• Korean Journal of Agricultural and Forest Meteorology
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• v.21 no.1
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• pp.55-64
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• 2019

The flowering seasons can be advanced due to climate change that would cause an abnormally warm winter. Such warm winter would increase the frequency of crop damages resulted from sudden occurrences of low temperature before and after the vegetative growth stages, e.g., the period from germination to flowering. The degree and pattern of freezing damage would differ by the development stage of each individual fruit tree even in an orchard. A critical temperature, e.g., killing temperature, has been used to predict freeze damage by low-temperature conditions under the assumption that such damage would be associated with the development stage of a fruit flower bud. However, it would be challenging to apply the critical temperature to a region where spatial variation in temperature would be considerably high. In the present study, a phenological model was used to estimate major bud development stages, which would be useful for prediction of regional risks for the freeze damages. We also derived a linear function to calculate a probabilistic freeze risk in spring, which can quantitatively evaluate the risk level based solely on forecasted weather data. We calculated the dates of freeze damage occurrences and spatial risk distribution according to main production areas by applying the spring freeze risk function to apple, peach, and pear crops in 2018. It was predicted that the most extensive low-temperature associated freeze damage could have occurred on April 8. It was also found that the risk function was useful to identify the main production areas where the greatest damage to a given crop could occur. These results suggest that the freezing damage associated with the occurrence of low-temperature events could decrease providing early warning for growers to respond abnormal weather conditions for their farm.

• Journal of the Korean earth science society
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• v.43 no.6
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• pp.691-711
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• 2022

The state-of-the-art Earth system model as a virtual Earth is required for studies of current and future climate change or climate crises. This complex numerical model can account for almost all human activities and natural phenomena affecting the atmosphere of Earth. The Unified Model (UM) from the United Kingdom Meteorological Office (UK Met Office) is among the best Earth system models as a scientific tool for studying the atmosphere. However, owing to the expansive numerical integration cost and substantial output size required to maintain the UM, individual research groups have had to rely only on supercomputers. The limitations of computer resources, especially the computer environment being blocked from outside network connections, reduce the efficiency and effectiveness of conducting research using the model, as well as improving the component codes. Therefore, this study has presented detailed guidance for installing a new version of the UM on high-performance parallel computers (Linux clusters) owned by individual researchers, which would help researchers to easily work with the UM. The numerical integration performance of the UM on Linux clusters was also evaluated for two different model resolutions, namely N96L85 (1.875° ×1.25° with 85 vertical levels up to 85 km) and N48L70 (3.75° ×2.5° with 70 vertical levels up to 80 km). The one-month integration times using 256 cores for the AMIP and CMIP simulations of N96L85 resolution were 169 and 205 min, respectively. The one-month integration time for an N48L70 AMIP run using 252 cores was 33 min. Simulated results on 2-m surface temperature and precipitation intensity were compared with ERA5 re-analysis data. The spatial distributions of the simulated results were qualitatively compared to those of ERA5 in terms of spatial distribution, despite the quantitative differences caused by different resolutions and atmosphere-ocean coupling. In conclusion, this study has confirmed that UM can be successfully installed and used in high-performance Linux clusters.

• Korean Journal of Remote Sensing
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• v.39 no.2
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• pp.193-205
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• 2023

A large amount of floating debris from land-based sources during heavy rainfall has negative social, economic, and environmental impacts, but there is a lack of monitoring systems for floating debris accumulation areas and amounts. With the recent development of artificial intelligence technology, there is a need to quickly and efficiently study large areas of water systems using drone imagery and deep learning-based object detection models. In this study, we acquired various images as well as drone images and trained with You Only Look Once (YOLO)v5s and the recently developed YOLO7 and YOLOv8s to compare the performance of each model to propose an efficient detection technique for land-based floating debris. The qualitative performance evaluation of each model showed that all three models are good at detecting floating debris under normal circumstances, but the YOLOv8s model missed or duplicated objects when the image was overexposed or the water surface was highly reflective of sunlight. The quantitative performance evaluation showed that YOLOv7 had the best performance with a mean Average Precision (intersection over union, IoU 0.5) of 0.940, which was better than YOLOv5s (0.922) and YOLOv8s (0.922). As a result of generating distortion in the color and high-frequency components to compare the performance of models according to data quality, the performance degradation of the YOLOv8s model was the most obvious, and the YOLOv7 model showed the lowest performance degradation. This study confirms that the YOLOv7 model is more robust than the YOLOv5s and YOLOv8s models in detecting land-based floating debris. The deep learning-based floating debris detection technique proposed in this study can identify the spatial distribution of floating debris by category, which can contribute to the planning of future cleanup work.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.32 no.6D
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• pp.679-686
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• 2012

