• Journal of Climate Change Research
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• v.3 no.1
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• pp.25-37
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• 2012

This research proposes the direction for the assessment of local government infrastructure vulnerabilities relating to climate change driven flood and analyzes the assessment result. In this research, the local government infrastructures are evaluated by three indices such as exposure, infrastructure sensitivity, adaptive capacity and each index is calculated by selected alternative variable. Climate change scenario(A1B) developed on National Institute of Environmental Research is used to calculate present and future(2020, 2050, 2100s) exposure. As the result of infrastructure vulnerability assessment on present, the infrastructures in Seoul, Northern Gyeonggi-do, Gangwon-do, coastal area of Gyeongsangnam-do are vulnerable to flooding. For future, although the spatial pattern of flooding vulnerable infrastructure are similar, the flooding vulnerabilities of infrastructure in Gyeonggido and Ganwon-do would be increased as close to 2100s. It is expected that this research can be utilized as the preliminary analysis for climate change adaptation in local government infrastructure because this research propose the method for the assessment of local government infrastructure vulnerability relating to climate change driven flood and the result such as a trend of infrastructure vulnerability to flooding and the level of contribution of each index and alternative variable.

• Journal of Ecology and Environment
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• v.43 no.1
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• pp.43-60
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• 2019

Background: Tropical montane forests played an important role in the provision of ecosystem services. The intense degradation and deforestation for the need of agricultural land expansion result in a significant decline of forest cover. However, the expansion of agricultural land did not completely destruct natural forests. There remain forests inaccessible for agricultural and grazing purpose. Studies on these forests remained scant, motivating to investigate biomass and soil carbon stocks. Data of biomass and soils were collected in 80 quadrats ($400m^2$) systematically in 5 forests. Biomass and disturbance gradients were determined using allometric equation and disturbance index, respectively. The regression modeling is employed to explore the spatial distribution of carbon stock along disturbance and environmental gradients. Correlation analysis is also employed to identify the relation between site factors and carbon stocks. Results: The result revealed that a total of 1655 individuals with a diameter of ${\geq}5cm$, representing 38 species, were measured in 5 forests. The mean aboveground biomass carbon stocks (AGB CS) and soil organic carbon (SOC) stocks at 5 forests were $191.6{\pm}19.7$ and $149.32{\pm}6.8Mg\;C\;ha^{-1}$, respectively. The AGB CS exhibited significant (P < 0.05) positive correlation with SOC and total nitrogen (TN) stocks, reflecting that biomass seems to be a general predictor of SOCs. AGB CS between highly and least-disturbed forests was significantly different (P < 0.05). This disturbance level equates to a decrease in AGB CS of 36.8% in the highly disturbed compared with the least-disturbed forest. In all forests, dominant species sequestrated more than 58% of carbon. The AGB CS in response to elevation and disturbance index and SOC stocks in response to soil pH attained unimodal pattern. The stand structures, such as canopy cover and basal area, had significant positive relation with AGB CS. Conclusions: Study results confirmed that carbon stocks of studied forests were comparable to carbon stocks of protected forests. The biotic, edaphic, topographic, and disturbance factors played a significant variation in carbon stocks of forests. Further study should be conducted to quantify carbon stocks of herbaceous, litter, and soil microbes to account the role of the whole forest ecosystem.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.39 no.1
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• pp.93-103
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• 2019

In metropolitan cities have been reorganizing the routes of the city buses periodically due to changes in the spatial structure and traffic pattern and the lack of supply due to new developments. However, there is a lack of system to comprehensively evaluate the routes. Even if the evaluation index is not clearly defined, it may not be reasonable. Therefore, it is difficult to set the improvement direction when the bus route is reorganized. In this study, the existing evaluation indexes were reviewed and further investigation was conducted on the problematic indexes. In particular, the degree of curvature has been used as a very important index in the evaluation of the route system, but the existing curvature based curvature has a limitation in considering the traffic characteristics of individual users. For this purpose, a virtual city bus network was set up and the degree of curvature was calculated and compared based on the point - based curvature and stopping point based on individual user O/D. In this study, it is considered that more efficient and practical analysis and evaluation are possible in the evaluation of the city bus route system through the curvature considering the individual user O/D based on the stopping point. It is expected that it will be used at the time of reorganization of city bus route performed by individual local governments in the future.

