• 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 Institute of Landscape Architecture
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• v.49 no.5
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• pp.1-11
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• 2021

The purpose of this study is to find out the main factors affecting air quality in urban physical space factors, and provide clues for environmental improvement. Nine monitoring stations in China's industrial city, Changchun, collected AQI concentration data from January 1, 2018 to December 31, 2019. This paper analyzes the types and distribution characteristics of urban physical facilities within a radius of 300m with the detection station as the center. The monitoring station is divided into three groups, and the difference in floating dust concentration among the three groups in different seasons is analyzed. The results show that AQI concentration is the highest in spring and winter, followed by summer, and the lowest in autumn. The place with the highest concentrations of AQI in spring are F (93.00), D (91.10), I (89.20), in summer are D (69.05), A (67.89), B (84.44), in autumn are I (62.80), G (60.84), D (53.27), D (53.27), in winter are I (95.82), H (95.60), f (94.04). Through SPSS analysis, it shows that the air index in a space with a diameter of 600 meters is related to forest land, grassland, bare land, water space, tree height, building area (average value), and building volume (average value). According to the statistical analysis results of spring and winter with the most serious pollution, forest land area (43,637m², 15.44%) and water surface area (18,736m², 6.63%) accounted for the majority, and group 1 (A, B, C) with the least average building area (448m², 0.17%) and average building volume (10,201m²) had the lowest pollution concentration. On the contrary, group 2 (D, E, F) had the highest AQI concentration, with less or no woodland (1,917m², 0.68%) and water surface area (0m², 0%), and the highest average building area (1,056m², 0.37%) and average building volume (17,470m³). It is confirmed that the characteristics of the area with the highest AQI concentration are that the more the site ratio of tree height above 12m, the smaller the site ratio of bare land, and the lower the pollution degree. On the contrary, the larger the area of bare land, the higher the pollution degree. By analyzing the characteristics of nine monitoring stations in Changchun, it can be seen that the air quality brought by the physical characteristics of urban space is closely related to the above factors.

• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.29 no.1
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• pp.59-70
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• 2011

The purpose of this study is to present raw data to systematically and rationally manage the protected trees located in Changwon-si, Korea. This study investigated about the present condition and the information of location, individual, management, health and soil. The results are as follows. The protected trees were located in 26 spots, and species of trees were 9 taxa; Zelkova serrata, Celtis sinensis, Aphananthe aspera, Ginkgo biloba, Carpinus tschonoskii, Pinus densiflora for. multicaulis, Quercus variabilis, Pinus densiflora and Salix glandulosa. In protected tree types, shade trees were the most, and the majority of theirs were 200 years or more in age. The range of altitude was 14~173m, and the number of trees located in flat fields was the most. For location types, village and field and mountain were presented in the order and, in land use, land for building was the most. The range of height was 8.0~30.0m, 0.6~5.1m in crown height, 240~700cm in diameter of breast and 210~800cm in diameter of root. In case of crown area, Zelkova serrata of No.5 was most large. The status boards were mostly installed except No.23 and No.26. The sites with fence were 9 spots, and the site with stonework were 14 spots. The sites with the support beam were 5 spots, and most sites were not covered up with soil. The materials of bottom were soil, gravel and vegetation in the order. The range of withering branch rate was 0~40%, and peeled bark rate was 0~60%. The sites made holes were 23 spots, and the hole size of Aphananthe aspera of No.12 was the largest. The sites disturbed by human trampling were 7 spots, the sites by disease and insects of 2 spots, the sites by injury of 23 spots and the sites by exposed roots of 13 spots. In the results of soil analysis, there showed that acidity was pH 4.5~8.0, organic matter content of 3.5~69.8g/kg, electrical conductivity(EC) of 0.11~2.87dS/m, available $P_2O_5$ of 3.0~490.6mg/kg, exchangeable K of 0.10~1.05cmol+/kg, exchangeable Ca of 1.41~16.45cmol+/kg, exchangeable Mg of 0.37~1.96cmol+/kg, exchangeable Na of 0.25~2.41cmol+/kg and cation exchange capacity(C.E.C) of 8.35~26.55cmol+/kg.

