• Journal of Advanced Navigation Technology
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• v.28 no.2
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• pp.172-177
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• 2024

This paper presents a method to overcome the problem of increasing elevation estimation error when estimating elevation in a multipath situation with radar. A multipath situation means that radar reception signals reflected from the same target come from multiple paths. In non-multipath, the monopulse method is accurate. For the opposite case, the least square error method is accurate. In multipath situation and when the elevation angle is very low, a singular occurs where the least square error estimate diverges. This singular was identified based on the propagation factor, and monopulse and least square error estimation methods were selectively used. As a result, we succeeded in increasing the accuracy of elevation estimation. MATLAB simulations were performed to verify the method proposed in this paper.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.3
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• pp.217-223
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• 2014

With precise sensor position, attitude element, and imaging resolution, a simulated geospatial image can be generated. In this study, a satellite image is simulated using SPOT ortho-image and global elevation data, and the geometric similarity between original and simulated images is analyzed. Using a SPOT panchromatic image and high-density elevation data from a 1/5K digital topographic map data an ortho-image with 10-meter resolution was produced. The simulated image was then generated by exterior orientation parameters and global elevation data (SRTM1, GDEM2). Experimental results showed that (1) the agreement of the image simulation between pixel location from the SRTM1/GDEM2 and high-resolution elevation data is above 99% within one pixel; (2) SRTM1 is closer than GDEM2 to high-resolution elevation data; (3) the location of error occurrence is caused by the elevation difference of topographical objects between high-density elevation data generated from the Digital Terrain Model (DTM) and Digital Surface Model (DSM)-based global elevation data. Error occurrences were typically found at river boundaries, in urban areas, and in forests. In conclusion, this study showed that global elevation data are of practical use in generating simulated images with 10-meter resolution.

• Korean Journal of Remote Sensing
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• v.17 no.2
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• pp.121-129
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• 2001

A DEM(digital elevation model) is generated from a SPOT panchromatic stereo-pair using automated algorithms over a 8 km$\times$10 km region around Mokpo city. The aims are to continue the accuracy assessment over diverse conditions and to examine the applicability of a SPOT DEM for coastal disaster monitoring. The accuracy is assessed with respect to three reference data sets: 10 global positioning system records, 19 leveling data, and 1:50,000 topography map. The planimetric error is 10.6m r.m.s. and the elevation erroer ranges from 12.4m to 14.4m r.m.s.. The DEM accuracy of the flat Mokpo region is consistent with that over a mountainous area, which supports the robustness of the algorithms. It was found that coordinate transformation errors are significant at a few meters when using the data from leveling and topographic maps. The error budget is greater than the requirements for coastal disaster monitoring. Exploiting that a sub-scene is used, the affine transformation improves the accuracy by 50% during the camera modeling.

• Korean Journal of Remote Sensing
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• v.37 no.5_1
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• pp.1043-1059
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• 2021

Forest and agricultural land are very important factors in the environmental ecosystem and securing food resources. Forest and agricultural land should be monitored regularly. CAS500-4 data are expected to be effectively used as a supplement of monitoring forest and agricultural land. Prior to the launch of the CAS500-4, the relative canopy height error analysis of the digital elevation model on South Korea was performed to determine the vertical target accuracy. Especially, by considering area of interest of the CAS500-4 (mountainous or agricultural area), it is conducted that vertical error analysis according to the slope and canopy. For Gongju, Jeju, and Samcheok, the average root mean squared differences were calculated compared to the drone LiDAR digitalsurface models, which were filmed in autumn and winter and the 5 m digital elevation model from the National Geographic Information Institute. As a result, the Shuttle radar topography mission digital elevation model showed a root mean squared differences of about 8.35, 8.19, and 7.49 m, respectively, while the Copernicus digital elevation model showed a root mean squared differences of about 5.65, 6.73, and 7.39 m, respectively. In addition, the root mean squared difference of shuttle radar topography mission digital elevation model and the Copernicus digital elevation model according to the slope angle were estimated on South Korea compared to the 5 m digital elevation model from the National Geographic Information Institute. At the slope angle of between 0° to 5°, root mean squared differences of the Shuttle radar topography mission digital elevation model and the Copernicus digital elevation model showed 3.62 and 2.52 m, respectively. On the other hands root mean squared differences of the Shuttle radar topography mission digital elevation model and the Copernicus digital elevation model respectively showed about 10.16 and 11.62 m at the slope angle of 35° or higher.

• Proceedings of the Korean Institute of Navigation and Port Research Conference
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• v.1
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• pp.535-540
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• 2006

The relationship between the observable noise model and the satellite elevation angle can be modeled quite well by an exponential function.[Jin, 1996] Noise size and dependence on the elevation angle are, however, different for each observation and receiver type. Therefore, the coefficient determination of this model is an issue, and various methods including PR-CP, single difference, and time difference have been suggested. The limitations of them are difficulty to model the carrier phase noise and to eliminate bias. To overcome these disadvantages for using Jin's model, we suggest zero baseline double difference (DD) and noise sorting algorithm. Data DD technique in zero baseline is useful to eliminate all the troublesome GPS biases, and the remaining error is the sum of GPS measurement noises from two satellites. These DD residuals for hours should be sorted by the combination of satellite elevation angles, and then variance value of the residual for each combination can be estimated. Using these values, we construct an over-determined linear equation whose solution is a set of noise variance for each satellite elevation angle. With 24hr Trimble 4000ssi data, we easily worked out the coefficients of the noise model not only for pseudorange but also for carrier phase. We estimated the standard deviation of the measurement DD using our model, and plotted 1 and 3 sigma lines for every epoch to verify the representation of the residual error. 63.3% of pseudorange residual and 65.9% of phase error did not exceed the 1 sigma lines. Additionally, 99.2% and 99.5% of them lied within 3sigma line. These figures prove that the Gaussian property of measurement noise, and that the suggested model by our algorithm corresponds to the observable noise information.

