• Journal of Korean Society for Geospatial Information Science
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• v.12 no.1 s.28
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• pp.69-77
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• 2004

Since there are a lot of changes in climate on domestic and natural disasters owing to the disturbance-development of the land, damages of properties and human life frequently occur due to the coastal floodings. Accordingly, it is necessary to find the area where the danger of flooding coasts is relatively high and to inform resident the characteristics of the area As a part of preventive land management to minimize the flooding damages of the coastal area, this study suggested the generation of the coastal flood hazard map that provides detailed information such as refuge path, a place of refuge, and the location of medical supplies, food, and main rescue equipment, etc. This study selected the southern region of Daebu-do as an exemplary area, conducted a document study to establish GIS data, secured pre-structured data, and suggested the method of establishing GIS data fit to the study area. In particular, it emphasized the efficient construction of the geographical spatial data that were accurate, economic, objective, and realistic in supporting the modeling to predict the flooding zone. The specific type of established database was divided into flooding risk area, flooding warning area, and flooding-volume area. The prototype of coastal flood hazard map can be widely used for efficient disaster management. Furthermore, it is considered that the map could be applied for arousing residents' attentions to the flooding, prior education, and local governments' management actions against the danger of flooding.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.4
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• pp.175-182
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• 2000

Spatial interpolation of daily temperature forecasts and observations issued by public weather services is frequently required to make them applicable to agricultural activities and modeling tasks. In contrast to the long term averages like monthly normals, terrain effects are not considered in most spatial interpolations for short term temperatures. This may cause erroneous results in mountainous regions where the observation network hardly covers full features of the complicated terrain. We developed a spatial interpolation model for daily minimum temperature which combines inverse distance squared weighting and elevation difference correction. This model uses a time dependent function for 'mountain slope lapse rate', which can be derived from regression analyses of the station observations with respect to the geographical and topographical features of the surroundings including the station elevation. We applied this model to interpolation of daily minimum temperature over the mountainous Korean Peninsula using 63 standard weather station data. For the first step, a primitive temperature surface was interpolated by inverse distance squared weighting of the 63 point data. Next, a virtual elevation surface was reconstructed by spatially interpolating the 63 station elevation data and subtracted from the elevation surface of a digital elevation model with 1 km grid spacing to obtain the elevation difference at each grid cell. Final estimates of daily minimum temperature at all the grid cells were obtained by applying the calculated daily lapse rate to the elevation difference and adjusting the inverse distance weighted estimates. Independent, measured data sets from 267 automated weather station locations were used to calculate the estimation errors on 12 dates, randomly selected one for each month in 1999. Analysis of 3 terms of estimation errors (mean error, mean absolute error, and root mean squared error) indicates a substantial improvement over the inverse distance squared weighting.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.2
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• pp.47-56
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• 2020

Decline of pine forests happens in Korea due to various disturbances such as insect pests, forest fires and extreme climate, which may further continue with ongoing climate change. For conserving and reestablishing pine forests, understanding climate-induced future shifts of pine tree distribution is a critical concern. This study predicts future geographical distribution of Pinus densiflora, using Maximum Entropy Model (MaxEnt). Input data of the model are locations of pine tree stands and their environmental variables such as climate were prepared for the model inputs. Alternative future projections for P. densiflora distribution were conducted with RCP 4.5 and RCP 8.5 climate change scenarios. As results, the future distribution of P. densiflora steadily decreased under both scenarios. In the case of RCP 8.5, the areal reductions amounted to 11.1% and 18.7% in 2050s and 2070s, respectively. In 2070s, P. densiflora mainly remained in Kangwon and Gyeongsang Provinces. Changes in temperature seasonality and warming winter temperature contributed primarily for the decline of P. densiflora., in which altitude also exerted a critical role in determining its future distribution geographic vulnerability. The results of this study highlighted the temporal and spatial contexts of P. densiflora decline in Korea that provides useful ecological information for developing sound management practices of pine forests.

• Journal of Korea Water Resources Association
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• v.44 no.12
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• pp.1015-1029
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• 2011

Conventional MCDA techniques have been used in the field of water resources in the past. A GIS can offer an effective spatial data-handling tool that can enhance water resources modeling through interfaces with sophisticated models. However, GIS systems have a limited capability as far as the analysis of the value structure is concerned. The MCDA techniques provide the tools for aggregating the geographical data and the decision maker's preferences into a one-dimensional value for analyzing alternative decisions. In other words, the MCDA allows multiple criteria to be used in deciding upon the best alternatives. The combination of GIS and MCDA capabilities is of critical importance in spatial multi-criteria analysis. The advantage of having spatial data is that it allows the consideration of the unique characteristics at every point. The purpose of this study is to identify, review, and evaluate the performance of a number of conventional MCDA techniques for integration with GIS. Even though there are a number of techniques which have been applied in many fields, this study will only consider the techniques that have been applied in floodplain decision-making problems. Two different methods for multi-criteria evaluation were selected to be integrated with GIS. These two algorithms are Compromise Programming (CP), Spatial Compromise Programming (SCP). The target region for a demonstration application of the methodology was the Suyoung River Basin in Korea.

• Journal of the Korean Society of Marine Environment & Safety
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• v.27 no.6
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• pp.721-730
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• 2021

Maritime air pollutants around port cities have gained a great deal of attention due to their direct impacts on regional air quality. This study aims to determine the geographical properties of sea/land breezes in different areas to discover overall ranges of maritime emission dispersion. The HOTMAC-RAPTAD modeling program was used to simulate regional-scale air dispersion considering non-linear and unsteady states during the general summer period for the target areas of the Yellow Sea (Incheon Port and Pyeongtaek·Dangjin Ports), archipelago region (Mokpo Port), South and East Sea (Busan and Masan Ports) and East Sea with mountainous area (Donghae·Mukho Ports). The resulting dispersion lengths of vessel emissions into the onshore regions around the target ports shed light on portal air quality management, because vessel emissions from the Incheon, Mokpo, Busan, and Donghae·Mukho ports were transported 27-31km (Western Seoul), 21-24km (Southern Muan), 20-26km (Gimhae and Yangsan), and 22-25km (Taebeak Mountains), respectively. Therefore, the results of this study provide useful data for regional air quality management and marine air pollution mitigation to improve the sustainability of port cities.

