• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.239-249
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• 2010

The Geostationary Ocean Color Imager (GOCI) data-processing system (GDPS), which is a software system for satellite data processing and analysis of the first geostationary ocean color observation satellite, has been developed concurrently with the development of th satellite. The GDPS has functions to generate level 2 and 3 oceanographic analytical data, from level 1B data that comprise the total radiance information, by programming a specialized atmospheric algorithm and oceanic analytical algorithms to the software module. The GDPS will be a multiversion system not only as a standard Korea Ocean Satellite Center(KOSC) operational system, but also as a basic GOCI data-processing system for researchers and other users. Additionally, the GDPS will be used to make the GOCI images available for distribution by satellite network, to calculate the lookup table for radiometric calibration coefficients, to divide/mosaic several region images, to analyze time-series satellite data. the developed GDPS system has satisfied the user requirement to complete data production within 30 minutes. This system is expected to be able to be an excellent tool for monitoring both long-term and short-term changes of ocean environmental characteristics.

• Korean Journal of Remote Sensing
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• v.26 no.2
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• pp.189-207
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• 2010

Several ocean color algorithms have been developed for GOCI (Geostationary Ocean Color Imager) using in-situ bio-optical data sets. These data sets collected around the Korean Peninsula between 1998 and 2009 include chlorophyll-a concentration (Chl-a), suspended sediment concentration (SS), absorption coefficient of dissolved organic matter ($a_{dom}$), and remote sensing reflectance ($R_{rs}$) obtained from 1348 points. The GOCI Chl-a algorithm was developed using a 4-band remote sensing reflectance ratio that account for the influence of suspended sediment and dissolved organic matter. The GOCI Chl-a algorithm reproduced in-situ chlorophyll concentration better than the other algorithms. In the SeaWiFS images, this algorithm reduced an average error of 46 % in chlorophyll concentration retrieved by standard chlorophyll algorithms of SeaWiFS. For the GOCI SS algorithm, a single band was used (Ahn et al., 2001) instead of a band ratio that is commonly used in chlorophyll algorithms. The GOCI $a_{dom}$ algorithm was derived from the relationship between remote sensing reflectance band ratio ($R_{rs}(412)/R_{rs}(555)$) and $a_{dom}(\lambda)$). The GOCI Chl-a fluorescence and GOCI red tide algorithms were developed by Ahn and Shanmugam (2007) and Ahn and Shanmugam (2006), respectively. If the launch of GOCI in June 2010 is successful, then the developed algorithms will be analyzed in the GOCI CAL/VAL processes, and improved by incorporating more data sets of the ocean optical properties data that will be obtained from waters around the Korean Peninsula.

• Proceedings of the Korea Water Resources Association Conference
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• 2018.05a
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• pp.395-395
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• 2018

