• Korean Journal of Soil Science and Fertilizer
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• v.45 no.6
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• pp.890-896
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• 2012

This study was conducted to evaluate soil erosion risk with a standard unit watershed in the upper Upper Nakdong River Basin according to soil characteristics and landuse using the spatial soil erosion map. The study area is $3,605.6km^2$, which consists of 2 subbasins, 35 standard unit watersheds (Andong basin 18, Imha basin 17). As a evaluation of soil erosion potential using the spatial soil erosion map, total annual soil loss and soil loss per area estimated $2,013{\times}10^3Mg\;yr^{-1}$ (Andong basin 979, Imha basin 1,034) and $6.1Mg\;ha^{-1}yr^{-1}$ (Andong basin 6.0, Imha basin 5.2), respectively. 54.2% of soil loss was originated from Arable land (Andong basin 49.0%, Imha basin 59.0%), and the area of regions, graded as higher "Moderate" for annual soil loss, was $201.7km^2$ (Andong basin 84.9, Imha basin 116.8). Average soil loss per area of unit watersheds by classification according to soil dominant parent material types ranked "Sedimentary rock group" > "Mixed group" > "Metamorphic rock group" > "Igneous rock group". In conclusion, the results of this study inform that the classification of soil parent material type would be effective for soil erosion analysis and interpretation in the Upper Nakdong River Basin.

• Journal of the Korean Institute of Landscape Architecture
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• v.13 no.1
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• pp.43-66
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• 1985

Korea needs proper and rational public policies on conservation and use of forest land and other natural resources because of the accelerating expansion of national land developments in recent years. Unfortunately, there is no systematic planning system to support the needs. Generally, forest land use planning needs suitability analysis based on efficient land classification system. The goal of this study was to classify a forest land using multivariate satistical methods. A case study was carried out in winter of 1983 on a mountainous area higher than 100m above sea level located at Mt. Kwanak in Anyang -city, Kyung-gi-do (province). The study area was 19.80 km$^2$wide and was divided into 1, 383 Operational Taxonomic Units (OTU's) by a 120m$\times$120m grid. Fourteen descriptors were identified and quantified for each OTU from existing national land data : elevation, slope, aspect, terrain form, geologic material, surface soil permeability, topsoil type, depth of the solum, soil acidity, forest cover type, stand size class, stand age class, stand density class, and simple forest soil capability class. For this study, a FORTRAN IV program was written for input and output map data, and the computer statistics packages, SPSS and BMD, were used to perform the multivariate statistical analysis. Fourteen variables were analyzed to investigate the characteristics of their fire quench distribution and to estimate the correlation coefficients among them. Principal component analysis was executed to find the dimensions of forest land characteristics, and factor scores were used for proper samples of OTU throughout the study area. In order to develop the classes of forest land classification based on 102 surrogates, cluster and discriminant analyses of principal descriptor variable matrix were undertaken. Results obtained through a series of multivariate statistical analyses were as follows ; 1) Principal component analysis was proved to be a useful tool for data selection and identification of principal descriptor variables which represented the characteristics of forest land and facilitated the selection of samples.

• Journal of Korean Tunnelling and Underground Space Association
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• v.21 no.2
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• pp.227-242
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• 2019

This paper presents a method to predict ground types ahead of a tunnel face utilizing operational data of the earth pressure-balanced (EPB) shield tunnel boring machine (TBM) when running through soil ground. The time series analysis model which was applicable to predict the mixed ground composed of soils and rocks was modified to be applicable to soil tunnels. Using the modified model, the feasibility on the choice of the soil conditioning materials dependent upon soil types was studied. To do this, a self-organizing map (SOM) clustering was performed. Firstly, it was confirmed that the ground types should be classified based on the percentage of 35% passing through the #200 sieve. Then, the possibility of predicting the ground types by employing the modified model, in which the TBM operational data were analyzed, was studied. The efficacy of the modified model is demonstrated by its 98% accuracy in predicting ground types ten rings ahead of the tunnel face. Especially, the average prediction accuracy was approximately 93% in areas where ground type variations occur.

• Journal of the Korean Association of Geographic Information Studies
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• v.6 no.1
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• pp.119-131
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• 2003

Rainfall-runoff process is under the control of hydrologic parameters having temporal and spatial variety. Accordingly, it is difficult to efficiently deal them since many parameters and various information are required to perform hydrologic simulation. So the purposes of this study is to estimate the runoff volume by frequency using GIS techniques and NRCS method. The analysis of frequency rainfall is analyzed using FARD 2002 program and the result of goodness of fit test show that Log-pearson type III is suitable distribute type for the applied area. TOPAZ program used for the analysis of DEM data examining into geological characteristic. NRCS curve numbers estimated using landuse map and soil map for the estimation of effective rain fall in the basin. The storm Type II and Type III were used as the type for the application of NRCS. The result of application show that the runoff volumes above 80 years frequency in return period have similar patterns regardless of Type II and Type III. In addition, the results of comparison with runoff volumes by frequency in the report of river improvement master plan show that it have similar volumes as the relative errors for them of 80, 100 years frequency are each 7.65%, 5.33%.

