95% of researchers rate our articles as excellent or good
Learn more about the work of our research integrity team to safeguard the quality of each article we publish.
Find out more
ORIGINAL RESEARCH article
Front. Mar. Sci.
Sec. Coastal Ocean Processes
Volume 12 - 2025 | doi: 10.3389/fmars.2025.1457016
This article is part of the Research Topic Innovative Approaches to Coastal Zone Monitoring and Geodata Management View all articles
An Assessment of the Long-term Change of the Mersin West Coastline Using Digital Shoreline Analysis System and Detection of Pattern Similarity Using Fuzzy C-Means Clustering
Provisionally accepted- 1 Mersin Provincial Directorate of National Education Research and Development Unit, Mersin, Mersin, Türkiye
- 2 Department of Geomatics Engineering, Faculty of Engineering, Mersin University, Mersin, Türkiye
The study focused on analyzing shoreline changes along the western beaches of Mersin Province, located on Turkey's Mediterranean coast. Landsat satellite imagery from 1985 to 2022 was used to detect long-term coastal alterations. The Google Earth Engine (GEE) platform facilitated data acquisition, classification, and edge detection. A Support Vector Machine (SVM) classification algorithm was applied to distinguish land from water. To enhance classification accuracy, additional indices-Normalized Difference Water Index (NDWI), Modified NDWI (MNDWI), and Normalized Difference Moisture Index (NDMI)-were incorporated alongside Landsat spectral bands. The Canny edge detection algorithm was employed to delineate shorelines from the classified images. Resulting shoreline positions were analyzed using the DSAS, an open-source ArcGIS extension, to quantify erosion and accretion. Key shoreline change metrics-Net Shoreline Movement (NSM), Shoreline Change Envelope (SCE), End Point Rate (EPR), and Linear Regression Rate (LRR)were derived from DSAS outputs. Over the 38-year study period, maximum shoreline advancement reached 588.59 meters, while maximum retreat was -130.63 meters. The highest erosion rates were -3.53 m/year (EPR) and -2.8 m/year (LRR), whereas the most pronounced accretion rates were 15.91 m/year (EPR) and 15.47 m/year (LRR). To identify spatial patterns in shoreline change, the Fuzzy C-Means (FCM) clustering algorithm was applied using the NSM, SCE, EPR, and LRR metrics. The resulting clusters were then interpreted in relation to land cover data provided by the European Space Agency (ESA) WorldCover dataset.
Keywords: Shoreline, GEE, SVM, DSAS, fuzzy c-means
Received: 29 Jun 2024; Accepted: 01 Apr 2025.
Copyright: © 2025 Zorlu and KUŞAK. This is an open-access article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.
* Correspondence:
Lütfiye KUŞAK, Department of Geomatics Engineering, Faculty of Engineering, Mersin University, Mersin, Türkiye
Disclaimer: All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article or claim that may be made by its manufacturer is not guaranteed or endorsed by the publisher.