• Analytical Science and Technology
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• v.16 no.6
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• pp.483-487
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• 2003

Amitrole is well-known as a non-selective herbicide and it is able to cause contamination of driking water as well as pollution of ground water and surface water. However, it is difficult to extract from water because it has a high solubility for water whereas a low solubility for general organic solvents. This method is described for the determination of amitrole in water samples by GC/MS. After evaporation of 10 mL water sample by a vacuum evaporator, amitrole was derivatized with isobutyl chloroformate (iso-BCF) on room temperature for 15~20 min. As a result, the sensitivity for GCfMS was improved as N-isobutoxycarbonyl amitrole derivative was formed. The linearity of the calibration curve showed good as 0.997. The recoveries were obtained more than 94.9% and relative standard deviations were less than 2.8% at $1.0{\mu}g/L$, $10.0{\mu}g/L$ and $100.0{\mu}g/L$. The limit of detection showed $0.1{\mu}g/L$ with a signal-to-noise ratio (S/N) of 3.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.44 no.4 s.316
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• pp.28-35
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• 2007

Airbone laser altimeters have been utilized for 3D topographic mapping of the earth, moon, and planets with high resolution and accuracy, which is a rapidly growing remote sensing technique that measures the round-trip time emitted laser pulse to determine the topography. The traveling time from the laser scanner to the Earth's surface and back is directly related to the distance of the sensor to the ground. When there are several objects within the travel path of the laser pulse, the reflected laser pluses are distorted by surface variation within the footprint, generating multiple echoes because each target transforms the emitted pulse. The shapes of the received waveforms also contain important information about surface roughness, slope and reflectivity. Waveform processing algorithms parameterize and model the return signal resulting from the interaction of the transmitted laser pulse with the surface. Each of the multiple targets within the footprint can be identified. Assuming each response is gaussian, returns are modeled as a mixture gaussian distribution. Then, the parameters of the model are estimated by LMS Method or EM algorithm However, each response actually shows the skewness in the right side with the slowly decaying tail. For the application to require more accurate analysis, the tail information is to be quantified by an approach to decompose the tail. One method to handle with this problem is proposed in this study.

• 한국지구물리탐사학회:학술대회논문집
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• 2001.09a
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• pp.122-140
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• 2001

We have applied the geophysical survey, mainly electric and electromagnetic (EM) methods, to a test site contaminated by hydrocarbon waste disposal and local spill. The multi-frequency, moving source & receiver EM survey along with ground penetrating radar (GPR) showed a fairly good performance in detection of buried metal pipes and objects. Magnetic survey measuring vertical and horizontal gradients were so sensitive to the small metallic objects spread over the surface that it's hard to discriminate the buried pipe. We chose electrical resistivity, EM and GPR survey to examine the soil contamination. Depth slices of resistivity distribution as the results of the inversion of resistivity and EM data coincided each other and closely matched the contaminated area determined by chemical analysis of the soil samples. GPR images did not show the reflection events related with contamination plume since there are no distinct spill in this site. We inferred the contamination using the penetration depth of the GPR energy, which could be used as auxiliary information to the resistivity and EM results. We summarized the applicability of each survey methods based on this results and proposed a desirable survey scheme for the determination of hydrocarbon contaminated site.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.34 no.5
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• pp.503-514
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• 2016

Delineation of accurate object boundaries is crucial to provide reliable spatial information products such as digital topographic maps, building models, and spatial database. In LiDAR(Light Detection and Ranging) data, real boundaries of the buildings exist somewhere between outer-most points on the roofs and the closest points to the buildings among points on the ground. In most cases, areas of the building footprints represented by LiDAR points are smaller than actual size of the buildings because LiDAR points are located inside of the physical boundaries. Therefore, building boundaries determined by points on the roofs do not coincide with the actual footprints. This paper aims to estimate accurate boundaries that are close to the physical boundaries using airborne LiDAR data. The accurate boundaries are determined from the non-gridded original LiDAR data using initial boundaries extracted from the gridded data. The similar method implemented in this paper is also found in demarcation of the maritime boundary between two territories. The proposed method consists of determining initial boundaries with segmented LiDAR data, estimating accurate boundaries, and accuracy evaluation. In addition, extremely low density data was also utilized for verifying robustness of the method. Both simulation and real LiDAR data were used to demonstrate feasibility of the method. The results show that the proposed method is effective even though further refinement and improvement process could be required.

• The KIPS Transactions:PartB
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• v.11B no.2
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• pp.141-148
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• 2004

The ground positions and elevations information called DEMs(Digital Elevation Models) which are extracted from the stereo aerial photographs and/or satellite images using image matching method have the natural errors caused by variant environments. This study suggests the method to evaluate DEMs using correlation values between the reference and the target DEMs. This would be strongly helpful for experts to correct these errors. To evaluate the whole area of DEMs in the horizontal and vertical errors, the target cell is matched for each reference cell using the correlation values of these two cells. When the target cell is matched for each reference cell, horizontal and vertical error was calculated so as to help experts to recognize a certain area of DEMs which should be corrected and edited. If the correlation value is low and tile difference in height is high, the target cell will be candidated as changed or corrupted cell. When the area is clustered with those candidated cells, that area will be regarded as changed or corrupted area to be corrected and edited. Using this method, the evaluation for all DEM cells is practicable, the horizontal errors as well as vertical errors is calculable and the changed or corrupted area can be detected more efficiently.

