDB) to understand the geomorphological changes that have occurred in a coastal region located along Puducherry, India, where a beach restoration project was taken up in 2017 to arrest the shoreline erosion that is prevalent due to installation of hard structures. In the study, multi-temporal bathymetry data is generated by applying a non-linear machine learning technique of Support Vector Regression (SVR) on Landsat 8 OLI satellite datasets of 30 m resolution. The empirically driven SVR is calibrated and validated using eco-sounder data collected during field measurement campaigns and fairly accurate SDBs with Root Mean Square Errors and Mean Absolute Errors ranging between 0.40-1.07 m and 0.31–0.85 m, respectively are obtained. Subsequently, the derived temporal depth maps are studied to understand the morphological changes that have occurred in this coastal stretch and the results clearly show the development of a beach, north of the pier, followed by the stabilization of the coastline. The outcomes are further validated through an independent ArcGIS- DSAS based shoreline change analysis which suggests similar trends of accretion and erosion as observed through the bathymetry change analysis. The study thus substantiates that the beach restoration step has yielded positive results between 2017 and 2018 and throws light on the significance of SDBs in coastal monitoring, modelling and assessment. Highlights: Ratio based single predictor driven SVM performs well for depth estimation. Low RMSE of <1 m is achieved in case of all Multi-date imageries used. Depth maps obtained from SVM can be used for coastal geomorphological analysis. Satellite bathymetry an effective alternative to repeated in-situ surveys.