• Journal of the Korean Institute of Landscape Architecture
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• v.51 no.6
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• pp.1-11
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• 2023

By comparing and analyzing the effects of the 2013 Suncheon Bay International Garden Expo and the 2023 Suncheon Bay International Garden Expo, designated as Korea's first national garden, this study aims to present basic data for the future operation direction and sustainability strategy. First, in both fairs, satisfaction throughout the event was high, 4.0 or higher. In particular, the satisfaction level of the 2023 Suncheon Bay International Garden Expo was higher than that of the 2013 Suncheon Bay International Garden Expo. As the longest international event held since the COVID-19 pandemic, it reflected the citizens' demand for healing and recharging in natural spaces. Second, as a result of comparing the types of perceptions that affected satisfaction, it was found that economic, environmental, and ecological types commonly affected satisfaction at the 2013 and 2023 Suncheon Bay International Garden Expo. The 2013 Suncheon Bay International Garden Expo established the brand value as an "ecological city" by creating a garden in the city center along with an ecological resource called Suncheon Bay. In addition, the 2023 Suncheon Bay International Garden Expo expanded the scope of the garden to the entire city center. It also attempted to create a city where humans and nature coexist by realizing values, such as responding to climate change and carbon neutrality. In other words, one of the ways to secure urban competitiveness is to attract corporate investment and tourists and build a differentiated brand in Suncheon by promoting the 2023 fair based on the potential ecological values of the region after the 2013 Suncheon Bay International Garden Expo. Therefore, if the Suncheon Bay International Garden Expo continues to develop environmental and ecological content and programs in line with changes in society and tries to establish itself in citizens' perception through cooperation with local governments and residents, it will be able to establish its identity and brand power.

• Journal of Environmental Impact Assessment
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• v.32 no.6
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• pp.577-589
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• 2023

In Korea, the technical techniques for assessing visual impacts are standardized, but the methods for assessing the marine landscape itself are not standardized and need to be improved. In particular, in the landscape impact assessment of offshore wind power generation in Korea, it is necessary to recognize the landscape itself as a receptor and prepare a system that can evaluate the characteristics and sensitivity of the landscape. In this study, we propose an evaluation method for preparing a marine landscape quality assessment document that reflects the project characteristics of offshore wind power projects, and examine the possibility of utilization by applying it to actual project sites as an example. To evaluate the quality of marine scenery in offshore wind power projects, evaluation items of landscape characteristics, physical characteristics, and socio-cultural characteristics were evaluated based on the preliminary survey contents, and the quality of marine scenery was divided into five grades. Next, the evaluation criteria of the evaluation items were synthesized and the quality of the marine landscape was classified into preservation grade (grade 5), semi-preservation grade (grade 4), buffer grade (grade 3), semi-improvement grade (grade 2), and improvement grade (grade 1). In addition, the Sinan-Ui Offshore Wind Farm, an actual project site, was randomly selected to conduct the evaluation process and examine its utilization. This study aims to complement the existing method of visual impact assessment in offshore wind power projects and evaluate the quality of the marine landscape itself to effectively conserve marine landscape resources during offshore wind power projects. Rather than relying on mechanical and quantitative evaluation, this study is expected to be used as a basis for comprehensive understanding of the location and socio-cultural characteristics of the project site and for communication and cooperation with stakeholders.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.589-597
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• 2023

Floating debris in large quantities from land during heavy rainfall has adverse social, economic, and environmental impacts, but the monitoring system for the concentration area and amount is insufficient. In this study, we proposed an efficient monitoring method for floating debris entering the river during heavy rainfall in Daecheong Lake, the largest water supply source in the central region, and applied remote sensing techniques to estimate the standing-stock of floating debris. To investigate the status of floating debris in the upper of Daecheong Lake, we used a tracking buoy equipped with a low-orbit satellite communication terminal to identify the movement route and behavior characteristics, and used a drone to estimate the potential concentration area and standing-stock of floating debris. The location tracking buoys moved rapidly during the period when the cumulative rainfall for 3 days increased by more than 200 to 300 mm. In the case of Hotan Bridge, which showed the longest distance, it moved about 72.8 km for one day, and the maximum moving speed at this time was 5.71 km/h. As a result of calculating the standing-stock of floating debris using a drone after heavy rainfall, it was found to be 658.8 to 9,165.4 tons, with the largest amount occurring in the Seokhori area. In this study, we were able to identify the main concentrations of floating debris by using location-tracking buoys and drones. It is believed that remote sensing-based monitoring methods, which are more mobile and quicker than traditional monitoring methods, can contribute to reducing the cost of collecting and processing large amounts of floating debris that flows in during heavy rain periods in the future.

