• KSCE Journal of Civil and Environmental Engineering Research
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• v.42 no.1
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• pp.35-44
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• 2022

The V_S30 map is used as a key variable for site amplification in the ShakeMap, which predicts ground motion at any site. However, no V_S30 map considering Korean geology and geomorphology has been developed yet. To develop a proxy-based V_S30 map, we used 1,101 V_S profiles obtained from a geophysical survey and collected proxy layers of geological and topographical information for the Korean Peninsula. Then, V_S30 prediction models were developed using linear regression analysis for each geological age considering the distribution of V_S30. As a result, models depending on geomorphology were suggested per each geologic group, including Quaternary, Fill, Ocean, Mesozoic group and Precambrian. Resolution of map is doubled from that of V_S30 map by U.S. Geological Survey (USGS). Standard deviation of residual in natural log of proxy-based V_S30 map is 0.233, whereas standard deviation of slope-based USGS V_S30 map is 0.387. Therefore, the proxy-based V_S30 map developed in this study is expected to have less uncertainty and to contribute to predicting more accurately the ground motion amplitude.

• Korean Journal of Remote Sensing
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• v.38 no.5_3
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• pp.901-912
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• 2022

The frequency of disasters is increasing due to global climate change, and unusual heavy rains and rainy seasons are occurring in Korea. Periodic monitoring and rapid detection are important because these weather conditions can lead to drought and flooding, causing secondary damage. Although research using optical images is continuously being conducted to determine the waterbody, there is a limitation in that it is difficult to detect due to the influence of clouds in order to detect floods that accompany heavy rain. Therefore, there is a need for research using synthetic aperture radar (SAR) that can be observed regardless of day or night in all weather. In this study, using Sentinel-1 SAR images that can be collected in near-real time as open data, the UNet model among deep learning algorithms that have recently been used in various fields was applied. In previous studies, waterbody detection studies using SAR images and deep learning algorithms are being conducted, but only a small number of studies have been conducted in Korea. In this study, to determine the applicability of deep learning of SAR images, UNet and the existing algorithm thresholding method were compared, and five indices and Sentinel-2 normalized difference water index (NDWI) were evaluated. As a result of evaluating the accuracy with intersect of union (IoU), it was confirmed that UNet has high accuracy with 0.894 for UNet and 0.699 for threshold method. Through this study, the applicability of deep learning-based SAR images was confirmed, and if high-resolution SAR images and deep learning algorithms are applied, it is expected that periodic and accurate waterbody change detection will be possible in Korea.

• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.311-326
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• 2022

The Compact Advanced Satellite 500-4 (CAS500-4) is under development to efficiently manage and monitor forests in Korea and is scheduled to launch in 2025. The National Institute of Forest Science is developing 36 types of forestry applications to utilize the CAS500-4 efficiently. The products derived using the remote sensing method require validation with ground reference data, and the quality monitoring results for the products must be continuously reported. Due to it being the first time developing the national forestry satellite, there is no official calibration and validation site for forestry products in Korea. Accordingly, the author designed a calibration and validation site for the forestry products following international standards. In addition, to install calibration and validation sites nationwide, the authors selected appropriate sensors and evaluated the applicability of the sensors. As a result, the difference between the ground observation data and the Sentinel-2 image was observed to be within ±5%, confirming that the sensor could be used for nationwide expansion.

• Journal of Korea Water Resources Association
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• v.56 no.9
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• pp.563-574
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• 2023

