• 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.

• Proceedings of the Korean Geotechical Society Conference
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• 2010.03a
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• pp.1302-1308
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• 2010

This study the change of the safety factor before and after the reinforcement were compared by performing the parameter research based on the limit equilibrium analysis regarding the same cross section after carrying out the safety factor before the reinforcement on the virtual section in order to observe the change of the safety factor of the slop reinforced with the slope planting reinforced earth, and the variation of the safety factor according to the increase of the length of the reinforcement materials and the change of the slope height was analyzed. As the result, the reinforcement effect was insignificant at no more than 0.6 of L/H, the reinforcement length ratio when the reinforcement length was increased, as the increase of the safety factor was slow comparing with the non-reinforced slope. At 3.0m of the slope height, reinforcement on the slope is not necessary, and at 3.0m to 5.0m of the slope height, the inclination was not influencing at no less than 0.6 of L/H. At 5.0m to 9.0m of the slope height, the safety factor was mostly secured on the slope at 0.8 of L/H and the over-reinforced slope appeared at no less than 1.0 of L/H. Also, the safety factor increased as the slope height increases and the slope gets steeper till 0.8 of L/H, but the slope steepness affects more on the increase of the safety factor than the reinforcement material, as the reinforcing force by the reinforcement material became steady.

• Journal of the Korean Society of Hazard Mitigation
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• v.9 no.6
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• pp.119-124
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• 2009

In this study, the characteristics of estimating methodology for RUSLE factors such as soil erodibility factor, slope length-steepness factor, and cover management factor were reviewed and then the relative error according to each methodology was analyzed. RUSLE was applied to experimental watershed for 42 storm events and their results were compared with measured sediment yield to examine the applicability of RUSLE. As a result, this paper found that it should be necessary to consider vegetation effect for forest application of RUSLE as cover management was the most sensitive factor. Also, soil erodbility factor was calculated from data of soil series by National Academy of Agricultural Science caused sediment yield to be overestimated because there were big differences between the soil series and on-site soil texture. The 22.7% of maximum relative error was shown according to selecting the rain energy equation. In addition, it will be necessary to verify the RUSLE factors with more data in order to improve their accuracy.

• 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.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.19 no.1
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• pp.27-37
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• 2001

Soil erosion by outflow of water or rainfall has caused many environmental problems as declining agricultural productivity, damaging pasture and preventing flow of water. As the interest in environment is increasing lately, soil erosion is considered as a serious problem, whereas the systematic regulation and analysis for that have not established yet. This research shows the method of extracting factor entered model which expects soil erosion by GSIS. There are several erosion model such as ANSWER, WEPP, RUSLE. The research used RUSLE erosion model which could expect general soil erosion connected easily with GSIS data. RUSLE's input factors are composed of rainfall runoff factor(R). soil erodibility factor(K), slope length factor(L), slope steepness factor(S), cover management factor(C) and support practice factor(P). The general equation used to extract L, S factor on the RUSLE to be oriented for agricultural area has some limitation to apply whole watershed. So, on this study we used a revised empirical equation applicable to the watershed by grid on the GSIS. Also, we analyzed RUSLE factors by watershed being analyzed with watershed extraction algorithm. Then we could calculate the minimum, maximum. mean and standard deviation of RUSLE factors by watershed.

• Proceedings of the National Institute of Ecology of the Republic of Korea
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• v.4 no.4
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• pp.159-176
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• 2023

The conservation of the raccoon dog (Nyctereutes procyonoides) in South Korea requires the protection and preservation of natural habitats while additionally ensuring coexistence with human activities. Applying habitat map modeling techniques provides information regarding the distributional patterns of raccoon dogs and assists in the development of future conservation strategies. The purpose of this study is to generate potential habitat distribution maps for the raccoon dog in South Korea using geospatial technology-based models. These models include the frequency ratio (FR) as a bivariate statistical approach, the group method of data handling (GMDH) as a machine learning algorithm, and convolutional neural network (CNN) and long short-term memory (LSTM) as deep learning algorithms. Moreover, the imperialist competitive algorithm (ICA) is used to fine-tune the hyperparameters of the machine learning and deep learning models. Moreover, there are 14 habitat characteristics used for developing the models: elevation, slope, valley depth, topographic wetness index, terrain roughness index, slope height, surface area, slope length and steepness factor (LS factor), normalized difference vegetation index, normalized difference water index, distance to drainage, distance to roads, drainage density, and morphometric features. The accuracy of prediction is evaluated using the area under the receiver operating characteristic curve. The results indicate comparable performances of all models. However, the CNN demonstrates superior capacity for prediction, achieving accuracies of 76.3% and 75.7% for the training and validation processes, respectively. The maps of potential habitat distribution are generated for five different levels of potentiality: very low, low, moderate, high, and very high.

• Spatial Information Research
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• v.14 no.4 s.39
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• pp.363-377
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• 2006

Mean annual soil loss was calculated and critical soil erosion areas were identified for the Congaree River Basin in South Carolina, USA using the Revised Universal Soil Loss Equation (RUSLE) model. In the RUSLE model, the mean annual soil loss (A) can be calculated by multiplying rainfall-runoff erosivity (R), soil erodibility (K), slope length and steepness (LS), crop-management (C), and support practice (P) factors. The critical soil erosion areas can be identified as the areas with soil loss amounts (A) greater than the soil loss tolerance (T) factor More than 10% of the total area was identified as a critical soil erosion area. Among seven subwatersheds within the Congaree River Basin, the urban areas of the Congaree Creek and the Gills Creek subwatersheds as well as the agricultural area of the Cedar Creek subwatershed appeared to be exposed to the risk of severe soil loss. As a prototype model for examining future effect of human and/or nature-induced changes on soil erosion, the RUSLE model customized for the area was embedded into ESRI ArcGIS ArcMap 9.0 using Visual Basic for Applications. Using the embedded model, users can modify C, LS, and P-factor values for each subwatershed by changing conditions such as land cover, canopy type, ground cover type, slope, type of agriculture, and agricultural practice types. The result mean annual soil loss and critical soil erosion areas can be compared to the ones with existing conditions and used for further soil loss management for the area.