• Journal of Korean Society for Geospatial Information Science
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• v.15 no.3
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• pp.77-85
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• 2007

Assessment of soil erosion is a cost and time-consuming task. There are many models developed to predict soil erosion from an area, but Universal Soil Loss Equation (USLE) is most widely used empirical equation for estimating annual soil erosion. Soil erosion depends upon-rainfall intensity, type of soil, land cover and land use, slope degree, slope length and soil conservation practice. All these parameters are have spatial distribution and hence satellite remote sensing and Geographic Information System (GIS) are applicable in the assessment of the influence on soil erosion. GIS has been integrated with the USLE (Universal Soil Loss Equation) model in identification of rainfall-based erosion to the Bocheong River which is the representative basin of IHP due to Typhoon Rusa. Similar studies are available in literature, ranging from those that use a simple model such as USLE to others of a more sophisticated nature.

• Proceedings of the Korean Society of Soil and Groundwater Environment Conference
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• 2004.04a
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• pp.289-292
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• 2004

In order to identify the origin and nature of the spilled oil in the potential source, we analyzed the pattern of alkyi PAM(Polynuclear Aromatic Hydrocarbons) in fuel standard and environmental samples. Alkyl PAM patterns are used for fuel-type identification in weathered environmental samples. Detection of alkyl PAH was achieved by operation CC/MS in the SIM mode. We chose ions of naphthalene(m/z 128), C1-naphthalene(m/z 142), C2-naphthalene(m/z 156), C3-naphthalene(m/z 170), C4-naphthalene(m/z 184) for the comparison of this pattern according to the type of fuel. We analyzed tile pattern of alkyl PAH in neat gasoline, kerosene, diesel, and JP-8, and in groundwater samples which were collected in monitoring wells. The distribution map of alkyl-naphthalene shows different patterns among four different fuel types (gasoline, kerosene, diesel, and JP-8). Particularly, tile distribution map of kerosene and JP-8 is found to be of value in identifying fuel type in that the difference is clear. Therefore distribution patterns of alkyl-PAH compounds provide another useful tool for fuel-type identification of petroleum fuels.

• Membrane and Water Treatment
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• v.10 no.4
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• pp.313-320
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• 2019

Increasing the concentration of nitrate ions in the soil solution and then leaching it to underground aquifers increases the concentration of nitrate in the water, and can cause many health and ecological problems. This study was conducted to evaluate the vulnerability of Meymeh aquifer to nitrate pollution. In this research, sampling of 10 wells was performed according to standard sampling principles and analyzed in the laboratory by spectrophotometric method, then; the nitrate concentration zonation map was drawn by using intermediate models. In the drastic model, the effective parameters for assessing the vulnerability of groundwater aquifers, including the depth of ground water, pure feeding, aquifer environment, soil type, topography slope, non-saturated area and hydraulic conductivity. Which were prepared in the form of seven layers in the ARC GIS software, and by weighting and ranking and integrating these seven layers, the final map of groundwater vulnerability to contamination was prepared. Drastic index estimated for the region between 75-128. For verification of the model, nitrate concentration data in groundwater of the region were used, which showed a relative correlation between the concentration of nitrate and the prepared version of the model. A combination of two vulnerability map and nitrate concentration zonation was provided a qualitative aquifer classification map. According to this map, most of the study areas are within safe and low risk, and only a small portion of the Meymeh Aquifer, which has a nitrate concentration of more than 50 mg / L in groundwater, is classified in a hazardous area.

• Journal of Environmental Science International
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• v.29 no.1
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• pp.25-32
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• 2020

The determination of soil parameters is important in predicting the simulated surface runoff using either a distributed or a lumped rainfall-runoff model. Soil characteristics can be collected using remote sensing techniques and represented as a digital map. There is no universal agreement with respect to the determination of a representative parameter from a gridded digital map. Two representative methods, i.e., arithmetic and predominant, are introduced and applied to both FLO-2D and HEC-HMS to improve the model's accuracy. Both methods are implemented in the Yongdam catchment, and the results show that the former seems to be more accurate than the latter in the test site. This is attributed to the high conductivity of the dominant soil class, which is A type.

• Spatial Information Research
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• v.20 no.3
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• pp.57-66
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• 2012

Gangwon-do has been suffering extensive landslide dam age, because its geography consists mainly of mountains. Analyzing the related factors is crucial for landslide prediction. We digitized the landslide and non-landslide spots on an aerial photo obtained right after a disaster in Inje, Gangwon-do. Three landslide factors-topographic, forest type, and soil factors-w ere statistically analyzed through GIS overlap analysis between topographic map, forest type map, and soil map. The analysis showed that landslides occurred mainly between the inclination of $20^{\circ}$ and $35^{\circ}$, and needleleaf tree area is more vulnerable to a landslide. About soil properties, an area with shallow effective soil depth and parent material of acidic rock has a greater chance of landslide.

