• The Journal of Engineering Geology
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• v.3 no.3
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• pp.279-293
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• 1993

In recent yeaes, the collapses of man made structures have been encountered from time to time due to the deformation of the ground in korea. Furthermore, the possibilities of casasters from the ground deformation suCh as landslide and active fault are atrracting our attention to the deformation monitoring. In this study, two-coordinate tilt which was monitored during six months in order to develop tediniques for prevention of disasters from the ground deformation. The two-coordinate tilt which was detected by a tilt-sensor installed in shallow depth on the slope with the sensitivity of 0.0001 arc.sec in every 10 minutes was recorded continously to PC through the interface with 200-m line coonection. The observed digital tilt data. together with the relevant meteorological data were analyzed in reference to engineering application. During the whole observation period of six months, the net tilt is 10.06 arc.sec to the west and 73.88 arc.sec to the south. Consequently the ground has a tilt of 74.56 arc.sec to the direction of $S7.75^{\circ}W$ with average tilting of 0.02 arc.sec/hour. In spite of such fast and large tilting, it is interpreted in view of engineering aspects that the site is much safe from danger, since both East-West and North-South components of tilt converge as time goes by. Two categories of deformational events are recognized ; one is toward the direction of surface slope and the other is to the direction of increased pore pressure. Tiks are acenain to have a close relation with precipitation of rain. The daily variation of two-coordinate tilt is delayed 4.3 hours in average after the variation of atmospheric temperature. A certain correlation between atmospheric pressure and deformation might be revealed.

• Journal of Korean Society of Transportation
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• v.32 no.6
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• pp.686-693
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• 2014

This study presents a model that enables to predict road surface temperature using neural network theory. Historical road surface temperature data were collected from Road Weather Information System. They used for the calibration of the model. The neural network was designed to predict surface temperature after 1-hour, 2-hour, and 3-hour from now. The developed model was performed on Cheongwon-Sangju highway to test. As a result, the standard deviation of the difference of the predicted and observed was $1.27^{\circ}C$, $0.55^{\circ}C$ and $1.43^{\circ}C$, respectively. Also, comparing the predicted surface temperature and the actual data, R2 was found to be 0.985, 0.923, and 0.903, respectively. It can be concluded that the explanatory power of the model seems to be high.

• Korean Journal of Remote Sensing
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• v.21 no.4
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• pp.341-369
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• 2005

Hyperspectral images have emerged as a new and promising remote sensing data that can overcome the limitations of existing optical image data. This study was designed to provide a comprehensive review on definition, data processing methods, and applications of hyperspectral data. Various types of airborne, spaceborne, and field hyperspectral image sensors were surveyed from the available literatures and internet search. To understand the current status of hyperspectral remote sensing technology and research development, we collected several hundreds research papers from international journals (IEEE Transactions on Geoscience and Remote Sensing, International Journal of Remote Sensing, Remote Sensing of Environment and AVIRIS Workshop Proceedings), and categorized them by sensor types, data processing techniques, and applications. Although several hyperspectral sensors have been developing, AVIRIS has been a primary data source that the most hyperspectral remote sensing researches were relied on. Since hyperspectral data have very large data volume with many spectral bands, several data processing techniques that are particularly oriented to hyperspectral data have been developed. Although atmospheric correction, spectral mixture analysis, and spectral feature extraction are among those processing techniques, they are still in experimental stage and need further refinement until the fully operational adaptation. Geology and mineral exploration were major application in early stage of hyperspectral sensing because of the distinct spectral features of rock and minerals that could be easily observed with hyperspectral data. The applications of hyperspectral sensing have been expanding to vegetation, water resources, and military areas where the multispectral sensing was not very effective to extract necessary information.