KTX step 1 April 2004, after the opening, the second phase of the project was opened in November 2010. High-speed rail after the opening and continue to increase the demand of high-speed rail, Have the speed of competitive advantage compared too the means of transportation. The opening of these high-speed rail has led to changes of the move, the company's position, and the spatial structure of the population of reorganization, such as the social, economic, transportation. In this study, survey data using the High Speed Rail Station EMME/2 of the program to take advantage of the 2-Dimentional Blancing trip distribution to investigate the passage through the trip distribution by the estimation of the parameters of the model to estimate the distribution of the means of access and high-speed rail station to reproduce and Analysis of the results by means of access parameters (${\theta}$) autos 0.0395, buses 0.0390, subway 0.0650, taxi 0.0415, the frequency distribution (Trip Length Frequency Distribution: TLFD) were analyzed survey data value model with the results of comparing $R^2$ cars analysis and model values similar survey data 0.909 bus 0.923, subway 0.745 to 0.922, taxi, F test P value analysis is smaller than 0.05 at the 95% confidence level as a note that was judged to have been. Trip frequency distribution analysis, but in the future, set the unit to 5km-trip frequency distribution middle zone Units from small zone units (administrative district) segmentation research is needed, and can reflect the trip distance 0~5 km interval combined function to take advantage of the gravity model and the 3-Dimentional Blancing applied research is needed to be considered.

• Journal of Environmental Policy
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• v.12 no.3
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• pp.21-47
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• 2013

The aim is to analysis landslide vulnerability in Inje, Korea, using GCI(Geospatial Correlative Integration) and probability rainfalls based on geographic information system (GIS). In order to achieve this goal, identified indicators influencing landslides based on literature review. We include indicators of exposure to climate(rainfall probability), sensitivity(slope, aspect, curvature, geology, topography, soil drainage, soil material, soil thickness and soil texture) and adaptive capacity(timber diameter, timber type, timber density and timber age). All data were collected, processed, and compiled in a spatial database using GIS. Karisan-ri that had experienced 470 landslides by Typhoon Ewinia in 2006 was selected for analysis and verification. The 50% of landslide data were randomly selected to use as training data, while the other 50% being used for verification. The probability of landslides for target years (1 year, 3 years, 10 years, 50 years, and 100 years) was calculated assuming that landslides are triggered by 3-day cumulative rainfalls of 449 mm. Results show that number of slope has comparatively strong influence on landslide damage. And inclination of $25{\sim}30^{\circ}C$, the highest correlation landslide. Improved previous landslide vulnerability methodology by adopting GCI. Also, vulnerability map provides meaningful information for decision makers regarding priority areas for implementing landslide mitigation policies.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.721-732
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• 2016

The accuracy and error characteristics of microwave Sea Surface Temperature (SST) measurements in the Northwest Pacific were analyzed by utilizing 162,264 collocated matchup data between GCOM-W1/AMSR2 data and oceanic in-situ temperature measurements from July 2012 to August 2016. The AMSR2 SST measurements had a Root-Mean-Square (RMS) error of about $0.63^{\circ}C$ and a bias error of about $0.05^{\circ}C$. The SST differences between AMSR2 and in-situ measurements were caused by various factors, such as wind speed, SST, distance from the coast, and the thermal front. The AMSR2 SST data showed an error due to the diurnal effect, which was much higher than the in-situ temperature measurements at low wind speed (<6 m/s) during the daytime. In addition, the RMS error tended to be large in the winter because the emissivity of the sea surface was increased by high wind speeds and it could induce positive deviation in the SST retrieval. Low sensitivity at colder temperature and land contamination also affected an increase in the error of AMSR2 SST. An analysis of the effect of the thermal front on satellite SST error indicated that SST error increased as the magnitude of the spatial gradient of the SST increased and the distance from the front decreased. The purpose of this study was to provide a basis for further research applying microwave SST in the Northwest Pacific. In addition, the results suggested that analyzing the errors related to the environmental factors in the study area must precede any further analysis in order to obtain more accurate satellite SST measurements.

• Journal of Korean Society of Forest Science
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• v.94 no.6
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• pp.410-419
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• 2005

Community structure of evergreen broad-leaved forest of Mt. Yeogwi in Jin-do was investigated to secure sustainable conservation of evergreen broad-leaved forest. Twenty survey plots of $20m{\times}20m$ were established along the elevation from 200 m to 390 m above the sea level in the northwestern slope. The woody species were tallied at each plots. DBH distribution of the major evergreen broad-leaved trees was investigated to find out the spatial distribution. Cluster analysis was applied to a set of vegetation data, that is, importance value to classify the forest community. Species diversity, evenness and species correlation were analysed. Canonical correspondence analysis was also applied to vegetation data and soil environmental data. Cluster analysis showed that the forests of Mt. Yeogwi were classified into 2 community groups, such as Camellia japonica community group and Quercus acuta community group. C japonica community group was subdivided into C japonica- evergreen broad-leaved trees community, C. japonica-Neolitsea sericea community, and C. japonica-deciduous broad-leaved trees community. Q. acuta community group was subdivided into Q. acuta-C. japonica community, and Q. acuta community. C. japonica, Q. acuta, N. sericea, Machilus thunbergii and Illicium religiosum were dominant evergreen broad-leaved tree species, while Styrax japonica, Lindera erythrocarpa, Cornus kousa, Prunus sargentii, Albizzia julibrisin and Quercus acutissima were major deciduous tree species. Species diversity was greater in Q. acuta community group (0.8231 for Q. acuta-C. japonica community, and 0.8135 for Q. acuta community) than in C. japonica community group (0.7674 for C. japonica-evergreen broad-leaved trees community, 0.6164 for C. japonica-N. sericea community, and 0.7931 for C. japonica-deciduous broad-leaved trees community). DBH 2 cm~10 cm of major evergreen broad-leaved trees occupied 80% of all. C. japonica of less than DBH 5 cm tended to aggregate but distribute randomly or regularly with increasing DBH. Q. acuta distributed more aggregately than C. japonica and Machilus thunbergii for more than 15 cm of DBH. C. japonica correlated negatively with Q. acuta, I. religiosum and C. kousa, but positively with N. sericea. with significance at 1% level. Q. acuta correlated negatively with C. japonica, N. sericea and M. thunbergii but positively with l. religiosum. There were also negative correlation between N. sericea and C. kousa. CCA showed that forest communities were correlated with soil variables such as CEC, soil organic matter and soil pH. With increased soil organic matter and CEC, and decreased soil pH, C. japonica was dominant and forest community and Q. acuta and N. sericea tended to develop in the evergreen broad-leaved forest.

