• Journal of Advanced Navigation Technology
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• v.25 no.6
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• pp.569-575
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• 2021

Deep learning shows differences in prediction performance depending on data quality and model. This study uses various input data and multiple deep learning models to build an optimal deep learning model for predicting solar radiation, which has the most influence on power generation prediction. did. As the input data, the weather data of the Korea Meteorological Administration and the clairvoyant meteorological image were used by segmenting the image of the Korea Meteorological Agency. , comparative evaluation, and predicting solar radiation by constructing multiple deep learning models connecting the models with the best error rate in each model. As an experimental result, the RMSE of model A, which is a multiple deep learning model, was 0.0637, the RMSE of model B was 0.07062, and the RMSE of model C was 0.06052, so the error rate of model A and model C was better than that of a single model. In this study, the model that connected two or more models through experiments showed improved prediction rates and stable learning results.

• Journal of Korea Water Resources Association
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• v.47 no.4
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• pp.371-384
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• 2014

This study developed a new algorithm of extreme rainfall extraction based on the Communication, Ocean and Meteorological Satellite (COMS) and the Tropical Rainfall Measurement Mission (TRMM) Satellite image data and evaluated its applicability for the heavy rainfall event in July-2011 in Seoul, South Korea. The power-series-regression-based Z-R relationship was employed for taking into account for empirical relationships between TRMM/PR, TRMM/VIRS, COMS, and Automatic Weather System(AWS) at each elevation. The estimated Z-R relationship ($Z=303R^{0.72}$) agreed well with observation from AWS (correlation coefficient=0.57). The estimated 10-minute rainfall intensities from the COMS satellite using the Z-R relationship generated underestimated rainfall intensities. For a small rainfall event the Z-R relationship tended to overestimated rainfall intensities. However, the overall patterns of estimated rainfall were very comparable with the observed data. The correlation coefficients and the Root Mean Square Error (RMSE) of 10-minute rainfall series from COMS and AWS gave 0.517, and 3.146, respectively. In addition, the averaged error value of the spatial correlation matrix ranged from -0.530 to -0.228, indicating negative correlation. To reduce the error by extreme rainfall estimation using satellite datasets it is required to take into more extreme factors and improve the algorithm through further study. This study showed the potential utility of multi-geostationary satellite data for building up sub-daily rainfall and establishing the real-time flood alert system in ungauged watersheds.

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1651-1669
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• 2023

Sea ice currently covers approximately 7% of the world's ocean area, primarily concentrated in polar and high-altitude regions, subject to seasonal and annual variations. It is very important to analyze the area and type classification of sea ice through time series monitoring because sea ice is formed in various types on a large spatial scale, and oil and gas exploration and other marine activities are rapidly increasing. Currently, research on the type and area of sea ice is being conducted based on high-resolution satellite images and field measurement data, but there is a limit to sea ice monitoring by acquiring field measurement data. High-resolution optical satellite images can visually detect and identify types of sea ice in a wide range and can compensate for gaps in sea ice monitoring using Geostationary Ocean Color Imager-II (GOCI-II), an ocean satellite with short time resolution. This study tried to find out the possibility of utilizing sea ice monitoring by training a rule-based machine learning model based on learning data produced using high-resolution optical satellite images and performing detection on GOCI-II images. Learning materials were extracted from Liaodong Bay in the Bohai Sea from 2021 to 2022, and a Random Forest (RF) model using GOCI-II was constructed to compare qualitative and quantitative with sea ice areas obtained from existing normalized difference snow index (NDSI) based and high-resolution satellite images. Unlike NDSI index-based results, which underestimated the sea ice area, this study detected relatively detailed sea ice areas and confirmed that sea ice can be classified by type, enabling sea ice monitoring. If the accuracy of the detection model is improved through the construction of continuous learning materials and influencing factors on sea ice formation in the future, it is expected that it can be used in the field of sea ice monitoring in high-altitude ocean areas.

• Journal of the Korean Association of Geographic Information Studies
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• v.15 no.3
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• pp.137-147
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• 2012

Recently, floating green algae (FGA) in open oceans and coastal waters have been reported over wide area, yet accurate detection of these using traditional ground based measurement and chemical analysis in the laboratory has been difficult or even impossible due to the lack of spatial resolution, coverage, and revisit frequency. In contrast, spectral reflectance measurement makes it possible to quickly assess the chlorophyll content in green algae. Our objectives are to investigate the spectral reflectance of the FGA observed in the Yellow Sea and to develop a new index to detect FGA from satellite imagery, namely floating green algae index (FGAI), which uses relatively simple reflectance ratio technique. The Moderate Resolution Imaging Spectroradiometer (MODIS) and Geostationary Ocean Color Imager (GOCI) satellite images at 500m spatial resolution were utilized to produce FGAI which is defined as the ratio between reflectance at 860nm and 660nm bands. Both FGAI results yielded reasonable green algae detection at the regional scale distribution. Especially houly GOCI observations can present more detaield information of FGAI than low-orbit satellite.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.259-274
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• 2014

On Aug 18, 2013, Sakurajima volcano in Japan erupted on a relatively large-scale. Geostationary Ocean Color Imager (GOCI) had used to detect volcanic ash in the surrounding area on the next day of this eruption. The geomagnetic variation has been analyzed using geomagnetic data from Cheongyang observatory in Korea and several geomagnetic observatories in Japan. First, we reconstruct geomagnetic data by principal component analysis and conduct semblance analysis by wavelet transform. Secondly, we minimize the error of solar effect by using wavelet based semblance filtering with Kp index. As a result of this study, we could confirm that the geomagnetic variation usually occur at the moment of Sakurajima volcano eruption. However, we cannot rule out the possibilities that it could have been impacted by other factors besides volcanic eruption in other variation's cases. This research is an exceptional study to analyze geomagnetic variation related with abroad volcanic eruption uncommonly in Korea. Moreover, we expect that it can help to develop further study of geomagnetic variation involved in earthquake and volcanic eruption.