Recently, climate change has affected functional responses of river basins to meteorological variables, emphasizing the importance of rainfall-runoff simulation research. Simultaneously, the growing interest in machine learning has led to its increased application in hydrological studies. However, it is not yet clear whether machine learning models are more advantageous than the conventional conceptual models. In this study, we compared the performance of the conventional GR6J model with the machine learning-based Random Forest model across 38 basins in Korea using both gauged and ungauged basin prediction methods. For gauged basin predictions, each model was calibrated or trained using observed daily runoff data, and their performance was evaluted over a separate validation period. Subsequently, ungauged basin simulations were evaluated using proximity-based parameter regionalization with Leave-One-Out Cross-Validation (LOOCV). In gauged basins, the Random Forest consistently outperformed the GR6J, exhibiting superiority across basins regardless of whether they had strong or weak rainfall-runoff correlations. This suggest that the inherent data-driven training structures of machine learning models, in contrast to the conceptual models, offer distinct advantages in data-rich scenarios. However, the advantages of the machine-learning algorithm were not replicated in ungauged basin predictions, resulting in a lower performance than that of the GR6J. In conclusion, this study suggests that while the Random Forest model showed enhanced performance in trained locations, the existing GR6J model may be a better choice for prediction in ungagued basins.

• Journal of Broadcast Engineering
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• v.28 no.3
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• pp.285-292
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• 2023

Recently, the impact of fine dust on health has become a major topic. Fine dust is dangerous because it can penetrate the body and affect the respiratory system, without being filtered out by the mucous membrane in the nose. Since fine dust is directly related to the industry, it is practically impossible to completely remove it. Therefore, if the concentration of fine dust can be predicted in advance, pre-emptive measures can be taken to minimize its impact on the human body. Fine dust can travel over 600km in a day, so it not only affects neighboring areas, but also distant regions. In this paper, wind direction and speed data and a time series prediction model were used to predict the concentration of fine dust in Seoul, and the correlation between the concentration of fine dust in Seoul and the concentration in each region was confirmed. In addition, predictions were made using the concentration of fine dust in each region and in Seoul. The lowest MAE (mean absolute error) in the prediction results was 12.13, which was about 15.17% better than the MAE of 14.3 presented in previous studies.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.43 no.2
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• pp.249-258
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• 2023

Solar power has the largest proportion of power generation and facility capacity among renewable energy in South Korea. Floating solar power plant is a new way to resolve weakness of land solar power plant. This study analyzes the power generation of the 18.7 MW floating solar power project located in Saemangeum, Gunsan-si. Since the solar power generation has a characteristic that is greatly affected by the climate, various methods have been applied to predict solar power generation. In general, variables necessary for predicting power generation are solar insolation on inclined surfaces, solar generation efficiency, and panel installation area. This study analyzed solar power generation using the monthly solar insolation data from the KMA (Korea Meteorological Administration) over the past 10 years. Monte Carlo simulation (MCS) was applied to predict the solar power generation with the variables including solar panel efficiency and insolation. In the case of Saemangeum solar power project, the most solar power generation was in May, the least was in December, the average solar power generation simulated on MCS is 2.1 GWh per month, the minimum monthly power generation is 0.3 GWh, and the maximum is 5.0 GWh.

• Journal of Sensor Science and Technology
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• v.31 no.3
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• pp.180-186
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• 2022

Photosynthetically active radiation flux density (PPFD) and daily light integral (DLI) values related to plant photosynthesis were obtained using the sunlight time and insolation data points in the agricultural weather sensor data for Jinan-gun, Jeollabuk-do, Korea from 2016 to 2020. The objective was to optimize the photo-environmental conditions for cultivating ginseng. The range of average monthly sunshine duration was 395.5-664.1 min, with the longest duration observed in June. The range of average annual accumulated daily insolation was 11.98-17.65 MJ·m^-2. The range of average daily external DLI calculated from the insolation and solar time data was 22.3-36.1 mol·m^-2·d^-1, and the annual cumulative DLI was 8,156-13,175 mol·m^-2·d^-1. Both the insolation and DLI values were the highest in 2016 and lowest in 2020. Based on the PPFD required for ginseng growth (111-185 µmol·m^-2·s^-1), the monthly average daily DLI and monthly cumulative DLI were 3.51-5.87 and 82-228 mol·m^-2·d^-1, respectively. The range of five-year average value for the external monthly cumulative DLI was 298-1,459 mol·m^-2·d^-1, and the monthly cumulative DLI values when a black double shading film and blue-white shading film were applied were 101-496 and 36-175 mol·m^-2·d^-1, respectively. A comparative analysis of DLI values indicated that shading was required to ginseng growth throughout the year under natural light. When the black double shading film was used, shading was required from March to October. When the blue-white shading film was applied from April to August, (i.e., the period with active ginseng growth) the appropriate DLI for ginseng growth could be continuously maintained. Regional weather differences due to climate change are gradually increasing, and even in one region, monthly and cumulative DLI values are different every year. Therefore, in order to implement a precise agricultural environment for ginseng cultivation, precise analysis and continuous research using agricultural weather sensor big data is required.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1389-1399
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• 2023

