• Korean Journal of Remote Sensing
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• v.38 no.3
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• pp.299-309
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• 2022

Recently, The sea water temperature around Korean Peninsula is steadily increasing. Water temperature changes not only affect the fishing ecosystem, but also are closely related to military operations in the sea. The purpose of this study is to suggest which model is more suitable for the field of water temperature prediction by attempting short-term water temperature prediction through various prediction models based on deep learning technology. The data used for prediction are water temperature data from the East Sea (Goseong, Yangyang, Gangneung, and Yeongdeok) from 2016 to 2020, which were observed through marine observation by the National Fisheries Research Institute. In addition, we use Long Short-Term Memory (LSTM), Bidirectional LSTM, and Gated Recurrent Unit (GRU) techniques that show excellent performance in predicting time series data as models for prediction. While the previous study used only LSTM, in this study, the prediction accuracy of each technique and the performance time were compared by applying various techniques in addition to LSTM. As a result of the study, it was confirmed that Bidirectional LSTM and GRU techniques had the least error between actual and predicted values at all observation points based on 1 hour prediction, and GRU was the fastest in learning time. Through this, it was confirmed that a method using Bidirectional LSTM was required for water temperature prediction to improve accuracy while reducing prediction errors. In areas that require real-time prediction in addition to accuracy, such as anti-submarine operations, it is judged that the method of using the GRU technique will be more appropriate.

• The Journal of the Acoustical Society of Korea
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• v.41 no.4
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• pp.426-434
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• 2022

Bubbles generated by various causes in the ocean are known to persist for long periods of time. Although the volume occupied by bubbles in the ocean is small, the presence of bubbles in ocean due to resonance and attenuation greatly affects the acoustic properties. Accordingly, bistatic reverberation experiment was performed in the ocean where artificial bubbles exist. A number of transducers and receivers were installed on 6 buoys arranged in a hexagonal shape, and blowing agents were dropped in the center of the buoy to generate bubbles. For reverberation modeling that reflects acoustic characteristics changed by bubbles, the spatial distribution of bubbles was estimated using video data and received signals. A measurement-based bubble spectral shape was used, and it was assumed that the bubble density within the spatial distribution of the estimated bubble was the same. As a result, it was confirmed that the bubble reverberation was simulated in a time similar to the measured data regardless of the bubble density, and the bubble reverberation level similar to the measured data was simulated at a void fraction of about 10^-7 ~ 10^-6.8.

• Journal of the Korean Association of Geographic Information Studies
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• v.19 no.4
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• pp.63-75
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• 2016

Aquaculture has historically delivered marine products because the country is surrounded by ocean on three sides. Surveys on production have been conducted recently to systematically manage aquaculture facilities. Based on survey results, pricing controls on marine products has been implemented to stabilize local fishery resources and to ensure minimum income for fishermen. Such surveys on aquaculture facilities depend on manual digitization of aerial photographs each year. These surveys that incorporate manual digitization using high-resolution aerial photographs can accurately evaluate aquaculture with the knowledge of experts, who are aware of each aquaculture facility's characteristics and deployment of those facilities. However, using aerial photographs has monetary and time limitations for monitoring aquaculture resources with different life cycles, and also requires a number of experts. Therefore, in this study, we investigated an automatic prototype system for detecting boundary information and monitoring aquaculture facilities based on satellite images. KOMPSAT-3 (13 Scene), a local high-resolution satellite provided the satellite imagery collected between October and April, a time period in which many aquaculture facilities were operating. The ANN classification method was used for automatic detecting such as cage, longline and buoy type. Furthermore, shape files were generated using a digitizing image processing method that incorporates polygon generation techniques. In this study, our newly developed prototype method detected aquaculture facilities at a rate of 93%. The suggested method overcomes the limits of existing monitoring method using aerial photographs, but also assists experts in detecting aquaculture facilities. Aquaculture facility detection systems must be developed in the future through application of image processing techniques and classification of aquaculture facilities. Such systems will assist in related decision-making through aquaculture facility monitoring.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.23 no.1
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• pp.1-19
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• 2018

In order to compare significant wave height (SWH) data from multi-satellites (GFO, Jason-1, Envisat, Jason-2, Cryosat-2, SARAL) and SWH measurements from Ieodo Ocean Research Station (IORS), we constructed a 12 year matchup database between satellite and IORS measurements from December 2004 to May 2016. The satellite SWH showed a root mean square error (RMSE) of about 0.34 m and a positive bias of 0.17 m with respect to the IORS wave height. The satellite data and IORS wave height data did not show any specific seasonal variations or interannual variability, which confirmed the consistency of satellite data. The effect of the wind field on the difference of the SWH data between satellite and IORS was investigated. As a result, a similar result was observed in which a positive biases of about 0.17 m occurred on all satellites. In order to understand the effects of topography and the influence of the construction structures of IORS on the SWH differences, we investigated the directional dependency of differences of wave height, however, no statistically significant characteristics of the differences were revealed. As a result of analyzing the characteristics of the error as a function of the distance between the satellite and the IORS, the biases are almost constant about 0.14 m regardless of the distance. By contrast, the amplitude of the SWH differences, the maximum value minus the minimum value at a given distance range, was found to increase linearly as the distance was increased. On the other hand, as a result of the accuracy evaluation of the satellite SWH from the Donghae marine meteorological buoy of Korea Meteorological Administration, the satellite SWH presented a relatively small RMSE of about 0.27 m and no specific characteristics of bias such as the validation results at IORS. In this paper, we propose a conversion formula to correct the significant wave data of IORS with the satellite SWH data. In addition, this study emphasizes that the reliability of data should be prioritized to be extensively utilized and presents specific methods and strategies in order to upgrade the IORS as an international world-wide marine observation site.

• Korean Journal of Remote Sensing
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• v.25 no.4
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• pp.339-357
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• 2009

The Saemangeum Reclamation Project was launched as a national project in 1991 to reclaim a large coastal area of 401 km$^2$ by constructing a 33-km long dyke. The final dyke enclosure in April 2006 has transformed the tidal flat into lake and land. The dyke construction has abruptly changed not only the estuarine tidal system inside the dyke, but also the coastal marine environment outside the dyke. In this study, we investigated the spatial change of SST distribution using the Landsat-5/7 and NOAA data before and after the dyke completion in the Saemangeum area. Satellite-induced SST was verified by compared with the various in situ measurements such as tower, buoy, and water sample. The correlation coefficient resulted in above 0.96 and RMSE was about 1$^{\circ}C$ in all data. 38 Landsat satellite images from 1985 to 2007 were analyzed to estimate the temporal and spatial change of SST distribution from the beginning to the completion of the Samangeum dyke's construction. The seasonal change in detailed spatial distribution of SST was measured, however, the estimation of change during the Saemangeum dyke's construction was hard to figure out owing to the various environmental conditions. Monthly averaged SST induced from NOAA data from 1998 to 2007 has been analyzed for a complement of Landsat's temporal resolution. At the inside of the dyke, the change of SST from summer to winter was large due to the relatively high temperature in summer. In this study, multi-sensor thermal remote sensing is an efficient tool for monitoring the temporal and spatial distribution of SST in coastal area.