K V S R PRASAD
Articles written in Journal of Earth System Science
Volume 132 All articles Published: 28 March 2023 Article ID 0052 Letter
Extraction of shorelines using satellite imagery is an effective method because customary digitization is a longand hectic process. This study focuses on extracting and detecting shoreline changes from Landsat-8 imageries ofthe Visakhapatnam–Kakinada coast along the east coast of India using an object-based approach. An object-based approach for the automatic detection of coastline from Landsat imagery using the Feature Extraction Workflow by Maximum Likelihood is implemented by the maximum classification method (MLC). The resulting vector polyline is smoothened for every 100 m using ArcGIS software. Delineation of multi-temporal satellite images was performed by visual interpretation from 2014 to 2019 to detect the shoreline changes. Different available techniques and methods are employed to observe shoreline changes. In addition to this, the shoreline information simulated by satellite remote sensing is in fair agreement with RTK GPS observations. The observed and remote sensing shoreline changes help to identify the areas of accretion and eroding zones overthe long term. During this study, erosion and deposition changes were observed along RK beach, Rushikonda beach, Uppada beach, and Kakinada beach. The spatial variation rates were calculated using the statistical methods of the Digital Shoreline Analysis System (DSAS) during specific periods. The maximum observed shoreline accretion and erosion rates at Kakinada are 5.3 and –4.35 m/year indicates slight accretion. The maximum observed accretion and erosion rates at Uppada beach are 3.8 and –6.78 m/year, respectively indicatingerosion. Similarly, at RK Beach the maximum observed shoreline accretion and erosion rates are 3.68 and –3.68 m/year, respectively indicating the beach is in a stable state. At Rushikonda beach, the maximum observed shoreline accretion and erosion rates are 2.24 and –3.04 m/year, respectively indicating erosion.
Volume 132, 2023
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