• Korean Journal of Remote Sensing
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• v.39 no.5_1
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• pp.481-493
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• 2023

In high-density urban areas, the urban heat island effect increases urban temperatures, leading to negative impacts such as worsened air pollution, increased cooling energy consumption, and increased greenhouse gas emissions. In urban environments where it is difficult to secure additional green spaces, rooftop greening is an efficient greenhouse gas reduction strategy. In this study, we not only analyzed the current status of the urban heat island effect but also utilized high-resolution satellite data and spatial information to estimate the available rooftop greening area within the study area. We evaluated the mitigation effect of the urban heat island phenomenon and carbon sequestration capacity through temperature predictions resulting from rooftop greening. To achieve this, we utilized WorldView-2 satellite data to classify land cover in the urban heat island areas of Busan city. We developed a prediction model for temperature changes before and after rooftop greening using machine learning techniques. To assess the degree of urban heat island mitigation due to changes in rooftop greening areas, we constructed a temperature change prediction model with temperature as the dependent variable using the random forest technique. In this process, we built a multiple regression model to derive high-resolution land surface temperatures for training data using Google Earth Engine, combining Landsat-8 and Sentinel-2 satellite data. Additionally, we evaluated carbon sequestration based on rooftop greening areas using a carbon absorption capacity per plant. The results of this study suggest that the developed satellite-based urban heat island assessment and temperature change prediction technology using Random Forest models can be applied to urban heat island-vulnerable areas with potential for expansion.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1635-1650
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• 2023

Under global warming, the steadily increasing sea surface temperature (SST) severely impacts marine ecosystems,such as the productivity decrease and change in marine species distribution. Recently, the catch of Todarodes Pacificus, one of South Korea's primary marine resources, has dramatically decreased. In this study, we analyze the marine environment that affects the formation of fishing grounds of Todarodes Pacificus and develop seasonal habitat suitability index (HSI) models based on various satellite data including Geostationary Ocean Color Imager (GOCI) data to continuously manage fisheries resources over Korean exclusive economic zone. About 83% of catches are found within the range of SST of 14.11-26.16℃,sea level height of 0.56-0.82 m, chlorophyll-a concentration of 0.31-1.52 mg m^-3, and primary production of 580.96-1574.13 mg C m^-2 day^-1. The seasonal HSI models are developed using the Arithmetic Mean Model, which showed the best performance. Comparing the developed HSI value with the 2019 catch data, it is confirmed that the HSI model is valid because the fishing grounds are formed in different sea regions by season (East Sea in winter and Yellow Sea in summer) and the high HSI (> 0.6) concurrences to areas with the high catch. In addition, we identified the significant increasing trend in SST over study regions, which is highly related to the formation of fishing grounds of Todarodes Pacificus. We can expect the fishing grounds will be changed by accelerating ocean warming in the future. Continuous HSI monitoring is necessary to manage fisheries' spatial and temporal distribution.

• Korean Journal of Remote Sensing
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• v.39 no.5_3
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• pp.933-948
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• 2023

