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 - Zorlu, Ozcan; Kusak, Lutfiye (Frontiers in Marine Science, 2025)
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.