• Proceedings of the KSRS Conference
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• 2007.10a
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• pp.274-277
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• 2007

This study is to estimate the spatial distribution of soil loss using the land use data produced from QuickBird satellite imagery. For a small agricultural watershed (1.16 $km^2$) located in the upstream of Gyeongan-cheon watershed, a precise agricultural land use map were prepared using QuickBird satellite image of April 5 of 2003. RUSLE (Revised Universal Soil Loss Equation) was adopted for soil loss estimation. The data (DEM, soil and land use) for the RUSLE were prepared for 5 m and 30 m spatial resolution. The results were compared with each other and the result of 30 m Landsat land use data.

• 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 Korea Water Resources Association
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• v.32 no.4
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• pp.457-467
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• 1999

No complete methods for estimating soil loss, included by rain storms, from a small watershed are available yet, and the best recommended method is to use measured data from the watershed. When no measured data is available from the watershed, empirical models for estimating the soil loss, such as the Universal Soil Loss Equation(USLE), is well recommended in practice. For using this equation, it is necessary to estimated the rainfall erosivity, commonly expressed as R, of the watershed. In this study, first we collected data of the probable rainfalls with the return periods of 2, 5, 10, 20, 30, 50, 80, 100, 200, and 500-yr and with the duration hours of 0.5, 1, 2, 3, 6, 12, and 24-hr. Using this data, we calculated the design values for R for the return period of 24-hr at each major rainfall-measuring station nationwide. Then we constructed the iso-erodent map of Korea for each return period of the 24-hr design storm. This study shows that the 24-hr duration iso-erodent map of the 5-yr return period is very similar to the annual average iso-erodent map of Korea. This study also shows that the 24-hr duration R-values of a certain return period can be estimated by multiplying certain parameters, obtained from this study, to the 24-hr duration R-values for the 5-yr return period or the annual average R-values. Finally, the R-values of the design storm with the 24-hr return period obtained from this study can be used for designing the settling basins at small watersheds.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.18 no.3
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• pp.225-231
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• 2000

Soil loss by the rains has effect on natural environment. But It is difficult to find out the data that is surveyed in watershed. In this study, we combine RUSLE and GSIS, develop a program to automatically extract geo-factors to predict soil loss, and perform recurrent analysis against actual sediment load to bring out the relativity between soil loss and sediment load. Each factors need to RUSLE conducted by grid analysis. As the process to extract terrain factor became programming, the efficiency is rised.

• Journal of the Korean Institute of Landscape Architecture
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• v.24 no.3
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• pp.115-132
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• 1996

The purpose of this study is to estimate the soil loss amount with Geographic Information System according to the land use change of Buju mountain area in Mokpo city. To estimate the soil loss, Universal Soil Loss Equation which is the most proper technique to predict soil loss in this site condition is adopted and IDRISI, a raster GIS software, is used. GIS application with USLE is very efficient to estimate soil loss accurately and fastly. In order to decide value and to find application method of USLE factors, we used existing rainfall erosion index, soil erodibility analysis, slope length, slope steepness, vegetation management and practices, which are rated by GIS through the analysis of various studies related USLE. The result of this study was compared with the previous other researches to verify our method of constructing numerical data of USLE's factors. The result of verification of our way showed significance for the soil loss in forest area. But the result of verification for the soil loss in forest area. But the result of verification for the soil loss of cultivated area showed some errors. It seems that this result was due to local variation of topographical map.

• Proceedings of the Korean Society of Agricultural Engineers Conference
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• 2001.10a
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• pp.34-38
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• 2001

The purpose of this study is to estimate temporal soil loss change according to long-term land cover changes using GIS and RS. Revised USLE(Universal Soil Loss Equation) factors were made by using point rainfall data, DEM(Digital Elevation Model), soil map and land cover map. Past two decades land cover changes were traced by using Landsat MSS and TM data. Soil loss in 2000 increased $6.3\;kg/m^{2}/yr$ compared with that in 1983. This was mainly caused by the increased upland area.

