Development of Automatic Shoreline Detection Algorithm Using KOMPSAT Satellite Data for Monitoring of Coastal Erosion in the East Sea

Abstract

Coastal erosion ratio in South Korea was about 44% of the national coast in 2014, and the coast around the East Sea showed very serious coastal erosion in the state 76%. Time-series aerial photographs and satellite images were used for monitoring of coastal erosion. Many preview studies on shoreline detection have used the visual interpretation methods which depend on the person and require a lot of time and money. This study aims to suggest an automatic shoreline detection algorithm for more accurate coastal erosion monitoring in the coastal region of the East Sea where belongs to Case 1 (open ocean) waters. This study uses one KOMPSAT-3A data provided high spatial resolutions from 0.55m to 5.5m and three KOMPSAT-2 data obtained from 2012 to 2015. Shoreline detection algorithm is based on wetness and texture information obtained from satellite multispectral data. Dry sand, wet sand and water were classified by using threshold values of wetness and variance texture data. Geospatial method is also applied in this study to increase the shoreline detection accuracy. Although this study uses temporal KOMPSAT data, the same threshold values of wetness (more than -1.5 and less than 1.0) and variance texture (more than 0.1) are determined in this automatic shoreline detection algorithm. The shoreline in 2012 shows high sea level compared with other years. For more accurate detection of shoreline, further study will try to rtoring of coastal erosion. Many preview studies on shoreline detection have used the visual interpretation methods which depend on the person and require a lot of time and money. This study aims to suggest an automatic shoreline detection algorithm for more accurate coastal erosion monitoring in the coastal region of the East Sea where belongs to Case 1 (open ocean) waters. This study uses one KOMPSAT-3A data provided high spatial resolutions from 0.55m to 5.5m and three KOMPSAT-2 data obtained from 2012 to 2015. Shoreline detection algorithm is based on wetness and texture information obtained from satellite multispectral data. Dry sand, wet sand and water were classified by using threshold values of wetness and variance texture data. Geospatial method is also applied in this study to increase the shoreline detection accuracy. Although this study uses temporal KOMPSAT data, the same threshold values of wetness (more than -1.5 and less than 1.0) and variance texture (more than 0.1) are determined in this automatic shoreline detection algorithm. The shoreline in 2012 shows high sea level compared with other years. For more accurate detection of shoreline, further study will try to r