• Korean Journal of Remote Sensing
• /
• v.38 no.6_2
• /
• pp.1653-1661
• /
• 2022

In this study, the classification of cold water and normal water based on Geo-Kompsat 2A images was performed. Daily mean surface temperature products provided by the National Meteorological Satellite Center (NMSC) were used, and convolution neural network (CNN) deep learning technique was applied as a classification algorithm. From 2019 to 2022, the cold water occurrence data provided by the National Institute of Fisheries Science (NIFS) were used as the cold water class. As a result of learning, the probability of detection was 82.5% and the false alarm ratio was 54.4%. Through misclassification analysis, it was confirmed that cloud area should be considered and accurate learning data should be considered in the future.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.24 no.6
• /
• pp.773-784
• /
• 2018

In this study, we examined the UA (upwelling age) using wind data of AWS/ASOS in the East Sea coast and the correlation between UA and SST (sea surface temperature) from May to August in 1995 to 2016. The data used the 6 observations of the wind data of AWS/ASOS and the SST data of the COD/RISA provided by the National Institute and Fisheries Science near the East Sea coast. The UA was calculated quantitatively low but it rose when the actual cold water mass occurred. Correlation analysis between UA and SST showed the negative (-) r (correlation coefficient) predominately. At the time of cold-water mass in June to August 2013, the r had a very high negative value of -0.65 to -0.89 in the 6 observations. It proved that as the UA increases, the SST is lower. By knowing the UA, we were able to evaluate the trend of upwelling in the cold-water mass of the East Sea coast in the long term and it will contribute to minimizing the damage to aquatic organisms according to the size and intensity of the upwelling.

• Journal of the Korean Society of Marine Environment & Safety
• /
• v.22 no.6
• /
• pp.731-737
• /
• 2016

To understand the characteristics and strength of the cold water that has caused damage to marine-culturing farms around Guryongpo, in the southwestern part of Korea, surface and water column temperatures were collected from temperature loggers deployed at a sea squirt farm during August-November 2007 and from a Real-time Information System for Aquaculture environment operated by NIFS (National Institute of Fisheries Science) during July-August 2015 and 2016. During the study period, surface temperature at Guryongpo decreased sharply when south/southwestern winds prevailed (the 18-26th of August and 20-22nd of September 2007 and the 13-15th of July 2015) as a result of upwelling. However, the deep-water (20-30m) temperature increased during periods of strong north/northeasterly winds (the 5-7th and 16-18th of September 2007) as a result of downwelling. Among the cold water events that occurred at Guryongpo, the mass death of cultured fish followed strong cold water events (surface temperatures below $10^{\circ}C$) that were caused by more than two days of successive south/southeastern winds with maximum speeds higher than 5 m/s. A Cold Water Index (CWI) was defined and calculated using maximum wind speed and direction as measured daily at Pohang Meteorological Observatory. When the average CWI over two days ($CWI_{2d}$) was higher than 100, mass fish mortality occurred. The four-day average CWI ($CWI_{4d}$) showed a high negative correlation with surface temperature from July-August in the Guryongpo area ($R^2=0.5$), suggesting that CWI is a good index for predicting strong cold water events and massive mortality. In October 2007, the sea temperature at a depth of 30 m showed a high fluctuation that ranged from $7-23^{\circ}C$, with frequency and spectrum coinciding with tidal levels at Ulsan, affected by the North Korean Cold Current. If temperature variations at the depth of fish cages also regularly fluctuate within this range, damage may be caused to the Guryongpo fish industry. More studies are needed to focus on this phenomenon.

• The Journal of Engineering Geology
• /
• v.31 no.4
• /
• pp.647-658
• /
• 2021

In this study, the change of temperature, chemical composition, and helium gas of thermal water in Pohang area was observed from January 2018 to June 2019 in order to interpret the relationship with earthquake events. During observation period earthquakes above M 2.0 within 100 km in a radius from a geothermal well occurred 58 including two earthquake events with a magnitude of 3.0~3.9 and two earthquake events with a magnitude of 4.0~4.9. We introduce a q-factor and earthquake effectiveness (ε) to express the influence of each earthquake as magnitude and distance factors. The geothermal well of 715 m deep was developed in the Bulguksa biotite granite, and the water temperature was observed in the variation from 51.8 to 56.3℃ during monitoring period. At M 4.1 and M 4.6 earthquake events, the increase of geothermal water temperature (𝜟T 2.6~4.5℃) was recorded, and slight change in specific ionic components such as SO₄ and Cl, and of chemical types on the Piper diagram were observed. In the ³He/⁴He vs ⁴He/²⁰Ne diagram, the original mixing ratio of helium isotope before and after the magnitude 4.1 earthquake was slightly changed from 83.0% to 83.2% of crust-origin ⁴He, and the from 16.3% to 16.7% of mantle-origin ³He. Hot-cold water mixing ratio before and after earthquakes by using the quartz and chalcedony solubility curves of the silica-enthalpy mixing model was calculated to interpret the temperature change of geothermal water. The model calculation shows the increase of 6.93~7.72% and 1.65~4.94% of hot water ratio at E1 and E2 earthquakes, respectively. Conclusively, the magnitude of earthquake for observable change in the temperature and helium isotope of thermal water is of 4.1 or higher and q-factor value of 30.0 or higher in the study site.