• Korean Journal of Agricultural and Forest Meteorology
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• v.2 no.4
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• pp.175-182
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• 2000

Spatial interpolation of daily temperature forecasts and observations issued by public weather services is frequently required to make them applicable to agricultural activities and modeling tasks. In contrast to the long term averages like monthly normals, terrain effects are not considered in most spatial interpolations for short term temperatures. This may cause erroneous results in mountainous regions where the observation network hardly covers full features of the complicated terrain. We developed a spatial interpolation model for daily minimum temperature which combines inverse distance squared weighting and elevation difference correction. This model uses a time dependent function for 'mountain slope lapse rate', which can be derived from regression analyses of the station observations with respect to the geographical and topographical features of the surroundings including the station elevation. We applied this model to interpolation of daily minimum temperature over the mountainous Korean Peninsula using 63 standard weather station data. For the first step, a primitive temperature surface was interpolated by inverse distance squared weighting of the 63 point data. Next, a virtual elevation surface was reconstructed by spatially interpolating the 63 station elevation data and subtracted from the elevation surface of a digital elevation model with 1 km grid spacing to obtain the elevation difference at each grid cell. Final estimates of daily minimum temperature at all the grid cells were obtained by applying the calculated daily lapse rate to the elevation difference and adjusting the inverse distance weighted estimates. Independent, measured data sets from 267 automated weather station locations were used to calculate the estimation errors on 12 dates, randomly selected one for each month in 1999. Analysis of 3 terms of estimation errors (mean error, mean absolute error, and root mean squared error) indicates a substantial improvement over the inverse distance squared weighting.

• Journal of The Geomorphological Association of Korea
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• v.25 no.3
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• pp.105-120
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• 2018

This study estimates possibility and limitation on production of DEM using aerial photo by comparison of DEMs using aerial photo and digital map. Mountain and urban areas show higher elevation in DEM using aerial photo than in DEM using digital map, due to height of vegetation cover and buildings, respectively. However, artificial affects due to bridge, embankment and road construction are responsible for areas with higher elevation in DEM using digital map than in DEM using aerial photo. This difference in elevation between DEMs seems to be caused by rapid change in real elevation that is not reflected in digital map. There is little difference in elevation between DEMs in plain and area with little or no vegetation cover. This study suggests that problems associated with vegetation cover and error by GCP should be fixed, although DEM using aerial photo can quantitatively and 3-dimensionally reconstruct topography with a high resolution.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.25 no.1
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• pp.1-5
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• 1989

A number of studies for the improvement of the accuracy of NNSS fix were carried out previously. But most studies were done when a ship was stationary at the fixed position. To investigate the accuracy of NNSS fix affected by the error of ship's speed and course when a ship was moving, the computer simulation was performed by each satellite, passing direction of satellite, and elevation angle. The obtained results are summarized as follows: 1. When elevation angle and passing direction of satellite were constant, there were little difference in the accuracy of NNSS fix from among those. 2. The accuracy of NNSS fox caused by the error of ship's speed was in proportion to the absolute value of it without regard to the magnitude of ship's speed, and it also became different according to the ship's course. 3. When the error of ship's speed was constant, the accuracy of the fixed position became different according to the passing direction of the satellite. 4. When the ship's course was south or north, the error of NNSS fix was greater than that of east or west, and the higher the elevation angle of the satellite, the greater the error of the NNSS fix.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.38 no.3
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• pp.223-235
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• 2020

In this study, we propose an approach for calibrating the elevation of a DEM (Digital Elevation Model), one of the key data in realizing unmanned aerial vehicle image-based precision agriculture. First of all, radiometric correction is performed on the orthophoto, and then ExG (Excess Green) is generated. The non-vegetation area is extracted based on the threshold value estimated by applying the Otsu method to ExG. Subsequently, the elevation of the DEM corresponding to the location of the non-vegetation area is extracted as EIFs (Elevation Invariant Features), which is data for elevation correction. The normalized Z-score is estimated based on the difference between the extracted EIFs to eliminate the outliers. Then, by constructing a linear regression model and correcting the elevation of the DEM, high-quality DEM is produced without GCPs (Ground Control Points). To verify the proposed method using a total of 10 DEMs, the maximum/minimum value, average/standard deviation before and after elevation correction were compared and analyzed. In addition, as a result of estimating the RMSE (Root Mean Square Error) by selecting the checkpoints, an average RMSE was derivsed as 0.35m. Comprehensively, it was confirmed that a high-quality DEM could be produced without GCPs.

• Proceedings of the KSRS Conference
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• 2008.10a
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• pp.259-261
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• 2008

GIS-based spatial data integration tasks have used exhaustive thematic maps generated from sparsely sampled data or satellite-based exhaustive data. Due to a simplification of reality and error in mapping procedures, such spatial data are usually imperfect and of different accuracy. The objective of this study is to carry out a sensitivity analysis in connection with input topographic data for landslide hazard mapping. Two different types of elevation estimates, elevation spot heights and a DEM from ASTER stereo images are considered. The geostatistical framework of kriging is applied for generating more reliable elevation estimates from both sparse elevation spot heights and exhaustive ASTER-based elevation values. The effects of different accuracy arising from different terrain-related maps on the prediction performance of landslide hazard are illustrated from a case study of Boeun, Korea.