• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.12 no.4
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• pp.370-379
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• 2007

Short-term variations of phytoplankton community structure in the southeast coastal waters of Korea from July to September in 2006 were investigated with data set of phytoplankton, chemical and physical water properties, and meterological data. A total of 11 sampling sites of 4 different depths (surface, 5 m, 10 m, and bottom) were visited on July 11-14, July 24-26, August 7-10, August 21-24 and September 5-8. We identified 151 species in 63 genera of phytoplankton in which diatoms were the most diverse group composed of 92 species in 37 genera. Dinoflagellates were the second diverse group of 52 species in 22 genera. The other groups include 7 species in 4 genera including Raphidophytes, and Euglenophyta. After rainy season, excessive nutrients from adjacent streams to the stratified water column proliferates Chaetoceros group in July. But biomass of phytoplankton and nutrient concentrations were decreased during the period of a drought in August. However, Chaetoceros was the most dominant genera in all depths of the first, second, third and fifth cruises, except the 4th cruise on August 21-24 when dominant group were dinoflagellates including Gymnodinium spp. and Cochlodinium polykrikoides. The characteristic of phytoplankton community and environment condition during summer 2006 can be summarized as: 1) low concentration of nutrients caused by a long lasting drought in August 2) no summer outbreak of C. polykrikoides because the strength of offshore waters was weak than other years, and 3) Chaetoceros spp. was the dominant species despite short period appearance of dinoflagellates.

• Korean Journal of Remote Sensing
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• v.39 no.6_1
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• pp.1255-1272
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• 2023

Satellite-based chlorophyll-a concentration, produced as a long-term time series, is crucial for global climate change research. The production of data without gaps through the merging of time-synthesized or multi-satellite data is essential. However, studies related to satellite-based chlorophyll-a concentration in the waters around the Korean Peninsula have mainly focused on evaluating seasonal characteristics or proposing algorithms suitable for research areas using a single ocean color sensor. In this study, a merging dataset of remote sensing reflectance from the geostationary sensor GOCI-II and polar-orbiting sensors (MODIS, VIIRS, OLCI) was utilized to achieve high spatial coverage of chlorophyll-a concentration in the waters around the Korean Peninsula. The spatial coverage in the results of this study increased by approximately 30% compared to polar-orbiting sensor data, effectively compensating for gaps caused by clouds. Additionally, we aimed to quantitatively assess accuracy through comparison with global chlorophyll-a composite data provided by Ocean Colour Climate Change Initiative (OC-CCI) and GlobColour, along with in-situ observation data. However, due to the limited number of in-situ observation data, we could not provide statistically significant results. Nevertheless, we observed a tendency for underestimation compared to global data. Furthermore, for the evaluation of practical applications in response to marine disasters such as red tides, we qualitatively compared our results with a case of a red tide in the East Sea in 2013. The results showed similarities to OC-CCI rather than standalone geostationary sensor results. Through this study, we plan to use the generated data for future research in artificial intelligence models for prediction and anomaly utilization. It is anticipated that the results will be beneficial for monitoring chlorophyll-a events in the coastal waters around Korea.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.17 no.2
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• pp.45-58
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• 2012

For the development of reference values and evaluation of water quality in various environmental conditions, we divided the coastal region around Korean peninsular into 5 distinctive ecological regions based on the influence of surface current, depth, tidal range, turbidity, and climate condition. We used national marine environment monitoring data collected by National Fisheries Research & Development Institute(NFRDI) from 2000-2009. For the reference values, we used maximum seasonal mean from 2000 to 2007 for DIN, DIP, and chlorophyll-a and minimum seasonal mean for secchi depth measured at stations without the influence of river runoff in each ecological regions. For the reference value of bottom dissolved oxygen saturation, we used minimum mean value of 90% calculated from minimal riverine influence stations of whole regions. We calculated enrichment score for each assessment criteria. The enrichment score of DIN, DIP, and Chlorophyll-a was 1 (=< reference value), 2 (< 110% of reference value), 3 (< 125% of reference value), 4 (< 150% of reference value), and 5 (> 150% of reference value). The enrichment score of DO saturation and Secchi depth was 1 (> reference value), 2 (> 90% of reference value), 3 (>75 % of reference value), 4 (> 50% of reference value), and 5 (< 50% of reference value). We calculated water quality index using weighted linear combination of five enrichment score for the comparison of whole regions. From the water quality index distribution calculated from all stations between 2000 and 2007 period, we classified into 5 grade based on the standard deviation calculated from total water quality index. We assigned grade very good(I), good(II), moderate(III), bad(IV), and very bad(V) when the water quality index was less than 23, minimum + 1 sd, +2 sd, +3 sd, and grater than minium+ 3 sd, respectively.

• Korean Journal of Remote Sensing
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• v.37 no.3
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• pp.603-614
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• 2021

Recently, the surface temperature in the seas around Korea has been continuously rising. This temperature rise causes changes in fishery resources and affects leisure activities such as fishing. In particular, high temperatures lead to the occurrence of red tides, causing severe damage to ocean industries such as aquaculture. Meanwhile, changes in sea temperature are closely related to military operation to detect submarines. This is because the degree of diffraction, refraction, or reflection of sound waves used to detect submarines varies depending on the ocean mixed layer. Currently, research on the prediction of changes in sea water temperature is being actively conducted. However, existing research is focused on predicting only the surface temperature of the ocean, so it is difficult to identify fishery resources according to depth and apply them to military operations such as submarine detection. Therefore, in this study, we predicted the temperature of the ocean mixed layer at a depth of 38m by using temperature data for each water depth in the upper mixed layer and meteorological data such as temperature, atmospheric pressure, and sunlight that are related to the surface temperature. The data used are meteorological data and sea temperature data by water depth observed from 2016 to 2020 at the IEODO Ocean Research Station. In order to increase the accuracy and efficiency of prediction, LSTM (Long Short-Term Memory), which is known to be suitable for time series data among deep learning techniques, was used. As a result of the experiment, in the daily prediction, the RMSE (Root Mean Square Error) of the model using temperature, atmospheric pressure, and sunlight data together was 0.473. On the other hand, the RMSE of the model using only the surface temperature was 0.631. These results confirm that the model using meteorological data together shows better performance in predicting the temperature of the upper ocean mixed layer.