• Proceedings of the Korea Water Resources Association Conference
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• 2021.06a
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• pp.453-453
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• 2021

도시지역 비점오염원관리, 물순환 회복, 침투 및 증발산량 증가, 열섬현상 저감을 위한 주요한 방안으로 저영향개발(low impact development, LID)과 그린인프라 기법의 적용되고 있다. LID 시설은 소규모 분산형 시설로써 넓은 지역에 많고 다양한 시설들이 적용되어 시설의 개수가 많으며, 수질 및 토양 내 기성제품에 대한 센서들의 가격은 고가로 형성되어 있어 기기의 경제성 및 유지관리 등 적용하는데 제한적이다. 따라서 과거 모니터링 자료를 기반으로 오염물질들과의 상관성 분석을 통하여 계측이 어려운 항목들을 계측가능한 항목들로부터 예측 가능하며, 선정된 항목들에 대한 비용효율적인 센서를 개발하여 실시간 LID 모니터링이 가능한 비용효율적 모니터링을 개발하였다. 공주대학교 천안캠퍼스의 LID 시설들은 2013년에 조성되어 현재까지 시설이 운영되고 있으며, 5년이상의 과거 강우시 모니터링 자료들을 이용하여 오염물질 상관성 분석을 수행가능 하기에 대상지로 선정하였다. 오염물질 상관성 분석은 2013년부터 2017년도에 침투도랑에서 수행된 강우시 모니터링 자료를 활용하여 각 오염물질들의 상관성을 분석을 수행하였다. 침투도랑 내 유입되는 평균 유입수는 TSS 286.1±318.3 mg/L, BOD 22.6±39.5 mg/L, TN 8.96±5.85 mg/L, TP 1.01±1.11 mg/L로 나타났다. 겨울철에 비해 여름철에서의 오염물질의 유입농도가 높은 것으로 분석되었다. 이는 여름철 고온건조로 인한 노면 내 차량의 주행으로 인한 중금속, 폐타이어 등과 장마철 강우 시 유출된 토사로 인하여 유입수의 농도가 높은 것으로 분석되었다. 오염물질 부하량은 TSS와 COD 0.66으로 유의성이 높은 것으로 나왔으며, COD와 TSS, TP, TN 등 유의성이 높은 것으로 분석되었다. Arduino와 Raspberry PI를 활용하여 저비용 센서와 LTE 모뎀통신과 데이터 베이스 연결하여 개발된 프로그램을 통해서 무선으로 LID 시설에 대한 모니터링을 침투화분2와 식생체류지에 조성하였다. 전력공급이 어려운 식생체류지의 경우 태양열(Solar system) 시스템과 보조 전력 배터리를 조성하여 장마철이나 장기적인 악천후로 인한 전력을 생산하지 못할 경우 보조전력배터리에서 전력을 제공하여 지속적인 모니터링이 이루어지도록 설계하였다. 토양함수량, 토양온도와 Conductivity 등 3종류의 센서를 적용하였으며, 프로그램은 현재 2단계를 통한 2차수정을 통하여 프로그램을 구축하였다. 오차, 오작동, 계측값에 대한 검·보정 작업이 필요하다. 또한 대기자료의 구축을 통해 보다 토양과 LID 시설에 대한 영향분석이 필요한 것으로 사료된다.

• Journal of Convergence for Information Technology
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• v.12 no.4
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• pp.260-269
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• 2022

In this paper, We propose an anomaly detection model using deep neural network to automate the identification of outliers of the national air pollution measurement network data that is previously performed by experts. We generated training data by analyzing missing values and outliers of weather data provided by the Institute of Environmental Research and based on the BeatGAN model of the unsupervised learning method, we propose a new model by changing the kernel structure, adding the convolutional filter layer and the transposed convolutional filter layer to improve anomaly detection performance. In addition, by utilizing the generative features of the proposed model to implement and apply a retraining algorithm that generates new data and uses it for training, it was confirmed that the proposed model had the highest performance compared to the original BeatGAN models and other unsupervised learning model like Iforest and One Class SVM. Through this study, it was possible to suggest a method to improve the anomaly detection performance of proposed model while avoiding overfitting without additional cost in situations where training data are insufficient due to various factors such as sensor abnormalities and inspections in actual industrial sites.

