• KSCE Journal of Civil and Environmental Engineering Research
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• v.40 no.2
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• pp.257-264
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• 2020

Vectorization is currently the main method in feature collection (extraction) during digital mapping using UAV-Photogrammetry. However, this method is time consuming and prone to gross elevation errors when extracted from a DSM (Digital Surface Model), because three-dimensional feature coordinates are vectorized separately: plane information from an orthophoto and height from a DSM. Consequently, the demand for stereo plotting method capable of acquiring three- dimensional spatial information simultaneously is increasing. However, this method requires an expensive equipment, a Digital Photogrammetry Workstation (DPW), and the technology itself is still incomplete. In this paper, we evaluated the accuracy of low-cost stereo plotting system, Menci's StereoCAD, by analyzing its three-dimensional spatial information acquisition. Images were taken with a FC 6310 camera mounted on a Phantom4 pro at a 90 m altitude with a Ground Sample Distance (GSD) of 3 cm. The accuracy analysis was performed by comparing differences in coordinates between the results from the ground survey and the stereo plotting at check points, and also at the corner points by layers. The results showed that the Root Mean Square Error (RMSE) at check points was 0.048 m for horizontal and 0.078 m for vertical coordinates, respectively, and for different layers, it ranged from 0.104 m to 0.127 m for horizontal and 0.086 m to 0.092 m for vertical coordinates, respectively. In conclusion, the results showed 1: 1,000 digital topographic map can be generated using a stereo plotting system with UAV images.

• Journal of Korean Association for Spatial Structures
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• v.12 no.1
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• pp.109-119
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• 2012

For membrane structure, there are three main steps in design and construction, which are form finding, statistical load analysis, and cutting patterning. Unlike the first two stages, the step of cutting pattern involves the translation of a double-curved surface in 3D space into a 2D plane with minimal error. For economic reasons, the seam lines of generated cutting patterns rely greatly on the geodesic line. Generally, as searching regions of the seam line are plane elements in the step of shape analysis, the seam line is not a smooth curve, but an irregularly divided straight line. So, it is how we make an irregularly divided straight line a smooth curve that defines the quality of the pattern. Accordingly, in this paper, we analyzed interpolation schemes using spline, and apply these methods to cutting pattern generation on the curved surface. To generate the pattern, three types of spline functions were used, i.e., cubic spline function, B-spline, and least-square spline approximation, and simple model and the catenary-shaped membrane was adopted to examine the result of generation. The result of comparing the approximation curves by the number of elements and the number of extracted nodes of simple model revealed that the seam line for less number of extracted nodes with large number of elements were more efficient, and the least-square spline approximation provided smoother seam line than other methods.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.30 no.6
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• pp.263-269
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• 2018

Changes in water depth caused by underwater earthquakes and landslides cause sea surface undulations, which in turn propagate to the coast and result in significant damage as wave heights normally increase due to the wave shoaling process. Various types of numerical models have been developed to simulate the generation and propagation of tsunami waves. Most of tsunami models determine the initial surface of the water based on the assumption that the movement of the seabed is immediately and identically transmitted to the sea surface. However, this approach does not take into account the characteristics of underwater earthquakes that occur with time history and spatial variation. Thus, such an incomplete description on the initial generation of tsunami waves is totally reflected in the error during the simulation. In this study, the analytical solution proposed by Hammack (1973) was applied in the tsunami model in order to simulate the generation of initial water surface elevation by the change of water depth with time history and its propagation. The developed solution is expected to identify the relationship among various type of seabed motions, initial surface undulations, and wave speeds of elevated water surfaces.

• Journal of Korean Society of Coastal and Ocean Engineers
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• v.32 no.6
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• pp.553-560
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• 2020

This paper presents an algorithm for identifying and eliminating errors by seagrasses in coastal bathymetry surveying using drone and HD camera. Survey errors due to seagrasses were identified, segmentated and eliminated using a L∗a∗b color space model. Bathymetry survey using a drone and HD camera has many advantages over conventional survey methods such as ship-board acoustic sounder or manual level survey which are time consuming and expensive. However, errors caused by sea bed reflectance due to seagrasses habitat hamper the development of new surveying tool. Seagrasses are the flowering plants which start to grow in November and flourish to maximum density until April in Korea. We developed a new algorithm for identifying seagrasses habitat locations and eliminating errors due to seagrasses to get the accurate depth survey data. We tested our algorithm at Wolpo beach. Bathymetry survey data which were obtained using a drone with HD camera and calibrated to eliminate errors due to seagrasses, were compared with depth survey data obtained using ship-board multi-beam acoustic sounder. The abnormal bathymetry data which are defined as the excess of 1.5 times of a standard deviation of random errors, are composed of 8.6% of the test site of area of 200 m by 300 m. By applying the developed algorithm, 92% of abnnormal bathymetry data were successfully eliminated and 33% of RMS errors were reduced.

