• Korean Journal of Remote Sensing
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• v.37 no.2
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• pp.321-335
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• 2021

Particulate matter (PM10 and PM2.5 with a diameter less than 10 and 2.5 ㎛, respectively) can be absorbed by the human body and adversely affect human health. Although most of the PM monitoring are based on ground-based observations, they are limited to point-based measurement sites, which leads to uncertainty in PM estimation for regions without observation sites. It is possible to overcome their spatial limitation by using satellite data. In this study, we developed machine learning-based retrieval algorithm for ground-level PM10 and PM2.5 concentrations using aerosol parameters from Geostationary Ocean Color Imager (GOCI) satellite and various meteorological parameters from a numerical weather prediction model during January to December of 2019. Gradient Boosted Regression Trees (GBRT) and Light Gradient Boosting Machine (LightGBM) were used to estimate PM concentrations. The model performances were examined for two types of feature sets-all input parameters (Feature set 1) and a subset of input parameters without meteorological and land-cover parameters (Feature set 2). Both models showed higher accuracy (about 10 % higher in R2) by using the Feature set 1 than the Feature set 2. The GBRT model using Feature set 1 was chosen as the final model for further analysis(PM10: R2 = 0.82, nRMSE = 34.9 %, PM2.5: R2 = 0.75, nRMSE = 35.6 %). The spatial distribution of the seasonal and annual-averaged PM concentrations was similar with in-situ observations, except for the northeastern part of China with bright surface reflectance. Their spatial distribution and seasonal changes were well matched with in-situ measurements.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.2
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• pp.47-61
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• 2000

Agroclimatic zoning was done for paddy rice culture in North Korea based on a simulation experiment. Daily weather data for the experiment were generated by 3 steps consisting of spatial interpolation based on topoclimatological relationships, zonal summarization of grid cell values, and conversion of monthly climate data to daily weather data. Regression models for monthly climatological temperature estimation were derived from a statistical procedure using monthly averages of 51 standard weather stations in South and North Korea (1981-1994) and their spatial variables such as latitude, altitude, distance from the coast, sloping angle, and aspect-dependent field of view (openness). Selected models (0.4 to 1.6$^{\circ}C$ RMSE) were applied to the generation of monthly temperature surface over the entire North Korean territory on 1 km$\times$l km grid spacing. Monthly precipitation data were prepared by a procedure described in Yun (2000). Solar radiation data for 27 North Korean stations were reproduced by applying a relationship found in South Korea ([Solar Radiation, MJ m$^{-2}$ day$^{-1}$ ] =0.344 + 0.4756 [Extraterrestrial Solar Irradiance) + 0.0299 [Openness toward south, 0 - 255) - 1.307 [Cloud amount, 0 - 10) - 0.01 [Relative humidity, %), $r^2$=0.92, RMSE = 0.95 ). Monthly solar irradiance data of 27 points calculated from the reproduced data set were converted to 1 km$\times$1 km grid data by inverse distance weighted interpolation. The grid cell values of monthly temperature, solar radiation, and precipitation were summed up to represent corresponding county, which will serve as a land unit for the growth simulation. Finally, we randomly generated daily maximum and minimum temperature, solar irradiance and precipitation data for 30 years from the monthly climatic data for each county based on a statistical method suggested by Pickering et a1. (1994). CERES-rice, a rice growth simulation model, was tuned to accommodate agronomic characteristics of major North Korean cultivars based on observed phenological and yield data at two sites in South Korea during 1995~1998. Daily weather data were fed into the model to simulate the crop status at 183 counties in North Korea for 30 years. Results were analyzed with respect to spatial and temporal variation in yield and maturity, and used to score the suitability of the county for paddy rice culture.

• Journal of the Korean Association of Geographic Information Studies
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• v.17 no.1
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• pp.35-49
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• 2014

In order to understand the impacts of surface characteristics and the distance from the urban heat island center to suburban areas on the mean night time air temperature, we analyzed GIS and AWS observational data. Spatial distributions of mean night time air temperature during the summer and winter periods of 2004-2011(six years) were utilized. Results show that the temperature gradients were different by distance and direction. We found high correlation between mean night-time air temperature and artificial land cover area within 1km and 200m radii during both summer(R=0.84) and winter(R=0.78) seasons. Regression models either from 1km and 200m land surface data explained the distribution of night-time temperature equally well if the input data had sufficient resolution with detailed attribute including building area and height.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.1-11
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• 2006

