Quantitative analysis of shoreline changes at the Mediterranean Coast in Turkey

Abstract

This research is focused on the coastline evolution monitoring and its potential change estimation by remote sensing techniques using multi-temporal Landsat images at the southeast coasts of the Mediterranean Sea in Turkey. The study area includes the coastal zone located in the Cukurova Delta coasts. The Cukurova Delta has accreted toward the Mediterranean Sea as a result of sediment discharge and transport from Seyhan and Ceyhan rivers. These processes have caused the morphological changes (accretion or erosion) of coastline along some parts of the southeast coasts of the Mediterranean Sea. In this study, coastline changes were researched by using radiometrically and geometrically corrected multi-temporal and multi-spectral data from Landsat Multispectral Scanner dated 1972, Thematic Mapper dated 1987, and Enhanced Thematic Mapper dated 2002. In the image processing steps, mosaicing, subset, Iterative Self-Organizing Data Analysis Technique classification, band ratioing (B5/B2), edge detection, and overlay techniques were used to carry out coastline extraction and the Digital Shoreline Analysis System was used to calculate rate of coastline changes. As a result of the analysis, in some parts of the research area, remarkable coastline changes (more than 2,900 m withdrawal and -24.50 m/year erosion) were observed for a 30-year period.

Application of remote sensing and GIS for detection of long-term mangrove shoreline changes in Mui Ca Mau, Vietnam

“…This detects an optimum threshold by minimizing weighted sum of within-class variances of the foreground and background pixels and gives satisfactory results when the numbers of pixels in each class are close to each other. The Otsu method remains one of the most cited threshold methods (Sezgin and Sankur, 2004;Kuleli, 2010;Kuleli et al, 2011). The border pixels between segmented vegetation/water or land regions can be delineated as mangrove shorelines.…”

“…In order to calculate erosion/accretion rates, many statistical methods have been applied, such as end point rate (EPR), average of rates (AOR), minimum description length (MDL), jackknifing (JK), linear regression rate (LRR), reweighted least squares (RLS), weighted least squares (WLS), reweighted weighted least squares (RWLS), least absolute deviation (LAD), and weighted least absolute deviation (WLAD) (Dolan et al, 1991;Thieler et al, 1995;Crowell et al, 1997;Coyne et al, 1999;Honeycutt et al, 2001;Genz et al, 2007;Kuleli, 2010;Kuleli et al, 2011;Sheik and Chandrasekar, 2011). The two most frequently cited methods are EPR and LRR.…”

“…Remote sensing and geographical information system (GIS) techniques have been used widely to assess changes in coastal shorelines (Chen and Rau, 1998;Ghosh et al, 2001;Lin et al, 2001; Van der Wal et al, 2002;Ali, 2003; Vanderstraete et al, 2006;Genz et al, 2007;Maiti and Bhattacharya, 2009;Sesli et al, 2009;Rebelo et al, 2009;Kuleli, 2010;Kuleli et al, 2011;and Hai-Hoa et al, 2013) and the boundaries of mangrove forests and other habitats over time (Woodroffe, 1995;Solomon et al, 1997;El-Raey et al, 1999;Wilton and Saintilan, 2000;Saintilan and Wilton, 2001;Cohen and Lara, 2003;Dahdouh-Guebas et al, 2004;Fromard et al, 2004;Souza Filho et al, 2006;Gilman et al, 2007;Satyanarayana et al, 2011;Giri et al, 2011;Heumann, 2011;and Nfotabong-Atheull et al, 2013). The Digital Shoreline Analysis System (DSAS) is an extension to ArcMap and was introduced to automatically or manually generate measurements of transects and metadata based on user-specified parameters, calculating rates of shoreline (2013) changes and to provide other statistical information (Thieler et al, 2009).…”

Characteristics of coastline changes in mainland China since the early 1940s

“…While coast erosion could damage lands with economic values, such as farmlands, salt pans and aquaculture ponds, destabilize buildings with cultural values, inundate and damage the coastal wetland ecosystem, or even destroy the coastal protection structures (e.g., coastal levees) and other infrastructural facilities (Ji, 1996), artificial modification and seaward expansion of shorelines also cause unprecedented risks to eco-environmental conditions of coastal zone: industrial and domestic wastewater leading to severe seawater eutrophication (Cui et al, 2005); land subsidence due to construction of large seawalls (Feng and Niu, 2004) and wetland degradation (Guan et al, 2008); and coastal reclamation projects encroaching the breeding sites or habitats of aquatic or wetland flora and fauna (Crowell et al, 2010). Under such context, analyzing the spatiotemporal characteristics of coastline changes, identifying vulnerable coastal sections in which coastline erosion or expansion is stronger, and exploring their impacts on coastal ecosystem and environment to provide information and policy-making supports for disaster prevention and reduction efforts, and to protect residents' lives and properties, have drawn increasing worldwide attention (Roeland and Piet, 1995;Moore et al, 1999;Morton and Mckenna, 1999;Kuleli, 2010;Kish and Donoghue, 2013). …”

Comparing Fuzzy Sets and Random Sets to Model the Uncertainty of Fuzzy Shorelines

“…Instantaneous shoreline locations extracted from remote sensing images have become popular since mapping shorelines using ground survey and photogrammetry is costly. Several methods have been proposed, for example, using manual digitization [7], spectral indices extraction such as water and vegetation indices [8], active contour segmentation [6], band ratios [9], and image classification [10,11]. Most of these methods are based on hard classifications, and only a few considered soft classifications in the context of shoreline mapping [4,5,12].…”