• Korean Journal of Environment and Ecology
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• 제22권3호
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• pp.309-319
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• 2008

Roads are an indicator of anthropogenic activity causing ecosystem disturbances and often lead to habitat fragmentation, habitat loss, and habitat isolation. The Hallasan National Park(153.4$km^2$) on Jeju Island being distinguished for its unique geology, topography, and biota has also been designated as a core area of UNESCO Man and the Biosphere(MAB) Reserve. Although the high conservation value of this park has contributed to a rapid growth of tourists and road construction, landscape changes due to roads have not been examined yet. We used GIS systems to examine the fragmentation pattern caused by roads, in relation to its zonation, elevation, and vegetation. When a buffer was applied to roads(112m width for paved roads and 60m width for both legal and illegal trails), the park consisted of 100 fragments. The ten fragments generated after applying buffer to only paved roads and legal trails ranged from $0.002km^2$ to $38.2km^2$ with a mean of $14.2km^2$, and about 7% of both nature conservation zone and nature environment zone of the park were edge. Fragments in both east and west ends of the park and around the summit exhibited relatively high shape indices with means of 5.19(for 100 fragments) and 7.22(for 10 fragments). All five legal trails are connected to the pit crater of the mountain and vegetation changed from broadleaf forests and conifer forests to grasslands with elevation, consequently resulting in dramatic fragment size reduction in grasslands at high elevation, in particular above 1,400m, where endemic and alpine plants are abundant. These results show that in Hallasan National Park the risks of habitat deterioration and habitat loss due to fragmentation may be more severe in the nature conservation zone dominated by Baengnokdam than in the nature environment zone. Therefore, current road networks of the park appear to fall short of the goal of the national park for ecosystem conservation and protection. Considering that the entire Hallasan National Park also serves as a MAB core area, conservation efforts should focus, first of all, on park rezoning and road management to mitigate habitat fragmentation.

• Journal of the Korean Society of Groundwater Environment
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• 제5권3호
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• pp.129-140
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• 1998

In this study two sites were selected to investigate groundwater contamination and spatial relationship between pollution level and its source. One is the Asan area, agricultural district where pollution sources are scattered. The other is the Gurogu area of Seoul city, industrial district where industrial complex and residential areas are located. Groundwater samples collected from these districts were analysis for chemical constituents. The attribute value files of the chemical constituents of groundwater and the spatial layers have been constructed and the pollution properties have been investigated to find out spatial relationships between the groundwater constituents and pollution sources using CIS. Relatively high contents of Si and HCO$_3$ in groundwater from the Asan area reflect the effect of water-rock interaction, whereas high contents of Cl, NO$_3$, SO$_4$and Ca in groundwater from the Gurogu area are due to the pollution of various sources. Pollution over the critical level of Korean Dinking Water Standard has been investigated from 15 sampling sites out of 40 in the Asan area, and 33 sampling sites out of 51 in the Gurogu area. There is pollution of NO$_3$, Cl, Fe, Mn, SO$_4$and Zn in groundwater from the Gurogu area, and that of NO$_3$, SO$_4$and Zn in groundwater from the Asan area. Principal pollution in both areas is NO$_3$contamination. Deep groundwater from the Asan area is not contaminated with NO$_3$except for one site and most of shallow groundwater near the potential point sources such as factory and stock farm is contaminated seriously. Groundwater from the Gurogu area has been already polluted seriously considering the fact of contamination of deep groundwater. This study reports a spatial relationship between the pollution level and pollution source using GIS.