Monitoring the change of Turkey’s tourism city Antalya’s Konyaaltı shoreline with multi-source satellite and meteorological data

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Abstract

Uncontrolled tourism activities cause the destruction of nature and deterioration of the ecological balance. Since coastal areas are both economically and socially important, monitoring shoreline changes has become one of the important research areas. Monitoring short-term and long-term changes in coastal areas is important to prevent damages that may occur due to natural and human factors and protect the shorelines. In this study, which is an important tourist city of Antalya, Turkey, and the world, coastal changes using historical and recent satellite data have been analyzed. The focus of the study is to analyze long-term coastal change with Landsat data and the data obtained every 5 years between 1985 and 2020 and to analyze short-term change with annual Sentinel-2 data between 2015 and 2020. In the study, water areas on the coastline were determined by object-based classification and multiresolution segmentation method with the Modified Normalized Difference Water Index (MNDWI). The obtained shorelines were analyzed with the Digital Shoreline Analysis System (DSAS) tool and the changes in the coastal areas were determined statistically. Five-year and 35-year changes were compared with Linear Regression Rate (LRR) and End Point Rate (EPR) analyses. As a result of the study, when the Pearson’s r value between EPR and LRR was examined, r was found 0.916 and 0.944 for 5-year and 35-year data, respectively. Spatial resolution in satellite images is vital for shoreline detection. However, the most optimal way to detect long-term changes in the shoreline is to use Landsat data. Although the spatial resolution of Landsat data, it has been observed that the power to analyze annual variation differences is lower than Sentinel-2.

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APA

Yiğit, A. Y., Kaya, Y., & Şenol, H. İ. (2022). Monitoring the change of Turkey’s tourism city Antalya’s Konyaaltı shoreline with multi-source satellite and meteorological data. Applied Geomatics, 14(2), 223–236. https://doi.org/10.1007/s12518-022-00431-5

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