• The Journal of the Korea Contents Association
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• v.19 no.8
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• pp.332-344
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• 2019

Based on the life of the furniture is to reflect a region and the important basis of ideological and cultural characteristics of The Times culture form the traditional concept of directly determine the style and features of furniture. Due to the geographical location, China and the Korean peninsula have a long history of cultural exchanges. Through long-term exchanges, Chinese traditional culture has penetrated into the daily life of the ancestors of the Korean peninsula in various ways. As one of the traditional Chinese cultures, Taoism began to spread in The Three Kingdoms period on the Korean peninsula. With the integration and development of Taoism on the Korean peninsula, Taoism culture with unique characteristics of the peninsula was formed and became part of the traditional ideological and cultural life of the ancestors on the peninsula. In the historical development of furniture on the Korean peninsula, Taoist theories such as yin-yang theory and five-element theory and geomantic geography theory have exerted an important influence on the use, shape, material and pattern of traditional furniture on the Korean peninsula. The late period of the joseon dynasty was the heyday of the handicraft industry on the Korean peninsula. During this period, the categories of furniture increased, and a large number of furniture with distinctive Taoist characteristics, beautiful shape, excellent design and different uses appeared. Through the study on the modeling, materials, patterns, seals and designs of furniture in the late period of joseon dynasty, this study confirms that Taoist thoughts are one of the main factors affecting the development of Korean traditional furniture forms and patterns. Using patterns of various natural objects or plants and animals for furniture design, it is to pray for family members to avoid disasters and disasters. Thus it can be seen that praying for blessings from heaven is the main Taoist thought.

• 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 the Korea Institute of Information Security & Cryptology
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• v.17 no.2
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• pp.125-137
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• 2007

This paper proposes a new scheme to provide the location privacy of sources in Wireless Sensor Networks (WSNs). Because the geographical location of a source sensor reveals contextual information on an 'event' in WSN, anonymizing the source location is an important issue. Despite abundant research efforts, however, about data confidentiality and authentication in WSN, privacy issues have not been researched well so far. Moreover, many schemes providing the anonymity of communication parties in Internet and Ad-hoc networks are not appropriate for WSN environments where sensors are very resource limited and messages are forwarded in a hop-by-hop manner through wireless channel. In this paper, we first categorize the type of eavesdroppers for WSN as Global Eavesdropper and Compromising Eavesdropper. Then we propose a novel scheme which provides the anonymity of a source according to the types of eavesdroppers. Furthermore, we analyze the degree of anonymity of WSN using the entropy-based modeling method. As a result, we show that the proposed scheme improves the degree of anonymity compared to a method without any provision of anonymity and also show that the transmission range plays a key role to hide the location of source sensors.

• Journal of Korean Society of Disaster and Security
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• v.13 no.4
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• pp.25-36
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• 2020

An increasing frequency and intensity of natural disasters have been observed due to climate change. To better prepare for these, the MOIS (ministry of the interior and safety) announced a comprehensive plan for minimizing damages associated with natural disasters, including drought and heavy snowfall. The spatial-temporal pattern of snowfall is greatly influenced by temperature and geographical features. Heavy snowfalls are often observed in Gangwon-do, surrounded by mountains, whereas less snowfall is dominant in the southern part of the country due to relatively high temperatures. Thus, snow depth data often contains zeros that can lead to difficulties in the selection of probability distribution and estimation of the parameters. A generalized mixture distribution approach to a maximum snow depth series over the southern part of Korea (i.e., Changwon, Tongyeoung, Jinju weather stations) are located is proposed to better estimate a threshold (𝛿) classifying discrete and continuous distribution parts. The model parameters, including the threshold in the mixture model, are effectively estimated within a Bayesian modeling framework, and the uncertainty associated with the parameters is also provided. Comparing to the Daegwallyeong weather station, It was found that the proposed model is more effective for the regions in which less snow depth is observed.

• Korean Journal of Environmental Biology
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• v.39 no.4
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• pp.581-589
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• 2021

Using visible-near infrared(Vis-NIR) spectra combined with machine learning methods, the feasibility of quick and non-destructive classification of Convolvulaceae species was studied. The main aim of this study is to classify six Convolvulaceae species in the field in different geographical regions of South Korea using a handheld spectrometer. Spectra were taken at 1.5 nm intervals from the adaxial side of the leaves in the Vis-NIR spectral region between 400 and 1,075 nm. The obtained spectra were preprocessed with three different preprocessing methods to find the best preprocessing approach with the highest classification accuracy. Preprocessed spectra of the six Convolvulaceae sp. were provided as input for the machine learning analysis. After cross-validation, the classification accuracy of various combinations of preprocessing and modeling ranged between 43.4% and 98.6%. The combination of Savitzky-Golay and Support vector machine methods showed the highest classification accuracy of 98.6% for the discrimination of Convolvulaceae sp. The growth stage of the plants, different measuring locations, and the scanning position of leaves on the plant were some of the crucial factors that affected the outcomes in this investigation. We conclude that Vis-NIR spectroscopy, coupled with suitable preprocessing and machine learning approaches, can be used in the field to effectively discriminate Convolvulaceae sp. for effective weed monitoring and management.