Atmospheric aerosols not only have adverse effects on human health but also exert direct and indirect impacts on the climate system. Consequently, it is imperative to comprehend the characteristics and spatiotemporal distribution of aerosols. Numerous research endeavors have been undertaken to monitor aerosols, predominantly through the retrieval of aerosol optical depth (AOD) via satellite-based observations. Nonetheless, this approach primarily relies on a look-up table-based inversion algorithm, characterized by computationally intensive operations and associated uncertainties. In this study, a novel high-resolution AOD direct retrieval algorithm, leveraging machine learning, was developed using top-of-atmosphere reflectance data derived from the Geostationary Ocean Color Imager-II (GOCI-II), in conjunction with their differences from the past 30-day minimum reflectance, and meteorological variables from numerical models. The Light Gradient Boosting Machine (LGBM) technique was harnessed, and the resultant estimates underwent rigorous validation encompassing random, temporal, and spatial N-fold cross-validation (CV) using ground-based observation data from Aerosol Robotic Network (AERONET) AOD. The three CV results consistently demonstrated robust performance, yielding R²=0.70-0.80, RMSE=0.08-0.09, and within the expected error (EE) of 75.2-85.1%. The Shapley Additive exPlanations(SHAP) analysis confirmed the substantial influence of reflectance-related variables on AOD estimation. A comprehensive examination of the spatiotemporal distribution of AOD in Seoul and Ulsan revealed that the developed LGBM model yielded results that are in close concordance with AERONET AOD over time, thereby confirming its suitability for AOD retrieval at high spatiotemporal resolution (i.e., hourly, 250 m). Furthermore, upon comparing data coverage, it was ascertained that the LGBM model enhanced data retrieval frequency by approximately 8.8% in comparison to the GOCI-II L2 AOD products, ameliorating issues associated with excessive masking over very illuminated surfaces that are often encountered in physics-based AOD retrieval processes.

• Journal of Korea Water Resources Association
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• v.56 no.11
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• pp.737-749
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• 2023

In this study, we analyzed the hydrological impacts of future climate change on Jeju Island using SSP-based climate change scenarios from 18 climate models and watershed modeling (SWAT-K). Despite discrepancies among climate models, we generally observed an increase in evapotranspiration due to rising future temperatures. Furthermore, a significant increase in runoff and recharge was noted due to increased precipitation. These increasing trends were particularly pronounced in the SSP5-8.5 scenario, and differences among GCM models became more significant in the late 21 century. When compared to the historical period (1981-2010), the projected changes for the far-future period (2071-2100) in the SSP5-8.5 scenario showed a 21.4% increase in precipitation, a 19.2% increase in evapotranspiration, a 40.9% increase in runoff, and a 16.6% increase in recharge on an annual average basis. On a monthly basis in the SSP5-8.5 scenario, precipitation was expected to increase by 24.5% in September, evapotranspiration by 34.1% in April, runoff by 58.1% in October, and recharge by 33.8% in September. To further assess projections based on extreme climate scenarios, we selected two models, CanESM5 and ACCESS-ESM1-5, which represented the maximum and minimum future precipitation forecasts, and compared the hydrological changes in the future scenarios. The results indicated that runoff and recharge rates were relatively higher in the CanESM5 model with the highest precipitation forecast, while evapotranspiration rates were higher in the ACCESS-ESM1-5 model with the lowest precipitation forecast. Based on the climate change scenarios used in this study, the overall available water resources on Jeju Island are more likely to increase. However, since results vary by season and region depending on the climate model and scenario, it is considered necessary to conduct a comprehensive analysis and develop response measures using various scenarios.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.18 no.2
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• pp.201-219
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• 2023

ESG (Environment, Social, Governance) is becoming the core of the interest of today's entrepreneurs concerning about the earth crisis. Numerous studies are going on these days about the importance of ESG, but most of them seem confined to the introductory level. This study concentrates on "ESG education" that will teach the learners how to put various ESG ideas into practice, knowing that the earth crisis would not be overcome without actual practice of those ideas. First, elementary and junior·senior high school, professors in university and educational consultants in the field designed educational programs and related content materials under "ESG entrepreneurship education" integrated with ESG and Entrepreneurship education, which have been implemented previously. Participatory learning methods are converged with the program. The researcher analyzed the learning effects in depth after implementing the programs in the education field. Thus, this study first examined the effects of key variables of ESG educational program i.e., ESG entrepreneurship education, student participatory learning, and team-based learning on creative problem-solving and social value recognition with an essential variant of ESG educational programs and identified the relations to creative problem-solving and social value recognition. Besides, this study investigated the moderating effects of school atmosphere, and teachers' enthusiasm, regarding traits of educational programs and social value recognition. Findings indicate that sub variants of the traits of educational programs i.e., ESG entrepreneurship education, student participatory learning, and team-based learning significantly affect creative problem-solving skills and social value recognition and that creative problem-solving impacts social value recognition. In addition, teachers' enthusiasm has moderating effects between traits of educational programs and social value recognition. This study provides content-program learning methods that can be practically applied in education, emphasizing practice in ESG in elementary and junior·senior high school education. Implications suggest that ESG entrepreneurship education and active participatory learning affect social value recognition and that teachers' enthusiasm plays a significant role in education.