• Journal of Korea Water Resources Association
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• v.56 no.4
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• pp.261-272
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• 2023

In recent years, natural disasters such as heavy rainfall and typhoons have occurred more frequently, and their severity has increased due to climate change. The Korea Meteorological Administration (KMA) currently uses the same criteria for all regions in Korea for watch and warning based on the maximum cumulative rainfall with durations of 3-hour and 12-hour to reduce damage. However, KMA's criteria do not consider the regional characteristics of damages caused by heavy rainfall and typhoon events. In this regard, it is necessary to develop new criteria considering regional characteristics of damage and cumulative rainfalls in durations, establishing four stages: blue, yellow, orange, and red. A classification model, called DSCM (Disaster Severity Classification Model), for the four-stage disaster severity was developed using four machine learning models (Decision Tree, Support Vector Machine, Random Forest, and XGBoost). This study applied DSCM to local governments of Seoul, Incheon, and Gyeonggi Province province. To develop DSCM, we used data on rainfall, cumulative rainfall, maximum rainfalls for durations of 3-hour and 12-hour, and antecedent rainfall as independent variables, and a 4-class damage scale for heavy rain damage and typhoon damage for each local government as dependent variables. As a result, the Decision Tree model had the highest accuracy with an F1-Score of 0.56. We believe that this developed DSCM can help identify disaster risk at each stage and contribute to reducing damage through efficient disaster management for local governments based on specific events.

• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.3
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• pp.21-36
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• 2023

As a solution to environmental issues, such as climate change response, the carbon neutrality strategy, urban heat islands, fine dust, and biodiversity enhancement, the value of urban green spaces and trees are becoming important, and various studies dealing with the effects of trees for environmental improvement are being conducted. This study comprehensively considers the preceding studies on planting tree species, planting structure, planting density, and planting base to propose a direction for the planting renewal of green areas in urban parks and applies the findings to a renewal plan to improve the urban environment through landscaping trees. A field survey was conducted on the planting status of Seoul Children's Grand Park, a large-scale neighborhood park in Seoul, and based on the survey data, a planting function evaluation was conducted, and areas needing improvement in planting function were identified. The planting function evaluation was carried out considering the park function setting, planting concept according to spatial function, and planting status. As a result of the study, the direction of planting renewal according to functional change was derived for each stage of planting function evaluation. Increasing the green area ratio is a priority in setting up park functions, but user convenience should also be considered. As a concept of planting, visual landscape planting involves planting species with beautiful tree shapes, high carbon absorption, and fine dust reduction effects. Ecological landscape planting should create a multi-layered planting site on a slope. Buffer planting should be created as multi-layered forests to improve carbon absorption and fine dust reduction effects. Green planting should consist of broad-leaved trees and herbaceous layers and aim for the natural planting of herbaceous species. For plant species, species with high urban environment improvement effects, local native species, and wild bird preferred species should be selected. As for the planting structure, landscape planting sites and green planting sites should be composed of trees, shrubs, and trees and herbaceous layers that emphasize ecology or require multi-layered buffer functions. A higher standard is applied based on the planting interval for planting density. Installing a rainwater recycling facility and using soil loam for the planting base improves performance. The results of this study are meaningful in that they can be applied to derive areas needing functional improvement by performing planting function evaluation when planning planting renewal of aging urban parks and can suggest renewal directions that reflect the paradigm of functional change of created green areas.

• Journal of Nutrition and Health
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• v.56 no.3
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• pp.300-314
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• 2023

Purpose: This study sought to predict the reasons for skipping breakfast by adolescents aged 13-18 years using the 7^th Korea National Health and Nutrition Examination Survey (KNHANES). Methods: The participants included 1,024 adolescents. The data were analyzed using a complex-sample t-test, the Rao Scott χ²-test, and the classification and regression tree (CART) algorithm for decision tree analysis with SPSS v. 27.0. The participants were divided into two groups, one regularly eating breakfast and the other skipping it. Results: A total of 579 and 445 study participants were found to be breakfast consumers and breakfast skippers respectively. Breakfast consumers were significantly younger than those who skipped breakfast. In addition, breakfast consumers had a significantly higher frequency of eating dinner, had been taught about nutrition, and had a lower frequency of eating out. The breakfast skippers did so to lose weight. Children who skipped breakfast consumed less energy, carbohydrates, proteins, fats, fiber, cholesterol, vitamin C, vitamin A, calcium, vitamin B1, vitamin B2, phosphorus, sodium, iron, potassium, and niacin than those who consumed breakfast. The best predictor of skipping breakfast was identifying adolescents who sought to control their weight by not eating meals. Other participants who had low and middle-low household incomes, ate dinner 3-4 times a week, were more than 14.5 years old, and ate out once a day showed a higher frequency of skipping breakfast. Conclusion: Based on these results, nutrition education targeted at losing weight correctly and emphasizing the importance of breakfast, especially for adolescents, is required. Moreover, nutrition educators should consider designing and implementing specific action plans to encourage adolescents to improve their breakfast-eating practices by also eating dinner regularly and reducing eating out.