• KSCE Journal of Civil and Environmental Engineering Research
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• v.41 no.6
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• pp.675-686
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• 2021

This study investigated the ground displacement occurring in a slope below a waste-rock dumping site and estimated the likelihood of a disaster due to a landslide. To start with, photogrammetry was conducted by unmanned aerial vehicles (UAVs) to investigate the size and extent of the ground displacement. From April 2019 to July 2020, the average error rate of the five UAV surveys was 0.011-0.034 m, and an elevation change of 2.97 m occurred due to the movement of the soil layer. Only some areas of the slope showedelevation change, and this was believed to be due to thegroundwater generated during rainfall rather than the effect of the waste-rock load at the top. Sensitivity analysis for LS-RAPID simulation was performed, and the simulation results were compared and analyzed by applying a digital elevation model (DEM) and a digital surface model (DSM)as terrain data with 10 m, 5 m, and 4 m grids. When data with high spatial resolution were used, the extent of the sedimentation of landslide material tended to be excessively expanded in the DEM. In contrast, in the result of applying a DSM, which reflects the topography in detail, the diffusion range was not significantly affected even when the spatial resolution was changed, and the sedimentation behavior according to the river shape could be accurately expressed. As a result, it was concluded that applying a DSM rather than a DEM does not significantly expand the sedimentation range, and results that reflect the site situation well can be obtained.

• Journal of the Korean Association of Geographic Information Studies
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• v.13 no.4
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• pp.157-169
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• 2010

In this study, the applicability of SRTM(The Shuttle Radar Topography Mission) DEM(Digital Elevation Model) which is one of the remotely sensed shuttle's radar digital elevation was tested for use as the input data in a grid-based rainfall-runoff model. The SRTM DEM and digital topographic map derived DEM(TOPO DEM) were building with 500m spatial resolution for the Chungju-Dam watershed which located in the middle east of South Korea, and stream-burning method was applied to delineate the proper flow direction for model application. Similar topographical characteristics were shown as a result of comparing elevation, flow-direction, hydrological slope, number of watershed cell, and profile between SRTM DEM and TOPO DEM. Two DEMs were tested by using a grid-based rainfall-runoff model named KIMSTORM with 6 storm events. The results also showed no significant differences in average values of relative error for both peak runoff(0.91 %) and total runoff volume(0.29 %). The results showed that the SRTM DEM has applicability like TOPO DEM for use in a grid-based rainfall-runoff modeling.

• Journal of Korea Water Resources Association
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• v.53 no.12
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• pp.1159-1172
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• 2020

This study applied deep convolution neural network based on U-Net and SegNet using long period weather radar data to very short-term rainfall prediction. And the results were compared and evaluated with the translation model. For training and validation of deep neural network, Mt. Gwanak and Mt. Gwangdeoksan radar data were collected from 2010 to 2016 and converted to a gray-scale image file in an HDF5 format with a 1km spatial resolution. The deep neural network model was trained to predict precipitation after 10 minutes by using the four consecutive radar image data, and the recursive method of repeating forecasts was applied to carry out lead time 60 minutes with the pretrained deep neural network model. To evaluate the performance of deep neural network prediction model, 24 rain cases in 2017 were forecast for rainfall up to 60 minutes in advance. As a result of evaluating the predicted performance by calculating the mean absolute error (MAE) and critical success index (CSI) at the threshold of 0.1, 1, and 5 mm/hr, the deep neural network model showed better performance in the case of rainfall threshold of 0.1, 1 mm/hr in terms of MAE, and showed better performance than the translation model for lead time 50 minutes in terms of CSI. In particular, although the deep neural network prediction model performed generally better than the translation model for weak rainfall of 5 mm/hr or less, the deep neural network prediction model had limitations in predicting distinct precipitation characteristics of high intensity as a result of the evaluation of threshold of 5 mm/hr. The longer lead time, the spatial smoothness increase with lead time thereby reducing the accuracy of rainfall prediction The translation model turned out to be superior in predicting the exceedance of higher intensity thresholds (> 5 mm/hr) because it preserves distinct precipitation characteristics, but the rainfall position tends to shift incorrectly. This study are expected to be helpful for the improvement of radar rainfall prediction model using deep neural networks in the future. In addition, the massive weather radar data established in this study will be provided through open repositories for future use in subsequent studies.

