• Geophysics and Geophysical Exploration
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• v.5 no.4
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• pp.236-249
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• 2002

We have extended the three-dimensional (3-D) resistivity imaging algorithm to cover the 3-D resistivity tomography problem, where resistivity data are acquired using electrodes installed in several boreholes as well as at the earth surface. The imaging algorithm consists of the 3-D finite element forward modeling and least-squares inversion scheme, where the ACB (Active Constraint Balancing) is adopted to enhance the resolving power of the inversion. Sensitivity analysis with numerical verifications shows that 3-D resistivity tomography is a very appealing method and can be used to get 3-D attitude of subsurface structures with very high-resolution. Moreover, we could accurately handle the topography effect, which could cause artifacts in the resistivity tomography. In the application of 3-D resistivity tomography to the real field data set acquired at the quarry mine, we could derive a very reasonable and accurate image of the subsurface.

• Korean Journal of Remote Sensing
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• v.14 no.4
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• pp.376-391
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• 1998

Images from satellites will have 1 to 3 meter ground resolution and will be very useful for analyzing current status of earth surface. An image processing system named GeoWatch with more intelligent image processing algorithms has been designed and implemented to support the detailed analysis of the land surface using high-resolution satellite imagery. The GeoWatch is a valuable tool for satellite image processing such as digitizing, geometric correction using ground control points, interactive enhancement, various transforms, arithmetic operations, calculating vegetation indices. It can be used for investigating various facts such as the change detection, land cover classification, capacity estimation of the industrial complex, urban information extraction, etc. using more intelligent analysis method with a variety of visual techniques. The strong points of this system are flexible algorithm-save-method for efficient handling of large size images (e.g. full scenes), automatic menu generation and powerful visual programming environment. Most of the existing image processing systems use general graphic user interfaces. In this paper we adopted visual program language for remotely sensed image processing for its powerful programmability and ease of use. This system is an integrated raster/vector analysis system and equipped with many useful functions such as vector overlay, flight simulation, 3D display, and object modeling techniques, etc. In addition to the modules for image and digital signal processing, the system provides many other utilities such as a toolbox and an interactive image editor. This paper also presents several cases of image analysis methods with AI (Artificial Intelligent) technique and design concept for visual programming environment.

• Journal of The Korean Society of Agricultural Engineers
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• v.63 no.5
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• pp.13-25
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• 2021

The particulate matters (PM₁₀ and PM_2.5) and ammonia emitted from livestock farms as dispersed to urban and residential areas can increase the public's concern over the health problem, social conflicts, and air quality. Understanding the atmospheric dispersion of such matters is important to prevent the problems for the regulatory purposes. In this study, AERMOD modeling was performed to predict the dispersion of livestock particulate matters and ammonia in Gwangju metropolitan city and five surrounding cities. The five cities were divided into 40 sub-zones to model the area-based emissions which varied with the number of livestock farms, species and growth stages of the animals. As a result, the concentrations of PM₁₀, PM_2.5 and ammonia resulted from livestock farms located in the surrounding cities were 2.00 ㎍ m^-3, 0.30 ㎍ m^-3 and 0.04 ppm in the southwestern part of Gwangju based on the average concentration of 1 hour. These values accounted for 0.7% of PM₁₀ concentration, 0.5% of PM_2.5 concentration, and 0.4% of the ammonia concentration in Gwangju, contributing to a small amount of air pollution compared to other sources. As preventive measures, the plantation was applied to high emission source areas to reduce particulate matters and ammonia emissions by 35% and 31%, respectively, and resulted in decrease of the area of influence by 57% for particulate matters and 59% for ammonia.

• Journal of Environmental Impact Assessment
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• v.28 no.3
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• pp.201-214
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• 2019

The concept of ecosystem services is important for the effective management of regional ecological resources. Multiple ecosystem services provided by regional ecosystems are represented as ecosystem service bundles, which define the co-occurrent ecosystem services in a specific region. Bundles provide useful information to identify regional characteristics of ecosystem services and categorize sub-regions with similar patterns of ecosystem service provision. We assessed eleven ecosystem services using modeling approaches and statistical data and produced bundles of cities in Gyeonggi-do.We also conducted principal component analysis and cluster analysis to categorize these cities according to the characteristics of ecosystem services. The results indicated that the cities in Gyeonggi-do were categorized into three groups depending on the types of provision,regulation, and cultural services, and were designated as urbanized, urban-forest, agriculture, or forest cities. These groups were influenced by land use patterns reflecting regional social-environmental features. The results provide useful information for identifying regional ecosystem services and facilitate decision-making in regional ecosystem service management.

