SHORELINE EXTRACTION USING TIME SERIES OF SENTINEL-2 SATELLITE IMAGES BY GOOGLE EARTH ENGINE PLATFORM
Keywords: Shoreline, Machine Learning, Sentinel-2, Google Earth Engine, SVM, Random Forest, DSAS
Abstract. In recent decades, global warming and sea level rise, population growth, and intensification of human activities, have directly affected the coasts and as such, their monitoring for the accretion and retreat are among the issues that are considered by the coastal countries. This study, compares two supervised classification algorithms for classifying Sentinel-2 satellite imagery for shoreline extraction. Median monthly images from 2020/01 to 2021/12 are taken and classified by Random Forest (RF) and Support Vector Machine (SVM) algorithms. By validating the maps, it is found that the RF algorithm has better accuracy and as such by averaging the accuracy of all maps, the overall accuracy (OA) values of 97.18% and the kappa coefficient (KC) of 0.97, and the mean overall accuracy and kappa coefficient of maps from SVM algorithm of 85.15% and 0.79, respectively, is obtained. After extracting the shorelines, the Digital Shoreline Analysis System (DSAS) is used to calculate the displacement rate. By calculating the Linear Regression Rate (LRR) factor, it is found that in 91% of transects (166 transects) we see the shoreline retreat to land. In 54% of them, the average rate of the retreat is 5.42 meters per year and in only 9% (16 transects) we see the accretion towards the sea.