• Journal of Korean Society for Geospatial Information Science
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• v.17 no.4
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• pp.75-80
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• 2009

The Beacon Mounds play a important role in defence and communication extending from the period of the Three States to the period of Chosun. About the research of beacon mounds have focused on investigation in old literature. This research analyzed geographic factors such as altitude, cross section, distance and visible distance affect in selecting location of beacon mounds. And it was presumed how each beacon mound geographic characteristics was considered in selecting location of beacon mounds. As a result, it is presumed that communicating among beacon mounds and watching the coast were affected by geographic characteristics and selecting location of beacon mounds was considered by several geographic factors.

• The Journal of Engineering Geology
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• v.15 no.2 s.42
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• pp.185-199
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• 2005

Properties of sliding materials are dependent on the lithology because debris is the product of rock weathering processes. In order to characterize transportation behavior of debris dependent of debris types, this study selected 26 debris flows over three areas composed with different rock weathering types and topographic conditions. Analyses of lithology, weathering, and topographic characteristics were performed by detailed field survey. Based on the field survey data, transportation behavior of debris was studied at the aspect of the relationship of grain size and volume of debris as well as topographic conditions. According to the study results, change of slope angle is very influential factor on runout distance of debris among the topographic factors. Because the sliding velocity and the energy of debris are frequently changed and more irregular on an undulating slope, the unout distance of debris is larger than that of an uniformly dipping slope. Runout distance of debris is also influenced by volume and grain size of debris. Volume of debris in the gabbro is four or five times larger than that of the granite area because it is controlled by the lithology. Considered with grain size distribution, runout distance of debris is longer in the gabbro area which is composed with irregular grain size bearing large corestones than that in the medium grained granite area.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.507-514
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• 2018

TSI (TPI-Slope Index) which is the combination of TPI (Topographic Position Index) and slope was newly proposed for landslide and applied to a landslide susceptibility model. To do this, we first compared the TPIs with various scale factors and found that TPI350 was the best fit for the study area. TPI350 was combined with slope to create TSI. TSI was evaluated using logistic regression. The evaluation showed that TSI can be used as a landslide factor. Then a logistic regression model was developed to assess the landslide susceptibility by adding other topographic factors, geological factors, and forestial factors. For this, landslide-related factors that can be extracted from DEM (Digital Elevation Model), soil map, and forest type map were collected. We checked these factors and excluded those that were highly correlated with other factors or not significant. After these processes, 8 factors of TSI, elevation, slope length, slope aspect, effective soil depth, tree age, tree density, and tree type were selected to be entered into the regression analysis as independent variables. Three models through three variable selection methods of forward selection, backward elimination, and enter method were built and evaluated. Selected variables in the three models were slightly different, but in common, effective soil depth, tree density, and TSI was most significant.

• Journal of The Korean Society of Agricultural Engineers
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• v.61 no.6
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• pp.55-65
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• 2019

Universal Soil Loss Equation (USLE) is to estimate potential soil loss and has benefit in use with its simplicity. The equation is composed of five factors, one of the factors is the slope length and steepness factor (LS factor) that is for topographic property of fields to estimate potential soil loss. Since the USLE was developed, many equations to compute LS was suggested with field measurement. Nowadays the factor is often computed in GIS software with digital elevation model, however it was reported that the factor is very sensitive to the resolution of digital elevation model. In addition, the digital elevation model of high resolution less than 3 meter is required in small field application, however these inputs are not associate with the empirical models' backgrounds since the empirical models were derived in 22.1 meter field measurements. In the study, four equation to compute LS factor and two approaches to determine slope length and steepness were examined, and correction factor was suggested to provide reasonable precision in LS estimations. The correction factor is computed with field area and cell size of digital elevation model, thus the correction factor can be adapted in any USLE-based models using LS factor at field level.

• Journal of Korean Society of Rural Planning
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• v.12 no.1 s.30
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• pp.37-48
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• 2006

The objectives of this study were to develop an evaluation method of regional vulnerability to agricultural drought and to classify the vulnerability patterns. In order to test the method, 24 city or county areas of Gyeonggi-do were chose. First, statistic data and digital maps referred for agricultural drought were defined, and the input data of 31 items were set up from 5 categories: land use factor, water resource factor, climate factor, topographic and soil factor, and agricultural production foundation factor. Second, for simplification of the factors, principal component analysis was carried out, and eventually 4 principal components which explain about 80.8% of total variance were extracted. Each of the principal components was explained into the vulnerability components of scale factor, geographical factor, weather factor and agricultural production foundation factor. Next, DVIP (Drought Vulnerability Index for Paddy), was calculated using factor scores from principal components. Last, by means of statistical cluster analysis on the DVIP, the study area was classified as 5 patterns from A to E. The cluster A corresponds to the area where the agricultural industry is insignificant and the agricultural foundation is little equipped, and the cluster B includes typical agricultural areas where the cultivation areas are large but irrigation facilities are still insufficient. As for the cluster C, the corresponding areas are vulnerable to the climate change, and the D cluster applies to the area with extensive forests and high elevation farmlands. The last cluster I indicates the areas where the farmlands are small but most of them are irrigated as much.

