• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.4
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• pp.78-86
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• 2011

The recovering of historical mountain fortress needs the maintenance of forest scape for achieving visibility. In the study, the methods for the maintenance of the forest around the fortress were proposed. The Cumulative Visibility Analysis and Multiple Sight Surface Analysis were tested to verify the methods using GIS on the Myeong-hwal Fortress in Kyungju. The results of the study are as follows. First, the Cumulative Visibility Analysis was made on the Myeong-hwal Fortress from surrounding major viewpoints. The Cumulative Visibility Analysis enables the selection of excellent visibility sectors on the fortress. The 6 excellent visibility sectors were 1,937m(which occupied 41.2% of the area). Second, two cases of pine tree height were compared in the Cumulative Visibility Analysis. One used the average height of pines and the other used the maximum growth height. The comparative result demonstrated that the case of average height would be more effective for deciding the pine removal zone as well as achieving visibility to the mountain fortress. Third, to examine the feasibility of the management method, the tree removal plan and removal execution were compared on the A zone which showed high visibility frequency. Asa comparative result, there was insignificant difference(3.3%) in area between the tree removal plan($10,935m^2$) and removal execution($11,296m^2$). This study proved the Cumulative Visibility Analysis and Multiple Sight Surface Analysis to be effective for forest scape maintenance around a mountain fortress.

• Atmosphere
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• v.23 no.2
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• pp.221-229
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• 2013

There has been an increasing need to assess the effects of climate change on human health. It is hard to use climate data to evaluate health effects because such data have a grid format, which could not represent specific cities or provinces. Therefore, the grid-format climate data of South Korea based on RCP (Representative Concentration Pathway) scenarios were modified into area-format climate data according to the major cities or provinces of the country, up to the year 2100. Moreover, heat index (HI) and discomfort index (DI) databases were developed from the modified climate database. These databases will soon be available for experts via a Website, and the expected HI and DI of any place in the country, or at any time, can be found in the country's climate homepage (http://www.climate.go.kr). The HI and DI were analyzed by plotting the average indices every ten years, and by comparing cities or provinces with index level changes, using the geographic information system (GIS). Both the HI and DI are expected to continually increase from 2011 to 2100, and to reach the most dangerous level especially in August 2100. Among the major cities of South Korea, Gwangju showed the highest HI and DI, and Gangwon province is expected to be the least affected area in terms of HI and DI among all the country's provinces.

• Journal of the Korean Society of Marine Environment & Safety
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• v.22 no.5
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• pp.389-401
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• 2016

This study attempted to present the monthly distribution of the inshore gill netters for the prevention of propeller failure caused by the damage of fishing gear by the merchant vessels in the West Sea. This study was conducted using the transmitting device of fishing location (V-Pass) data for 1 year in 2014. The grid intervals are $30minute{\times}30minute$ ($latitude{\times}longitude$) based on the section of the sea. A total of 56 sections were analyzed by constructing a grid. The results revealed that the section no. 194 with the highest density index showed no fishing gear damage accident. But the section no. 193 with a relatively lower density index showed more frequent fishing gear damage accident. It is because the section no. 193 is included in the main route of the merchant vessels. Based on this analysis, it is found that the fishing gears are not damaged in proportion to density index but risk of damage exists according to the main routes of merchant vessels. Therefore, for the safe navigation of the merchant vessels, notifying the waters of $34.5^{\circ}{\sim}35.5^{\circ}$ at latitude and $125.67^{\circ}{\sim}126^{\circ}$ at longitude, as a navigational warning area from May through September, will prevent marine accident, such as propeller failure and fishing gear damage. Accordingly, when the merchant vessels navigate in the section no. 193 and 203, the area of $34.5^{\circ}{\sim}35.5^{\circ}$ at latitude and $125.5^{\circ}{\sim}125.67^{\circ}$ at longitude is recommended for the safe navigation.

• Korean Journal of Remote Sensing
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• v.36 no.6_3
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• pp.1643-1652
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• 2020

To investigate the characteristics of detailed flows in a building-congested district, we coupled a computation fluid dynamics (CFD) model to the local data assimilation and prediction system (LDAPS), a current operational numerical weather prediction model of the Korea Meteorological Administration. For realistic numerical simulations, we used the meteorological variables such as wind speeds and directions and potential temperatures predicted by LDAPS as the initial and boundary conditions of the CFD model. We trilinearly interpolated the horizontal wind components of LDAPS to provide the initial and boudnary wind velocities to the CFD model. The trilinearly interpolated potential temperatures of LDAPS is converted to temperatures at each grid point of the CFD model. We linearly interpolated the horizontal wind components of LDAPS to provide the initial and boundary wind velocities to the CFD model. The linearly interpolated potential temperatures of LDAPS are converted to temperatures at each grid point of the CFD model. We validated the simulated wind speeds and directions against those measured at the PKNU-SONIC station. The LDAPS-CFD model reproduced similar wind directions and wind speeds measured at the PKNU-SONIC station. At 07 LST on 22 June 2020, the inflow was east-north-easterly. Flow distortion by buildings resulted in the east-south-easterly at the PKNU-SONIC station, which was the similar wind direction to the measured one. At 19 LST when the inflow was southeasterly, the LDAPS-CFD model simulated southeasterly (similar to the measured wind direction) at the PKNU-SONIC station.