• Journal of Forest and Environmental Science
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• v.34 no.6
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• pp.461-471
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• 2018

In this study, the deforestation and forest degradation areas have been obtained in Myanmar using a land cover lamp (LCM) and a tree cover map (TCM) to get the $CO_2$ potential reduction and the strength of occurrence was evaluated by using the geostatistical technique. By applying a multiple criteria decision-making method to the regions having high strength of occurrence for the $CO_2$ potential reduction for the deforestation and forest degradation areas, the priority was selected for candidate lands for REDD+ project. The areas of deforestation and forest degradation were 609,690ha and 43,515ha each from 2010 to 2015. By township, Mong Kung had the highest among the area of deforestation with 3,069ha while Thlangtlang had the highest in the area of forest degradation with 9,213 ha. The number of $CO_2$ potential reduction hotspot areas among the deforestation areas was 15, taking up the $CO_2$ potential reduction of 192,000 ton in average, which is 6 times higher than that of all target areas. Especially, the township of Hsipaw inside the Shan region had a $CO_2$ potential reduction of about 772,000 tons, the largest reduction potential among the hotpot areas. There were many $CO_2$ potential reduction hot spot areas among the forest degradation area in the eastern part of the target region and has the $CO_2$ potential reduction of 1,164,000 tons, which was 27 times higher than that of the total area. AHP importance analysis showed that the topographic characteristic was 0.41 (0.40 for height from surface, 0.29 for the slope and 0.31 for the distance from water area) while the geographical characteristic was 0.59 (0.56 for the distance from road, 0.56 for the distance from settlement area and 0.19 for the distance from Capital). Yawunghwe, Kalaw, and Hsi Hseng were selected as the preferred locations for the REDD+ candidate region for the deforestation area while Einme, Tiddim, and Falam were selected as the preferred locations for the forest degradation area.

• The Journal of the Korea institute of electronic communication sciences
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• v.16 no.3
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• pp.525-532
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• 2021

In the ocean, it is difficult to separate the effects of one cause due to the multiple causes, but the self-organizing map can be analyzed by adding other factors to the cluster result. Therefore, in this study, the results of the clustering of sea level data were applied to sea surface temperature. Sea level data was clustered into a total of 6 nodes. The difference between sea surface temperature and sea level height has a one-month delay, which applied sea surface temperature data a month ago to the clustered results. As a result of comparing the mean of sea surface temperature of 140 to 150°E, where the sea surface temperature was variously distributed, in the case of nodes 1, 3, and 5, it was possible to find a meandering sea surface temperature distribution that is clearly distinguished from the sea level data. While nodes 2, 4 and 6, the sea surface temperature distribution was smooth. In this study, sea surface temperature data were applied to the clustered results of sea level data, but later it is necessary to apply wind or geostrophic velocity data to compare.

• Journal of the Korean Institute of Educational Facilities
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• v.26 no.4
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• pp.3-10
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• 2019

In school facilities, space is the basic framework. The structure and arrangement of space will provide the form and feel of school facilities. In order to become a high-quality school facility, it is necessary to focus on the educational, human and ecological aspects of the instructor and learner until the space is conceived, designed and completed. However, even when public education was introduced in the past, it did not become a school space considering this aspect. The school space, which focuses on efficiency and labor production, is a school space that reflects the characteristics of a factory-type school, which has been occupied by a large number until recently. Although efforts to improve the quality of school facilities have been attempted in recent years, there is also a need to pursue more active changes. Future-oriented and progressive school spaces include flexibility, connectivity, individualization, diversity(creativity). In other words, space should be flexible so that it can be used faithfully according to the educational situation, not the fixed and limited school space as in the past. In the future, the school space should be open and securely linked to the place where it is essential to complete community relations with the community. In addition, space should be flexible so that the school can meet the needs of each student as much as possible. And the school space should be transformed from the space design of the past fixed pattern to reflect the close relationship between spatial, psychological, physiological, and behavioral areas. When school space needs to shift away from the past and change in a new future-oriented direction, the remaining tasks should be presented with specific characteristics and content of the direction. And the function of the consignment should be handled by related research. Although the text of this study reveals the characteristics of future-oriented school space, more concrete and empirical research results should be presented by subsequent research at home and abroad. It is necessary to reduce trial and error in creating a future-oriented school space where both professors and learners can be satisfied by analyzing the common points and differences between the results of the study. In order to do this, it is necessary to make efforts to approach such research based on the participation of the subjects who teach and learn directly at the school site.