• Asia-Pacific Journal of Business Venturing and Entrepreneurship
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• v.17 no.3
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• pp.147-163
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• 2022

The relocation policy of public Institutions throughout provincial areas that implemented for the purpose of "balanced national development" finished in 2019 with the last relocation of the Korea Institute of Science & Technology Evaluation and Planning, which moved to Chungbuk Innovation City. Electric power public corporations also completed relocation program over eight regions across the nation. This study was conducted empirically to identify the structural relationship between the public service motivation and job attitude. In this, the relationship of organizational change, particularly occurred by the regional relocation, with the psychological state of these organization members (experienced direct changes and got substantial impacts in various aspects such as psychological, economic and living environment, etc.,) was studied. This study aims to seek early organizational stabilization ideas for electric power public corporations after relocation, and to present some implications that can contribute to the secondary relocation of public institutions to local areas. This study shows the statistically significant relationship between the psychological state occurred by relocation and organizational commitment. The result shows that the higher the expectation levels, the higher the degree of organizational commitment, while anxious psychological state has no relation with that. Additionally, expectation level has no significant functional relation with turnover intention. Followings are the major conclusions revealed in this study; The stronger the anxious psychological state, the higher the turnover inducement goes up. The higher the expectation levels, the higher the public service motivation grows, and the higher the anxiety psychological state, the public service motivation lowers. The organizational commitment grows according to the public service motivation proportionally, but the turnover inducement intention is weak. The moderating effect of public service motivation between the expectation of organizational change and turnover intention was not significant, but it was analyzed that the moderating effect of public service motivation formed a significant relationship with other anxiety psychology. The expectation levels of employees of electric power public corporations has grown up after moving to provincial areas. Relationship between the expectation mind and the turnover inducement has decreased after local relocation.

• Journal of Korean Tunnelling and Underground Space Association
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• v.26 no.2
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• pp.129-152
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• 2024

In the application of deep learning object detection via CCTV in tunnels, a large number of false positive detections occur due to the poor environmental conditions of tunnels, such as low illumination and severe perspective effect. This problem directly impacts the reliability of the tunnel CCTV-based accident detection system reliant on object detection performance. Hence, it is necessary to reduce the number of false positive detections while also enhancing the number of true positive detections. Based on a deep learning object detection model, this paper proposes a false positive data training method that not only reduces false positives but also improves true positive detection performance through retraining of false positive data. This paper's false positive data training method is based on the following steps: initial training of a training dataset - inference of a validation dataset - correction of false positive data and dataset composition - addition to the training dataset and retraining. In this paper, experiments were conducted to verify the performance of this method. First, the optimal hyperparameters of the deep learning object detection model to be applied in this experiment were determined through previous experiments. Then, in this experiment, training image format was determined, and experiments were conducted sequentially to check the long-term performance improvement through retraining of repeated false detection datasets. As a result, in the first experiment, it was found that the inclusion of the background in the inferred image was more advantageous for object detection performance than the removal of the background excluding the object. In the second experiment, it was found that retraining by accumulating false positives from each level of retraining was more advantageous than retraining independently for each level of retraining in terms of continuous improvement of object detection performance. After retraining the false positive data with the method determined in the two experiments, the car object class showed excellent inference performance with an AP value of 0.95 or higher after the first retraining, and by the fifth retraining, the inference performance was improved by about 1.06 times compared to the initial inference. And the person object class continued to improve its inference performance as retraining progressed, and by the 18th retraining, it showed that it could self-improve its inference performance by more than 2.3 times compared to the initial inference.