ABSTRACT

Park, S.-J.; Achmad, A.R.; Syifa, M., and Lee, C.-W., 2019. Machine learning application for coastal area change detection in Gangwon province, South Korea using high-resolution satellite imagery. In: Jung, H.-S.; Lee, S.; Ryu, J.-H., and Cui, T. (eds.), Advances in Remote Sensing and Geoscience Information Systems of Coastal Environments. Journal of Coastal Research, Special Issue No. 90, pp. 228-235. Coconut Creek (Florida), ISSN 0749-0208.

The coastal area is one of an essential region for the ecosystem. Various kinds of effects that occur due to natural phenomena and human intervention make an impact on the change in the coastal area. In recent years, coastal erosion caused severe problems along the Korea coastline with 143 sites are experienced erosion throughout 2013. In this study, six beaches from 3 cities in Gangwon province, Republic of Korea are chosen to monitor its coastal area in 2016 and 2018. Those areas were chosen due to the high erosion severity grades according to Coastal Maintenance Project of the Ministry of Land, Infrastructure, and Transport. By using a support vector machine (SVM) classifier, high-resolution imagery from PlanetScope satellite was processed to generate the map that separate coastal area with others. The coastal area is successfully detected and calculated using SVM classifier. The result of the coastal area map is beneficial for the government or the authorities as mitigation and prevention plan.

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