Variation Characteristics of Haeundae Beach using Video Image Haeundae beach;Video image;Ground control point;Beach nourishment;Erosion; In this study, we analyzed the real time video image obtained from the video monitoring system to grasp the shoreline, beach width, and area change of Haeundae beach. The video monitoring techniques enabled continuous monitoring for a long period at a much lower cost than general survey methods. It was possible to grasp quantitative beach variation characteristics of Haeundae beach through image acquisition, rectification, and image processing of video images. According to the monitoring results, the erosion rate of Haeundae beach in spring and summer was -19.8% in 2014 and -6.7% in 2015. The erosion rate in 2016 was -6.4%, which showed that the erosion rate in spring and summer continued to decrease. In particular, the influence of the erosion at the time of typhoon CHABA was revealed to be smaller than in the past. It can be concluded that these variations were due to beach width expansion by beach nourishment and the installation of submerged breakwaters.

Kang, T.S.; Oh, H.M.; Lee, E.I., and Jeong, K.Y., 2018. Disaster Vulnerability Assessment in Coastal Areas of Korea. In: Shim, J.-S.; Chun, I., and Lim, H.S. (eds.), Proceedings from the International Coastal Symposium (ICS) 2018 (Busan, Republic of Korea). Journal of Coastal Research, Special Issue No. 85, pp. 886–890. Coconut Creek (Florida), ISSN 0749-0208. In this study, the vulnerability to disaster in the coastal areas of Korea was assessed for the safe utilization and value enhancement of coastal areas. The framework of the coastal disaster vulnerability assessment has been adopted from the concept of climate change vulnerability of the IPCC (2001). Coastal Disaster Impact Index(CDII) is defined as a function of exposure and sensitivity, and the Coastal Disaster Vulnerability Index (CDVI) includes those as well as adaptive capacity. Indicators of exposure consisted of typhoon, surge, wave, tide, SLR and coastal erosion. Indicators of sensitivity consisted of human, property and geography. ...

Coastal erosion is caused by various factors, such as harbor development along coastal areas and climate change. Erosion has been accelerated recently due to sea level rises, increased occurrence of swells, and higher-power storm waves. Proper understanding of the complex coastal erosion process is vital to prepare measures when they are needed. Monitoring systems have been widely established around a high portion of the Korean coastline, supported by several levels of governments, but valid analysis of the collected data and the following preparation of measures have not been highly effective yet. In this paper, we use a drone to obtain bed material images, and an analysis system to predict the representative grain size of beach sands from the images based on artificial intelligence (AI) analysis. The predicted grain sizes are verified via field samplings. Field bed material samples for the particle size analysis are collected during two seasons, while a drone takes photo images and the exact positions are simultaneously measured at Jangsa beach, Republic of Korea. The learning and testing results of the AI technology are considered satisfactory. Finally, they are used to diagnose the overall stability of Jangsa beach. A beach diagnostic grade is proposed here, which reflects the topography of a beach and the distribution of sediments on the beach. The developed beach diagnostic grade could be used as an indicator of any beach stability on the east coast of the Republic of Korea. When the diagnostic grade changes rapidly at a beach, it is required to undergo thorough investigation to understand the reason and foresee the future of the beach conditions, if we want the beach to function as well as before.