• Journal of People, Plants, and Environment
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• v.24 no.4
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• pp.441-450
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• 2021

Background and objective: The purpose of study is to analyze the three-dimensional (3D) structure by creating a 3D model for green spaces in a park using unmanned aerial vehicle (UAV) images. Methods: After producing a digital surface model (DSM) and a digital terrain model (DTM) using UAV images taken in Mureung Park in Chuncheon-si, we generated a digital tree height model (DHM). In addition, we used the mean shift algorithm to test the classification accuracy, and obtain accurate tree height and volume measures through field survey. Results: Most of the tree species planted in Mureung Park were Pinus koraiensis, followed by Pinus densiflora, and Zelkova serrata, and most of the shrubs planted were Rhododendron yedoense, followed by Buxus microphylla, and Spiraea prunifolia. The average height of trees measured at the site was 7.8 m, and the average height estimated by the model was 7.5 m, showing a difference of about 0.3 m. As a result of the t-test, there was no significant difference between height values of the field survey data and the model. The estimated green coverage and volume of the study site using the UAV were 5,019 m² and 14,897 m³, respectively, and the green coverage and volume measured through the field survey were 6,339 m² and 17,167 m³. It was analyzed that the green coverage showed a difference of about 21% and the volume showed a difference of about 13%. Conclusion: The UAV equipped with RTK (Real-Time Kinematic) and GNSS (Global Navigation Satellite System) modules used in this study could collect information on tree height, green coverage, and volume with relatively high accuracy within a short period of time. This could serve as an alternative to overcome the limitations of time and cost in previous field surveys using remote sensing techniques.

• Wind and Structures
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• v.35 no.1
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• pp.21-34
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• 2022

Gust factor is an important parameter for the conversion between peak gust wind and mean wind speed used for the structural design and wind-related hazard mitigation. The gust factor of typhoon wind is observed to show a significant dispersion and some differences with large-scale weather systems, e.g., monsoons and extratropical cyclones. In this study, insitu measurement data captured by 13 meteorological towers during a strong typhoon Morakot are collected to investigate the statistical characteristics, height and wind speed dependency of the gust factor. Onshore off-sea and off-land winds are comparatively studied, respectively to characterize the underlying terrain effects on the gust factor. The theoretical method of peak factor based on Gaussian assumption is then introduced to compare the gust factor profiles observed in this study and given in some building codes and standards. The results show that the probability distributions of gust factor for both off-sea winds and off-land winds can be well described using the generalized extreme value (GEV) distribution model. Compared with the off-land winds, the off-sea gust factors are relatively smaller, and the probability distribution is more leptokurtic with longer tails. With the increase of height, especially for off-sea winds, the probability distributions of gust factor are more peaked and right-tailed. The scatters of gust factor decrease with the mean wind speed and height. AS/NZ's suggestions are nearly parallel with the measured gust factor profiles below 80m, while the fitting curve of off-sea data below 120m is more similar to AIJ, ASCE and EU.

• Journal of the Korean Institute of Rural Architecture
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• v.23 no.4
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• pp.1-12
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• 2021

Recently, the use of unmanned aerial vehicles (UAVs) is increasing in the field of land information acquisition and terrain exploration through high-altitude aerial photography. High-altitude aerial photography is suitable for large-scale geographic information collection, but has the disadvantage that it is difficult to accurately collect small-scale geographic information. Therefore, this study used low-altitude UAV to monitor changes in small rural spaces around rural resources, and the results are as follows. First, the low-altitude aerial imagery had a very high spatial resolution, so it was effective in reading and analyzing topographic features. Second, an area with a large number of aerial images and a complex topography had a large amount of point clouds to be extracted, and the number of point clouds affects the three-dimensional quality of rural space. Third, 3D mapping technology using point cloud is effective for monitoring rural space and rural resources because it enables observation and comparison of parts that cannot be read from general aerial images. In this study, the possibility of rural space analysis of low-altitude UAV was verified through aerial photography and analysis, and the effect of 3D mapping on rural space monitoring was visually analyzed. If data acquired by low-altitude UAV are used in various forms such as GIS analysis and topographic map production it is expected to be used as basic data for rural planning to maintain and preserve the rural environment.

• Proceedings of the Korea Water Resources Association Conference
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• 2023.05a
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• pp.164-164
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• 2023

This study investigates the importance of flood-influencing factors on the accuracy of flood risk mapping using the integration of remote sensing-based and machine learning techniques. Here, the Extreme Gradient Boosting (XGBoost) and Random Forest (RF) algorithms integrated with GIS-based techniques were considered to develop and generate flood risk maps. For the study area of NAPA County in the United States, rainfall data from the 12 stations, Sentinel-1 SAR, and Sentinel-2 optical images were applied to extract 13 flood-influencing factors including altitude, aspect, slope, topographic wetness index, normalized difference vegetation index, stream power index, sediment transport index, land use/land cover, terrain roughness index, distance from the river, soil, rainfall, and geology. These 13 raster maps were used as input data for the XGBoost and RF algorithms for modeling flood-prone areas using ArcGIS, Python, and R. As results, it indicates that XGBoost showed better performance than RF in modeling flood-prone areas with an ROC of 97.45%, Kappa of 93.65%, and accuracy score of 96.83% compared to RF's 82.21%, 70.54%, and 88%, respectively. In conclusion, XGBoost is more efficient than RF for flood risk mapping and can be potentially utilized for flood mitigation strategies. It should be noted that all flood influencing factors had a positive effect, but altitude, slope, and rainfall were the most influential features in modeling flood risk maps using XGBoost.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.26 no.1D
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• pp.203-209
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• 2006