This study conducts a theoretical research on road traffic noise. Also, the domestic road noise forecast models were compared each other and analyzed with advanced countries' models to indicate the application possibility and problems. For the establishment of a general formula, we compared the forecasted value with the actual value applied in the formula proposed by the National Environment Institute, and examined the necessary improvement of the domestic road traffic noise forecast model. Also, a regression model was built to examine the relationship between traffic factors and noise. The traffic volume and speed are the main traffic factors used in this formula to affect the noise. From the results, it was found that the speed had a closer relationship with the noise rather than the traffic volume. Therefore, to decrease road noise, it is more important to control traffic speed. The spatial effect of road traffic noise within the apartment complexes was used in the case study to derive location-specific adjustment values. We surveyed the road traffic noise of three apartment complexes, and found that the road traffic noise within each complex was affected at plane level as well as at three-dimensionally. In other words, as the distance from the sound origin grows farther, noise level decreases. Also, it was found that noise increases as heigt goes up, but drops when the height goes beyond a certain level, and that the effect of noise decreases if there are obstacles along the path of the noise direction. Therefore, apartment site design should be done with consideration of the effects of noise in the future.

• Journal of the Korean Geographical Society
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• v.41 no.2 s.113
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• pp.168-187
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• 2006

Windy meteorological conditions and dried fire fuels due to higher atmospheric instability and dryness in the lower troposphere can exacerbate fire controls and result in more losses of forest resources and residential properties due to enhanced large wildland fires. Long-term (1979-2005) climatology of the Haines Index reconstructed in this study reveals that spatial patterns and intra-annual variability of the atmospheric instability and dryness in the lower troposphere affect the frequency of wildland fire incidences over the Korean Peninsula. Exponential regression models verify that daily high Haines Index and its monthly frequency has statistically significant correlations with the frequency of the wildland fire occurrences during the fire season (December-April) in South Korea. According to the climatic maps of the Haines Index created by the Geographic Information System (GIS) using the Digital Elevation Model (DEM), the lowlands below 500m from the mean sea level in the northwestern regions of the Korean Peninsula demonstrates the high frequency of the Haines Index equal to or greater than five in April and May. The annual frequency of the high Haines Index represents an increasing trend across the Korean Peninsula since the mid-1990s, particularly in Gyeongsangbuk-do and along the eastern coastal areas. The composite of synoptic weather maps at 500hPa for extreme events, in which the high Haines Index lasted for several days consecutively, illustrates that the cold low pressure system developed around the Sea of Okhotsk in the extreme event period enhances the pressure gradient and westerly wind speed over the Korean Peninsula. These results demonstrate the need for further consideration of the spatial-temporal characteristics of vertical atmospheric components, such as atmospheric instability and dryness, in the current Korean fire prediction system.

• Journal of the Korean Institute of Landscape Architecture
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• v.42 no.3
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• pp.1-12
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• 2014

Urban Green space takes charge of the important role for the physical activity and promotion of health to the residents. Therefore, this study is trying to examine the relationship between the various characteristics of green space and green space usage for physical activity and health promotion. A questionnaire survey was conducted to obtain the information about patterns of green space usage and perceived neighborhood environments for the residents living in Changwon-si, Gyeongsangnam-do(n=541). Geographic Information System(GIS) was used to construct spatial data about green space accessibility and physical neighborhood environments. A Multiple Linear Regression model was used to examine the association between the characteristics of green space and physical activity, perceived health status and BMI(Body Mass Index). The study results revealed that the residents' physical activities are positively and directly influenced by the number of available public parks and green spaces in the vicinity(${\leq}200m$). The frequency at which residents witness others exercising nearby or the perceived abundance of low-cost gym facilities also factor as positive influences. The closer to the park, the higher the number of parks and area of green spaces, the more comfortable the walk thereto and the denser the neighboring residential area distribution, the perceived health level was found to be the more positively influenced. Further, it was verified that BMI is correlated with the number of public parks and green spaces within 400 m of the resident's home as well as the safety of walkways, the density of neighboring residential areas, the ratio of road, and the density of crosswalk. The significant multiple regression models between the characteristics of green spaces and physical activities and perceived health level were extracted within the significance level of 10%. This study will contribute to provide better understanding the ways in which green space and neighborhood characteristics are associated with physical activity and health. The result of this research will be available in the landscape architecture plan aimed at improving the use of green space for physical activity and reducing obesity.

