• ALGAE
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• v.28 no.3
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• pp.289-296
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• 2013

For the quantitative detection of algal toxin, microcystin, a chemiluminescence immunochromatographic assay method was developed. The developed system consists of four parts, chemiluminescence assay strip (nitrocellulose membrane), horse radish peroxidase labeled microcystin monoclonal antibodies, chemiluminescence substrate (luminol and hydrogen peroxide), and luminometer. The performance of the chemiluminescence immunochromatographic assay system was compared with high performance liquid chromatography (HPLC) detection. The detection limit of chemiluminescence immunochromatographic assay system is several orders of magnitude lower than with HPLC. The chemiluminescence immunochromatography and HPLC results correlated very well with the correlation coefficient ($r^2$) of 0.979.

• Journal of Microbiology and Biotechnology
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• v.27 no.4
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• pp.808-815
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• 2017

We demonstrated the quantitative detection of a toxin-producing Microcystis aeruginosa (M. aeruginosa) strain with the laboratory protocol of the NanoGene assay. The NanoGene assay was selected because its laboratory protocol is in the process of being transplanted into a portable system. The mcyD gene of M. aeruginosa was targeted and, as expected, its corresponding fluorescence signal was linearly proportional to the mcyD gene copy number. The sensitivity of the NanoGene assay for this purpose was validated using both dsDNA mcyD gene amplicons and genomic DNAs (gDNA). The limit of detection was determined to be 38 mcyD gene copies per reaction and 9 algal cells/ml water. The specificity of the assay was also demonstrated by the addition of gDNA extracted from environmental algae into the hybridization reaction. Detection of M. aeruginosa was performed in the environmental samples with environmentally relevant sensitivity (${\sim}10^5$ algal cells/ml) and specificity. As expected, M. aeruginosa were not detected in nonspecific environmental algal gDNA over the range of $2{\times}10^0$ to $2{\times}10^7$ algal cells/ml.

• Proceedings of the KSRS Conference
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• 2005.10a
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• pp.249-252
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• 2005

Microphytobenthos bloom from late January to early March in Korean tidal flats. KOMPSAT-2 will provide multi-spectral images with a spatial resolution of 4 m comparable with IKONOS. Using IKONOS and Landsat data, algal mat detection was tested in the Saemangeum area~ Micro-benthic diatoms are abundant and a major primary product in the tidal flats. A linear spectral unmixing (LSU) method was applied to the test data. LSU was effective to detect algal mat and the classified algal mat fraction well correlated with NDVI image. Fine grained upper tidal flats are generally known to be the best environment for algal mat. Algal mat thriving in coarse grained lower tidal flats as well as upper tidal flats were reported in this study. A high resolution multi-spectral sensor in KOMPSAT-2 will provide useful data for long-term monitoring of microphytobenthos in tidal flats.

• The Journal of the Korea institute of electronic communication sciences
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• v.12 no.3
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• pp.457-464
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• 2017

The large breeding of algae in rivers has caused the algal bloom and has becoming a serious national problem for the safety of water sources. Therefore, in order to supply stable water resources through securing clean water, it is necessary to develop technology for prevention of water pollution caused by algal bloom. The purpose of this study is to improve the water quality management ability of river by applying the algal bloom detection technique using UAV. Unmanned aerial images were acquired for the Dodong in the middle region of the Nakdong River where algal bloom are frequent. In addition, the phytoplankton concentration was acquired through the sampling of algal bloom and the examination of water quality. Correlation between phytoplankton concentrations and the results of applying the algal bloom index to the Unmanned aerial images showed a strong positive correlation. The remote sensing method suggested in this study is expected to improve the initial response capability of river water pollution.

• ALGAE
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• v.20 no.2
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• pp.113-120
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• 2005

The freshwater blooms mainly blue-green algal blooms occur frequently in the lower Naktong River in summer, which provoke many socio-economical problems; therefore, the early detection of bloom events are demanding through the quantitative and qualitative analyses of blue green algal species. The in vivo fluorescence properties of cultured strains of Microcystis aeruginosa, M. viridis, M. wesenbergii, M. ichthyoblabe, Anabaena cylindrica, A. flos-aquae, and Synedra sp. were investigated. Wild phytoplankton communities of the lower Naktong River were also monitored at four stations in terms of their standing stocks, biomass and fluorescence properties compared with its absorption spectram. The 77K fluorescence emission spectra of each cultured strains normalized at 620 nm was very specific and enabled to detect of blue green algal biomass qualitatively and quantitatively. The relative chlorophyll a concentration determined by chlorophyll fluorescence analysis method showed significant relationship with chlorophyll a concentration determined by solvent extraction method ($R^2$ = 0.906), and the blue-green algal cell number determined by microscopic observation ($R^2$ = 0.588), which gives insight into applications to early detection of blue green algal bloom.