• Journal of the Korean Institute of Landscape Architecture
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• v.26 no.3
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• pp.162-177
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• 1998

Conentionally, LS factor for the USLE suggested by Wischmeier has been computed manually on topographic maps based on one dimensional approach. But outcomes of the equation could be severely affected by the convergence and divergence of surface runoff at complex terrains. Thus the objective of this research are to develop a method to automatically compute LS factor based on the multiple flow algorithm, and to test the accuracy of this method by comparing outcomes of this method to previous measurements or estimations of soil erosion. The program for the automatic calculation of LS factor was developed by utilizing Fox Pro 4.5, and outcomes of the program is designed to input to IDRISI. The accuracy test of LS factor was carried out by comparing the actual measurements of soil loss at two test sites in and around of Suwon. The calculated volume of soil erosion at Buju mountain, Mokpo, was also compared to the outcome of a previous research based on the LS factor calculated by the conventional onedimensional approach. The outcomes of this research are as follows. First, the computed L based on the multiple flow algorithm for concae slopes are greater than those of convex slopes,. Second, the estimated soil loss based on this method at the test site in Mokpo is much greater than the outcomes based on the conventional one-dimensional approach. It can e concluded that the application of this automatic calculation method of LS factor can improve the accuracy of USLE and facilitate soil erosion prevention methods.

• Korean Journal of Remote Sensing
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• v.19 no.2
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• pp.117-133
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• 2003

A field survey and Landsat ETM+ image acquisition carried out simultaneously. Using these data, we attempted to establish relationships between tidal flat environmental factors and reflectance observed by ETM+, and to set up a new critical grain size useful for optical remote sensing. Although the grain size of 4 $\Phi$ has been conventionally used as a critical size by sedimentologists, the correlation with optical reflectance was very low. Instead, the grain size of 2 $\Phi$ showed a relatively high correlation coefficient, 0.699, with ETM+ band 4, except near tidal channels in upper tidal flat. We concluded that the grain size of 2 $\Phi$ would be better to use for a critical grain size in Gomso Bay. The grain size also correlated well with moisture content having a correlation coefficient of -0.811 when the 2 $\Phi$ criterion was used. The results of factor analysis showed moisture content was more important parameter than topographic relief, and they were different from German tidal flats in which topographic relief was the prior factor This can be explained by finer grain composition of the Gomso bay tidal flat. In short, moisture content and topography as well as grain size should be considered in tidal flat remote sensing.

• Journal of Korean Society of Rural Planning
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• v.11 no.2 s.27
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• pp.9-19
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• 2005

The purpose of this study is to analyze the potential soil loss and hazard zone by the Revised Universal Soil Loss Equation(RUSLE) for preservation and management of land resources which is the base of ecosystem, and to grasp the relationship between RUSLE factors in the Nakdong River Basin. All thematic maps used in RUSLE are constructed through GIS and spatial analysis method derived from digital topographic maps, detailed soil maps, land-cover maps, and mean annual precipitation of 30 years collected respectively from National Geographic Information Institute, National Institute of Agricultural Science and Technology, and Ministry of Environment. The slope length of LS-factor that takes much times by the study area's wideness was calculated automatically through AML(Arc Macro Language) program developed by Van Remortel et al.(2001, 2003). The results are as follows; First, according to the soil loss estimation by the RUSLE, it shows that approximately 82% of the study area have relatively lower possibility of soil loss which is the 1 ton/ha in annual soil loss. While, 9.4% ($2,228km^2$) needed intensive and continuous management for soil loss. Because the amount of their annual soil loss was greater than 10 ton/ha that is optimum level suggested by Morgan(1995). For these areas, the author believe that a new approach which can minimize environmental impacts from soil loss through improvement of cultivation process and buffer forest zone should be applied. Second, according to the relationship between the RUSLE factors, topographical(LS-factor) and cover management(C-factor) conditions have a lot of influence on soil loss in case of the Nakdong River Basin. However, because of RUSLE factor's influence that affect to soil loss might be different based on the variety of spatial hierarchy and extent, it is necessary to analyze and evaluate factor's relationship in terms of spatial hierarchy and extent through field observations and further studies.

• Journal of Environmental Science International
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• v.29 no.1
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• pp.1-13
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• 2020

Recently, a heavy rainfall with high spatial variation occurred frequently in the Korean Peninsula. The meteorological event that occurred in Busan on 3 May 2016 is characterized by heavy rain in a limited area. In order to clarify the reason of large spatial variation associated with mountain height and location of low level jet, several numerical experiments were carried out using the dynamic meteorological Weather Research and Forecasting (WRF) model. In this case study, the raised topography of Mount Geumjeong increased a barrier effect and air uplifting due to topographic forcing on the windward side. As a result, wind speed reduced and precipitation increased. In contrast, on the downwind side, the wind speed was slightly faster and since the total amount of water vapor is limited, the precipitation on the downwind side reduced. Numerical experiments on shifting the location of the lower jet demonstrated that if the lower jet is close to the mountain, its core becomes higher due to the effect of friction. Additionally, the water vapor convergence around the mountain increased and eventually the precipitation also increased in the area near the mountain. Hence, the location information of the lower jet is an important factor for accurately predicting precipitation.

• Ecology and Resilient Infrastructure
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• v.4 no.2
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• pp.115-121
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• 2017

This study estimated potential natural vegetation in Gayasan National Park through the occurrence probability distribution by using geographic information system (GIS). in Gayasan National Park. Correlation and factor analysis were analyzed to estimate probability distribution. The presence of the Gaya National Park Vegetation survey results showed that 128 communities were distributed. The analyzed relationship between actual vegetation and distribution factors such as elevation, aspect, slope, topographic index, annual mean temperature, warmth index and potential evapotranspiration in Gayasan national park. The probability distribution of potential natural vegetation communities at least 0.3 odds were the advent of Pinus densiflora communities with the highest 55.80%, Quercus mongolica community is 44.05%, 0.09% is Quercus acutissima communities, Quercus variabilis communities are found to be 0.06%. If you want to limit the factors that affect the distribution of vegetation by factors presented in this study, the potential natural vegetation of the Gaya National Park was expected to appear in Quercus mongolica community (43.1%) and Pinus densiflora communities (56.9%).