• Proceedings of the Korea Water Resources Association Conference
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• 2006.05a
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• pp.1940-1944
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• 2006

현지관측을 통한 지속적이고 광범위한 지역에 대해 정확하고 정밀하게 조사하여 종합적인 분석과 예측, 결정과정에 있어서, 복잡한 해양의 특성, 여러가지 조사 작업상의 난점, 경제적, 시간적으로 많은 어려움이 따르게 된다. 하지만, 위성원격탐사와 GIS를 이용한 해양환경파악기법은 현지관측에서 얻을 수 있는 제한적인 자료이외의 다량의 자료를 정성 및 정량적으로 데이터베이스화하여 분석함과 동시에 가시화함으로써 해양개발로 인해 불가피하게 초래될 수밖에 없는 환경을 보다 정확하게, 객관적으로 분석하여 장기적으로 예측할 수 있는 고도화된 환경조사 및 평가 기술이라고 할 수 있다. 본 연구에서는 고해상도 위성자료인 Landsat TM 영상과 NOAA AVHRR 자료를 이용하여 수온 및 클로로필을 추출하였으며, GIS를 이용하여 현지관측자료 및 수치해도를 기초로 공간분포도를 작성함으로서 그 외의 수질환경요소를 산출하였다. 위성영상분석은 현장조사와 같은 시점의 Landsat TM 위성영상을 획득하여, 위성 영상은 지구의 곡률과 자전, 위성체의 자세와 고도 및 속도, 그리고 센서의 기하 특성으로 인하여 실제의 지형에 대하여 기하학적 왜곡을 가지고 있으므로 지형도에서 지상기준점(Ground Control Point, GCP)를 추출하여 ERDAS Imagine으로 UTM좌표체계에 따른 기하보정(Geometric Correction)을 실시하였으며, 동일한 시기의 NOAA AVHRR영상을 데이터로 처리하여 수온자료를 추출하였다. 표층수온과 현장관측에 의한 클로로필을 수치 지도화하기 위하여 열적외선영역인 TM band 6의 분광특성값(Digital Number)과 동일한 위치의 수온자료를 기초로 회귀분석을 실시함으로써 수온추출 알고리즘을 도출하여, 분석데이터의 신뢰도를 검증하였으며, 수온, 클로로필, 투명도 등을 위성원격탐사 자료와 GIS를 이용하여 공간분석을 실시하고, 공간분포도를 작성함으로써 대상해역의 해양환경을 파악하였다. 본 연구결과, 분석된 위성자료가 현장조사에 의한 검증이 이루어지지 않을 경우, 영상자료분석을 통한 표층수온 추출은 대기 중의 수증기와 에어로졸에 의한 계산치의 오차가 반영되기 때문에 실측치 보다 낮게 평가 될 수 있으므로, 반드시 이에 대한 검증이 필요함을 알 수 있었다. 현지관측에 비해 막대한 비용과 시간을 절약할 수 있는 위성영상해석방법을 이용한 방법은 해양수질파악이 가능할 것으로 판단되며, GIS를 이용하여 다양하고 복잡한 자료를 데이터베이스화함으로써 가시화하고, 이를 기초로 공간분석을 실시함으로써 환경요소별 공간분포에 대한 파악을 통해 수치모형실험을 이용한 각종 환경영향의 평가 및 예측을 위한 기초자료로 이용이 가능할 것으로 사료된다.

• Korean Journal of Remote Sensing
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• v.14 no.4
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• pp.366-375
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• 1998

Optical characteristics of the yellow sand and their influences on the ocean color remote sensing has been studied using ocean color sensor SeaWiFS measurements. Two cases of April 18 and April 25, 1998, representing yellow sand and background aerosol, are selected for emphasizing the impact of high aerosol concentration on the ocean color remote sensing. It was shown that NASA's standard atmospheric correction algorithm treats yellow sand area as either too high radiance or cloud area, in which ocean color information is not generated. Optical thickness of yellow sand arrived over the East Asian sea waters in April 18 indicates that there are two groups loaded with relatively homogeneous yellow sand, i.e.: heavy yellow sand area with optical thickness peak around 0.8 and mild area with about 0.4, which are consistent with ground observations. The movement of the yellow sand area obtained from surface weather maps and backward trajectory analysis manifest the notion that the weak yellow sand area was originated from the outer region of the dust storm. It is also noted that high optical thickness associated with the yellow sand is significantly different from what we may observe from background aerosol, which is about 0.2. These characteristics allow us to determine the yellow sand area with an aid of atmospheric correction parameter. Results indicate that the yellow sand area can be determined by applying the features revealed in scattergrams of atmospheric correction parameter and optical thickness.