• Journal of Korean Society on Water Environment
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• v.37 no.4
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• pp.275-285
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• 2021

Algal bloom is an important issue in maintaining the safety of the drinking water supply system. Fast detection and classification of algae images are essential for the management of algal blooms. Conventional visual identification using a microscope is a labor-intensive and time-consuming method that often requires several hours to several days in order to obtain analysis results from field water samples. In recent decades, various deep learning algorithms have been developed and widely used in object detection studies. YOLO is a state-of-the-art deep learning algorithm. In this study the third version of the YOLO algorithm, namely, YOLOv3, was used to develop an algae image detection model. YOLOv3 is one of the most representative one-stage object detection algorithms with faster inference time, which is an important benefit of YOLO. A total of 1,114 algae images for 30 genera collected by microscope were used to develop the YOLOv3 algae image detection model. The algae images were divided into four groups with five, 10, 20, and 30 genera for training and testing the model. The mean average precision (mAP) was 81, 70, 52, and 41 for data sets with five, 10, 20, and 30 genera, respectively. The precision was higher than 0.8 for all four image groups. These results show the practical applicability of the deep learning algorithm, YOLOv3, for algae image detection.

• Korean Journal of Remote Sensing
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• v.21 no.5
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• pp.405-415
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• 2005

It is difficult to classify tidal flat surface that is composed of a mixture of mud, sand, water and microphytobenthos. We used a Linear Spectral Unmixing (LSU) method for effectively classifying the tidal flat surface characteristics within a pixel. This study aims at 1) detecting algal mat using LSU in the Saemangeum tidal flats, 2) determining a suitable end-member selection method in tidal flats, and 3) find out a habitual characteristics of algal mat. Two types of end-member were built; one is a reference end-member derived from field spectrometer measurements and the other image end-member. A field spectrometer was used to measure spectral reflectance, and a spectral library was accomplished by shape difference of spectra, r.m.s. difference of spectra, continuum removal and Mann-Whitney U-test. Reference end-members were extracted from the spectral library. Image end-members were obtained by applying Principle Component Analysis (PCA) to an image. The LSU method was effective to detect microphytobenthos, and successfully classified the intertidal zone into algal mat, sediment, and water body components. The reference end-member was slightly more effective than the image end-member for the classification. Fine grained upper tidal flat is generally considered as a rich habitat for algal mat. We also identified unusual microphytobenthos that inhabited coarse grained lower tidal flats.

• Fisheries and Aquatic Sciences
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• v.7 no.3
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• pp.122-129
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• 2004

Harmful Cochlodinium polykrikoides is a notorious harmful algal bloom (HAB) species that is causing mass mortality of farmed fish along the Korean coast with increasing frequency. We analyzed the sequence of the large subunit (LSD) rDNA D1-D3 region of C. polykrikoides and conducted phylogenetic analyses using Bayesian inference of phylogeny and the maximum likelihood method. The molecular phylogeny showed that C. polykrikoides had the genetic relationship to Amphidinium and Gymnodinium species supported only by the relatively high posterior probabilities of Bayesian inference. Based on the LSU rDNA sequence data of diverse dinoflagellate taxa, we designed the C. polykrikoides-specific PCR primer set, CPOLY01 and CPOLY02 and developed PCR detection assays for its sensitive, accurate HAB monitoring. CPOLY01 and CPOLY02 specifically amplified C. polykrikoides and did not cross-react with any dinoflagellates tested in this study or environmental water samples. The effective annealing temperature $(T_{p})$ of CPOLY01 and CPOLY02 was $67^{\circ}C$. At this temperature, the conventional and nested PCR assays were sensitive over a wide range of C. polykrikoides cell numbers with detection limits of 0.05 and 0.0001 cells/reaction, respectively.

• Journal of Wetlands Research
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• v.25 no.4
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• pp.267-273
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• 2023

Algae are an important component of the ecosystem. However, the excessive growth of cyanobacteria has various harmful effects on river environments, and diatoms affect the management of water supply processes. Algal monitoring is essential for sustainable and efficient algae management. In this study, an object detection model was developed that detects and classifies images of four types of harmful cyanobacteria used for the criteria of the algae alert system, and one diatom, Synedra sp.. You Only Look Once(YOLO) v8, the latest version of the YOLO model, was used for the development of the model. The mean average precision (mAP) of the base model was analyzed as 64.4. Five models were created to increase the diversity of the input images used for model training by performing rotation, magnification, and reduction of original images. Changes in model performance were compared according to the composition of the input images. As a result of the analysis, the model that applied rotation, magnification, and reduction showed the best performance with mAP 86.5. The mAP of the model that only used image rotation, combined rotation and magnification, and combined image rotation and reduction were analyzed as 85.3, 82.3, and 83.8, respectively.

• 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.