• Journal of Environmental Impact Assessment
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• v.21 no.1
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• pp.105-117
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• 2012

Future climate according to land-use change was simulated by regional climate model. The goal of study was to predict the distribution of meteorological elements using the Weather Research & Forecasting Model (WRF). The KME (Korea Ministry of Environment) medium-category land-use classification was used as dominant vegetation types. Meteorological modeling requires higher and more sophisticated land-use and initialization data. The WRF model simulations with HyTAG land-use indicated certain change in potential vegetation distribution in the future (2086-2088). Compared to the past (1986-1988) distribution, coniferous forest area was decreased in metropolitan and areas with complex terrain. The research shows a possibility to simulate regional climate with high resolution. As a result, the future climate was predicted to $4.5^{\circ}$ which was $0.5^{\circ}$ higher than prediction by Meteorological Administration. To improve future prediction of regional area, regional climate model with HyTAG as well as high resolution initial values such as urban growth and CO2 flux simulation would be desirable.

• Journal of The Korean Society of Agricultural Engineers
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• v.59 no.6
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• pp.29-37
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• 2017

In this study, we analyzed the impacts of land surface characteristics on spatially and temporally distributed soil moisture values at the Yongdam and Soyang-river dam watersheds in 2014 and 2015. The soil moisture, NDVI (Normalized Difference Vegetation Index) and temperature values at the spatio-temporal scales were estimated using satellite-based MODIS (MODerate Resolution Imaging Spectroradiometer) products. Then the Pearson correlations between soil moisture and land surface characteristics (NDVI, temperature and DEM-digital elevation model) were estimated and analyzed, respectively. Overall, the monthly soil moisture values at the time step were highly influenced by the precipitation amounts. Also, the results showed that the soil moisture has the strong correlation with DEM while the temperature was inversely correlated with the soil moisture. However the monthly correlations between NDVI and soil moisture were highly varied along the time step. These findings indicated that water loss near the land surface are highly occurred by soil and plant activities as evapotranspiration and infiltration during the no/less precipitation period. But the high precipitation amounts reduce the impacts of land surface characteristics because of saturated condition of land surface. Thus these results demonstrated that soil moisture values are highly correlated with land surface characteristics. Our findings can be useful for water resources/environmental management, agricultural drought, etc.

• Journal of Korean Society of Environmental Engineers
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• v.36 no.3
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• pp.162-171
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• 2014

In this study, the uncertainty analysis of present land pollutant load estimation with simplified land category in TMDLs was performed and the enhanced method for land pollutant load estimation with level II land cover consisting of 23 categories was suggested, which was verified by L-THIA model. For land TP load estimation in Jinwi stream basin, the result of comparison between existing method with simplified land category (Scenario 1) and enhanced method with level II land cover (Scenario 2) showed high uncertainty in existing method. TP loads estimated by Scenario 2 for land covers included in the site land category were in the range of 3.45 to 56.69 kg/day, in which TP loads differed by sixteen times as much among them. For application of scenario 2 to TMDLs, Land TP loads were estimated by matching level II land cover to 28 land categories in serial cadastral map (Scenario 3). In order to verify accuracy of TP load estimation by scenario 3, the simulation result of L-THIA was compared with that and the difference between the two was as little as 10%. The result of this study is expected to be used as primary data for accurate estimation of land pollutant load in TMDLs.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.729-741
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• 2023

The Operational Linescan System (OLS)sensor is a sensor aboard satellites launched through the Defense Meteorological Satellite Program (DMSP) that detects light in the visible and infrared bands emitted at night. Studies by several researchers have shown a high correlation between nighttime light data from OLS sensors and gross domestic product values. In this study, we investigated the correlation of nighttime light data with the total amount of individual land prices, which is one of the various indicators related to economic development. The study found that most cities and provinces showed a high correlation with a correlation coefficient of more than 0.7, and the correlation coefficient of 0.7837 between the total amount of individual land price and nighttime light data for the entire South Korea was also high. However, unlike other cities and provinces, Seoul has a low correlation coefficient of 0.5648 between nighttime light and the total amount of individual land price, which is analyzed as a reason that the digital number value of the OLS sensor is close to the maximum value and cannot show further brightness changes. This study is expected to help identify announced land prices in areas where announced land prices are not systematically organized and to analyze land use changes in such areas.

