• Journal of The Geomorphological Association of Korea
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• v.24 no.1
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• pp.51-62
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• 2017

Liao River with the largest basin area in the northeastern part of China has constructed huge floodplain along the lower reach. Especially a vast marsh was developed around estuaries and coastline near Liaodong Bay. The marsh was called as Yotaek(or Liaoze) before the modern time, which is meaningful for understanding human life since prehistorian times. By the analysis of historical documents and geomorphic data, it can be assumed that the height of Yotaek of landward boundary reached 20~30m from Heishan to Liaoyang during Han dynasty. The shoreline of 7,000 yr BP is estimated to coincide with the contour line between 20m and 30m at present. And the ancient shoreline during Christ era indicates 10m.a.s.l., which is corresponding to the seaside boundary of the Yotaek. The shoreline of Liaodong Bay was progressed seaward 30km/ka during 1000~1100 AD, while 10~40km/ka during late 19 century ~ early 20 century.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.