• Ocean and Polar Research
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• v.35 no.1
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• pp.15-26
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• 2013

The toxic, Harmful Algal Blooms (HABs) caused by the Cochlodinium polykrikoides have a serious impact on the coastal waters of Korea. In this study, the acoustic detection system was developed for rapid HABs detection, based on the acoustic backscattering properties of the C. polykrikoides. The developed system was mainly composed of a pulser-receiver board, a signal processor board, a control board, a network board, a power board, ultrasonic sensors (3.5 and 5.0 MHz), an environmental sensor, GPS, and a land-based control unit. To evaluate the performance of the system, a trail was done at a laboratory, and two in situ trials were conducted: (1) when there was no red tide, and (2) when there was red tide. In the laboratory evaluation, the system performed well in accordance with the number of C. polykrikoides in the received level. Second, under the condition when there was no red tide in the field, there was a good correlation between the acoustic data and sampling data. Finally, under the condition when there was red tide in the field, the system successfully worked at various densities in accordance with the number of C. polykrikoides, and the results corresponded with the sampling data and monitoring result of NFRDI (National Fisheries Research & Development Institute). From the laboratory and field evaluations, the developed acoustic detection system for early detecting HABs has demonstrated that it could be a significant system to monitor the occurrence of HABs in coastal regions.

• 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.

• Proceedings of the KSRS Conference
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• v.1
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• pp.471-474
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• 2006

Several planktonic dinoflagellates, including Cochlodinium polykrikoides (p), are known to produce red tides responsible for massive fish kills and serious economic loss in turbid Northwest Pacific (Korean and neighboring) coastal waters during summer and fall seasons. In order to mitigate the impacts of these red tides, it is therefore very essential to detect, monitor and forecast their development and movement using currently available remote sensing technology because traditional ship-based field sampling and analysis are very limited in both space and temporal frequency. Satellite ocean color sensors, such as Sea-viewing Wide Field-of-view Sensor (SeaWiFS), are ideal instruments for detecting and monitoring these blooms because they provide relatively high frequency synoptic information over large areas. Thus, the present study attempts to evaluate the red tide index methods (previously developed by Ahn and Shanmugam et al., 2006) to identify potential areas of red tides from SeaWiFS imagery in Korean and neighboring waters. Findings revealed that the standard spectral ratio algorithms (OC4 and LCA) applied to SeaWiFS imagery yielded large errors in Chl retrievals for coastal areas, besides providing false information about the encountered red tides in the focused waters. On the contrary, the RI coupled with the standard spectral ratios yielded comprehensive information about various ranges of algal blooms, while RCA Chl showing a good agreement with in-situ data led to enhanced understanding of the spatial and temporal variability of the recent red tide occurrences in high scattering and absorbing waters off the Korean and Chinese coasts. The results suggest that the red tide index methods for the early detection of red tides blooms can provide state managers with accurate identification of the extent and location of blooms as a management tool.

• Journal of Korean Society for Geospatial Information Science
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• v.22 no.3
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• pp.129-136
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• 2014

This study deals with red tide detection by using the remote sensing imagery from the Geostationary Ocean Color Imager (GOCI), the world's first geostationary orbit satellite, around the southern coast of Korea where the most severe red tide occurred recently. The red tide zone was determined by the available data selection from the GOCI imagery during the period of red tide occurrence and also the severe red tide zone was detected through the spatial analysis by temporal change out of the red tide zone. This study results showed that the coast in the vicinity of the Hansan and Yokji in Tongyeong-si was classified into the severe red tide zone, and that the red tide was likely to spread from the coast of Hansan and Yokji to the one of Sanyang-eub. In addition, the comparative analysis between the area of red tide occurrence, the prevention activities of Gyeongsangnam-do provincial government and the amount of the damage cost over time showed close correlation among them. It is still early to conclude that the study is showing the severe red tide zone and the spread path exactly due to various factors for red tide occurrence and activities. In order to improve the reliability of the results, the more data analysis is required.

• Korean Journal of Remote Sensing
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• v.28 no.5
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• pp.531-548
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• 2012

To identify Cochlodinium polykrikoides red tide from non-red tide water (satellite high chlorophyll waters) in the South Sea of Korea (SSK), we improved a spectral classification method proposed by Son et al.(2011) for the world first Geostationary Ocean Color Imager (GOCI). C. polykrikoides blooms and non-red tide waters were classified based on four different criteria. The first step revealed that the radiance peaks of potential red tide water occurred at 555 and 680 nm (fluorescence peak). The second step separated optically different waters that were influenced by relatively low and high contributions of colored dissolved organic matter (CDOM) (including detritus) to chlorophyll. The third and fourth steps discriminated red tide water from non-red tide water based on the blue-to-green ratio, respectively. After applying the red tide classification, the spectral response of C. polykrikoides red tide water, which is influenced by pigment concentration as well as CDOM (detritus), showed different slopes for the blue and green bands (lower slope at blue bands and higher slope at green bands). The opposite result was found for non-red tide water. This modified spectral classification method for GOCI led to increase user accuracy for C. polykrikoides and non-red tide blooms and provided a more reliable and robust identification of red tides over a wide range of oceanic environments than was possible using chlorophyll a concentration, or proposed red tide detection algorithms. Maps of C. polykrikoides red tide in SSK outlined patches of red tide covering the area near Naro-do and Tongyeong during the end of July and early of August, 2012 and extending into from Wan-do and Geoje-do during the middle of August, 2012.

