• Ocean and Polar Research
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• v.29 no.4
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• pp.391-400
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• 2007

To identify the spatial distribution pattern of water quality in Masan Bay, Pearson's correlation as a common statistic method and Moran's I as a spatial autocorrelation statistics were applied to the hydrological data seasonally collected from Masan Bay for two years ($2004{\sim}2005$). Spatial distribution of salinity, DO and silicate among the hydrological parameters clustered strongly while chlorophyll a distribution displayed a weak clustering. When the similarity matrix of Moran's I was compared with correlation matrix of Pearson's r, only the relationships of temperature vs. salinity, temperature vs. silicate and silicate vs. total inorganic nitrogen showed significant correlation and similarity of spatial clustered pattern. Considering Pearson's correlation and the spatial autocorrelation results, water quality distribution patterns of Masan Bay were conceptually simplified into four types. Based on the simplified types, Moran's I and Pearson's r were compared respectively with spatial distribution maps on salinity and silicate with a strong clustered pattern, and with chlorophyll a having no clustered pattern. According to these test results, spatial distribution of the water quality in Masan Bay could be summed up in four patterns. This summation should be developed as spatial index to be linked with pollutant and ecological indicators for coastal health assessment.

• Korean Journal of Remote Sensing
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• v.37 no.5_2
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• pp.1307-1315
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• 2021

The Changjiang Diluted Water (CDW) spreads over the East China Sea every summer and significantly affects the sea surface salinity changes in the seas around Jeju Island and the southern coast of Korea peninsula. Sometimes its effect extends to the eastern coast of Korea peninsula through the Korea Strait. Specifically, the CDW has a significant impact on marine physics and ecology and causes damage to fisheries and aquaculture. However, due to the limited field surveys, continuous observation of the CDW in the East China Sea is practically difficult. Many studies have been conducted using satellite measurements to monitor CDW distribution in near-real time. In this study, an algorithm for estimating Sea Surface Salinity (SSS) in the East China Sea was developed using the Geostationary Ocean Color Imager (GOCI). The Multilayer Perceptron Neural Network (MPNN) method was employed for developing an algorithm, and Soil Moisture Active Passive (SMAP) SSS data was selected for the output. In the previous study, an algorithm for estimating SSS using GOCI was trained by 2016 observation data. By comparison, the train data period was extended from 2015 to 2020 to improve the algorithm performance. The validation results with the National Institute of Fisheries Science (NIFS) serial oceanographic observation data from 2011 to 2019 show 0.61 of coefficient of determination (R²) and 1.08 psu of Root Mean Square Errors (RMSE). This study was carried out to develop an algorithm for monitoring the surface salinity of the East China Sea using GOCI and is expected to contribute to the development of the algorithm for estimating SSS by using GOCI-II.

• Proceedings of the KSRS Conference
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• 2004.10a
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• pp.696-700
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• 2004

In the Korean seas, Sea Surface Temperature (SST) and Thermal Fronts (TF) were analyzed temporally and spatially during 8 years from 1993 to 2000 using NOAA/AVHRR MCSST. As the result of harmonic analysis, distributions of the mean SST were $10~25^{\circ}C,$ and generally SST decreased as latitude increased. SST increased in the order as following; the South Sea $(20\~23^{\circ}C),$ the East Sea $(17\~19^{\circ}C)$, and the West $Sea(13\~16^{\circ}C).$ Annual amplitudes and phases were $4\~11^{\circ}C,\;210\~240^{\circ}$ and high values were shown as following; the West Sea $(A1,\;9\~11^{\circ}C),$ the Northern East Sea $(A5,\;8\~9^{\circ}C),$ the Southern East Sea $(A4,\;6\~8^{\circ}C),$ the South Sea $(A3,\;6\~7^{\circ}C),$ the East China Sea $(A2,\;4\~7^{\circ}C)$ and phases; $A3\;(238\~242^{\circ}),\;A4\;(235\~240^{\circ}),\;A5\;(225\~235^{\circ}),\;Al\;(220\~230^{\circ}),\;A2\;(210\~235^{\circ}),$ respectively, Both of them were related inversely except the area A2, therefore the rest areas were affected by seasonal variations. TF were detected by Soble Edge Detection Method using gradient of SST. Consequently, TF were divided into 4 fronts; the Subpolar Front (SPF) based on the Cold Water Mass (low SST and salinity Subartic Water), resulting from the North Korea Cold Current (NKCC) and the East Sea Proper Cold Water in the middle and low layer, and the Warm Water Mass (high SST and salinity Subtropical Water), resulting from the Tsushima Warm Current (TWC) in area A4 and 5, the Kuroshio Front (KF) based on the Kuroshio Current (KC) and shelf waters in the East China Sea (ESC) in A2, and the South Sea Coastal Front (SSCF) based on the South Sea Coastal Water (SSCW) and TWC in A3. Also, the Tidal Front was weakly appeared in AI. TF located in steep slope of submarine topography. Annual amplitudes and phases were bounded in the same place, and these results should be considered to influence of seasonal variations.

