• Journal of the Korean Association of Geographic Information Studies
• /
• v.8 no.1
• /
• pp.75-87
• /
• 2005

This study aims at predicting and mapping of the landslide susceptibility in the Geumho river watershed using GIS and Remote Sensing techniques. We constructed the spatial database of affecting factors such as slope angle, slope aspect, lithology, landuse, and vegetation index (NDVI) at a $30m{\times}30m$ resolution. The landslide susceptibility of the study area was predicted through overlay analysis and adding up estimation matrix, and the predicted map of landslide susceptibility with six categories (stable, very low, low, moderate, high, very high) was constructed. As the results, it showed that the very high susceptibility zones made up approximately 0.3% of the total study area, and these zones were mainly distributed in the forest area with the high slope angle and low vegetation index.

• Korean Journal of Environment and Ecology
• /
• v.22 no.2
• /
• pp.119-127
• /
• 2008

This research managed to establish the space information on incidence factors of landslide targeting Bukhansan National Park and aimed at suggesting a basic data for disaster prevention of a landslide for the period to come in Bukhansan National Park through drawing up the map indicating vulnerability to a landslide and ecological risks by the use of overlay analysis and adding-up estimation matrix analysis methods. This research selected slope angle, slope aspect, slope length, drainage, vegetation index(NDVI) and land use as an assessment factor of a landslide and constructed the spatial database at a level of '$30m\times30m$' resolution. The analysis result was that there existed high vulnerability to a landslide almost all over Uidong and Dobong valleys. As for ecological risks, Dobong valley, Yongueocheon valley, Jeongneung valley and Pyeongchang valley were analyzed to be higher, so it is judged that the impact on a landslide risk should be also considered in time of establishing a management plan for these districts for the time to come.

• Journal of Environmental Science International
• /
• v.29 no.12
• /
• pp.1185-1198
• /
• 2020

This study aimed to suggest a framework for biotope classification and evaluation based on habitat values to rationally establish management areas of national parks. The factors and indicators related to the biotope classification and evaluation were established based on integration from those of previous studies. The decision tree evaluation process was applied to the classification and evaluation of the biotope type level. The evaluation of the biotope group level was carried out to determine the weight and the AUEM (Adding Up Estimation Matrix) was applied for the final grades. As a result, the biotope type of Seolaksan National Park was classified into 43 types and Odaesan National Park was classified into 41 types. Bukhansan National Park, which is located in a metropolitan city, was classified into 49 types. In terms of biotope evaluation, grade III had a ratio of 50.6%, the highest in Seolaksan national park. The ratios of grade I and grade II, which have great ecological-value, were respectively 12.0% and 36.5%. Grade II was 48.2%, the highest ratio in Odaesan National Park. The ratios of grades I and II were 10.8% and 37.9%, respectively. Grade III was 54.8%, the highest ratio in Bukhansan national park, and the ratios of grade I and grade II, which have great ecological-value were, respectively, 11.4% and 25.7%. The biotope values of major national parks were evaluated according to the type focusing on the actual vegetation. This framework can be considered for application to the spatial management of other types of protected areas.

• Journal of the Korean Association of Geographic Information Studies
• /
• v.11 no.4
• /
• pp.101-111
• /
• 2008

To provide more natural elements in the harsh urban environment, 'planting trees as urban forests' has been emphasized as having an important role, and trees are expected to be as functional as the trees under more natural conditions in rural areas, and provide people with benefits. To do this, urban forest policies needed a better idea of planting methods and management of trees through the theory of landscape ecology, and also the feedback system according to the evaluation and assessment of urban forests. In this case study, a new principle and assessment indices for the evaluation are applied for the 4 urban forests in two Korean metropolitan cities, Daejeon and Ulsan. The evaluation of Korean urban forest-function as biotope and the assessment for the classification of biotope diversity types are carried out. The AUEM(Adding Up Estimation Matrix) is applied for the analysis of urban forests. Unlikely previous researches on the other Korean metropolitan cities, the size of urban forest has less influence on the vegetation diversity. The most frequent biotope grade is the 3rd grade at Namseon park and Mt. Hamwol, while Mt. Bomun and Mt. Yeompo show the 4th grade. The grades of forest-function as biotope are from 3rd to 5th in which lower than average in forest-function grades. This means that the 4 sites are still not-matured forests and less-functional forests as the urban biotope.

• Korean Journal of Environment and Ecology
• /
• v.24 no.2
• /
• pp.178-185
• /
• 2010

The purpose of this study is to verify the effectiveness of the biotope model in applying and developing Korean urban forests. We found that there are 17 biotope assessment indicators, including forest layer structure, site conditions, ratio of broad-leaved trees, species richness, etc. In terms of correlation analysis between indicators, the stand ages and the period of space formation have the highest relativity(coefficient 0.684). On the other hand, indicators that have negative relativity are layer structure and risk, with a coefficient of -0.412. Ten models were developed for the multiple regression analysis. 10 variables(site conditions(X2), ratio of broad-leaved trees(X3) and so forth except layer structure(X1), species richness(X4)) were found to have a 95% significance level The results from comparing the regression model and adding-up estimation matrix, the most accurate one was Model 3, which has a 91.7% out of the 10 models. However more monitoring will be needed to improve the accuracy of models for the Korean urban forests in future.