• The Journal of the Korea institute of electronic communication sciences
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• v.11 no.7
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• pp.723-728
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• 2016

This study is analyzed the optical property of red tide pixel by using Landsat-7 ETM+, Landsat-8 OLI and COMS/GOCI image. In order to sample red tide pixel, Landsat-7, 8 true color image were used and obtained coordinate of red tide pixel in the true color image. Normalized water leaving radiance(nLw) and absorption coefficient were obtained from GOCI image in the same coordinate of the true color image. When red tide was not occurred the main absorption range was 412nm and 660nm but when red tide occurred it was 660nm and absorption coefficient in 412nm are drastically reduced. It made no difference of nLw spectrum between red tide pixel and non red tide pixel in nLw, but the absolute value of nLw was low than non red tide pixel, especially 660nm and 680nm wavelength sharply decrease.

• Journal of the Korean Society of Surveying, Geodesy, Photogrammetry and Cartography
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• v.25 no.3
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• pp.257-266
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• 2007

The research on red tide is generally in progress through field work, such as the naked eye and sampling. It was difficult to forecast exactly the course, from appearance of red tide to disappearance. with the established ways of investigation and analysis. Accordingly it is need to analyze environmental factors in time and space, the appearance of red tide and the path of its migration by more objective and scientific methods. In this study, GIS is applied to analyse the space character of red tide and the interpolation of IDW(Inverse Distance Weight) is applied to assume the density distribution of red tide after gather data by using Arc/Info. After IDW interpolation, the sea area occurred over 1,000 cells/ml of red tide density is extracted with CON and SUM Function of Grid Module, and the density of the sea area is accumulated daily. As a result of this study, the distribution condition of red tide is found timely and spacially by applying GIS to the sea area of red tide, the results indicated that the spatial density and the cumulative frequency about the origin of red tide using GIS, the sea area demonstrated that the maximum density and the maximum frequency varied significantly over the Nammyun of Namhae-Is. with the maximum frequency being 49 times. accordingly if data about the areas of red tide will occur from the present are accumulated, the shifting route of red tide occurrence and extinction can be predicted.

• Smart Media Journal
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• v.5 no.1
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• pp.130-136
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• 2016

In this paper, the real time monitoring system for the abnormal state of marine algae does not detect the plankton which may directly cause the red tide or the water bloom. But checks both oxygen reduction and nitrogen reduction in water, which indicates the characteristics of zooplankton and phytoplankton respectively, and this system makes a module that monitors in real time the temperature and the illumination of the water, which are indirect factors, with sensors placed in and outside the water, and this module transmits signals periodically at specific intervals to a sever that builds up data base, and the data collected in these ways will be analyzed and compared with the standard data from Ministry of Oceans and Fisheries, and then these data will be made the adequate form of information to be provided to the users as visual information, thus, this system intends to make a red tide and water bloom monitoring system tailored for individual fish farm businesses that has local characteristics and can quickly operate outside working hours, which differs from the existing wide area detecting and monitoring systems.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.6
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• pp.1161-1170
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• 2019

In this study, we propose a model for predicting Cochlodinium polykrikoides red tide occurrence using deep neural networks. A deep neural network with eight hidden layers was constructed to predict red tide occurrence. The 59 marine and meteorological factors were extracted and used for neural network model training using satellite reanalysis data and meteorological model data. The red tide occurred in the entire dataset is very small compared to the case of no red tide, resulting in an unbalanced data problem. In this study, we applied over sampling with adding noise based data augmentation to solve this problem. As a result of evaluating the accuracy of the model using test data, the accuracy was about 97%.

• The Journal of the Korea institute of electronic communication sciences
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• v.13 no.6
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• pp.1353-1364
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• 2018

This study proposed a method to detect Cochlodinium polykrikoides red tide pixels in satellite images using a logistic regression model of machine learning technique under Imbalanced data. The spectral profiles extracted from red tide, clear water, and turbid water were used as training dataset. 70% of the entire data set was extracted and used for as model training, and the classification accuracy of the model was evaluated using the remaining 30%. At this time, the white noise was added to the spectral profile of the red tide, which has a relatively small number of data compared to the clear water and the turbid water, and over-sampling was performed to solve the unbalanced data problem. As a result of the accuracy evaluation, the proposed algorithm showed about 94% classification accuracy.