KOMPSAT-3/3A (K3/K3A) absolute radiometric calibration study was conducted based on a Field Line of sight Automated Radiance Exposure (FLARE) system. FLARE is a system, which has been developed by Labsphere, Inc. adopted a SPecular Array Radiometric Calibration (SPARC) concept. The FLARE utilizes a specular mirror target resulting in a simplified radiometric calibration method by minimizing other sources of diffusive radiative energies. Several targeted measurements of K3/3A satellites over a FLARE site were acquired during a field campaign period (July 5-15, 2021). Due to bad weather situations, only two observations of K3 were identified as effective samples and they were employed for the study. Absolute radiometric calibration coefficients were computed using combined information from the FLARE and K3 satellite measurements. Comparison between the two FLARE measurements (taken on 7/7 and 7/13) showed very consistent results (less than 1% difference between them except the NIR channel). When additional data sets of K3/K3A taken on Aug 2021 were also analyzed and compared with gain coefficients from the metadata which are used by current K3/K3A, It showed a large discrepancy. It is assumed that more studies are needed to verify usefulness of the FLARE system for the K3/3A absolute radiometric calibration.

• Korean Journal of Remote Sensing
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• v.39 no.5_2
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• pp.755-770
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• 2023

Due to recent severe climate change, abnormal weather phenomena, and other factors, the frequency and magnitude of natural disasters are increasing. The need for disaster management using artificial satellites is growing, especially during large-scale disasters due to time and economic constraints. In this study, we have summarized the current status of next-generation medium-sized satellites and microsatellites in operation and under development, as well as trends in satellite imagery analysis techniques using a large volume of satellite imagery driven by the advancement of the space industry. Furthermore, by utilizing satellite imagery, particularly focusing on recent major disasters such as floods, landslides, droughts, and wildfires, we have confirmed how satellite imagery can be employed for damage analysis, thereby establishing its potential for disaster management. Through this study, we have presented satellite development and operational statuses, recent trends in satellite imagery analysis technology, and proposed disaster response strategies that utilize various types of satellite imagery. It was observed that during the stages of disaster progression, the utilization of satellite imagery is more prominent in the response and recovery stages than in the prevention and preparedness stages. In the future, with the availability of diverse imagery, we plan to research the fusion of cutting-edge technologies like artificial intelligence and deep learning, and their applicability for effective disaster management.

• Journal of Navigation and Port Research
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• v.47 no.6
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• pp.331-340
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• 2023

Predicting the survival time of a person in water (PIW) in maritime search and rescue (SAR) operations is an important concern. Although there have been many studies on survival models in marine-developed countries, it is difficult to apply them to Koreans in Korea's oceans because they were developed using marine distress data from the United Kingdom, United States, and Canada. Data on the survival time of a P IW were collected through interviews and surveys with a special rescue team from the Korea Coast Guard, SAR cases, press releases, and Korea Meteorological Administration data to address these issues. The maximum survival time (Korean) equation was developed by performing a regression analysis of this data, and the applicability to actual marine distress was reviewed and compared to the overseas survival model. By comprehensively using the maximum survival time (Korean), domestic SAR cases, and overseas survival models, guidelines for survival time and intensive and recommended search time were suggested. The study findings can contribute to decision-making, such as the input for search and rescue units. The findings can also help to determine the end of or reductions in SAR operations and explain policy decisions to the public and families of a PIW.