• Economic and Environmental Geology
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• v.47 no.1
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• pp.39-48
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• 2014

In coastal region, estuaries have complex environments where dissolved and particulate matters are mixed with marine water and substances. Suspended sediment (SS) dynamics in coastal water, in particular, plays a major role in erosion/deposition processes, biomass primary production and the transport of nutrients, micropollutants, heavy metals, etc. Temporal variation in suspended sediment concentration (SSC) can be used to explain erosion/sedimentation patterns within coastal zones. Remotely sensed data can be an efficient tool for mapping SS in coastal waters. In this study, we analyzed the variation in SSC in coastal water using the Geostationary Ocean Color Imager (GOCI) and Landsat Enhanced Thematic Mapper Plus (ETM+) in Gyeonggi-bay. Daily variations in GOCI-derived SSC showed low values during ebb time. Current velocity and water level at 9 and 10 am is 37.6, 28.65 $cm{\cdot}s^{-1}$ and -1.23, -0.61 m respectively. Water level has increased to 1.18 m at flood time. In other words, strong current velocity and increased water level affected high SSC value before flood time but SSC decreased after flood time. Also, we compared seasonal SSC with the river discharge from the Han River and the Imjin River. In summer season, river discharge showed high amount, when SSC had high value near the inland. At this time SSC in open sea had low value. In contrast, river discharge amount from inland showed low value in winter season and, consequently, SSC in the open sea had high value because of northwest monsoon.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.33 no.5
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• pp.463-472
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• 2015

Solar radiation data are important data that can be used as basic research data in diverse areas. In particular, solar radiation data are essential for diverse studies that have been recently conducted in South Korea including those for new and renewable energy resource map making and crop yield forecasting. So purpose of this study is modification of Heliosat-II method to estimate solar irradiance in Korea by using COMS-MI imagery. For this purpose, in this study, errors appearing in ground albedo images were corrected through linear transformation. And method of producing background albedo map which is used in Heliosat-II method is modified to get more finely tuned one. Through the study, ground albedo correction could be successfully performed and background albedo maps could be successfully derived. Lastly, In this study, solar irradiance was estimated by using modified Heliostat-II method. And it was compared with actually measured values to verify the accuracy of the methods. Accuracy of estimated solar irradiance was 30.8% RMSE(%). And this accuracy level means that solar irradiance was estimated on 10% higher level than previous Heliosat-II method.

• Korean Journal of Remote Sensing
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• v.30 no.2
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• pp.303-314
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• 2014

Mt. SAKRAJIMA in southern Kagosima, japan is one of the most active volcanoes in the world. On 18 August 2013, the SAKRAJIMA volcano recently went into the largest scaled eruption with a huge plume of volcanic ash. Therefore, the concern arises if this considerable amount of ashes might flow into the Korea peninsula as well as Japan. In this paper, we performed numeric experiment to analyze how volcanic product resulted from the SAKRAJIMA volcano has impacted on Korea. In order to predict the spread pathway of ash, HYSPLIT model and UM data has been used and 17th September 2013 has been selected as observation date since it is expected that the volcanic ash would flow into the South Korea. In addition, we have detected ash dispersion by using optical Communication, Ocean and Meteorological Satellite- Geostationary Ocean Color Imager (COMS-GOCI) images. As the results, we come to a very satisfactory conclusion that the spread pathway of volcanoes based on HYSPLIT model are matched 63.52 % with ash dispersion area detected from GOCI satellites image.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.3
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• pp.669-676
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• 2018

The Geostationary Ocean Color Imager(: GOCI) detects the sea fog at a high horizontal resolution of $500m{\times}500m$ using the Rayleigh corrected reflectance of 8 bands. The visible and the near infrared waves strongly reflect the characteristics of the earth surface, causing errors in cloud and fog detection. A threshold of the Band7 reflectance was set to detect the sea fog entering the land. When the region on which Band4 reflectance is larger than Band8 is determinated as cloud, the error over-estimated as sea fog is corrected by comparing the average reflectance with the surrounding region. The improved algorithm has been verified by comparing the fog images of the Cheollian satellite (COMS: Communication, Ocean, and Meteorological Satellite) as well as the visibility data from the Korea Meteorological Administration.

• Aerospace Engineering and Technology
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• v.12 no.2
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• pp.156-162
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• 2013

In the communication between satellite and ground station, data rate can be determined from the data volume and required transmission time. Increasing data rate can be limited according to the available bandwidth. For the reason, it has been popularly studying on high spectral-efficient modulation scheme in large volume data application such as digital video broadcasting service. This paper presents the conceptual design of UHRIT broadcasting in GEO-KOMPSAT-2A (GK2A) mission by using DVB-S2 standard. Based on the recently determined data rate, UHRIT bandwidth was calculated at the various modulation schemes and code rates of DVB-S2 standard. Receiving performance of global user station was also evaluated thorough link analysis by considering that user station is located at the edge of beam coverage. Finally, maximum data rate was analyzed in a situation that COMS HRIT bandwidth should be alternatively applied for UHRIT downlink.