• Journal of the Korean Association of Geographic Information Studies
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• v.9 no.4
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• pp.129-141
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• 2006

The image data amount is increasing rapidly that used geography, sea information etc. with great development of a remote sensing technology using artificial satellite. Therefore, people need automatic method that use image processing description than macrography for analysis remote sensing image. In this paper, we propose that acquire texture information to use GLCM(Gray Level Co-occurrence Matrix) in red tide area of artificial satellite remote sensing image, and detects red tide area by PCA(principal component analysis) automatically from this data. Method by sea color that one feature of remote sensing image of existent red tide area detection was most. but in this paper, we changed into 2 principal component accumulation images using GLCM's texture feature information 8. Experiment result, 2 principal component accumulation image's variance percentage is 90.4%. We compared with red tide area that use only sea color and It is better result.

• Korean Journal of Remote Sensing
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• v.32 no.6
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• pp.673-679
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• 2016

This study propose a new method to detect Cochlodinium polykrikoides red tide occurring in South Sea of Korea using Water-leaving Radiance data and Absorption Coefficients data of Geostationary Ocean Color Imager (GOCI). C. polykrikoides were analyzed and the irradiance and light emission characteristics of the wavelength range from 412 nm to 555 nm were confirmed. The detection technique proposed in this study detects the red tide occurring in the optically complex South Sea. Based on these results, it can be used for future red tide prevention.

• Korean Journal of Remote Sensing
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• v.29 no.3
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• pp.331-335
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• 2013

This study detected red tide areas using the existing Moderate-Resolution Imaging Spectroradiometer(MODIS) and Geostationary Ocean Color Imager(GOCI), and then compared the results between results of two sensors. The coasts of Jeollanam-do in the South Sea of Korea were set as the study area based on the red tide data which occurred on Aug. 26th, 2012. This study compared the results of sensors to detect red tides by using a satellite. In the results of analyzing MODIS by limiting it as chlorophyll concentration and the sea surface temperature which is considered to have red tides by the existing researches, it was possible to delete considerable amount of errors compared to the case of detecting red tides by using only chlorophyll while still there were differences from the range of red tides actually observed. In the results of GOCI by using empirical algorithm for detecting red tides, currently used by Korea Institute of Ocean Science & Technology(KIOST), it was possible to obtain more detailed results than MODIS. However, there was an area misjudged as red tides due to the influence of clouds. Also both MODIS and GOCI extracted red tides were not actually occurring, which might be because they were not able to perfectly distinguish red tides from turbid water in coastal areas with high turbidity.

• The Sea:JOURNAL OF THE KOREAN SOCIETY OF OCEANOGRAPHY
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• v.21 no.1
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• pp.44-47
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• 2016

Real-time PCR was applied to early warning of red tide. For early warning of red tide at $10cells\;mL^{-1}$, Cochlodinium polykrikoides specific real-time PCR was used as a complement of microscopy that has a lower detection sensitivity. C. polykrikoides appeared extensively in Tongyeong, Namhae waters at low densities in the end of June, and early warning of C. polyrkrikoides blooms was issued on 2 August 2015.

• The KIPS Transactions:PartB
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• v.14B no.2
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• pp.65-70
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• 2007

Red tide is becoming hot issue of environmental problem worldwide since the 1990. Advanced nations, are progressing study that detect red tide area on early time using satellite for sea. But, our country most seashores bends serious. Also because there are a lot of turbid method streams on coast, hard to detect small red tide area by satellite for sea that is low resolution. Also, method by sea color that use one feature of satellite image for sea of existent red tide area detection was most. In this way, have a few feature in image with sea color and it can cause false negative mistake that detect red tide area. Therefore, in this paper, acquired texture information to use GLCM(Gray Level Co occurrence Matrix)'s texture 6 information about high definition land satellite south Coast image. Removed needless component reducing dimension through principal component analysis from this information. And changed into 2 principal component accumulation images, Experiment result 2 principal component conversion accumulation image's eigenvalues were 94.6%. When component with red tide area that uses only sea color image and all principal component image. displayed more correct result. And divided as quantitative,, it compares with turbid stream and the sea that red tide does not exist using statistical feature analysis about texture.