기후변화 및 토지이용변화에 따라 강우량 및 증발산량 등과 같은 물순환계 구성요소가 변화하면 유역에서의 물순환계가 영향을 받게 된다. 이렇게 변화된 유역의 물순환계를 종합적으로 관리하기 위해서는 물순환 개선 기술을 통한 지속가능하고 건전한 물순환체계의 구축이 필요하다. 유역 물순환 개선 기술은 기후변화가 진행 중에 있거나 예상되는 지역에 대하여 강우-유출수를 지연, 저류, 침투시켜 지속가능한 물순환체계를 유지 회복하도록 하는 기술이라 할 수 있다. 한국건설기술연구원에서는 기후변화 대비 수자원 적응기술 개발 연구단(CCAW, Climate Change Adaptation for Water resources)의 연구비 지원을 받아 유역 건전성 및 취약성을 평가 하고 취약한 유역에 대한 물순환 개선기술을 확보하기 위한 연구를 수행 중에 있다. 특히, 수년간 국가연구개발사업을 통해 개발되고 사업화에 성공한바 있는 유역 물순환 평가 모형인 CAT(Catchment hydrologic cycle Assessment Tool)을 수정 개선하여 수요자 중심의 활발한 현장 적용을 도모하고 있다. 본 연구에서는 개발된 유역 물순환 개선 및 평가시스템의 적용성 평가를 위하여 대상유역으로 태국의 Lam Takhong 저수지 유역을 선정하였다. Lam Takhong 저수지 유역은 유역면적은 $1,423km^2$이며 저류량은 약 $440{\times}106m^3$이다. 입력자료인 DEM, Land Cover 자료는 USGS Hydro1K (https://earthexplorer.usgs.gov/), 하천망 및 유역경계 자료는 USGS HydroSHEDS (https://hydrosheds.cr.usgs.gov/dataavail.php), 기상 및 관측 유입량, 저수지 제원 등의 자료는 APEC 기후센터의 협조를 받아 1976년부터 2016년까지의 일단위 자료를 이용하였다. 모의결과는 저수지 월별 관측 유입량과 상류 유역의 모의 유출량을 이용하여 비교-분석 하였다. Lam Takhong 저수지 상류 유역은 APEC 기후센터에서 SWAT 모형을 이용하여 저수지 유입량 분석을 수행한 바 있다. 따라서 본 연구의 결과를 SWAT 모의결과와 비교하여 그 적용성을 검증하였다. 월별 관측 유입량과 저수지 상류 유역 모의 유출량을 비교한 결과 CAT의 경우 결정계수(R2) 값이 0.86, SWAT은 0.76으로 나타나 CAT의 적용 결과가 좀 더 우수한 것으로 나타났다. 모의 결과는 매개변수 최적화 과정을 거치지 않은 결과이며 SWAT 모형과의 결과 비교를 위하여 매개변수는 동일하게 적용하였다. 향후 매개변수 최적화 모듈을 통해 검 보정 단계를 거친다면 정밀한 분석이 가능할 것으로 판단된다.

• Proceedings of the Korea Water Resources Association Conference
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• 2015.05a
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• pp.46-46
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• 2015

강우가 지역별 계절별로 편중되어 있는 우리나라는 수자원의 안정적인 확보와 이용을 위해 다양한 형태의 댐을 건설하여 운영하고 있다. 그러나 대부분의 댐건설을 통해 형성된 저수지들은 탁수 장기화 및 녹조 발생 등의 환경, 생태적인 문제를 겪고 있으며, 그에 따른 사회적 우려로 인해 신규댐 건설을 통한 수자원확보는 더 이상 어려운 실정이다. 이러한 문제에 대응하기 위한 대안으로 기존 댐 저수지들(안동호-임하호)의 구조적 연계운영방안이 진행되고 있다. 본 연구의 목적은 2차원 CE-QUAL-W2모형을 활용하여 안동호와 임하호의 구조적 연결에 따른 탁수의 이동과 각 저수지 내에서의 유동 변화를 해석하는데 있다. 저수지 연계 시나리오는 EL. 138 m 위치에 길이 2 km, 직경 5.5 m 의 콘크리트관(마찰계수 0.05)이 안동호 좌안인 임동면 마리와 임하호 우안 망천리를 연결하는 것으로 가정하였다. 모델의 보정은 실측자료가 풍부한 2006년도 수문사상을 대상으로, 개별 저수지에 대해 수행하였고, 탁수 유동 시나리오 해석은 임하호에 심각한 탁수장기화 문제가 발생했던 2002년을 대상으로 댐 연계 탁수모의를 수행하였다. 안동호와 임하호의 댐 앞에서 모의값과 실측값을 오차를 분석한 결과 탁수예측오차는 AME 0.5~24 mg/L, RMSE 0.7~30.2mg/L의 범위로 비교적 실측값을 잘 반영한 것으로 나타났다. 임하댐의 경우 탁수층의 위치와 두께, 그리고 최고 탁도값을 적절히 재현 하였지만, 안동댐은 최고 탁도값 예측에서 다소 오차가 발생하는 것으로 나타났다. 안동호와 임하호 단독 운영시와 연계 운영시의 탁수변화 파악을 위해 초기 홍수사상이 발생한 8월 이후부터 저수지내의 TSS농도 분포를 비교하였다. 안동호의 경우 댐앞지점의 탁수분포는 수온성층구조에 영향을 받아, 단독 운영시(EL. 130 m)보다 연계운영시(EL. 140 m)에 탁수의 중심이 높은 위치에 형성되었다. 단독 운영시 10월 이후에 전도현상으로 인해 침강되지 않은 잔류 탁수층이 저수지 하부로 확산되었지만, 연계 운영시에는 재부상 되어 상층으로 확산되는 것으로 모의되었다. 또한 연계운영시 유량이동으로 인해 안동호의 탁수 댐앞 도달시간이 짧아지는 것으로 나타났다. 반면 임하호는 연계 운영시 안동댐으로 유출이 생기면서 중층에서 탁수량이 저감되는 것으로 모의되었다. 저수지 내 탁수량 분석을 위해 SS 15 mg/L 이상의 잔류 탁수량을 분석한 결과, 연계운영시 안동호의 평균 잔류탁수량 비율은 11.8% 증가, 임하호의 경우 11.7% 감소하였다. 또한, 탁수의 댐하류 방류일수도 SS 15 mg/L 기준 임하호 9일 저감, 안동호는 70일 증가하여 임하호의 탁수가 안동호의 탁수 장기화에 영향을 주는 것으로 나타났다.

