• Ecology and Resilient Infrastructure
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• v.5 no.4
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• pp.229-245
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• 2018

This study aims to investigate the changes in the riparian vegetated area in the Naeseong stream, an unregulated river, in order to analyze the main factors leading to these changes. For this purpose, the land surface cover in the channel area of the Naeseong stream was classified into 9 categories using past aerial photographs collected between 1970 and 2016, which recorded the long-term changes of the Naeseong stream. The increase or decrease in the vegetated area was calculated for each category using a pair of before and after images. The changes in the vegetated area were divided into 6 periods: the unvegetated channel period (1970 - 1980), the first rapid increase (1980 - 1986), the period of decrease due to flood (1986 - 1988), the period of repetitive man-induced disturbance and vegetation increase (1988 - 2008), the period of gradual vegetation increase (2008 - 2013), and the period of second rapid increase (2013 - 2016). Multiple regression analysis was performed using independent variables representing hydrology, climate, and geomorphology. The major variables found to be involved in the changes in the vegetated area of the Naeseong stream were the discharge during June - July, channel width, and temperature during April - June. Among the three variables, discharge and temperature were respectively the main independent variables in the downstream and the upstream reaches as per a single variable model. Channel width was the variable that distinguished the upstream and downstream reaches of the stream. The implication of the long-term increase in the vegetated area in the Naeseong stream was discussed based on the result of this study.

• Journal of Urban Science
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• v.7 no.2
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• pp.53-60
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• 2018

Digital Twin is a technology that creates a photocopy of real-world objects on a computer and analyzes the past and present operational status by fusing the structure, context, and operation of various physical systems with property information, and predicts the future society's countermeasures. In particular, 3D rendering technology (UAS, LiDAR, GNSS, etc.) is a core technology in digital twin. so, the research and application are actively performed in the industry in recent years. However, UAS (Unmanned Aerial System) and LiDAR (Light Detection And Ranging) have to be solved by compensating blind spot which is not reconstructed according to the object shape. In addition, the terrestrial LiDAR can acquire the point cloud of the object more precisely and quickly at a short distance, but a blind spot is generated at the upper part of the object, thereby imposing restrictions on the forward digital twin modeling. The UAS is capable of modeling a specific range of objects with high accuracy by using high resolution images at low altitudes, and has the advantage of generating a high density point group based on SfM (Structure-from-Motion) image analysis technology. However, It is relatively far from the target LiDAR than the terrestrial LiDAR, and it takes time to analyze the image. In particular, it is necessary to reduce the accuracy of the side part and compensate the blind spot. By re-optimizing it after fusion with UAS and Terrestrial LiDAR, the residual error of each modeling method was compensated and the mutual correction result was obtained. The accuracy of fusion-based 3D model is less than 1cm and it is expected to be useful for digital twin construction.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1343-1352
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• 2018

This paper is designed for hardware to be applied to JPEG2000 standard in the fields of digital photography, remote sensing, aerial remote telemetry, medical imaging, high resolution, and high compression telemetry applications. The software implementation of the JPEG2000 standard for image compression has disadvantages that the processing speed is very slow compared to the conventional JPEG, also the degradation occurs when the DWT level of the JPEG2000 standard is improved. In order to solve this problem, we designed and applied JPEG2000 compression/decompressor. In this paper, the hardware of the JPEG 2000 compression/storage device shows optimal compression speed, faster processing speed, and the image quality for still images by changing the optimal DWT level.

• Korean Journal of Remote Sensing
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• v.35 no.3
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• pp.359-373
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• 2019

Bursaphelenchus xylophilus(Pine wilt disease) is a serious threat to the pine forest in Korea. However, dead wood observation by Pine wilt disease is based on field survey. Therefore, it is difficult to observe large-scale forests due to physical and economic problems. In this paper, high resolution images were obtained using the unmanned aerial vehicle (UAV) in the area where the pine wilt disease recurred. The damaged tree due to pine wilt disease was detected using Artificial Neural Network (ANN), Support Vector Machine (SVM) supervision classification technique. Also, the accuracy of supervised classification results was calculated. After conducting supervised classification on accessible forests, the reliability of the accuracy was verified by comparing the results of field surveys.

• Journal of Cadastre & Land InformatiX
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• v.51 no.1
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• pp.125-136
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• 2021

Recently, Unmanned Aerial Vehicle utilization and image processing technology for remote sensing have diversified remarkably with Orthophoto and Digital Surface Model. In particular, It uses more application fields such as spatial information analysis and hazardous areas as well as land surveying. This study analyses the accuracy of the coordinate on Orthophoto and DSM height on slope area with high and low differences by using UAV images. As the result of this study, in the case of GCP on 2D orthophoto, the location error was not produced significantly. The vertical position of the DSM showed the highest accuracy when the height difference between GCPs is under 30m(RMSEZ=0.07m). The location of the GCPs was divided into approximately 10m, 20m, 30m, and 40m with analysis for each of the eight points of GCP and inspection points in general. This study expects that producing both horizontal accuracy of Orthophoto and vertical accuracy of DSM using UAV on the sloped area which similar to this research area will help in spatial information fields.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1059-1070
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• 2020

River cross-section measurement data is one of the most important input data in research related to hydraulic and hydrological modeling, such as flow calculation and flood forecasting warning methods for river management. However, the acquisition of accurate and continuous cross-section data of rivers leading to irregular geometric structure has significant limitations in terms of time and cost. In this regard, a primary objective of this study is to develop a methodology that is able to measure the spatial distribution of continuous river characteristics by minimizing the input of time, cost, and manpower. Therefore, in this study, we tried to examine the possibility and accuracy of continuous cross-section estimation by estimating the water depth for each cross-section through multiple linear regression analysis using RGB-based aerial images and actual data. As a result of comparing with the actual data, it was confirmed that the depth can be accurately estimated within about 2 m of water depth, which can capture spatially heterogeneous relationships, and this is expected to contribute to accurate and continuous river cross-section acquisition.