• The Journal of the Korea institute of electronic communication sciences
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• v.14 no.3
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• pp.559-572
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• 2019

In the southeast sea of Korea, the cold water is concentrated in every summer, showing in abnormal oceanic conditions. Cold water occurred in the southeast sea is dominantly influenced by wind, which occurs when the south wind is continuously blowing for 3 to 7 days more. In this study, water temperature, wind speed and direction data of KMA, KHOA and KHNP, Chlorophyll-a of COMS/GOCI, GHRSST Level 4 SST of NASA, and red tide alert data of the National Institute of Fisheries Science were used to analyze the correlation between occurrence and change of the cold water and the red tide of Cochlodinium polykrikoides. The upwelling cold water mass showed a characteristic of moving northward along the current and occurrence a high concentration of chlorophyll along the water mass. Also, when the warm current were strong, the characteristic of red tide showed a northward moving.

• The Journal of the Korea institute of electronic communication sciences
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• v.15 no.6
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• pp.1089-1098
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• 2020

In this study, we propose a method to detect red tide Cochlodinium Polykrikoide using by machine learning and geostationary marine satellite images. To learn the machine learning model, GOCI Level 2 data were used, and the red tide location data of the National Fisheries Research and Development Institute was used. The machine learning model used logistic regression model, decision tree model, and random forest model. As a result of the performance evaluation, compared to the traditional GOCI image-based red tide detection algorithm without machine learning (Son et al., 2012) (75%), it was confirmed that the accuracy was improved by about 13~22%p (88~98%). In addition, as a result of comparing and analyzing the detection performance between machine learning models, the random forest model (98%) showed the highest detection accuracy.It is believed that this machine learning-based red tide detection algorithm can be used to detect red tide early in the future and track and monitor its movement and spread.

• Journal of the Institute of Electronics Engineers of Korea CI
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• v.49 no.2
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• pp.1-6
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• 2012

In recent years global climate change of hurricanes and torrential rains are going to significantly, that increase damages to property and human life. The disasters have been several claimed in every field. In future, climate changes blowing are keen to strike released to the world like in several movies. Reducing the damage of long-term weather phenomena are emerging with predicting changes in weather. In this study, it is shown how to predict the red tide phenomenon with multiple linear regression analysis and artificial neural network techniques. The red tide phenomenon causing risk could be reduced by filtering sensor data which are transmitted and forecasted in real time. It could be ubiquitous driven custom marine information service system, and forecasting techniques to use throughout the meteorological disasters to minimize damage.

• The Journal of Korean Institute of Communications and Information Sciences
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• v.38C no.3
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• pp.263-270
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• 2013

Recently, the prediction and analysis technology of marine environment are actively being studied since the ocean resources in the world is taken notice. The prediction of marine disaster by automatic collecting marine environment data and analyzing the collected data can contribute to minimized the damages with respect to marine pollution of oil spill and fisheries damage by red tide blooms and marine environment upsets. However the studies of the marine environment monitoring and analysis system are limited in South Korea. In this paper, we study the marine disasters prediction system model to analyze collection marine information of out sea and near sea. This paper proposes the models for the marine disasters prediction system as communication system model, a marine environment data monitoring system model, prediction and analyzing system model, and situations propagation system model. The red tide prediction model and summarizing and analyzing model is proposed for prediction and analyzing system model.

• Journal of Environmental Impact Assessment
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• v.14 no.3
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• pp.127-134
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• 2005

Recently, aquaculture farm sites have been increased with demand of the expensive fish species and sea food like as seaweed, laver and oyster. Therefore coastal water quality have been deteriorated by organic contamination from marine aquaculture farm sites. For protecting of coastal environment, we need to control the location of aquaculture sites. The purpose of this study is to detect the laver aquaculture sites using multispectral remotely sensed data with autodetection algorithm. In order to detect the aquaculture sites, density slice and contour and vegetation index methods were applied with SPOT and IKONOS data of Shinan area. The marine aquaculture farm sites were extracted by density slice and contour methods with one band digital number(DN) carrying 65% accuracy. However, vegetation index algorithm carried out 75% accuracy using near-infra red and red bands. Extraction of the laver aquaculture site using remotely sensed data will provide the efficient digital map for coastal water management strategies and red tide GIS management system.