• Korean Journal of Optics and Photonics
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• v.31 no.1
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• pp.13-19
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• 2020

To measure underwater acoustics using a fiber-optic Sagnac interferometric sensor, the sensitivities of ring-type probes are investigated by theoretical and experimental studies. A ring-type probe was fabricated by packaging a single-mode fiber wound around an acrylate cylinder of diameter 5 cm with epoxy bond. The probes were prepared as A-type, which was packaged with 46.84 m of sensing optical fiber, and B-type, which was packaged with 112.22 m of sensing fiber. The underwater acoustic test was performed at frequencies of 50, 70, and 90 kHz, and over a range of acoustic pressure of 20-100 Pa, to study the sensitivity. A commercial acoustic generator was located 1 m from the acoustic sensor, such as the ring-type probe or a commercial acoustic sensor. From the experimental test, the acoustic sensitivity of the ring-type probe had different values due to acoustic frequencies, unlike the theoretical prediction. Therefore, the experimental sensitivities were averaged for comparison to the theoretical values. These averaged sensitivities are 25.48 × 10^-5 rad/Pa for the A-type probe and 60.79 × 10^-5 rad/Pa for the B-type probe. The correction coefficient of Young's modulus c was determined to be 0.35.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1317-1328
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• 2021

This study analyzes the early satellite mission marine fog detection results from Geostationary Ocean Color Imager-II (GOCI-II). We investigate optical characteristics of the GOCI-II spectral bands for marine fog between October 2020 and March 2021 during the overlapping mission period of Geostationary Ocean Color Imager (GOCI) and GOCI-II. For Rayleigh-corrected reflection (R_rc) at 412 nm band available for the input of the GOCI-II marine fog algorithm, the inter-comparison between GOCI and GOCI-II data showed a small Root Mean Square Error (RMSE) value (0.01) with a high correlation coefficient (0.988). Another input variable, Normalized Localization Standard (NLSD), also shows a reasonable correlation (0.798) between the GOCI and GOCI-II data with a small RMSE value (0.007). We also found distinctive optical characteristics between marine fog and clouds by the GOCI-II observations, showing the narrower distribution of all bands' R_rc values centered at high values for cloud compared to marine fog. The GOCI-II marine fog detection distribution for actual cases is similar to the GOCI but more detailed due to the improved spatial resolution from 500 m to 250 m. The validation with the automated synoptic observing system (ASOS) visibility data confirms the initial reliability of the GOCI-II marine fog detection. Also, it is expected to improve the performance of the GOCI-II marine fog detection algorithm by adding sufficient samples to verify stable performance, improving the post-processing process by replacing real-time available cloud input data and reducing false alarm by adding aerosol information.