• Journal of Veterinary Clinics
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• v.36 no.1
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• pp.7-14
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• 2019

In South Korea, six large outbreaks of highly pathogenic avian influenza (HPAI) have occurred since the first confirmation in 2003 from chickens. For the past 15 years, HPAI outbreaks have become an annual phenomenon throughout the country and has extended to wider regions, across rural and urban environments. An understanding of the spatial epidemiology of HPAI occurrence is essential in assessing and managing the risk of the infection; however, local spatial variations of relationship between HPAI incidences in Korea and related risk factors have rarely been derived. This study examined whether spatial heterogeneity exists in this relationship, using a geographically weighted Poisson regression (GWPR) model. The outcome variable was the number of HPAI-positive farms at 252 Si-Gun-Gu (administrative boundaries in Korea) level notified to government authority during the period from January 2014 to April 2016. This response variable was regressed to a set of sociodemographic and topographic predictors, including the number of wild birds infected with HPAI virus, the number of wintering birds and their species migrated into Korea, the movement frequency of vehicles carrying animals, the volume of manure treated per day, the number of livestock farms, and mean elevation. Both global and local modeling techniques were employed to fit the model. From 2014 to 2016, a total of 403 HPAI-positive farms were reported with high incidence especially in western coastal regions, ranging from 0 to 74. The results of this study show that local model (adjusted R-square = 0.801, AIC = 954.5) has great advantages over corresponding global model (adjusted R-square = 0.408, AIC = 2323.1) in terms of model fitting and performance. The relationship between HPAI incidence in Korea and seven predictors under consideration were significantly spatially non-stationary, contrary to assumptions in the global model. The comparison between global Poisson and GWPR results indicated that a place-specific spatial analysis not only fit the data better, but also provided insights into understanding the non-stationarity of the associations between the HPAI and associated determinants. We demonstrated that an empirically derived GWPR model has the potential to serve as a useful tool for assessing spatially varying characteristics of HPAI incidences for a given local area and predicting the risk area of HPAI occurrence. Considering the prominent burden of HPAI this study provides more insights into spatial targeting of enhanced surveillance and control strategies in high-risk regions against HPAI outbreaks.

• Journal of Cadastre & Land InformatiX
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• v.48 no.2
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• pp.197-211
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• 2018

We conducted this research to examine how well regional housing prices are suited to use as an alternative to conventional census-based regional deprivation indices in health and medical geography studies. To examine the relative performance of mean regional housing prices compared to conventional census-based regional deprivation indices, we compared several multilevel logistic regression models, where the first level was individuals and the second was health districts in the Seoul Metropolitan Area (SMA) in Korea, for the sake of adjusting the regional clustering tendency of unknown factors. In these models, we predicted two dichotomous variables that represented individuals' after-lunch tooth brushing behavior and use of dental floss by individual characteristics and regional indices. Then, we compared the relative predictive performance of the models using the Akaike Information Criterion (AIC) and Bayesian Information Criterion (BIC). The results from the estimations showed that mean regional housing prices and census-based deprivation indices were correlated with the two types of dental health behavior in a statistical sense. The results also revealed that the model with mean regional housing prices showed smaller AIC and BIC compared with other models with conventional census-based deprivation indices. These results imply that it is possible for housing prices summarized using aerial units to be used as an alternative to conventional census-based deprivation indices when the census variables employed cannot properly reflect the characteristics of the aerial units.