• Journal of the Korean Association of Geographic Information Studies
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• v.23 no.4
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• pp.156-172
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• 2020

The purpose of this study was to predict road surface temperature using high-resolution solar radiation data. The road surface temperature prediction model (RSTPM) was applied to predict road surface temperature; this model was developed based on the heat-balance method. In addition, using SOLWEIG (SOlar and LongWave Environmental Irradiance Geometry-model), the shadow patterns caused by the terrain effects were analyzed, and high-resolution solar radiation data with 10 m spatial resolution were calculated. To increase the accuracy of the shadow patterns and solar radiation, the day that was modeled had minimal effects from fog, clouds, and precipitation. As a result, shadow areas lasted for a long time at the entrance and exit of a tunnel, and in a high-altitude area. Furthermore, solar radiation clearly decreased in areas affected by shadows, which was reflected in the predicted road surface temperatures. It was confirmed that the road surface temperature should be high at topographically open points and relatively low at higher altitude points. The results of this study could be used to forecast the freezing of sections of road surfaces in winter, and to inform decision making by road managers and drivers.

• 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.

• Journal of Navigation and Port Research
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• v.46 no.3
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• pp.251-258
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• 2022

Donghae Port is adjacently located to a residential area wherein 26,933 generations are creating a living environment. The areas comprise Song-jeong village (5,754 generations) and Bukp-yeong village (21,179 generations). Major cargoes handled in Donghae Port are dusty limestone, cement, anthracite, and bituminous coal, etc. In the process of handling such cargoes, air pollutants including oxide dust and fine dust which adversely impact the living conditions and health of residents are generated, causing air pollution in the vicinity of the port. Currently, Donghae Port is making an effort to improve the operation environment of the infrastructure and equipment in stages, for the purpose of reducing air pollutant emissions caused by the port industries in a long-term perspective. In this study, the sphere of influence of fine dust exposure and the degree of air pollution in the surrounding area were analyzed such as the state of fine dust concentration and diffusion in the vicinity of Donghae Port, fine dust diffusion pattern and spatial distribution of high-concentration considering wind direction and speed characteristics during the day and seasonal cycles. A more effective plan to reduce the concentration of fine dust in nearby areas by combining reduction plan, is being developed in terms of improvement regarding port infrastructure and equipment, and reduction measures considering the characteristics of the atmosphere environment according to the daytime, nighttime and season.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1407-1422
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• 2022

This study was conducted to classify tree species and assess the classification accuracy, using SE-Inception, a classification-based deep learning model. The input images of the dataset used Worldview-3 and GeoEye-1 images, and the size of the input images was divided into 10 × 10 m, 30 × 30 m, and 50 × 50 m to compare and evaluate the accuracy of classification of tree species. The label data was divided into five tree species (Pinus densiflora, Pinus koraiensis, Larix kaempferi, Abies holophylla Maxim. and Quercus) by visually interpreting the divided image, and then labeling was performed manually. The dataset constructed a total of 2,429 images, of which about 85% was used as learning data and about 15% as verification data. As a result of classification using the deep learning model, the overall accuracy of up to 78% was achieved when using the Worldview-3 image, the accuracy of up to 84% when using the GeoEye-1 image, and the classification accuracy was high performance. In particular, Quercus showed high accuracy of more than 85% in F₁ regardless of the input image size, but trees with similar spectral characteristics such as Pinus densiflora and Pinus koraiensis had many errors. Therefore, there may be limitations in extracting feature amount only with spectral information of satellite images, and classification accuracy may be improved by using images containing various pattern information such as vegetation index and Gray-Level Co-occurrence Matrix (GLCM).