• Korean Journal of Remote Sensing
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• v.25 no.5
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• pp.445-454
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• 2009

Localized unprecedented torrential rain and heavy rainfall cause repeated damages and make it difficult to detect and predict the landslide caused by heavy rainfall. To analyze the landslide characteristics of Inje area this study used satellite images photographed after the occurrence of landslide caused by the typhoon Ewiniar occurred in July, 2006, and for GIS analysis purpose, interpreted the satellite images (SPOT5) visually to digitize into developing parts, water traveling parts and sediment parts. For analysis of spatial characteristics, landslide areas obtained from visual interpretation of digital map, 3rd & 4th forest vegetation maps and detailed soil map and grids were overlaid and analyzed. As a result, in regard to topographic features, landslide occurred at places, of which average slope is $26.34^{\circ}$, had south, south-east, south-west aspects and average altitude of 627m. From hydrological analysis, it was found out that water traveling area rapidly spread approaching water traveling area and sediment area. From forest type analysis, it was found out that landslide occurrence was high in pine woods, and in terms of girth class attribute, landslide occurred in small-sized woods, in which the crown occupancy of trees that have the diameter at breast height, 6~16cm, was greater than 50%. From the analysis of soil series, landslide areas constitute 37.85% of OdF and 37.35% of SmF, which had sandy loam soil and excellent drainage capacity. Through this study, landslides in Inje area were characterized and SPOT5 images of 2.5m resolution could be used. But there was a difficulty in determining water traveling parts adjacent to urban 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 Korean Society for Geospatial Information Science
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• v.25 no.1
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• pp.19-27
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• 2017

This research was aimed to analyze landslide susceptibility and compare the prediction accuracy using ensemble frequency ratio (FR) and logistic regression at the Inje area, Korea. The landslide locations were identified with the before and after aerial photographs of landslide occurrence that were randomly selected for training (70%) and validation (30%). The total twelve landslide-related factors were elevation, slope, aspect, distance to drainage, topographic wetness index, stream power index, soil texture, soil sickness, timber age, timber diameter, timber density, and timber type. The spatial relationship between landslide occurrence and landslide-related factors was analyzed using FR and ensemble model. The produced LSI maps were validated and compared using relative operating characteristics (ROC) curve. The prediction accuracy of produced ensemble LSI map was about 2% higher than FR LSI map. The LSI map produced in this research could be used to establish land use planning and mitigate the damages caused by disaster.

• Journal of Wetlands Research
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• v.7 no.1
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• pp.107-117
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• 2005

Soil erosion is influenced from a variety of factors such as rainfall distribution, soil type, land use, etc. This paper is aimed at analyzing the soil erosion hazard zone in cropland. RUSLE was used for an analysis of soil erosion amount, and for the spatial data of basin, soil erosion amount was calculated by extracting the respect topography space related factors of RUSLE using DEM, Landuse, Soil map as base map. This paper is targeting at the watershed of Gyeongan stream in Gyeonggi-do The result of an analysis of soil erosion amount showed that soil erosion occurred in the order of crop field(1210) planting area, orchard(1220), non-adjusted paddy fields(1120), and adjusted paddy fields(1110), and also the average soil erosion in these planting areas has the most amount in crop field planting area. As a result of analysis on soil erosion hazard zone of farm land by classifying it into 5 classes using the result of that result of analysis on the amount of soil erosion, in case of Class 5 in which the hazard of soil erosion is the highest, approximately 72.5ha that corresponds to 2.4% of the total farm land was decided as erosion hazard zone. For this erosion hazard zone, it was analyzed that dry field crop planting area was 72.4ha and orchard was 0.1ha, and Class 5 hazard zone did not appear in other farming areas. Also, it showed that Class II(1~50ton/ha/yr) area had the most ratio of the entire farm land, i.e., 70.2%, regardless of land use state. According to the result of analysis on soil erosion hazard zone of farm land by classifying it into 5 classes, the Class V has the highest soil erosion hazard, approximately 72.5ha that corresponds to 2.4% of the total farm land was estimated as an erosion hazard zone. This erosion hazard shows 72.4ha in dry field crop planting area, 0.1ha in an orchard, but the highest hazard zone, the Class V was not shown in other farming areas. Also, it showed that Class II area had the most ratio of the entire farm land, i.e., 70.2%, regardless of land use state.

• Fire Science and Engineering
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• v.19 no.3 s.59
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• pp.64-69
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• 2005

In order to decrease the area damaged by forest fires and to prevent the occurrence of forest fires, we are making an effort to improve prevention measures for forest fires. The objective of this study is developing the forest fire occurrence probability model by means of forest site characteristics such as soil type, topography, soil texture, slope, and drainage and forest fire sites. Conditional probability analysis and GIS were used in developing the forest fire occurrence probability model that was used in the classification of forest fire occurrence risk regions.

• Korean Journal of Soil Science and Fertilizer
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• v.37 no.4
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• pp.199-206
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• 2004

Factors of universal soil loss equation, USLE, and its revised version, RUSLE for Korean soils were reevaluated to estimate the national scale of soil loss based on digital soil maps. Rainfall erosivity factor, R, of 158 locations of cities and counties were spacially interpolated by the inverse distance weight method. Soil erodibility factor, K, of 1321 soil phases of 390 soil series were calculated using the data of soil survey and agri-environmental quality monitoring. Topographic factor, LS, was estimated using soil map of 1:25,000 scale with soil phase and land use type. Cover management factor, C, of major crops and support practice factor, P, were summarized by analyzing the data of lysimeter and field experiments for 27 years (1975-2001) in the National Institute of Agricultural Science and Technology. R factor varied between 2322 and 6408 MJ mm $ha^{-1}$ $yr^{-1}$ $hr^{-1}$ and the average value was 4276 MJ mm $ha^{-1}$ $yr^{-1}$ $hr^{-1}$. The average K value was evaluated as 0.027 MT hr $MJ^{-1}$ $mm^{-1}$. The highest K factor was found in paddy rice fields, 0.034 MT hr $MJ^{-1}$ $mm^{-1}$, and K factors in upland fields, grassland, and forest were 0.026, 0.019, and 0.020 MT hr $MJ^{-1}$ $mm^{-1}$, respectively. C factors of upland crops ranged from 0.06 to 0.45 and that of grassland was 0.003. P factor varied between 0.01 and 0.85.