• Journal of the Korea Convergence Society
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• v.10 no.7
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• pp.243-249
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• 2019

This study is aimed at offering a typology of changes in the social participation of middle-aged and older population and explore determinants for each type of such changes. The data employed for analysis are the 1st survey (2006) through the 6th version (2016) of the Korea Aging Research Panel Survey. Among the respondents of the panel survey, 1,327 males and 1,520 females with a total of 2,847 respondents were analyzed. As a result of applying the growth mixture modelling through the SPS 22.0 and M-plus 8.0 statistical programs, the changes in the social participation of middle-aged and older population have been classified into the 'high-decreasing', 'moderate-increasing' and 'low-stable' trajectory classes. Analysis of the determinants for each class shows that higher the education level, the more likely they are to belong to the high-decreasing and moderate-increasing classes than the low-stable class, and the more the population lives in urban areas, the more likely they are to belong to high-decreasing trajectory class than to low-stable class. Also, it was found that the probability of belonging to moderate-increasing trajectory class was higher than that of the low-stable class when there was no occupation. Through the results of these analyses, the implications of promoting social participation of middle-aged and older population were discussed.

• Korean Journal of Ecology and Environment
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• v.52 no.2
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• pp.94-104
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• 2019

In this study, we investigated the spatial variation in land use and topographic effects on water quality at the Geum river watershed in South Korea, using the ordinary least squares(OLS) and geographically weighted regression (GWR) models. Understanding the complex interactions between land use, slope, elevation, and water quality is essential for water pollution control and watershed management. We monitored four water quality indicators -total phosphorus, total nitrogen, biochemical oxygen demand, and dissolved oxygen levels - across three land use types (urban, agricultural, and forested) and two topographic features (elevation and mean slope). Results from GWR modeling revealed that land use and topography did not affect water quality consistently through space, but instead exhibited substantial spatial non-stationarity. The GWR model performed better than the OLS model as it produced a higher adjusted $R^2$ value. Spatial variation in interactions among variables could be visualized by mapping $R^2$ values from the GWR model at fine spatial resolution. Using the GWR model, we were able to identify local pollution sources, determine habitat status, and recommend appropriate land-use planning policies for watershed management.

• Journal of Cadastre & Land InformatiX
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• v.51 no.1
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• pp.39-51
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• 2021

Recently, there has been much interest in practical use of standardized HD map that can effectively define roads, lanes, junctions, road signs, and road facilities in autonomous driving. Various kinds of de jure or de facto standards such as ISO 22726-1, ISO 14296, HERE HD Live map, NDS open lane model, OpenDRIVE, and NGII HD map are currently being used. However, there are lots of differences in data modeling among these standards, it makes difficult to use them together in autonomous driving. Therefore, we propose a data model conversion method to enable an efficient use of various kinds of HD map standards in autonomous driving in this study. Specifically, we propose a conversion method between the NGII HD map model, which is easily accessible in the country, and the OpenDRIVE model, which is commonly used in the autonomous driving industry. The proposed method consists of simple conversion of NGII HD map layers into OpenDRIVE objects, new OpenDRIVE objects creation corresponding to NGII HD map layers, and linear transformation of NGII HD map layers for OpenDRIVE objects creation. Finally, we converted some test data of NGII HD map into OpenDRIVE objects, and checked the conversion results through Carla simulator. We expect that the proposed method will greatly contribute to improving the use of NGII HD map in autonomous driving.

• Journal of Korea Water Resources Association
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• v.55 no.1
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• pp.71-84
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• 2022

The objective of this study is to develop a two-dimensional (2D) flood model that can perform accurate flood analysis with simple input data. The 2D flood inundation models currently used to create flood forecast maps require complex input data and grid generation tools. This sometimes requires a lot of time and effort for flood modeling, and there may be difficulties in constructing input data depending on the situation. In order to compensate for these shortcomings, in this study, a grid-based model that can derive accurate and rapid flood analysis by reflecting correct topography as simple input data was developed. The calculation efficiency was improved by extending the existing 2×2 sub-grid model to a 5×5. In order to examine the accuracy and applicability of the model, it was applied to the Gamcheon Basin where both urban and river flooding occurred due to Typhoon Rusa. For efficient flood analysis according to user's selection, flood wave propagation patterns, accuracy and execution time according to grid size and number of sub-grids were investigated. The developed model is expected to be highly useful for flood disaster mapping as it can present the results of flooding analysis for various situations, from the flood inundation map showing accurate flooding to the flood risk map showing only approximate flooding.