• Journal of Intelligence and Information Systems
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• v.16 no.3
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• pp.77-97
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• 2010

Market timing is an investment strategy which is used for obtaining excessive return from financial market. In general, detection of market timing means determining when to buy and sell to get excess return from trading. In many market timing systems, trading rules have been used as an engine to generate signals for trade. On the other hand, some researchers proposed the rough set analysis as a proper tool for market timing because it does not generate a signal for trade when the pattern of the market is uncertain by using the control function. The data for the rough set analysis should be discretized of numeric value because the rough set only accepts categorical data for analysis. Discretization searches for proper "cuts" for numeric data that determine intervals. All values that lie within each interval are transformed into same value. In general, there are four methods for data discretization in rough set analysis including equal frequency scaling, expert's knowledge-based discretization, minimum entropy scaling, and na$\ddot{i}$ve and Boolean reasoning-based discretization. Equal frequency scaling fixes a number of intervals and examines the histogram of each variable, then determines cuts so that approximately the same number of samples fall into each of the intervals. Expert's knowledge-based discretization determines cuts according to knowledge of domain experts through literature review or interview with experts. Minimum entropy scaling implements the algorithm based on recursively partitioning the value set of each variable so that a local measure of entropy is optimized. Na$\ddot{i}$ve and Booleanreasoning-based discretization searches categorical values by using Na$\ddot{i}$ve scaling the data, then finds the optimized dicretization thresholds through Boolean reasoning. Although the rough set analysis is promising for market timing, there is little research on the impact of the various data discretization methods on performance from trading using the rough set analysis. In this study, we compare stock market timing models using rough set analysis with various data discretization methods. The research data used in this study are the KOSPI 200 from May 1996 to October 1998. KOSPI 200 is the underlying index of the KOSPI 200 futures which is the first derivative instrument in the Korean stock market. The KOSPI 200 is a market value weighted index which consists of 200 stocks selected by criteria on liquidity and their status in corresponding industry including manufacturing, construction, communication, electricity and gas, distribution and services, and financing. The total number of samples is 660 trading days. In addition, this study uses popular technical indicators as independent variables. The experimental results show that the most profitable method for the training sample is the na$\ddot{i}$ve and Boolean reasoning but the expert's knowledge-based discretization is the most profitable method for the validation sample. In addition, the expert's knowledge-based discretization produced robust performance for both of training and validation sample. We also compared rough set analysis and decision tree. This study experimented C4.5 for the comparison purpose. The results show that rough set analysis with expert's knowledge-based discretization produced more profitable rules than C4.5.

• Korean Journal of Remote Sensing
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• v.32 no.3
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• pp.221-234
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• 2016

In Korea, most industrial parks and major cities are located in coastal areas, which results in serious environmental problems in both coastal land and ocean. In order to effectively manage such problems especially in coastal ocean, water quality should be monitored. As there are many factors that influence water quality, the Korean Government proposed an integrated Water Quality Index (WQI) based on in situmeasurements of ocean parameters(bottom dissolved oxygen, chlorophyll-a concentration, secchi disk depth, dissolved inorganic nitrogen, and dissolved inorganic phosphorus) by ocean division identified based on their ecological characteristics. Field-measured WQI, however, does not provide spatial continuity over vast areas. Satellite remote sensing can be an alternative for identifying WQI for surface water. In this study, two schemes were examined to estimate coastal WQI around Korea peninsula using in situ measurements data and Geostationary Ocean Color Imager (GOCI) satellite imagery from 2011 to 2013 based on machine learning approaches. Scheme 1 calculates WQI using estimated water quality-related factors using GOCI reflectance data, and scheme 2 estimates WQI using GOCI band reflectance data and basic products(chlorophyll-a, suspended sediment, colored dissolved organic matter). Three machine learning approaches including Random Forest (RF), Support Vector Regression (SVR), and a modified regression tree(Cubist) were used. Results show that estimation of secchi disk depth produced the highest accuracy among the ocean parameters, and RF performed best regardless of water quality-related factors. However, the accuracy of WQI from scheme 1 was lower than that from scheme 2 due to the estimation errors inherent from water quality-related factors and the uncertainty of bottom dissolved oxygen. In overall, scheme 2 appears more appropriate for estimating WQI for surface water in coastal areas and chlorophyll-a concentration was identified the most contributing factor to the estimation of WQI.

