• Korean Journal of Environment and Ecology
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• v.36 no.6
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• pp.553-565
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• 2022

Awareness of the ecological value and importance of protected areas has increased as climate change accelerates, and there is a need for research on ecosystem services provided by nature. The natural park, which is a representative protected area in Korea, has a system of national parks, provincial parks, and county parks. National parks are managed systematically by the Korea National Park Service, but local governments manage provincial parks and county parks. There may be the same hierarchical differences in naturalness (habitat quality) depending on the hierarchy of the natural parks, but it has not been verified. To identify differences, we examined 22 mountain-type natural parks using habitat quality using the INVEST model developed by Stanford University. The analysis of the habitat quality, regardless of the type and area of the natural park, showed that it was higher in the order of Taebaeksan National Park (0.89), Juwangsan National Park (0.87), Woongseokbong County Park (0.86), and Gayasan National Park (0.85). The larger the area, the higher the value of habitat quality. A comparison of natural parks with similar areas showed that the habitat quality of national parks was higher than that of provincial parks and parks. On the other hand, the average habitat quality of county parks was 0.83±0.02, which was 0.05 higher than that of provincial parks at 0.78±0.03. Furthermore, the higher the proportion of forest areas within the natural park, the higher the habitat quality. The results confirmed that the naturalness of natural parks was independent of their hierarchy and that there are differences in naturalness depending on land use, land coverage, and park management.

• Journal of The Korean Society of Grassland and Forage Science
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• v.43 no.1
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• pp.11-21
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• 2023

This study was conducted to estimate the damage of Whole Crop Corn (WCC; Zea Mays L.) according to abnormal climate using machine learning as the Representative Concentration Pathway (RCP) 4.5 and present the damage through mapping. The collected WCC data was 3,232. The climate data was collected from the Korea Meteorological Administration's meteorological data open portal. The machine learning model used DeepCrossing. The damage was calculated using climate data from the automated synoptic observing system (ASOS, 95 sites) by machine learning. The calculation of damage was the difference between the dry matter yield (DMY)_normal and DMY_abnormal. The normal climate was set as the 40-year of climate data according to the year of WCC data (1978-2017). The level of abnormal climate by temperature and precipitation was set as RCP 4.5 standard. The DMY_normal ranged from 13,845-19,347 kg/ha. The damage of WCC which was differed depending on the region and level of abnormal climate where abnormal temperature and precipitation occurred. The damage of abnormal temperature in 2050 and 2100 ranged from -263 to 360 and -1,023 to 92 kg/ha, respectively. The damage of abnormal precipitation in 2050 and 2100 was ranged from -17 to 2 and -12 to 2 kg/ha, respectively. The maximum damage was 360 kg/ha that the abnormal temperature in 2050. As the average monthly temperature increases, the DMY of WCC tends to increase. The damage calculated through the RCP 4.5 standard was presented as a mapping using QGIS. Although this study applied the scenario in which greenhouse gas reduction was carried out, additional research needs to be conducted applying an RCP scenario in which greenhouse gas reduction is not performed.