• The Journal of Engineering Geology
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• v.32 no.4
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• pp.483-498
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• 2022

Soil moisture is defined as water in the pores of the soil's unsaturated zone, and it is closely related to various hydrological processes. This study aims to provide meaningful data by identifying factors affecting soil moisture through comparing soil moisture content and soil permeability in a study area covering six different land use types in an agricultural region that is highly dependent on groundwater. We conduct auto-correlation analysis, spectral density analysis, and cross-correlation analysis using time-series data. Soil moisture content shows to have weak auto-correlation and memory effects, and precipitation appears to have a substantial influence on soil moisture content. Saturation hydraulic conductivity does not vary markedly with changing land use, and instead appears to be affected by the inhomogenous soil structure.

• Magazine of the Korean Society of Agricultural Engineers
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• v.37 no.E
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• pp.63-71
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• 1995

Three types of land use were investigated to describe the effect of land use on both surface and ground water quality. Typical land uses of a grazing pasture, Sudan grass field and paddy in Kangwon province were selected and flumes and monitoring wells were installed. Land managements were carefully monitored, water samples were collected periodically and analyzed with respect to nitrate, TP and TKN at a laboratory of Kangwon Provincial Institute of Health and Environment from August, 1993 to May, 1994. Runoff from the pasture was formed mostly with seeping subsurface flow in the lower areas of the pasture. A few overland flows were observed during heavy storms, and when it occurred, runoff increased sharply. For the Sudan grass field, runoff was formed with overland flow. Nitrate concentration in runoff from both land uses seemed not affected by runoff and ranged from 0.241 to 4.137mg'/1. TP and TKN concentrations from the pasture were affected by overland flow. When overland flow occurred, TP and TKN concentrations abruptly increased to 5.726 and 12.841mg/1, respectively, from less than 1.0mg/l. However, these concentrations from the Sudan grass field were quite stable ranging from 0.191 to 0.674mg/l for TP and 0A70 and 1.650mg/l for TKN. Nitrate concentration was significantly affected by land use(Sudan grass field) and the concentration increase reached about 2mg/l per lOOm ground water flow. Nitrate concentration from a well located in the middle of rice fields also was significantly higher than that measured from a well located relatively undisturbed mountain toe area. TP and TKN concentrations in shallow ground water affected by the depth of the monitoring wells. The deeper the monitoring wells, the less TP and TKN concentrations were measured.

• Journal of Environmental Impact Assessment
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• v.14 no.3
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• pp.97-107
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• 2005

The purpose of this study is to develop a system, which estimates watershed pollutant loading rate through the combination of GIS and computational mode. Also, the applicability of this study was estimated by the application of the above system for Chuncheon City. The detailed results of these studies are as follows; The pollutant loading estimation system was developed for more convenient estimation of pollutant loading rate in watershed, and the system load was minimized by the separation of estimation module for point and non-point source. This system on the basis of GIS is very economical and efficient because it can be applied to other watershed with the watershed map. System modification is not needed. The pollutant loading estimation system for point source was developed to estimate the pollutant loading rate in watershed through the extraction of the proper data from all districts and yearly data and the execution of spatial analysis which is main function of GIS. From the verification result of spatial analysis, real watershed area and the administrative districtarea extracted by spatial analysis were $1,114,893,340.15m^2$ and $1,114,878,683.68m^2$, respectively. It shows that the spatial analysis results were very exact with only 0.001% error. The pollutant loading estimation system for non-point source was developed to calculate the pollutant loading rate through the overlaying of land-use and watershed map after the construction of new land-use map using the land register database with most exact land use classification. Application result for Chuncheon City shows that the proposed system results in one percent land use error while the statistical method results in five percent. More exact nonpoint source pollutant loading was estimated from this system.

