• Korean Journal of Remote Sensing
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• v.38 no.6_1
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• pp.1245-1255
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• 2022

Terra Nova Bay polynya (TNBP) is a representative coastal polynya in East Antarctica, which is formed by strong katabatic winds. As the TNBP is one of the major sea ice factory in East Antarctica and has a great impact on regional ocean circulation and surrounding marine ecosystem, it is very important to analyze its area change and development characteristics. In this study, we detected the TNBP from synthetic aperture radar (SAR) and optical images obtained from April 2007 to April 2022 by visually analyzing the stripes caused by the Langmuir circulation effect and the boundary between the polynya and surrounding sea ice. Then, we analyzed the area change and development characteristics of the TNBP. The TNBP occurred frequently but in a small size during the Antarctic winter (April-July) when strong katabatic winds blow, whereas it developed in a large size in March and November when sea ice thickness is thin. The 12-hour mean wind speed before the satellite observations showed a correlation coefficient of 0.577 with the TNBP area. This represents that wind has a significant effect on the formation of TNBP, and that other environmental factors might also affect its development process. The direction of TNBP expansion was predominantly determined by the wind direction and was partially influenced by the local ocean current. The results of this study suggest that the influences of environmental factors related to wind, sea ice, ocean, and atmosphere should be analyzed in combination to identify the development characteristics of TNBP.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.27 no.2
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• pp.49-70
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• 2022

Sea Surface Temperature (SST), one of the ocean features, has a significant impact on climate, marine ecosystem and human activities. Therefore, SST prediction has been always an important issue. Recently, deep learning has drawn much attentions, since it can predict SST by training past SST patterns. Compared to the numerical simulations, deep learning model is highly efficient, since it can estimate nonlinear relationships between input data. With the recent development of Graphics Processing Unit (GPU) in computer, large amounts of data can be calculated repeatedly and rapidly. In this study, Short-term SST will be predicted through Convolutional Neural Network (CNN)-based U-Net that can handle spatiotemporal data concurrently and overcome the drawbacks of previously existing deep learning-based models. The SST prediction performance depends on the seasonal and interannual SST variabilities around the southern coast of Korea. The predicted SST has a wide range of variance during spring and summer, while it has small range of variance during fall and winter. A wide range of variance also has a significant correlation with the change of the Pacific Decadal Oscillation (PDO) index. These results are found to be affected by the intensity of the seasonal and PDO-related interannual SST fronts and their intensity variations along the southern Korean seas. This study implies that the SST prediction performance using the developed deep learning model can be significantly varied by seasonal and interannual variabilities in SST.

• Journal of the Institute of Convergence Signal Processing
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• v.25 no.2
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• pp.94-99
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• 2024

Recently, wind power has been gaining attention as a highly efficient renewable energy source, leading to various technological developments worldwide. Typically, wind power is operated in the form of large wind farms with many wind turbines installed in areas rich in wind resources. However, in developing countries or regions isolated from the power grid, off-grid small wind power systems are emerging as an efficient solution. To efficiently operate and expand off-grid small-scale power systems, the development of real-time monitoring systems is required. For the efficient operation of small wind power systems, it is essential to develop real-time monitoring systems that can actively respond to excessive wind speeds and various environmental factors, as well as ensure the stable supply of produced power to small areas or facilities through an Energy Storage System (ESS). The implemented system monitors turbine RPM, power generation, brake operation, and more to create an optimal operating environment. The developed small wind power system can be utilized in remote road lighting, marine leisure facilities, mobile communication base stations, and other applications, contributing to the development of the RE100 industry ecosystem.

• Korean Journal of Environmental Biology
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• v.30 no.1
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• pp.15-25
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• 2012

From April to November 2009, we performed field investigation to survey the characteristics of fish fauna and fish community structure inhabited in Yongdam reservoir in the upper Geumgang, which is changed into flat-water zone from flow-water zone by blocking the continuity by the gigantic submerged weir built in the upstream of Geumgang. 15 species belonging to 8 families were collected from natural habitat (St. 1) where its natural characteristics is well preserved, and 11 species were korean endemic fish species. 24 species belonging to 10 families were collected at the down region of Yongdam dam (St. 3), which might be affected by the change of water environment due to the dam, and 11 species were korean endemic fish species. On the other hand, 20 species belonging to 7 families were collected inside Yongdam reservoir (St. 2) which is changed into flat-water zone from flow-water zone by the dam reservoir, and 6 species were korean endemic fish species. In the dam reservoir, due to Yongdam dam built in the upper Geumgang, the original flow-water zone fish such as $Acheilognathus$ $koreensis$, $Pseudopungtungia$ $nigra$, $Coreoleuciscus$ $splendidus$, and $Gobiobotia$ $macrocephala$ were disappeared, and instead, the kinds of fish habitating in the flat-water zone tend to increase rapidly, such as $Carassius$ $auratus$, $Opsarichthys$ $uncirostris$ $amurensis$, $Hemiculter$ $eigenmanni$, $Zacco$ $platypus$, and $Lepomis$ $macrochirus$. Relative abundance of the insective fish was 66.7% at St. 1, 40.0% at St. 2, and 54.2% at St. 3. In order to preserve endemic fish species and aquatic ecosystem, it is desirable to minimize the artificial installation in the upper river, such as a large scale dam which can affect the habitat and if inevitable, it is required to prepare preservation measures when building facilities.