• Korean Journal of Remote Sensing
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• v.34 no.2_2
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• pp.419-429
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• 2018

An unmanned autonomous maritime surface system can move the vehicle to the areas for observing the ocean accidents, disasters, and severe weather conditions. Detection and monitoring technologies have been developed by the converging of the regional and local surveillance system. Wave Glider, one of the autonomous maritime surface systems, is ocean-wave propelled autonomous surface vehicle and controlled using Iridium satellite communication. In this study, we carried out two-time Wave Glider observations for 2016 and 2017 summer in the East China Sea that the area was influenced by low-salinity water. We observed the sea surface warming effect due to the low-salinity water using the regional (satellite) and local (Wave Glider) surveillance system. We also monitored the effect of the typhoon and understood the change of the ocean-atmosphere environments in real-time. New unmanned surface system with autonomous system and high endurance structure can measure comprehensively and usefully a long observation in complicated ocean environments because of connecting with other surveillance systems.

• Proceedings of the KSRS Conference
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• 2000.04a
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• pp.13-18
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• 2000

We observed sea level variation of the long time at Kerguelen island in the South Indian Ocean with ARGOS data and meteorological data during about 1 year(May 1993~March 1994) through using filter, spectral analysis, coherency and phase, and found characteristics for the two oceanic signal levels(detided oceanic signal level, h$_{detided}$ and seasonal oceanic level, h$_{corr.ib}$). The forms of variations are very well agreed to between ARGOS data and meteorological data for atmospheric pressure in the observed periods. The seasonal difference of sea level between Summer and Winter is about 1.6cm. Both the detided oceanic signal level(h$_{detided}$) variation and the inverted barometer level(h$_{ib}$) variation have a strong correlation for T>1day period bands. Characteristics of h$_{detided}$ variation are decided not by the influence of any meteorological distributions (pressure, winds, etc), but the influence of another factors(temperature, salinity, etc.) for T>2days periods bands. h$_{corr.ib}$ plays an very important role of sea level variation of the long time term(especially T>about 180days period bands).

• Korean Journal of Remote Sensing
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• v.39 no.6_2
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• pp.1605-1613
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• 2023

The physical properties of the ocean interior are determined by temperature and salinity. To observe them, we rely on satellite observations for broad regions of oceans. However, the satellite for salinity measurement, Soil Moisture Active Passive (SMAP), has low temporal and spatial resolutions; thus, more is needed to resolve the fast-changing coastal environment. To overcome these limitations, the algorithm to use the Geostationary Ocean Color Imager-II (GOCI-II) of the Geo-Kompsat-2B (GK-2B) was developed as the inputs for a Multi-layer Perceptron Neural Network (MPNN). The result shows that coefficient of determination (R²), root mean square error (RMSE), and relative root mean square error (RRMSE) between GOCI-II based sea surface salinity (SSS) (GOCI-II SSS) and SMAP was 0.94, 0.58 psu, and 1.87%, respectively. Furthermore, the spatial variation of GOCI-II SSS was also very uniform, with over 0.8 of R² and less than 1 psu of RMSE. In addition, GOCI-II SSS was also compared with SSS of Ieodo Ocean Research Station (I-ORS), suggesting that the result was slightly low, which was further analyzed for the following reasons. We further illustrated the valuable information of high spatial and temporal variation of GOCI-II SSS to analyze SSS variation by the 11th typhoon, Hinnamnor, in 2022. We used the mean and standard deviation (STD) of one day of GOCI-II SSS, revealing the high spatial and temporal changes. Thus, this study will shed light on the research for monitoring the highly changing marine environment.