• Magazine of the Korean Society of Agricultural Engineers
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• v.45 no.4
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• pp.78-88
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• 2003

The land use changes from non-urban areas to urban areas lead to the increased impervious areas, consequently increased direct runoff and higher peak runoff. Urban areas have also been recognized as significant sources of Nonpoint Source (NPS) pollution, while agricultural activities have been known as the primary sources of NPS pollution. Many features of the L-THIA/NPS GIS, L-THIA/NPS WWW system have been enhanced to provide easy-to-use system. The L-THIA model was applied to the Little Eagle Creek (LEC) watershed in Indiana to evaluate the accuracy of the model. The L-THIA/NPS GIS estimated yearly direct runoff values match the direct runoff separated from U.S. Geological Survey stream flow data reasonably. The $R^2$ and Nash-Sutcliffe values are 0.67 and 0.60, respectively. The L-THIA estimated runoff volume and total nitrogen loading for each land use classification in the LEC watershed were computed. The estimated runoff volume and total nitrogen loading in the LEC watershed increased by 180% and 270% for the 20 years. Urbanized areas -"Commercial", "High Density Residential", and "Low Density Residential"- of the LEC watershed made up around 68% of the 1991 total land areas, however contributed more than 92% of average annual runoff and 86% of total nitrogen loading. Therefore, it is essential to consider the impacts of land use change on hydrology and water quality in land use planning of urbanizing watershed.nning of urbanizing watershed.

• Journal of Korean Society on Water Environment
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• v.30 no.2
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• pp.212-219
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• 2014

In this study, a land pollutant load calculation method in TMDLs was improved to consider climate change scenarios. In order to evaluate the new method, future change in rainfall patterns was predicted by using SRES A1B climate change scenarios and then post-processing methods such as change factor (CF) and quantile mapping (QM) were applied to correct the bias between the predicted and the observed rainfall patterns. Also, future land pollutant loads were estimated by using both the bias corrected rainfall patterns and the enhanced method. For the results of bias correction, both methods (CF and QM) predicted the temporal trend of the past rainfall patterns and QM method showed future daily average precipitation in the range of 1.1~7.5 mm and CF showed it in the range of 1.3~6.8 mm from 2014 to 2100. Also, in the result of the estimation of future land pollutant loads using the enhanced method (2020, 2040, 2100), TN loads were in the range of 4316.6~6138.6 kg/day and TP loads were in the range of 457.0~716.5 kg/day. However, each result of TN and TP loads in 2020, 2040, 2100 was the same with the original method. The enhanced method in this study will be useful to predict land pollutant loads under the influence of climate change because it can reflect future change in rainfall patterns. Also, it is expected that the results of this study are used as a base data of TMDLs in case of applying for climate change scenarios.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1053-1065
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• 2019

Land use statistics calculation is very informative data as the activity data for calculating exact carbon absorption and emission in post-2020. To effective interpretation by land use category, This study classify automatically image interpretation by land use category applying forest aerial photography (FAP) to deep learning model and calculate national unit statistics. Dataset (DS) applied deep learning is divided into training dataset (training DS) and test dataset (test DS) by extracting image of FAP based national forest resource inventory permanent sample plot location. Training DS give label to image by definition of land use category and learn and verify deep learning model. When verified deep learning model, training accuracy of model is highest at epoch 1,500 with about 89%. As a result of applying the trained deep learning model to test DS, interpretation classification accuracy of image label was about 90%. When the estimating area of classification by category using sampling method and compare to national statistics, consistency also very high, so it judged that it is enough to be used for activity data of national GHG (Greenhouse Gas) inventory report of LULUCF sector in the future.