• Journal of the Korean Institute of Traditional Landscape Architecture
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• v.28 no.2
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• pp.67-74
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• 2010

Law-protected trees are our precious asset as natural resources with history and tradition and natural heritage which should be protected and maintained well to bequeath next generation. Law-protected trees have not only thremmatologic and genetic meaning but also environmental and emotional meaning for their value to be high. This study investigated location, vitality, wrapping condition of root area and status of maintenance of the trees to figure out their growth environment and status of maintenance in a small-middle city through survey on those of law-protected trees in Je-cheon. There showed 300 more year old trees in Je-cheon mostly and the number of trees located in flat fields was the highest. For location type, village, hill and road types were presented in the order and for degree of development, land for building was found most frequently. The average electric resistance of the formative layer was measured to be $8.4k{\Omega}$ and four trees showed bark separation. Most law-protected trees underwent tree surgery, and complete bareness of root area was observed in a tree. The root area of two trees was covered with concrete. pH of soil was recorded to be 5.0~8.4 with its average of 7.1 and electric conductivity(EC) was less than 0.5 dS/m. For status of maintenance rearing facilities were placed for 16 trees out of totally 48 ones and stone fence was done for three ones. Tree surgery was conducted for 33 trees to prevent and to treat decomposed parts of holes. Direction boards were installed for 23 trees. Based on these results, measures to manage systematically law-protected trees in Jecheon could be suggested as follows. First, a sufficient space for growth of low part of trees should be secured. Second, a voluntary management should be induced by advertising them to residents in a community. Third, rearing facilities and direction boards of law-protected trees should be placed and related education should be conducted. Fourth, through operation of the department for law-protected trees consisting of related professions and cooperation among related departments the trees should be maintained continuously.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.959-974
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• 2021

Forel-Ule Index (FUI) is an index which classifies the colors of inland and seawater exist in nature into 21 gradesranging from indigo blue to cola brown. FUI has been analyzed in connection with the eutrophication, water quality, and light characteristics of water systems in many studies, and the possibility as a new water quality index which simultaneously contains optical information of water quality parameters has been suggested. In thisstudy, Ocean Colour-Climate Change Initiative (OC-CCI) based 4 km FUI was spatially downscaled to the resolution of 500 m using the Geostationary Ocean Color Imager (GOCI) data and Random Forest (RF) machine learning. Then, the RF-derived FUI was examined in terms of its correlation with various water quality parameters measured in coastal areas and its spatial distribution and seasonal characteristics. The results showed that the RF-derived FUI resulted in higher accuracy (Coefficient of Determination (R²)=0.81, Root Mean Square Error (RMSE)=0.7784) than GOCI-derived FUI estimated by Pitarch's OC-CCI FUI algorithm (R²=0.72, RMSE=0.9708). RF-derived FUI showed a high correlation with five water quality parameters including Total Nitrogen, Total Phosphorus, Chlorophyll-a, Total Suspended Solids, Transparency with the correlation coefficients of 0.87, 0.88, 0.97, 0.65, and -0.98, respectively. The temporal pattern of the RF-derived FUI well reflected the physical relationship with various water quality parameters with a strong seasonality. The research findingssuggested the potential of the high resolution FUI in coastal water quality management in the Korean Peninsula.

• Korean Journal of Agricultural and Forest Meteorology
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• v.23 no.3
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• pp.149-155
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• 2021

Early warning systems for weather risk management in the agricultural sector have been developed to predict potential wind damage to crops. These systems take into account the daily maximum wind speed to determine the critical wind speed that causes fruit drops and provide the weather risk information to farmers. In an effort to increase the accuracy of wind risk predictions, an artificial neural network for binary classification was implemented. In the present study, the daily wind speed and other weather data, which were measured at weather stations at sites of interest in Jeollabuk-do and Jeollanam-do as well as Gyeongsangbuk- do and part of Gyeongsangnam- do provinces in 2019, were used for training the neural network. These weather stations include 210 synoptic and automated weather stations operated by the Korean Meteorological Administration (KMA). The wind speed data collected at the same locations between January 1 and December 12, 2020 were used to validate the neural network model. The data collected from December 13, 2020 to February 18, 2021 were used to evaluate the wind risk prediction performance before and after the use of the artificial neural network. The critical wind speed of damage risk was determined to be 11 m/s, which is the wind speed reported to cause fruit drops and damages. Furthermore, the maximum wind speeds were expressed using Weibull distribution probability density function for warning of wind damage. It was found that the accuracy of wind damage risk prediction was improved from 65.36% to 93.62% after re-classification using the artificial neural network. Nevertheless, the error rate also increased from 13.46% to 37.64%, as well. It is likely that the machine learning approach used in the present study would benefit case studies where no prediction by risk warning systems becomes a relatively serious issue.