• Korean Journal of Remote Sensing
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• v.33 no.3
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• pp.267-274
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• 2017

The purpose of this research was to develop and present methods to detect sinkholes which can exist underneath the surface of the ground. First, we buried a water tank with dimensions $1.8{\times}0.8{\times}0.8m$ at a distance of 1.8 m from the surface. This played the role of the sinkhole. Secondly, we created a square zone with sides 12 meters away from the buried water tank. Within this zone, we measured the gravity at 1-meter intervals using a Scintrex CG5 relative gravimeter with a resolution of 0.001 mGal. Additionally, we performed three-dimensional (3-D) gravity modeling to calculate the theoretical values of the relative gravity around our model sinkhole. The resulting values for the relative gravity around the sinkhole depended on the method used. The measured effect of gravity was 0.036 mGal and the effect calculated using 3-D modeling was 0.024 mGal. Our results suggest that sinkholes that are similar in size to the water tank used in this study can be detected using relative gravity surveys. Smaller sinkholes can be detected by reducing the intervals between the relative gravity measurements.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.32 no.4_1
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• pp.319-326
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• 2014

The accurate geo-referencing processes that apply ground control points is prerequisite for effective end use of HRSI (High-resolution satellite imagery). Since the conventional control point acquisition by human operator takes long time, demands for the automated matching to existing reference data has been increasing its popularity. Among many options of reference data, the airborne LiDAR (Light Detection And Ranging) data shows high potential due to its high spatial resolution and vertical accuracy. Additionally, it is in the form of 3-dimensional point cloud free from the relief displacement. Recently, a new matching method between LiDAR data and HRSI was proposed that is based on the image projection of whole LiDAR data into HRSI domain, however, importing and processing the large amount of LiDAR data considered as time-consuming. Therefore, we wmotivated to ere propose a local LiDAR chip generation for the HRSI geo-referencing. In the procedure, a LiDAR point cloud was rasterized into an ortho image with the digital elevation model. After then, we selected local areas, which of containing meaningful amount of edge information to create LiDAR chips of small data size. We tested the LiDAR chips for fully-automated geo-referencing with Kompsat-2 and Kompsat-3 data. Finally, the experimental results showed one-pixel level of mean accuracy.

• Journal of the Korean Society of Fisheries and Ocean Technology
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• v.27 no.4
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• pp.225-231
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• 1991

In order to suggest the useful information on the fishing ground of the bigeye tuna Thunnus obesus(LOWE), a data base system was formed with catch data of the Korean tuna long liners during from 1975 to 1987 by using a set of 16 bits personal computer. This data base was constructed of the handling program and 4 types of data file processed from the monthly and yearly catch data of the whole tunas and the bigeye tuna. And when the system was started, the map of one among various Oceans such as the Pacific, the Atlantic and the Indian Ocean. is drawn on the monitor. And then the catch rates of the whole tunas or the catch ratios of bigeye tunas are indicated by the figured symbols and the colors on the sea divisions of 5$^{\circ}$ space of longitude and latitude respectively at the same time. Also this system has the preestimating program on the catch rates of the whole tunas and the bigeye tuna in the desired month and sea divisions. In the results than this data base system was handled and tested, very useful informations were obtained for the detection of tunas, especially bigeye tuna, and the preestimation was possible in a desired level.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.123-131
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• 2010

In this paper we applied Ground-Based Synthetic Aperture Radar(GB-SAR) interferometry to detect artificial displacement of a reflector and performed an atmospheric humidity correction to improve the accuracy. A series of GB-SAR images were obtained using a center frequency of 5.3 GHz with a range resolution of 25 cm and a azimuth resolution of $0.324^{\circ}$, all in full-polarization (HH, VV, VH, HV) modes. A triangular trihedral corner reflector was located 160 m away from the system, and the artificial displacements of 0-40 mm was implemented during the GB-SAR image acquisition. The result showed that the RMS error between the actual and measured displacements, averaged in all polarization data, was 1.22 mm, while the maximum error in case of the 40 mm displacement was 2.72 mm at HH-polarization. After the atmospheric correction with respect to the humidity, the RMS error was reduced to 0.52 mm. We conclude that a GB-SAR system can be used to monitor the possible displacement of artificial/natural scatterers and the stability assessment with sub-millimeter accuracy.

• Korean Journal of Remote Sensing
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• v.23 no.4
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• pp.297-309
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• 2007

The first Korean geostationary weather satellite, Communications, Oceanography and Meteorology Satellite (COMS) will be launched in 2008. The ground station for COMS needs to perform geometric correction to improve accuracy of satellite image data and to broadcast geometrically corrected images to users within 30 minutes after image acquisition. For such a requirement, we developed automated and fast geometric correction techniques. For this, we generated control points automatically by matching images against coastline data and by applying a robust estimation called RANSAC. We used GSHHS (Global Self-consistent Hierarchical High-resolution Shoreline) shoreline database to construct 211 landmark chips. We detected clouds within the images and applied matching to cloud-free sub images. When matching visible channels, we selected sub images located in day-time. We tested the algorithm with GOES-9 images. Control points were generated by matching channel 1 and channel 2 images of GOES against the 211 landmark chips. The RANSAC correctly removed outliers from being selected as control points. The accuracy of sensor models established using the automated control points were in the range of $1{\sim}2$ pixels. Geometric correction was performed and the performance was visually inspected by projecting coastline onto the geometrically corrected images. The total processing time for matching, RANSAC and geometric correction was around 4 minutes.