• Korean Journal of Remote Sensing
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• v.33 no.5_2
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• pp.741-750
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• 2017

The objective of this study was to estimate corn and soybean yield in Illinois and Iowa in USA using satellite and meteorological data. MODIS products for NDVI were downloaded from a NASA website. Each layer was processed to convert projection and extract layers for NDVI. Relations of NDVI from 2002 to 2012 with corn and soybean yield were investigated to find informative days for rice yield estimation. Weather data for the county of study state duration from 2002 to 2012 to correlate crop yield. Multiple regression models based on MODIS NDVI and rainfall were made to estimate corn and soybean yields in study site. Corn yields estimated for 2013 were $10.17ton\;ha^{-1}$ in Illinois, $10.21ton\;ha^{-1}$ in Iowa and soybean yields estimated were $3.11ton\;ha^{-1}$ in Illinois, $2.58ton\;ha^{-1}$ in Iowa, respectively. Corn and Soybean yield distributions in 2013 were mapped to show spatial variability of crop yields of the Illinois and Iowa state.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.709-720
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• 2018

In order to optimize the evaluation of biomass in crop monitoring, accurate and timely data of the crop-field are required. Evaluating above-ground biomass helps to monitor crop vitality and to predict yield. Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study reports on the development of remote sensing techniques for evaluating the biomass of winter crop. Specific objective was to develop statistical models for estimating the dry weight of barley and wheat using a Excess Green index ($E{\times}G$) based Vegetation Fraction (VF) and a Crop Surface Model (CSM) based Plant Height (PH) value. As a result, the multiple linear regression equations consisting of three independent variables (VF, PH, and $VF{\times}PH$) and above-ground dry weight provided good fits with coefficients of determination ($R^2$) ranging from 0.86 to 0.99 with 5 cultivars. In the case of the barley, the coefficient of determination was 0.91 and the root mean squared error of measurement was $102.09g/m^2$. And for the wheat, the coefficient of determination was 0.90 and the root mean squared error of measurement was $110.87g/m^2$. Therefore, it will be possible to evaluate the biomass of winter crop through the UAV image for the crop growth monitoring.

• Journal of the Korean Geographical Society
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• v.42 no.4
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• pp.506-525
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• 2007

Incision into bedrock channel is the primary control of landform evolution, but research into bedrock incision process stagnated for long time. Due to the scaling problem of the application of results from flume studies to bedrock channel, there is a strong need to simulate the bedrock incision process with more realistic models. As a part of investigation into controls of bedrock channel incision, three-dimensional changes of rock surface with abrasion was investigated with physical modelling. 18 rock plates were abraded with various sediment particle size and sediment load and abraded surfaces of the plates were scanned with high resolution 3-D scanner. To identify the spatial pattern of erosion of the rock plates, various methods were used. There was no synthetic or holistic method that showed all features of bedrock plate produced by abrasion, so each plate was analyzed using some available methods. Contour maps, shaded relief maps and profiles show that abrasion concentrated on the centre of plate (cross profile) and upstream and downstream edges (longitudinal profile) and eroded area extended inwards. It also found that the cracks and boundaries of forming materials easily eroded than other parts. Changing patterns of surface roughness were investigated with profiles, regression analysis and spectral analysis. Majority of plates showed decrease in small-scale roughness, but it depends on microstructures of the plates rather than general hardness or other factors. SEM inspection results supported this idea.

• Journal of the Economic Geographical Society of Korea
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• v.13 no.3
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• pp.457-469
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• 2010

The study aims to estimate interregional trade coefficient of service industry using the gravity model. The gravity model has been widely used for prediction of the level of human interaction between two regions which is positively related to attraction of them and negatively related to the distance between them. To apply the gravity model for explaining the interregional trade flow of service industry, the choice of proper proxy variables which represent a dependent variable and independent variables is most important. However, the literature shows that there are few studies on this issue. Four models concerned to the choice of proxy variables are considered. Finally, this paper employs the least-squares regression analysis to test the model's goodness-of-fit, and suggests the most appropriate model based on the result from the analysis. The result shows that the interregional trade of service industry in regional input-output table developed by The Bank of Korea is desirable as a dependent variable, the service industry output of export region, the population of import region, and the spatial distance between regions are desirable as independent variables.