• Journal of Environmental Health Sciences
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• v.50 no.1
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• pp.6-15
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• 2024

Background: People's activities have been restricted due to the COVID-19 pandemic. These changes in activity patterns may lead to a decrease in fine particulate matter (PM_2.5) concentrations. Additionally, the level of population exposure to PM_2.5 may be changed. Objectives: This study aimed to analyze the impact of population movement and meteorological factors on the distribution of PM_2.5 concentrations before and after the outbreak of COVID-19. Methods: The study area was Guro-gu in Seoul. The research period was selected as January to March 2020, a period of significant population movement changes caused by COVID-19. The evaluation of the dynamic population was conducted by calculating the absolute difference in population numbers between consecutive hours and comparing them to determine the daily average. Ambient PM_2.5 concentrations were estimated for each grid using ordinary kriging in Python. For the population exposure assessment, the population-weighted average concentration was calculated by determining the indoor to outdoor population for each grid and applying the indoor to outdoor ratio to the ambient PM_2.5 concentration. To assess the factors influencing changes in the ambient PM_2.5 concentration, a statistical analysis was conducted, incorporating population mobility and meteorological factors. Results: Through statistical analysis, the correlation between ambient PM_2.5 concentration and population movement was positive on both weekends and weekdays (r=0.71, r=0.266). The results confirmed that most of the relationships were positive, suggesting that a decrease in human activity can lead to a decrease in PM_2.5 concentrations. In addition, when population-weighted concentration averages were calculated and the exposure level of the population group was compared before and after the COVID-19 outbreak, the proportion of people exceeding the air quality standard decreased by approximately 15.5%. Conclusions: Human activities can impact ambient concentrations of PM_2.5, potentially altering the levels of PM_2.5 exposure in the population.

• 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 Korean Society of Rural Planning
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• v.11 no.4 s.29
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• pp.47-57
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• 2005

The goal of this research is to evaluate landscape-ecological characteristics of watersheds in the Nakdong River Basin by using Geogaphic Information System (GIS) and landscape indices for integation of spatio-temporal informations and multivariate statistical techniques for quantitative analysis of forest landscape. Fragmentation index and change matrix techniques using factor analysis and grid overlay method were used to efficiently analyze and manage huge amount of information for ecological-environmental assessment (land-cover and forest landscape patterns). According to the results based on the pattern analysis of land-cover changes using the change detection matrix between 1980s and 1990s, addition on 750km$^2$ became urbanized areas. The altered 442.04km$^2$ was agricultural areas which is relatively easy for shifting of land-use, and 205.1km$^2$ of forests became urbanized areas, and average elevation and slope of the whole altered areas were 75m and 4$^{\circ}$. On the other hand, 120km$^2$ of urban areas were changed into other areas (i.e., agricultural areas and green space), and fortunately, certain amount of naturalness had been recovered. But still those agricultural areas and fallow areas, which were previously urban areas, had high potential of re-development for urbanization due to their local conditions. According to the structural analysis of forest landscape using the landscape indices, the forest fragmentation of watersheds along the main stream of the Nakdong River was more severe than my other watersheds. Furthermore, the Nakdong-sangju and Nakdong-miryang watersheds had unstable forest structures as well as least amount of forest quantity. Thus, these areas need significant amount of forest through a new forest management policy considering local environmental conditions.

• Korean Journal of Remote Sensing
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• v.33 no.4
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• pp.423-436
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• 2017

This paper presents a deep learning-based road segmentation framework from very high-resolution orthophotos. The proposed method uses Deep Convolutional Autoencoders for end-to-end mapping of orthophotos to road segmentations. In addition, a set of post-processing steps were applied to make the model outputs GIS-ready data that could be useful for various applications. The optimization of the model's parameters is explained which was conducted via grid search method. The model was trained and implemented in Keras, a high-level deep learning framework run on top of Tensorflow. The results show that the proposed model with the best-obtained hyperparameters could segment road objects from orthophotos at an average accuracy of 88.5%. The results of optimization revealed that the best optimization algorithm and activation function for the studied task are Stochastic Gradient Descent (SGD) and Exponential Linear Unit (ELU), respectively. In addition, the best numbers of convolutional filters were found to be 8 for the first and second layers and 128 for the third and fourth layers of the proposed network architecture. Moreover, the analysis on the time complexity of the model showed that the model could be trained in 4 hours and 50 minutes on 1024 high-resolution images of size $106{\times}106pixels$, and segment road objects from similar size and resolution images in around 14 minutes. The results show that the deep learning models such as Convolutional Autoencoders could be a best alternative to traditional machine learning models for road segmentation from aerial photographs.