• Korean Journal of Agricultural and Forest Meteorology
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• v.22 no.4
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• pp.327-339
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• 2020

Transpiration is the movement of water into the atmosphere through leaf stomata of plant, and it accounts for more than half of evapotranspiration from the land surface. The measurements of transpiration could be conducted in various ways including eddy covariance and water balance method etc. However, the transpiration measurements of individual trees are necessary to quantify and compare the water use of each species and individual component within stands. For the measurement of the transpiration by individual tree, the thermometric methods such as heat dissipation and heat pulse methods are widely used. However, it is difficult and labor consuming to maintain the transpiration measurements of individual trees in a wide range area and especially for long-term experiment. Therefore, the sharing of sapflow data through database should be useful to promote the studies on transpiration and water balance for large spatial scale. In this paper, we present sap flow database, which have Granier type sap flux data from 18 Korean pine (Pinus koraiensis) since 2011 and 16 (Quercus aliena) since 2013 in Mt.Taehwa Seoul National University forest and 18 needle fir (Abies holophylla), seven (Quercus serrata), three (Carpinus laxiflora and C. cordata each since 2013 in Gwangneung. In addition, the database includes the sapling transpiration of nine species (Prunus sargentii, Larix kaempferii, Quercus accutisima, Pinus densiflora, Fraxinus rhynchophylla, Chamecypans obtuse, P. koraiensis, Betulla platyphylla, A. holophylla, Pinus thunbergii), which were measured using heat pulse method since 2018. We believe this is the first database to share the sapflux data in Rep. of Korea, and we wish our database to be used by other researchers and contribute a variety of researches in this field.

• Korean Journal of Remote Sensing
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• v.39 no.6_3
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• pp.1779-1790
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• 2023

Satellite-based fog detection algorithms are being developed to detect fog in real-time over a wide area, with a focus on the Korean Peninsula (KorPen). The GEO-KOMPSAT-2A/Advanced Meteorological Imager (GK2A/AMI, GK2A) satellite offers an excellent temporal resolution (10 min) and a spatial resolution (500 m), while GEO-KOMPSAT-2B/Geostationary Ocean Color Imager-II (GK2B/GOCI-II, GK2B) provides an excellent spatial resolution (250 m) but poor temporal resolution (1 h) with only visible channels. To enhance the fog detection level (10 min, 250 m), we developed a fused GK2AB fog detection algorithm (FDA) of GK2A and GK2B. The GK2AB FDA comprises three main steps. First, the Korea Meteorological Satellite Center's GK2A daytime fog detection algorithm is utilized to detect fog, considering various optical and physical characteristics. In the second step, GK2B data is extrapolated to 10-min intervals by matching GK2A pixels based on the closest time and location when GK2B observes the KorPen. For reflectance, GK2B normalized visible (NVIS) is corrected using GK2A NVIS of the same time, considering the difference in wavelength range and observation geometry. GK2B NVIS is extrapolated at 10-min intervals using the 10-min changes in GK2A NVIS. In the final step, the extrapolated GK2B NVIS, solar zenith angle, and outputs of GK2A FDA are utilized as input data for machine learning (decision tree) to develop the GK2AB FDA, which detects fog at a resolution of 250 m and a 10-min interval based on geographical locations. Six and four cases were used for the training and validation of GK2AB FDA, respectively. Quantitative verification of GK2AB FDA utilized ground observation data on visibility, wind speed, and relative humidity. Compared to GK2A FDA, GK2AB FDA exhibited a fourfold increase in spatial resolution, resulting in more detailed discrimination between fog and non-fog pixels. In general, irrespective of the validation method, the probability of detection (POD) and the Hanssen-Kuiper Skill score (KSS) are high or similar, indicating that it better detects previously undetected fog pixels. However, GK2AB FDA, compared to GK2A FDA, tends to over-detect fog with a higher false alarm ratio and bias.