• Journal of The Korean Society of Agricultural Engineers
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• v.64 no.1
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• pp.15-26
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• 2022

3D open-field farm model developed from UAV (Unmanned Aerial Vehicle) data could make crop monitoring easier, also could be an important dataset for various fields like remote sensing or precision agriculture. It is essential to separate crops from the non-crop area because labeling in a manual way is extremely laborious and not appropriate for continuous monitoring. We, therefore, made a 3D open-field farm model based on UAV images and developed a crop segmentation model using a supervised machine learning algorithm. We compared performances from various models using different data features like color or geographic coordinates, and two supervised learning algorithms which are SVM (Support Vector Machine) and KNN (K-Nearest Neighbors). The best approach was trained with 2-dimensional data, ExGR (Excess of Green minus Excess of Red) and z coordinate value, using KNN algorithm, whose accuracy, precision, recall, F1 score was 97.85, 96.51, 88.54, 92.35% respectively. Also, we compared our model performance with similar previous work. Our approach showed slightly better accuracy, and it detected the actual crop better than the previous approach, while it also classified actual non-crop points (e.g. weeds) as crops.

• Korean Journal of Construction Engineering and Management
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• v.23 no.2
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• pp.45-53
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• 2022

The purpose of this study is to present a model for analyzing construction errors of reinforced concrete pile foundations using drones. First, a drone is used to obtain an aerial image of the construction site, and an orthomosaic image is generated based on those images. Then, the circular pile foundation is automatically recognized from the orthomosaic image by using the Hough transform circle detection method. Finally, the distance is calculated based on the the center point of the reinforced concrete pile foundation in the overlapped data. As a case study, the proposed concrete concrete pile foundation construction quality control model was applied to the real construction site in Incheon to evaluate the proposed model.

• Proceedings of the Korea Water Resources Association Conference
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• 2020.06a
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• pp.85-85
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• 2020

South Korea experiences few large scale erosion and sedimentation problems, however, there are numerous local sedimentation problems. A reliable and consistent approach to modelling and management for sediment processes are desirable in the country. In this study, field measurements of sediment concentration from 34 alluvial river basins in South Korea were used with the Modified Einstein Procedure (MEP) to determine the total sediment load at the sampling locations. And then the Flow Duration-Sediment Rating Curve (FD-SRC) method was used to estimate the specific degradation for all gauging stations. The specific degradation of most rivers were found to be typically 50-300 tons/㎢·yr. A model tree data mining technique was applied to develop a model for the specific degradation based on various watershed characteristics of each watershed from GIS analysis. The meaningful parameters are: 1) elevation at the middle relative area of the hypsometric curve [m], 2) percentage of wetland and water [%], 3) percentage of urbanized area [%], and 4) Main stream length [km]. The Root Mean Square Error (RMSE) of existing models is in excess of 1,250 tons/㎢·yr and the RMSE of the proposed model with 6 additional validations decreased to 65 tons/㎢·yr. Erosion loss maps from the Revised Universal Soil Loss Equation (RUSLE), satellite images, and aerial photographs were used to delineate the geospatial features affecting erosion and sedimentation. The results of the geospatial analysis clearly shows that the high risk erosion area (hill slopes and construction sites at urbanized area) and sedimentation features (wetlands and agricultural reservoirs). The result of physiographical analysis also indicates that the watershed morphometric characteristic well explain the sediment transport. Sustainable management with the data mining methodologies and geospatial analysis could be helpful to solve various erosion and sedimentation problems under different conditions.

• Journal of The Geomorphological Association of Korea
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• v.26 no.2
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• pp.69-84
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• 2019

The purpose of this study is to extract lineaments in the southeastern part of the Gaeum Fault System, and to understand their characteristics and a relationship between them and fault activation. The lineaments were extracted using a multi-layered analysis based on a digital elevation model (5 m resolution), aerial photos, and satellite images. First-grade lineaments inferred as an high-activity along them were classified based on the displacement of the Quaternary deposits and the distribution of fault-related landforms. The results of classifying the first-grade lineaments were verified by fieldwork and electrical resistivity survey. In the study area of 510 km², a total of 222 lineaments was identified, and their total length was 333.4 km. Six grade lineaments were identified, and their total length was 11.2 km. The lineaments showed high-density distribution in the region along the Geumcheon, Gaeum, Ubo fault, and a boundary of the Hwasan cauldron consisting the Gaeum Fault System. They generally have WNW-ESE trend, which is the same direction with the strike of Gaeum Fault System. Electrical resistivity survey was conducted on eight survey lines crossing the first-grade lineament. A low-resistivity zone, which is assumed to be a fault damage zone, has been identified across almost all survey lines (except for only one survey line). The visual (naked eyes) detecting of the lineament was evaluated to be less objectivity than the automatic extraction using the algorithm. However, the results of electrical resistivity survey showed that first-grade lineament extracted by visual detecting was 83% reliable for inferred fault detection. These results showed that objective visual detection results can be derived from multi-layered analysis based on tectonic geomorphology.