• Journal of Korea Water Resources Association
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• v.51 no.12
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• pp.1181-1194
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• 2018

This study suggests a novel approach of estimating stream flow discharge using the Synthetic Aperture Radar (SAR) images taken from 2015 to 2017 by European Space Agency Sentinel-1 satellite. Fifteen small to medium sized rivers in the Han River basin were selected as study area, and the SAR satellite images and flow data from water level and flow observation system operated by the Korea Institute of Hydrological Survey were used for model construction. First, we apply the histogram matching technique to 12 SAR images that have undergone various preprocessing processes for error correction to make the brightness distribution of the images the same. Then, the flow estimation model was constructed by deriving the relationship between the area of the stream water body extracted using the threshold classification method and the in-situ flow data. As a result, we could construct a power function type flow estimation model at the fourteen study areas except for one station. The minimum, the mean, and the maximum coefficient of determination ($R^2$) of the models of at fourteen study areas were 0.30, 0.80, and 0.99, respectively.

• Geophysics and Geophysical Exploration
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• v.24 no.3
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• pp.89-97
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• 2021

Fault detection in seismic data is well suited to the application of machine learning algorithms. Accordingly, various machine learning techniques are being developed. In recent studies, machine learning models, which utilize synthetic data, are the particular focus when training with deep learning. The use of synthetic training data has many advantages; Securing massive data for training becomes easy and generating exact fault labels is possible with the help of synthetic training data. To interpret real data with the model trained by synthetic data, the synthetic data used for training should be geologically realistic. In this study, we introduce a method to generate realistic synthetic seismic data. Initially, reflectivity models are generated to include realistic fault structures, and then, a one-way wave equation is applied to efficiently generate seismic stack sections. Next, a migration algorithm is used to remove diffraction artifacts and random noise is added to mimic actual field data. A convolutional neural network model based on the U-Net structure is used to verify the generated synthetic data set. From the results of the experiment, we confirm that realistic synthetic data effectively creates a deep learning model that can be applied to field data.

• The Journal of The Korea Institute of Intelligent Transport Systems
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• v.20 no.1
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• pp.119-131
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• 2021

This study quantitatively derived the direct socio-economic effects of the Gyeongbu Expressway, which opened in 1970, and suggested a methodological approach for more reliable results. The scenario was set when the Gyeongbu Expressway was not constructed in 1970, the opening of the Gyeongbu Expressway was delayed by 10 years, and the toll road between Seoul and Daejeon, or between Seoul and Gangneung was opened instead of the Gyeongbu Expressway as suggested by the World Bank. In addition, direct benefits were estimated by calculating and comparing the current vehicle operating costs, travel time costs, traffic accident costs, and environmental pollution costs. As a result, it was estimated that about 351 trillion won in direct benefits occurred, and it can be seen that the promotion of the construction project of the Gyeongbu Expressway at that time had a huge impact on South Korea's social economy.

• Journal of the Korea Academia-Industrial cooperation Society
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• v.22 no.1
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• pp.703-712
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• 2021

In this study, a formula was derived to calculate the damage impact distance using the Chemical Accident Response Information System (CARIS) so that local governments can decide on the evacuation and notification of 13 types of substances. The National Institute of Chemical Safety selected 16 out of 97 types of accident preparedness substances in 2018 and called them residents' evacuation preparedness substances. In a chemical accident, local governments should prepare for resident notification, such as emergency disaster texts. Using the CARIS in Chungju, this study modeled the damage-affected distances of 13 types of substances for the evacuation of residents. Under all conditions, the coefficient of determination R2 was 0.99 or higher, representing a range of at least 0.9921 to a maximum 0.9999. The relative standard deviation between the damage impact distance obtained using the calculation formula, and the CARIS result was compared. The minimum separation distance was corrected considering the actual chemical accident response situation, and the range was found to be between 0.58 and 5.97%. The damage impact distance can be calculated at the site using the calculation formula derived from the research, and local governments can determine whether to evacuate or notify residents.