SAR (Synthetic Aperture Radar) technology, which is not influenced by cloud cover because of using electromagnetic wave of long wavelength, has an advantage in mapping the earth. NASA, recognizing these strong points of SAR, launched SRTM (Shuttle Radar Topography Mission), and acquired the topographic information of the earth. SRTM and NED (National Elevation Data) of USGS were used for the research and vegetation height map was produced through differentiating the two data. Correlation between SRTM-NED and planting year was analyzed to see the relationship. Strong correlation was detected and it shows the feasibility of estimating timber age and eventually creating timber age map from SRTM-NED. Additional analyses were conducted to check if the linearity is influenced by regional characteristics and forest uniformity. As results, the correlation between SRTM-NED and timber age is influenced by roughness of the terrain. Overall, this paper shows that timber age estimation using SRTM and NED can be sufficiently practical.

• Journal of The Geomorphological Association of Korea
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• v.28 no.3
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• pp.51-61
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• 2021

Following the previous study, we report a complementary dating data on the silty layers deposited in paleo-tidal conditions of the study area, Dangjeong-ri, Seocheon-gun and suggest coastal terrain development processes over the past 200,000 years. Based on the dating results, the silty layers distributed up to 25 m above mean sea level were deposited between 171 and 183 ka, and the gravel layer deposited in a fluvial environment of a paleo-Dangjeong stream was found to have formed between 78 and 83 ka. Considering relative altitudes of distribution, an uplift rate of the study area in the western coast is judged to be relatively 0.5~0.7 low to that of Pohang area in the eastern coast. Compared to Busan and Sacheon areas in the southern coast, it is assumed that an uplift rate of the study area shows a similar level with those during the late Quaternary.

• Journal of Advanced Navigation Technology
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• v.26 no.6
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• pp.393-403
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• 2022

Various studies suggest that the initial operating form of UAM, which is being presented as a solution to the urban traffic problem, will be similar to VTOL aircraft among current aircraft. In a form similar to determining the direction of the runway where fixed-wing aircraft take off and land, the vertiport where take-off and landing of VTOL aircraft takes place determines the flight direction of departure and arrival in consideration of the direction of the wind. Unlike areas where airports are generally built, in the case of downtown areas, it is expected that the characteristics of wind may continuously change depending on the environment of changing terrain or obstacles such as the construction of new buildings. In this study, long-term actual observation data for reviewing the take-off and landing directions at the city center where the location of the vertiport is expected are compared using a wind speed map, and the characteristics of the ground wind and the possibility of change in the direction of the predominant wind depending on the observation period and observation location confirmed.

• Smart Media Journal
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• v.12 no.9
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• pp.159-170
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• 2023

It takes a lot of time and manpower to search for the missing. As part of the solution, a missing person search AI system was implemented using a YOLO-based model. In order to train object detection models, the model was learned by collecting recognition images (road fixation) of drone mobile objects from AI-Hub. Additional mountainous terrain datasets were also collected to evaluate performance in training datasets and other environments. In order to optimize the missing person search AI system, performance evaluation based on model size and hyperparameters and additional performance evaluation for concerns about overfitting were conducted. As a result of performance evaluation, it was confirmed that the YOLOv5-L model showed excellent performance, and the performance of the model was further improved by applying data augmentation techniques. Since then, the web service has been applied with the YOLOv5-L model that applies data augmentation techniques to increase the efficiency of searching for missing people.

• Journal of Aerospace System Engineering
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• v.18 no.2
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• pp.40-46
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• 2024

This study suggests a method to enhance drone flight path planning and safety evaluation in urban areas using Computational Fluid Dynamics (CFD). As the use of drones in urban environments has been growing rapidly, there is a lack of established methods for path planning and safety evaluation, which leads to a risky approach relying on experimental methods. Therefore, this research takes into account the intricate 3D fluid dynamics between drones and buildings by employing CFD to quantitatively plan flight paths and evaluate their safety. To accomplish this, the study focuses on Gimcheon Innovation City as the target area and collects relevant terrain and building data, and selects prospective flight routes. CFD analysis is then carried out to gather essential data for flight simulations and safety assessment. The safety assessments are conducted based on environmental fluid dynamics when the drone operates along the proposed flight paths

• The Journal of Engineering Geology
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• v.34 no.1
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• pp.107-115
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• 2024

This study assesses forest fire susceptibility in Gangwon-do, South Korea, which hosts the largest forested area in the nation and constitutes ~21% of the country's forested land. With 81% of its terrain forested, Gangwon-do is particularly susceptible to wildfires, as evidenced by the fact that seven out of the ten most extensive wildfires in Korea have occurred in this region, with significant ecological and economic implications. Here, we analyze 480 historical wildfire occurrences in Gangwon-do between 2003 and 2019 using 17 predictor variables of wildfire occurrence. We utilized three machine learning algorithms—random forest, logistic regression, and support vector machine—to construct wildfire susceptibility prediction models and identify the best-performing model for Gangwon-do. Forest and soil map data were integrated as important indicators of wildfire susceptibility and enhanced the precision of the three models in identifying areas at high risk of wildfires. Of the three models examined, the random forest model showed the best predictive performance, with an area-under-the-curve value of 0.936. The findings of this study, especially the maps generated by the models, are expected to offer important guidance to local governments in formulating effective management and conservation strategies. These strategies aim to ensure the sustainable preservation of forest resources and to enhance the well-being of communities situated in areas adjacent to forests. Furthermore, the outcomes of this study are anticipated to contribute to the safeguarding of forest resources and biodiversity and to the development of comprehensive plans for forest resource protection, biodiversity conservation, and environmental management.