• The Korean Journal of Quaternary Research
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• v.21 no.1
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• pp.27-36
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• 2007

The severe damage of cultural heritages induced by natural hazards like heavy rain has been dramatically increased since 1990. The number of the repair works of stone heritage of 2005 was six times as many as those of 1986 year. Especially the ratio of the repair works of Gyeongsang Province and Jeolla Province stood 63% of those of all over the country. Since 1990, the typhoons usually struck the southern part of Korea and went northward. The heavy damage of stone heritages in two provinces was caused by them. We made a preliminary survey the stone heritages that exposed to the natural hazards on the basis of repair works of them and a field survey. The analysis results indicate that the natural hazards such as landslide and soil disaster of the stone heritages related to a sloping surface stood 58% of all kind of natural hazards. The reasons are caused by the 59 % of all the stone heritages distributed in a sloping surface resulted in natural hazards like landslide and soil disaster. The bases of stone heritages can be easily eroded by the surface water with high energy induced by heavy rainfall. Most of the stone heritages like Maebul were engraved on a natural rock wall(outcrop). But some of them engraved on rolling stones are very vulnerable in a change of a base condition caused by erosion and ground subsidence and they can be tilted or fell down. The distribution of the stone heritages vulnerable in natural hazard is related to that of the rainfall distribution compounded five typhoons after 1990. Most of them are included in level two on the rainfall distribution map except those of Taean peninsula and some of Gyeonggi Province. They seem to be rather related to the rainfall distribution of the Typhoon Olga.

• Korean Journal of Remote Sensing
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• v.36 no.5_3
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• pp.1109-1123
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• 2020

In South Korea with forest as a major land cover class (over 60% of the country), many wildfires occur every year. Wildfires weaken the shear strength of the soil, forming a layer of soil that is vulnerable to landslides. It is important to identify the severity of a wildfire as well as the burned area to sustainably manage the forest. Although satellite remote sensing has been widely used to map wildfire severity, it is often difficult to determine the severity using only the temporal change of satellite-derived indices such as Normalized Difference Vegetation Index (NDVI) and Normalized Burn Ratio (NBR). In this study, we proposed an approach for determining wildfire severity based on machine learning through the synergistic use of Sentinel-1A Synthetic Aperture Radar-C data and Sentinel-2A Multi Spectral Instrument data. Three wildfire cases-Samcheok in May 2017, Gangreung·Donghae in April 2019, and Gosung·Sokcho in April 2019-were used for developing wildfire severity mapping models with three machine learning algorithms (i.e., Random Forest, Logistic Regression, and Support Vector Machine). The results showed that the random forest model yielded the best performance, resulting in an overall accuracy of 82.3%. The cross-site validation to examine the spatiotemporal transferability of the machine learning models showed that the models were highly sensitive to temporal differences between the training and validation sites, especially in the early growing season. This implies that a more robust model with high spatiotemporal transferability can be developed when more wildfire cases with different seasons and areas are added in the future.