• Korean Journal of Environmental Biology
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• v.28 no.4
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• pp.179-187
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• 2010

The Theory of Island Biogeography describes that the number of species on an island affected by island area and distance from the mainland. This study was performed to compare and analyze the community structure of the macro-invertebrates in three isolated islands, around Korean waters in terms of the Theory of Island Biogeography. Macrobenthic animals were collected using a modified underwater quadrat in August 2009. A total of 104 macrobenthic species were sampled with a mean density of 399 individuals $m^{-2}$ and biomass of 1,506.70 g $m^{-2}$. Based on the abundance and biomass data, there were 10 dominant species accounting for approximately 67.17% of total individuals. The highest densities were found in the amphipoda Amphipoda spp., the bivalvia Modiolus agripetus and Mytilus coruscus, the Sipunculida Phascolosoma scolops and the polychaeta Syllidae unid.. On the contrary, the top ten species made up 95.66% of the total biomass while the three most abundant, the bivalves M. coruscus, Streostria circumpicta and M. agripetus. The conventional multi-variate statistics (cluster analysis and non-metric multi-dimensional scaling) applied to assess spatial variation in macrobenthic assemblages. Cluster analysis and nMDS ordination analysis based on the Bray-Curtis similarity identified 2 station groups. The group 1 was consisted with Gageodo (except for lower station at Transect 2) and Dokdo station and was numerically dominated by the polychaetes Eunice antennata and Syllidae unid., the cirripedia Megabalanus rosa and the bivalvia M. coruscus. However, group 2 was associated with Sohwado station and was characterized by high abundance of the anomura Petrolisthes japonicus, the gastropoda Lirularia pygmaea and the brachiopoda Coptothyris grayi. In conclusion, these results suggested that the species diversity and community structure of macrobenthos in three isolated island seemed slightly related to island area and distance from the mainland.

• Korean Journal of Environmental Agriculture
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• v.17 no.3
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• pp.260-267
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• 1998

This experiment used the enclosed bench-scale reactors of 242 liters was conducted to obtain basic data on temporal and spatial variations in temperature, oxygen and moisture content, which were important factors of biological activities, during composting of mixture of dairy manure and rice straw. The reactors with thermocouples, oxygen sensor and datalogger were aerated at four different rates of 0.09, 0.18, 0.90 and 1.79 l $min^{-1}kg$ dry $solids^{-1}$. The higher aeration rates were, the faster the rates of increase and decrease in composting temperature were in both of initial and turnover stage, and the smaller the temperature difference between exhaust air and composting materials. Composting temperature of initial stage increased suddenly in all aeration rates, then stationary phase of temperature in materials and exhaust air showed at $50{\sim}53^{\circ}C$ for 5 hours and at $45^{\circ}C$ between 5 and 15 hours, respectively. In initial stage the maximum temperature was decreased with increasing aeration rates but in the stage after turnover it was the opposite except for 1.79 l $min^{-1}kg^{-1}$. Time arrived at the maximum temperature of composting materials was later in low-aeration rates than high-aeration rates at both stages. Time maintained high-temperature more than $45^{\circ}C$ was rapidly decreased with increasing aeration rates. In initial stage of composting maintaining time of $65^{\circ}C$ or more was the longest in the treatments of 0.09 and 0.18 l $min^{-1}kg{-1}$, while those of $55{\sim}65^{\circ}C$ and $45{\sim}55^{\circ}C$ was in 0.90 and 1.79 l $min^{-1}kg{-1}$, respectively. The minimum oxygen content and the maximum oxygen consumption rate in exhaust air through composting materials showed the increased trends with increasing aeration rates. In initial stage the minimum oxygen content was ranged from 0.9% to 7.4% for 32 to 59.5 hours and the maximum oxygen consumption rate was $1.89{\sim}6.48$ $gh^{-1}kgVS^{-1}$. In the stage after turnover their levels were $2.1{\sim}19.9%$ and $1.76{\sim}3.49 %$g/h-㎏ VS, respectively, for 16 to 49.5 hours.

• 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.