• Journal of Environmental Science International
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• v.23 no.12
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• pp.2057-2069
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• 2014

This study was carried out to estimate the BVOCs emissions with the emission factors which reflected the native conditions of forests in Jeju Island. This study made effective use of the previous data for the weather data and the emission rate of each organic volatile component measured at 10 species of conifers and broad leaved trees. The CORINAIR method and the grid system of $1km{\times}1km$ for whole area of Jeju Island were adopted in calculating the BVOCs emission emitted from forest. The vegetation information for Jeju Island was referred to GIS and a government report. By the results of BVOCs emission for Jeju Island, the 85% of monoterpene emission was emitted from conifers and the others was from broad leaved trees. Most of monoterpene emission was attributed to Pinus thunbergii and Cryptomeria japonica. The broad leaved trees greatly contributed to the isoprene emission and Quercus serrata played a dominant role in emission of isoprene. The total amount of BVOCs emission was estimated as $3612ton\;yr^{-1}$ in Jeju Island. The 51.1% of total emission was contributed to conifers, the 44.9% to broad leaved trees, and the 4.0% to grassland. Of total emission of BVOCs, monoterpene accounted for 32.3%, isoprene for 28.0%, and OVOCs for 39.7%. The BVOCs emission estimated by this study was less than that estimated by other previous study. This means that it is important to survey the emission rate at native conditions and gather the detailed information for various species of vegetation on target region.

• Proceedings of the Korea Water Resources Association Conference
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• 2011.05a
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• pp.335-335
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• 2011

토양유실은 농업환경지표를 비롯한 국제 규범에서 농업에 의한 환경오염의 핵심문제로 제기되고 있다. 이러한 토양유실의 문제점을 해결하기 위해 USLE를 기반으로 한 SATEEC모형을 사용하여 토양유실량 및 유사량을 산정하였다. SATEEC모형은 USLE입력자료와 DEM을 이용하여 산정된 토양유실량 과 GA-SDR모듈을 통해 산정된 유달률(Sediment Delivery Ratio, SDR)을 통하여 최종유출구에서의 유사량을 산정한다. 많은 연구자들은 최종유출구에서의 유사량을 실측유사량과 비교하여 비슷하게 모의되면 유역의 특성을 잘 반영한다고 판단하므로 SATEEC모형의 단점인 토양유실량이 과하게 산정되는 문제점을 중요하게 생각하지 않는다. 하지만 SATEEC 모형의 결과값인 유사량의 신뢰도를 향상시키기 위해서는 토양유실량 검증을 통한 정확한 입력자료 구축이 필요하다. 따라서 본 연구에서는 여러개의 토양유실량 시나리오를 만들어 이에 따른 SATEEC 모형의 유사량을 비교/평가하고, 이를 이용하여 토양유실량의 검증이 필요함을 제시하고자 한다. 본 연구에서 사용한 토양유실량 시나리오는 총 4개로써, 시나리오 1은 SATEEC모형을 이용하여 산정된 토양유실량이며, 시나리오2~4는 ArcGIS를 사용하여 기존의 토양유실량 값에 ${\pm}0.25$의 범위를 주어 새롭게 산정된 토양유실량으로 SATEEC모형을 이용한 유사량 산정시 입력자료로 활용하였다. 그 결과 SATEEC모형을 이용하여 산정된 토양유실량은 시나리오별로 차이를 보였다. 또한 SATEEC GA-SDR모듈을 통해 예측된 토양유실량 값과 실측유사량을 이용하여 유달률을 산정하였으며 유달률도 토양유실량 시나리오별로 차이를 보였다. 따라서 SATEEC모형을 이용하여 최종유출구에서의 유사량 산정 결과 토양유실량의 차이에도 불구하고 유사량은 거의 비슷한 값을 나타내고 있으며, 최종유출구에서의 모의된 예측 유사량이 실측유사량과 비교 시 R2=0.688, EI=0.643 정도로 실측유사량과 비슷한 경향을 나타냈다. 따라서 SATEEC모형을 이용하여 유사량 산정시 먼저 문헌을 통한 토양유실량 검증이 필요하리라 판단되며, 문헌을 통해 토양유실량 검증 후 정확한 입력자료를 구축하여 유역에서의 유사량 저감을 위한 최적관리 기법 분석에 사용할 수 있도록 프로세서를 구축해야 할 것이라 판단된다.