• Journal of Environmental Impact Assessment
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• v.24 no.3
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• pp.233-243
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• 2015

The ecosystem fragmentation due to transportation infrastructure causes road-kill phenomenon. When making policy for reducing road-kill it is important to select target-species in order to enhance its efficiency. However, many corridors installed in Korea have been raised issues about their effectiveness due to lack of considerations such as target-species selection, site selection, and management, etc. In this study, we are to grasp relationship between road-kill area and habitat patches and suggest that spatial location of habitat patches should be considered as one of the important factors when making policy for reducing road-kill. Habitat patches were presumed from overlying suitability index that chosen by literature review and road-kill hotspot was determined by Getis-Ord $G_i^*$ analysis. Afterwards, we performed a correlation analysis between $G_i$ Z-score and the distance from habitat patches to the roads. As a result, there is a negative correlation between two variables, It affects the $G_i^*$ Z-score going up if the habitat patches and the roads become closer.

• Journal of Forest and Environmental Science
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• v.35 no.4
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• pp.281-284
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• 2019

Efficient management of road-kill data is difficult at national scale when there are many organizations that are in charge of different road types. Here, we described the first case to integrate road-kill data through Korea Road-kill Observation System (KROS) by the Korean government. The system was launched in June 2018 to approximately 3,000 road menders. During 15 months, 5,812 road-kill observations were registered on KROS including mammals, birds, reptiles and amphibians. Of them, about 86 % was occupied by five species (Hydropotes inermis, Felis catus, Capreolus pygargus, Nyctereutes procyonoides, and Canis lupus familiaris) listed in number of occurrences. The observed road-kill frequency rapidly increased until April 2019 and peaked on May 2019. However, as the system is just starting, the results from KROS cannot be treated as the exact representation of road-kill trend in the country. Although the efficient method to manage national road-kill statistic is arranged, still there are some limitations to overcome to make the system stable.

• Journal of the Korean Society of Environmental Restoration Technology
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• v.26 no.5
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• pp.19-32
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• 2023

The fragmentation of habitats resulting from human activities leads to the isolation of wildlife and it also causes wildlife-vehicle collisions (i.e. Road-kill). In that sense, it is important to predict potential habitats of specific wildlife that causes wildlife-vehicle collisions by considering geographic, environmental and transportation variables. Road-kill, especially by large mammals, threatens human safety as well as financial losses. Therefore, we conducted this study on roe deer (Capreolus pygargus tianschanicus), a large mammal that causes frequently Road-kill in Jeju Island. So, to predict potential wildlife habitats by considering geographic, environmental, and transportation variables for a specific species this study was conducted to identify high-priority restoration sites with both characteristics of potential habitats and road-kill hotspot. we identified high-priority restoration sites that is likely to be potential habitats, and also identified the known location of a Road-kill records. For this purpose, first, we defined the environmental variables and collect the occurrence records of roe deer. After that, the potential habitat map was generated by using Random Forest model. Second, to analyze roadkill hotspots, a kernel density estimation was used to generate a hotspot map. Third, to define high-priority restoration sites, each map was normalized and overlaid. As a result, three northern regions roads and two southern regions roads of Jeju Island were defined as high-priority restoration sites. Regarding Random Forest modeling, in the case of environmental variables, The importace was found to be a lot in the order of distance from the Oreum, elevation, distance from forest edge(outside) and distance from waterbody. The AUC(Area under the curve) value, which means discrimination capacity, was found to be 0.973 and support the statistical accuracy of prediction result. As a result of predicting the habitat of C. pygargus, it was found to be mainly distributed in forests, agricultural lands, and grasslands, indicating that it supported the results of previous studies.