• Korean Journal of Environmental Biology
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• v.39 no.1
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• pp.100-107
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• 2021

Along with an increase in the frequency of high-concentration fine particulate matter in Korea, interest and research on ammonia (NH₃) are actively increasing. It is obvious that agriculture has contributed significantly to NH₃ emissions. However, studies on the long-term effect of fertilizer use on the ambient NH₃ concentration of agricultural land are insufficient. Therefore, in this study, NH₃ concentration in the atmosphere of agricultural land was monitored for 11 months using a passive sampler. The average ambient NH₃ concentration during the total study period was 2.02 ㎍ m^-3 and it was found that the effect of fertilizer application on the ambient NH₃ concentration was greatest in the month immediately following fertilizer application (highest ambient NH₃ concentration as 11.36㎍ m^-3). After that, it was expected that the NH₃ volatilization was promoted by increases in summer temperature and the concentration in the atmosphere was expected to increase. However, high NH₃ concentrations in the atmosphere were not observed due to strong rainfall that lasted for a long period. After that, the ambient NH₃ concentration gradually decreased through autumn and winter. In summary, when studying the contribution of fertilizer to the rate of domestic NH₃ emissions, it is necessary to look intensively for at least one month immediately after fertilizer application, and weather information such as precipitation and no-rain days should be considered in the field study.

• Journal of Intelligence and Information Systems
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• v.27 no.1
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• pp.177-190
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• 2021

Due to the development of the fourth industrial revolution technology, efforts are being made to improve areas that humans cannot handle by utilizing artificial intelligence techniques such as machine learning. Although on-demand production companies also want to reduce corporate risks such as delays in delivery by predicting total production time for orders, they are having difficulty predicting this because the total production time is all different for each order. The Theory of Constraints (TOC) theory was developed to find the least efficient areas to increase order throughput and reduce order total cost, but failed to provide a forecast of total production time. Order production varies from order to order due to various customer needs, so the total production time of individual orders can be measured postmortem, but it is difficult to predict in advance. The total measured production time of existing orders is also different, which has limitations that cannot be used as standard time. As a result, experienced managers rely on persimmons rather than on the use of the system, while inexperienced managers use simple management indicators (e.g., 60 days total production time for raw materials, 90 days total production time for steel plates, etc.). Too fast work instructions based on imperfections or indicators cause congestion, which leads to productivity degradation, and too late leads to increased production costs or failure to meet delivery dates due to emergency processing. Failure to meet the deadline will result in compensation for delayed compensation or adversely affect business and collection sectors. In this study, to address these problems, an entity that operates an order production system seeks to find a machine learning model that estimates the total production time of new orders. It uses orders, production, and process performance for materials used for machine learning. We compared and analyzed OLS, GLM Gamma, Extra Trees, and Random Forest algorithms as the best algorithms for estimating total production time and present the results.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.4
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• pp.330-336
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• 2022

It is likely that the heading would occur early when air temperature increases. In 2022, however, the heading date was delayed unusually, e.g., by 3 to 5 days although temperature during the vegetative growth stage was higher than normal years. The objective of this study was to identify the cause of such event analyzing weather variables including average temperature, sunshine hours, and day-length for each growth stage. The observation data were collected for medium-late maturing varieties, which has been grown at crop yield experiment sites including Daegu, Andong, and Yesan. The difference in heading date was compared between growing seasons in 2021 and 2022 because crop management options, e.g., the cultivars and cultivation methods, were identical at those sites during the study period. It appeared that the heading date was delayed due to the difference in temperature responsiveness under a given day-length condition The effect of the temperature increase on the heading date differed between the periods during which when the day-length was more than 14.3 hours before and after the summer-solstice.. The effect of the temperature decrease during the period from which the day-length decreased to less than 14.3 hours to the heading date was relatively greater. This merits further studies to examine the response of rice to the temperature change under different day-length and sunshine duration in terms of heading.

• Korean Journal of Agricultural and Forest Meteorology
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• v.24 no.2
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• pp.63-77
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• 2022

Our aim was to reduce estimation errors of a wind velocity model used as an early warning system for weather risk management in the agricultural sector. The Rural Development Administration (RDA) agricultural weather observation network's wind velocity data and its corresponding estimated data from January to December 2020 were used to calculate linear regression equations (Y = aX + b). In each linear regression, the wind estimation error at 87 points and eight time slots per day (00:00, 03:00, 06:00, 09.00, 12.00, 15.00, 18.00, and 21:00) is the dependent variable (Y), while the estimated wind velocity is the independent variable (X). When the correlation coefficient exceeded 0.5, the regression equation was used as the wind velocity correction equation. In contrast, when the correlation coefficient was less than 0.5, the mean error (ME) at the corresponding points and time slots was substituted as the correction value instead of the regression equation. To enable the use of wind velocity model at a national scale, a distribution map with a grid resolution of 250 m was created. This objective was achieved b y performing a spatial interpolation with an inverse distance weighted (IDW) technique using the regression coefficients (a and b), the correlation coefficient (R), and the ME values for the 87 points and eight time slots. Interpolated grid values for 13 weather observation points in rural areas were then extracted. The wind velocity estimation errors for 13 points from January to December 2019 were corrected and compared with the system's values. After correction, the mean ME of the wind velocities reduced from 0.68 m/s to 0.45 m/s, while the mean RMSE reduced from 1.30 m/s to 1.05 m/s. In conclusion, the system's wind velocities were overestimated across all time slots; however, after the correction model was applied, the overestimation reduced in all time slots, except for 15:00. The ME and RMSE improved b y 33% and 19.2%, respectively. In our system, the warning for wind damage risk to crops is driven by the daily maximum wind speed derived from the daily mean wind speed obtained eight times per day. This approach is expected to reduce false alarms within the context of strong wind risk, by reducing the overestimation of wind velocities.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.1
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• pp.17-27
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• 2023