• Korean Journal of Agricultural and Forest Meteorology
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• v.9 no.3
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• pp.195-202
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• 2007

The normalized difference in incident solar energy between a target surface and a level surface (overheating index, OHI) is useful in eliminating estimation error of site-specific maximum temperature in complex terrain. Due to the complexity in its calculation, however, an empirical proxy variable called northern exposure index (NEI) which combines slope and aspect has been used to estimate OHI based on empirical relationships between the two. An experiment with real-world landscape and temperature data was carried out to evaluate performance of the NEI - derived OHI (N-OHI) in reduction of spatial interpolation error for daily maximum temperature compared with that by the original OHI. We collected daily maximum temperature data from 7 sites in a mountainous watershed with a $149 km^2$ area and a 795m elevation range ($651{\sim}1,445m$) in Pyongchang, Kangwon province. Northern exposure index was calculated for the entire 166,050 grid cells constituting the watershed based on a 30-m digital elevation model. Daily OHI was calculated for the same watershed ana regressed to the variation of NEI. The regression equations were used to estimate N-OHI for 15th of each month. Deviations in daily maximum temperature at 7 sites from those measured at the nearby synoptic station were calculated from June 2006 to February 2007 and regressed to the N-OHI. The same procedure was repeated with the original OHI values. The ratio sum of square errors contributable by the N-OHI were 0.46 (winter), 0.24 (fall), and 0.01 (summer), while those by the original OHI were 0.52, 0.37 and 0.15, respectively.

• Journal of Cadastre & Land InformatiX
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• v.50 no.1
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• pp.125-141
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• 2020

This study investigated the time-series relationship between housing consumer sentiment and housing prices in the five major districts in Seoul and also analyzed the effect of the housing consumer sentiment on housing prices using Granger Causality and VEC (Vector Error Correction) models. To describe the key results, first of all, housing consumer sentiment and regional housing market prices were closely related to each other, and the consumer sentiment strongly affected the change of housing prices. Second, the housing consumer sentiment was confirmed to have a discriminatory effect on the housing prices among the districts in Seoul in the short term. Specifically, the housing price of the east southern district (ESD) was the main reason for the change in housing consumer sentiment in Seoul, and that the resulting impact was transferred to other districts. Third, it was analyzed that regions other than the ESD would increase the housing prices in the long term as the housing consumer sentiment turned positive, but that the ESD would see a steady tone. Fourth, in the case of relative influence by district, housing (apartment) price fluctuation in a district was generally found to be most affected by adjacent or competitive districts. Through these findings, this study confirmed that there is a clear causality between housing consumer sentiment and housing prices in each district of Seoul and that there is a discriminatory influence on housing consumer sentiment among the districts.

• Korean Journal of Remote Sensing
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• v.34 no.5
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• pp.709-720
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• 2018

In order to optimize the evaluation of biomass in crop monitoring, accurate and timely data of the crop-field are required. Evaluating above-ground biomass helps to monitor crop vitality and to predict yield. Unmanned Aerial Vehicle (UAV) imagery are being assessed for analyzing within field spatial variability for agricultural precision management, because UAV imagery may be acquired quickly during critical periods of rapid crop growth. This study reports on the development of remote sensing techniques for evaluating the biomass of winter crop. Specific objective was to develop statistical models for estimating the dry weight of barley and wheat using a Excess Green index ($E{\times}G$) based Vegetation Fraction (VF) and a Crop Surface Model (CSM) based Plant Height (PH) value. As a result, the multiple linear regression equations consisting of three independent variables (VF, PH, and $VF{\times}PH$) and above-ground dry weight provided good fits with coefficients of determination ($R^2$) ranging from 0.86 to 0.99 with 5 cultivars. In the case of the barley, the coefficient of determination was 0.91 and the root mean squared error of measurement was $102.09g/m^2$. And for the wheat, the coefficient of determination was 0.90 and the root mean squared error of measurement was $110.87g/m^2$. Therefore, it will be possible to evaluate the biomass of winter crop through the UAV image for the crop growth monitoring.