• Korean Journal of Remote Sensing
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• v.36 no.3
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• pp.461-474
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• 2020

Korea launched Geostationary Environmental Monitoring Satellite (GEMS), a UV/visible spectrometer that measure pollution gases on 18 February 2020. Because satellite retrieval is an ill-posed inverse solving process, the validation with ground-based measurements or other satellite measurements is essential to obtain reliable products. For this purpose, satellite-based OMI and OMPS total column ozone (TCO), and ground-based Pandora TCO in Busan and Seoul were selected for future GEMS validation. First of all, the goal of this study is to validate the ground ozone data using characteristics that satellite data provide coherent ozone measurements on a global basis, although satellite data have a larger error than the ground-based measurements. In the cross validation between Pandora and OMI TCO, we have found abnormal deviation in ozone time series from Pandora #29 observed in Seoul. This shows that it is possible to perform inverse validation of ground data using satellite data. Then OMPS TCO was compared with verified Pandora TCO. Both data shows a correlation coefficient of 0.97, an RMSE of less than 2 DU and the OMPS-Pandora relative mean difference of >4%. The result also shows the OMPS-Pandora relative mean difference with SZA, TCO, cross-track position and season have insignificant dependence on those variables.In addition, we showed that appropriate thresholds depending on the spatial resolution of each satellite sensor are required to eliminate the impact of the cloud on Pandora TCO.

• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1423-1444
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• 2022

In this study, the sensitivity of the mid-infrared radiance to atmospheric and surface factors was analyzed using the radiative transfer model, MODerate resolution atmospheric TRANsmission (MODTRAN6)'s simulation data. The possibility of retrieving the land surface temperature (LST) using only the mid-infrared bands at night was evaluated. Based on the sensitivity results, the LST retrieval algorithm that reflects various factors for night was developed, and the level of the LST retrieval algorithm was evaluated using reference LST and observed LST. Sensitivity experiments were conducted on the atmospheric profiles, carbon dioxide, ozone, diurnal variation of LST, land surface emissivity (LSE), and satellite viewing zenith angle (VZA), which mainly affect satellite remote sensing. To evaluate the possibility of using split-window method, the mid-infrared wavelength was divided into two bands based on the transmissivity. Regardless of the band, the top of atmosphere (TOA) temperature is most affected by atmospheric profile, and is affected in order of LSE, diurnal variation of LST, and satellite VZA. In all experiments, band 1, which corresponds to the atmospheric window, has lower sensitivity, whereas band 2, which includes ozone and water vapor absorption, has higher sensitivity. The evaluation results for the LST retrieval algorithm using prescribed LST showed that the correlation coefficient (CC), the bias and the root mean squared error (RMSE) is 0.999, 0.023K and 0.437K, respectively. Also, the validation with 26 in-situ observation data in 2021 showed that the CC, bias and RMSE is 0.993, 1.875K and 2.079K, respectively. The results of this study suggest that the LST can be retrieved using different characteristics of the two bands of mid-infrared to the atmospheric and surface conditions at night. Therefore, it is necessary to retrieve the LST using satellite data equipped with sensors in the mid-infrared bands.

• Korean Journal of Remote Sensing
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• v.37 no.6_1
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• pp.1631-1645
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• 2021

This research aims to provide the characteristics of the world's first active lidar sensor Atmospheric Laser Doppler Instrument (ALADIN) wind data and Geostationary Korea Multi Purpose Satellite 2A (GK2A) Atmospheric Motion Vector (AMV) data by comparing two wind data. As a result of comparing the data from September 2019 to August 1, 2020, The total number of collocated data for the AMV (using IR channel) and Mie channel ALADIN data is 177,681 which gives the Root Mean Square Error (RMSE) of 3.73 m/s and the correlation coefficient is 0.98. For a more detailed analysis, Comparison result considering altitude and latitude, the Normalized Root Mean Squared Error (NRMSE) is 0.2-0.3 at most latitude bands. However, the upper and middle layers in the lower latitudes and the lower layer in the southern hemispheric are larger than 0.4 at specific latitudes. These results are the same for the water vapor channel and the visible channel regardless of the season, and the channel-specific and seasonal characteristics do not appear prominently. Furthermore, as a result of analyzing the distribution of clouds in the latitude band with a large difference between the two wind data, Cirrus or cumulus clouds, which can lower the accuracy of height assignment of AMV, are distributed more than at other latitude bands. Accordingly, it is suggested that ALADIN wind data in the southern hemisphere and low latitude band, where the error of the AMV is large, can have a positive effect on the numerical forecast model.