Weeds are one of the factors that reduce crop yield through nutrient and photosynthetic competition. Quantification of weed density are an important part of making accurate decisions for precision weeding. In this study, we tried to quantify the density of weeds in images of maize fields taken by unmanned aerial vehicle (UAV). UAV image data collection took place in maize fields from May 17 to June 4, 2021, when maize was in its early growth stage. UAV images were labeled with pixels from maize and those without and the cropped to be used as the input data of the semantic segmentation network for the maize detection model. We trained a model to separate maize from background using the deep learning segmentation networks DeepLabV3+, U-Net, Linknet, and FPN. All four models showed pixel accuracy of 0.97, and the mIOU score was 0.76 and 0.74 in DeepLabV3+ and U-Net, higher than 0.69 for Linknet and FPN. Weed density was calculated as the difference between the green area classified as ExGR (Excess green-Excess red) and the maize area predicted by the model. Each image evaluated for weed density was recombined to quantify and visualize the distribution and density of weeds in a wide range of maize fields. We propose a method to quantify weed density for accurate weeding by effectively separating weeds, maize, and background from UAV images of maize fields.

• Korean Journal of Environmental Biology
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• v.41 no.1
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• pp.18-30
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• 2023

Forest destruction is an inevitable result of the development processes. According to the environmental impact assessment, over 10% of the destroyed trees need to be recycled and transplanted to minimize the impact of forest destruction. However, the rate of successful transplantation is low, leading to a high rate of tree death. This is attributable to a lack of consideration for environmental factors when choosing a temporary site for transplantation and inadequate management. To monitor transplanted trees, a field survey is essential; however, the spatio-temporal aspect is limited. This study evaluated the applicability of remote sensing for the effective monitoring of transplanted trees. Vegetation indices based on satellite remote sensing were derived to detect time-series changes in the status of the transplanted trees at three temporary transplantation sites. The mortality rate and vitality of transplanted trees before and after the transplant have a similar tendency to the changes in the vegetation indicators. The findings of this study showed that vegetation indices increased after transplantation of trees and decreased as the death rate increased and vitality decreased over time. This study presents a method for assessing newly transplanted trees using satellite images. The approach of utilizing satellite photos and the vegetation index is expected to detect changes in trees that have been transplanted across the country and help to manage tree transplantation for the environmental impact assessment.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.3
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• pp.129-141
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• 2023

Crop models have been used to predict yield under diverse environmental and cultivation conditions, which can be used to support decisions on the management of forage crop. Cultivar parameters are one of required inputs to crop models in order to represent genetic properties for a given forage cultivar. The objectives of this study were to compare calibration and ensemble approaches in order to minimize the uncertainty of crop yield estimates using the SIMPLE crop model. Cultivar parameters were calibrated using Log-likelihood (LL) and Generic Composite Similarity Measure (GCSM) as an objective function for Metropolis-Hastings (MH) algorithm. In total, 20 sets of cultivar parameters were generated for each method. Two types of ensemble approach. First type of ensemble approach was the average of model outputs (E_em), using individual parameters. The second ensemble approach was model output (E_pm) of cultivar parameter obtained by averaging given 20 sets of parameters. Comparison was done for each cultivar and for each error calculation methods. 'Jowoo' and 'Yeongwoo', which are forage rice cultivars used in Korea, were subject to the parameter calibration. Yield data were obtained from experiment fields at Suwon, Jeonju, Naju and I ksan. Data for 2013, 2014 and 2016 were used for parameter calibration. For validation, yield data reported from 2016 to 2018 at Suwon was used. Initial calibration indicated that genetic coefficients obtained by LL were distributed in a narrower range than coefficients obtained by GCSM. A two-sample t-test was performed to compare between different methods of ensemble approaches and no significant difference was found between them. Uncertainty of GCSM can be neutralized by adjusting the acceptance probability. The other ensemble method (E_pm) indicates that the uncertainty can be reduced with less computation using ensemble approach.