• Journal of the Korean Society for Aeronautical & Space Sciences
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• v.43 no.10
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• pp.928-935
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• 2015

This paper presents low temperature structural tests of a UAV wing which has room temperature-curing adhesive bond. The wing structure is made of carbon fiber reinforced composites, and the skins are bonded to the inner structures (such as ribs and spars) using room temperature-curing adhesive bond. Also, to verify damage tolerance design of the wing structure, barely visible impact damages are intentionally created in the critical areas. The attachment fittings of the wing are fixed in a specially designed chamber which can simulate the low temperature environments of the operating altitudes. The test load is applied by hydraulic actuators which are placed outside the chamber. The structural tests consist of strain survey tests and a durability test for 1-life fatigue load spectrum. During the tests, strains of major parts are measured by strain gauges and FBG sensors. The change of the initial impact damages is also monitored using piezoelectric sensors. The 1-life damage tolerance of the composite structure is verified by the structural tests under the simulated environments.

• Korean Journal of Remote Sensing
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• v.35 no.6_2
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• pp.1031-1035
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• 2019

Nowadays, the utilization infrastructure of domestic satellite information is expanding rapidly. Especially, the development of agriculture and forestry satellite is expected to drastically change the utilization of satellite information in the forest sector. The launch of the satellite is expected in 2023. Therefore, NIFoS and academic experts in forest sectors have prepared "Special Issue on Forest Management Research using Optical Sensors and Remote Sensing Technologies" in order to understand new remote sensing technologies and suggest the future direction of forest research and decision-making. This special issue is focused on a variety of fields in forest remote sensing research, including forest resources survey, forest disaster detection, and forest ecosystem monitoring. The new research topics for remote sensing technologies in forest sector focuses on three points: development of new indicators and information for accurate detection of forest conditions and changes, the use of new information sources such as UAV and new satellites, and techniques for improving accuracy through the use of artificial intelligence techniques.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.36 no.6
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• pp.565-572
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• 2018

Recently, UAVs (Unmanned Aerial Vehicles) or drones have gained popularity for the engineering surveying and mapping because they enable the rapid data acquisition and processing as well as their operation cost is low. The applicable fields become much wider including the topographic monitoring, agriculture, and forestry. It is reported that the high geospatial accuracy is achievable with the drone photogrammetry for many applications. However most studies reported the best achievable mapping results using well-distributed ground control points though some studies investigated the impact of control points on the accuracy. In this study, we focused on the drone mapping of corridors such as roads and pipelines. The distribution and the number of control points along the corridor were diversified for the accuracy assessment. In addition, the effects of the camera self-calibration and the number of the image strips were also studied. The experimental results showed that the biased distribution of ground control points has more negative impact on the accuracy compared to the density of points. The prior camera calibration was favored than the on-the-fly self-calibration that may produce poor positional accuracy for the case of less or biased control points. In addition, increasing the number of strips along the corridor was not helpful to increase the positional accuracy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.20 no.5
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• pp.27-35
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• 2019

The purpose of this study is to analyze the displacement characteristics in slow-moving landslide area using digital elevation model and airborne LiDAR when unpredictable disaster such as slow-moving landslide occurred. We also aimed to provide basic data for establishing a rapid, reasonable and effective restoration plan. In this study, slow-moving landslide occurrence cracks were selected through the airborne LiDAR data, and the topographic changes and the scale of occurrence were quantitatively analyzed. As a result of the analysis, the study area showed horseshoe shape similar to the general form of slow-moving landslide occurrence in Korea, and the direction of movement was in the north direction. The total area of slow-moving landslide damage was estimated to about 2.5ha, length of landsldie scrap 327.3m, average width 19.3m, and average depth 8.6m. The slow-moving landslides did not occur on a large scale but occurred on the adjacent slope where roads were located, caused damage to retaining walls and roads. The field survey of slow-moving landslides was limited by accessibility and safety issues, but there was an advantage that accurate analysis was possible through the airborne LiDAR. However, because airborne LiDAR has costly disadvantages, it has proposed a technique to mount LiDAR on UAV for rapidity, long-term monitoring. In a slow-moving landslide damage area, information such as direction of movement of cracks and change of scale should be acquired continuously to be used in restoration planning and prevention of damage.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.39 no.6
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• pp.541-548
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• 2021

Recently, multispectral cameras are being actively utilized in various application fields such as agriculture, forest management, coastal environment monitoring, and so on, particularly onboard UAV's. Resultant multispectral images are typically georeferenced primarily based on the onboard GPS (Global Positioning System) and IMU (Inertial Measurement Unit)or accurate positional information of the pixels, or could be integrated with ground control points that are directly measured on the ground. However, due to the high cost of establishing GCP's prior to the georeferencing or for inaccessible areas, it is often required to derive the positions without such reference information. This study aims to provide a means to improve the georeferencing performance of a multispectral camera images without involving such ground reference points, but instead with the simultaneously onboard high resolution RGB camera. The exterior orientation parameters of the drone camera are first estimated through the bundle adjustment, and compared with the reference values derived with the GCP's. The results showed that the incorporation of the images from a high resolution RGB camera greatly improved both the exterior orientation estimation and the georeferencing of the multispectral camera. Additionally, an evaluation performed on the direction estimation from a ground point to the sensor showed that inclusion of RGB images can reduce the angle errors more by one order.

• Korean Journal of Agricultural and Forest Meteorology
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• v.25 no.4
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• pp.245-257
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• 2023

The representative crop in the Republic of Korea, rice, is cultivated over extensive areas every year, which resulting in reduced resistance to pests and diseases. One of the major rice diseases, rice blast disease, can lead to a significant decrease in yields when it occurs on a large scale, necessitating early detection and effective control of rice blast disease. Drone-based crop monitoring techniques are valuable for detecting abnormal growth, but frequent image capture for potential rice blast disease occurrences can consume significant labor and resources. The purpose of this study is to early detect rice blast disease using remote sensing data, such as drone and satellite images, along with weather data. Satellite images was helpful in identifying rice cultivation fields. Effective detection of paddy fields was achieved by utilizing vegetation and water indices. Subsequently, air temperature, relative humidity, and number of rainy days were used to calculate the risk of rice blast disease occurrence. An increase in the risk of disease occurrence implies a higher likelihood of disease development, and drone measurements perform at this time. Spectral reflectance changes in the red and near-infrared wavelength regions were observed at the locations where rice blast disease occurred. Clusters with low vegetation index values were observed at locations where rice blast disease occurred, and the time series data for drone images allowed for tracking the spread of the disease from these points. Finally, drone images captured before harvesting was used to generate spatial information on the incidence of rice blast disease in each field.