• Journal of Environmental Impact Assessment
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• v.11 no.4
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• pp.271-280
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• 2002

The purpose of this study is to explore the hydrological impacts and soil loss variation due to the land use change of Namak New Town development area. The analysis of hydrological effects and soil loss variation has been carried out using GIS in this study. In order to estimate the peak runoff volume, the Rational Method which is the most popular technique to predict runoff amounts is used. To estimate the soil loss in the study area, Universal Soil Loss Equation(USLE), which is one of the most comprehensive and useful technique to predict soil erosion is adopted. The result of this study has shown that the peak runoff volume and the total soil loss increase according to the land use change. The peak runoff volume and the total soil loss have been increased about 2 times and about 48 times more than that of pre development. The increasing of the peak runoff volume can be effective erosion, flooding and so on. A careful city planning is the first essential step to minimize the environmental impacts and to construct the ecological city.

• Korean Journal of Soil Science and Fertilizer
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• v.9 no.1
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• pp.9-16
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• 1976

In order to investigate inherent erodibility of the soil, which is a major factor is soil erosion prediction, a survey on runoff and soil loss of reclaimed upland soil was carried out by using a portable rainulator. The relations of soil loss and some physical properties of the soil were discussed. The soil erodibility factor for Universal soil loss equation was calculated and compared with that of Wischmeier's nomograph. The result were as follows: 1. Total runoff increased for finer textured soil in order of Jeonnam silty clay loam, Songjeong clay loam, Yesan loam, Samgag and Sangju sandy loam. Total soil loss and soil content in runoff were not correspondently related with textural characteristic in order of Jeonnam, Samgag, Sangju, Yesan, and Songjeong. Total runoff, soil loss, and soil content in runoff were increased for steeper slope. 2. Soil loss and soil content in runoff negatively correlated with organic matter content of surface soil, while positively correlated with dispersion ratio, clay ratio, silt content, and significantly correlated with Middleton erosion ratio for coarser textured soil but not correctly related for finer textured soil. 3. The soil erodibilty factor K values for Universal soil loss equation were 0.32 for Jeonnam, 0.22 for Samgag, 0.17 for Sangju, 0.15 for Yesan, and 0.13 for Songjeong respectively. These values were close to those from Wischmeier's nomograph. So, it seems that the nomograph is useful for estimation of soil loss in Korea.

• KCID journal
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• v.21 no.1
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• pp.89-98
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• 2014

This study is to evaluate the future potential impact of climate change on soil erosion loss in a metropolitan area using Revised Universal Soil Loss Equation(RUSLE) with land use information of the Ministry of Environment and rainfall data for present and future years(30-year period). The spatial distribution map of vulnerable areas to soil erosion was prepared to provide the basis information for soil conservation and long-term land use planning. For the future climate change scenario, the MIROC3.2 HiRes A1B($CO_2720ppm$ level 2100) was downscaled for 2040-2069(2040s) and 2070-2099(2080s) using the stochastic weather generator(LARS-WG) with average rainfall data during past 30 years(1980-2010, baseline period). By applying the climate prediction to the RUSLE, the soil erosion loss was evaluated. From the results, the soil erosion loss showed a general tendency to increase with rainfall intensity. The soil loss increased up to 13.7%(55.7 ton/ha/yr) in the 2040s and 29.8%(63.6 ton/ha/yr) in the 2080s based on the baseline data(49.0 ton/ha/yr).

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.357-360
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• 2005

Soil loss is widely recognized as a threat to farm livelihoods and ecosystem integrity worldwide. Soil loss prediction models can help address long-range land management planning under natural and agricultural conditions. Even though it is hard to find a model that considers all forms of erosion, some models were developed specifically to aid conservation planners in identifying areas where introducing soil conservation measures will have the most impact on reducing soil loss. Revised Universal Soil Loss Equation (RUSLE) computes the average annual erosion expected on hillslopes by multiplying several factors together: rainfall erosivity (R), soil erodibility (K), slope length and steepness (LS), cover management (C), and support practice (P). The value of these factors is determined from field and laboratory experiments. This study calculated soil erosion using GIS-based RUSLE model in Imha basin and examined soil erosion source area using SPOT 5 high-resolution satellite image and land cover map. As a result of analysis, dry field showed high-density soil erosion area and we could easily investigate source area using satellite image. Also we could examine the suitability of soil erosion area applying field survey method in common areas (dry field & orchard area) that are difficult to confirm soil erosion source area using satellite image.