• Ocean and Polar Research
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• v.35 no.2
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• pp.147-155
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• 2013

The purpose of this study is to investigate seasonal difference in linear trends in satellite-derived chlorophyll-a concentration (Chl-a) and their related environmental changes in the South Sea of Korea (SSK) and East China Sea (ECS) for recent 15 years (Jan. 1998~Dec. 2012) by analyzing climatological data of Chl-a, Rrs(555), sea surface wind (SSW) and nutrient. A linear trend analysis of Chl-a data reveals that, during recent 15 years, the spring bloom was enhanced in most of the ECS, while summer and fall blooms were weakened. The increased spring (Mar. - May) Chl-a was associated with strengthened winter (Dec. - Feb.) wind that probably provided more nutrient into the upper ocean from the deep. The causes of decreased summer (Jun. - Aug.) Chl-a in the northern ECS were uncertain, but seemed to be related with the nutrient limitation. Recently (after 2006), low-salinity Changjiang diluted water in the south of Jeju and the SSK had lower phosphate that caused increase in N/P ratio with Chl-a decrease. The decreased fall (Sep. - Nov.) Chl-a was associated with weakened wind that tends to entrain less nutrient into the upper ocean from the deep. This study suggests that phytoplankton in the ECS differently changes in response to environmental changes depending on season and region.

• Proceedings of the KSRS Conference
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• 2006.03a
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• pp.91-94
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• 2006

China Coastal Waters (CCW) usually appears in the seas surrounding Jeju Island annually(June to October) and is very pronounced in August. Generally, low-salinity water appears to the western seas of Jeiu Island from June through October and gradually propagates to the eastern seas, where CCW meets the Tsushima Current. Empirical orthogonal function (EOF) analysis of SLAs and SSTs indicated that the valiance in SLAs and SSTs was 95.05%(the first mode to third mode) and 98.09%(the first mode), respectively The PSD of the western waters for the first mode of EOF analysis of SLAs was stronger than that of the eastern waters because of the influence of CCW. The PSD for the EOF analysis of SSTs was similar in all areas (the Yangtze Estuary and the seas to the west and east of Jeju Island), with a period of approximately 260 days.

• Journal of the Korean Association of Geographic Information Studies
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• v.7 no.1
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• pp.20-27
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• 2004

The hydro-dynamical and ecological coupled model were applied in the Tokyo Bay, to evaluate the flow pattern including water quality parameters and the distribution of biomass flux, and to compare with the results obtained from the satellite data during March 2001. The flow pattern and salinity distribution obtained from the present model were nearly identical with those of the previous studies. SST from NOAA/AVHRR was $2.5^{\circ}C$ higher than model results in the mouth of bay and $0.5^{\circ}C$ lower than model results in the inner bay, respectively. It was found that the concentration of chlorophyll-a estimated from SeaWiFS was considerably higher than that of model result, regardless similar distribution pattern. This disagreement will be studied through the more elaborate investigation in the future.

• Journal of Ecology and Environment
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• v.36 no.4
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• pp.407-419
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• 2013

Cyclotella species were collected at 51 sites from July 2010 to June 2013 in Korean coastal waters. A total of five Cyclotella species (C. atomus var. marina, C. baltica, C. litoralis, C. meduanae, and C. meneghiniana) were identified in this study. The diagnostic characteristics for five Cyclotella species are described, focusing on the spacing, position, number of satellite pores of the mantle fultoportula and valve face fultoportula. In addition, we put the salinity ranges of five species of Cyclotella together. Of the five Cyclotella species, C. baltica, C. litoralis and C. meduanae are newly recorded in Korea.