• Korean Journal of Remote Sensing
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• v.31 no.6
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• pp.501-512
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• 2015

Landfast sea ice (LFI) in Terra Nova Bay, East Antarctica where the Jangbogo Antarctic Research Station is located, has significant influences on marine ecosystem and the sailing of an icebreaker. Therefore, it is essential to analyze the spatio-temporal variation of the LFI in Terra Nova Bay. In this study, we chose interferometric pairs with the temporal baseline from 1 to 9 days out of a total of 62 COSMO-SkyMed synthetic aperture radar (SAR) images over Terra Nova Bay obtained from December 2010 to January 2012, and then constructed the coherence image of each pair. The LFI showed coherence values higher than 0.3 even in the interferometric SAR (InSAR) pairs of up to 9-days of temporal baseline. This was because the LFI was fixed at coastline and thus showed low temporal phase decorrelation. Based on the characteristics of the coherence on LFI, We defined the areas of LFI that show spatially homogeneous coherence values higher than 0.5. Pack ice (PI) and open water showed low coherence values due to large temporal phase decorreation caused by current and wind. Distinguishing PI from open water in the coherence images was difficult due to their similarly low coherence values. PI was identified in SAR amplitude images by investigating cracks on the ice. The extents of the LFI and PI were estimated from the coherence and SAR amplitude images and their temporal variations were analyzed. The extent of the LFI increased from March to July (maximum extent of $170.7km^2$) and decreased from October. The extent of the PI increased from February to May and decreased from May to July when the LFI increases dramatically. The extent of the LFI and air temperature showed an inverse correlation with a time lag of about 2 months, i.e., the extent of the LFI decreases after 2 months of the increase in the air temperature. Meanwhile the correlation between wind speed and the extent of the LFI was very low. This represents that the extent of LFI in Terra Nova Bay are influenced more by the air temperature than wind speed.

• Korean Journal of Fisheries and Aquatic Sciences
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• v.28 no.3
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• pp.279-293
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• 1995

The most obvious and easily recognizable sources of potential water pollution are point sources such as domestic and industrial wastes. But recently, the potential effects of nonpoint sources on water quality have been increased apparently. In order to evaluate the characteristics and the effects of nonpoint sources on water quality, this study was performed in Suyeong Bay from May, 1992 to July, 1992. The depth-averaged 2-dimensional numerical model, which consists of the hydrodynamic model and the diffusion model was applied to simulate the water quality in Suyeong Bay. When flowrate was $65.736m^3/s,$ the concentration of pollutants (COD, TSS and VSS) at Oncheon stream (Sebeong bridge) during second flush were very high as much as 121.4mg/l of COD, 1148.0mg/l of TSS and 262.0mg/1 of VSS. When flowrate was 4.686m^3/s, the concentration of pollutants $(TIN,\;NH_4\;^+-\;N,\;NO_2\;^--N\;and\;PO_4\;^{3-}-P)$ during the first flush were very high as much as 20.306mg/1 of TIN, 14.154mg/1 of $NH_4\;^+-N$, 9.571mg/l of $NO_2\;^--N$ and l.785mg/l of $PO_2\;^{3-}-P$ As results of the hydrodynamic model simulation, the computed maximum velocity of tidal currents in Suyeong Bay was 0.3m/s and their direction was clockwise flow for ebb tide and counter clockwise flow for Hood tide. Four different methods were applied for the diffusion simulation in Suyeong Bay. There were the effects for the water quality due to point loads, annual nonpoint loads and nonpoint loads during the wet weather and the investigation period, respectively. The efforts of annual nonpoint loads and nonpoint loads during the wet weather seem to be slightly deteriorated in comparison with the effects of point loads. However, the bay was significantly polluted by the nonpoint loads during the investigation period. In this case, COD and SS concentrations ranged 2.0-30.0mg/l, 7.0- 200.0mg/l in ebb tide, respectively. From these results, it can be emphasized that the large amount of pollutants caused by nonpoint sources during the wet weather were discharged into the bay, and affected significantly to both the water quality and the marine ecosystem. Therefore, it is necessary to consider the loadings of nonpoint pollutants to plan wastewater treatment plant.