• 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.

• Journal of the Korean Association of Geographic Information Studies
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• v.14 no.4
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• pp.63-76
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• 2011

This study purposes to develop a model to analyze soil loss in estimating prior disaster influence. The model of analyzing soil loss develops the soil loss analysis system on the basis of Internet by introducing cloud computing system, and also develops a standalone type in connection with HyGIS. The soil loss analysis system is developed to draw a distribution chart without requiring a S/W license as well as without preparing basic data such as DEM, soil map and land cover map. Besides, it can help users to draw a soil loss distribution chart by applying various factors like direct rain factors. The tools of Soil Loss Anaysis Model in connection with HyGiS are developed as add-on type of GMMap2009 in GEOMania, and also are developed to draw Soil Loss Hazard Map suggested by OECD. As a result of using both models, they are developed very conveniently to analyze soil loss. Hereafter, these models will be able to be improved continuously through researches to analyze sediment a watershed outlet and to calculate R value using data of many rain stations.

• Journal of Korean Society of Forest Science
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• v.83 no.3
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• pp.322-330
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• 1994

The Universal Soil Loss Equation (USLE) has been widely used to predict long-term soil loss by incorporating several erosion factors, such as rainfall, soil, topography, and vegetation. This study is aimed to introduce the LISLE within geographic information system(GIS) environment. The Kwangneung Experimental Forest located in Kyongki Province was selected for the study area. Initially, twelve years of hourly rainfall records that were collected from 1982 to 1993 were processed to obtain the rainfall factor(R) value for the LISLE calculation. Soil survey map and topographic map of the study area were digitized and subsequent input values(K, L, S factors) were derived. The cover type and management factor (C) values were obtained from the classification of Landsat Thematic Mapper(CM) satellite imagery. All these input values were geographically registered over a common map coordinate with $25{\times}25m^2$ ground resolution. The USLE was calculated for every grid location by selecting necessary input values from the digital base maps. Once the LISLE was calculated, the resultant soil loss values(A) were represented by both numerical values and map format. Using GIS to run the LISLE, it is possible to pent out the exact locations where soil loss potential is high. In addition, this approach can be a very effective tool to monitor possible soil loss hazard under the situations of forest changes, such as conversion of forest lands to other uses, forest road construction, timber harvesting, and forest damages caused by fire, insect, and diseases.

• Geomechanics and Engineering
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• v.13 no.3
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• pp.475-488
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• 2017

Geotechnical data contributes substantially to the cost of engineering projects due to increasing cost of site investigations. Existing information in the form of soil maps can save considerable time and expenses while deciding the scope and extent of site exploration for a proposed project site. This paper presents spatial interpolation of data obtained from soil investigation reports of different construction sites and development of soil maps for geotechnical characterization of Multan area using ArcGIS. The subsurface conditions of the study area have been examined in terms of soil type and standard penetration resistance. The Inverse Distance Weighting method in the Spatial Analyst extension of ArcMap10 has been employed to develop zonation maps at different depths of the study area. Each depth level has been interpolated as a surface to create zonation maps for soil type and standard penetration resistance. Correlations have been presented based on linear regression of standard penetration resistance values with depth for quick estimation of strength and stiffness of soil during preliminary planning and design stage of a proposed project in the study area. Such information helps engineers to use data derived from nearby sites or sites of similar subsoils subjected to similar geological process to build a preliminary ground model for a new site. Moreover, reliable information on geometry and engineering properties of underground layers would make projects safer and economical.

• Economic and Environmental Geology
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• v.38 no.1
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• pp.33-43
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• 2005

The purpose of this study is to make and validate landslide susceptibility map using artificial neural network and GIS in Kangneung area. For this, topography, soil, forest, geology and land cover data sets were constructed as a spatial database in GIS. From the database, slope, aspect, curvature, water system, topographic type, soil texture, soil material, soil drainage, soil effective thickness, wood type, wood age, wood diameter, forest density, lithology, land cover, and lineament were used as the landslide occurrence factors. The weight of the each factor was calculated, and applied to make landslide susceptibility maps using artificial neural network. Then the maps were validated using rate curve method which can predict qualitatively the landslide occurrence. The landslide susceptibility map can be used to reduce associated hazards, and to plan land use and construction as basic data.