• 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.

• Korean Journal of Environmental Biology
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• v.40 no.4
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• pp.464-476
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• 2022

In recent years, macroalgal bloom occurs frequently in coastal oceans worldwide. It might be attributed to accelerating climate change. "Green tide" events caused by proliferation of green macroalgae (Ulva spp.) not only damage the local economy, but also harm coastal environments. These nuisance events have become common across several coastal regions of continents. In Korea, green tide incidences are readily seen throughout the year along the coastlines of Jeju Island, particularly the northeastern coast, since the 2000s. Ulva species are notorious to be difficult for morphology-based species identification due to their high degrees of phenotypic plasticity. In this study, to investigate temporal variation in Ulva community structure on Jeju Island between 2015 and 2020, chloroplast barcode tufA gene was sequenced and phylogenetically analyzed for 152 specimens from 24 sites. We found that Ulva ohnoi and Ulva pertusa known to be originated from subtropical regions were the most predominant all year round, suggesting that these two species contributed the most to local green tides in this region. While U. pertusa was relatively stable in frequency during 2015 to 2020, U. ohnoi increased 16% in frequency in 2020 (36.84%), which might be associated with rising sea surface temperature from which U. ohnoi could benefit. Two species (Ulva flexuosa, Ulva procera) of origins of Europe should be continuously monitored. The findings of this study provide valuable information and molecular genetic data of genus Ulva occurring in southern coasts of Korea, which will help mitigate negative influences of green tide events on Korea coast.

• Journal of Environmental Science International
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• v.27 no.9
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• pp.737-742
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• 2018

Livestock manure treatments have become a more serious problem because massive environmental pollutions such as green and red tides caused by non-point pollution sources from livestock manures have emerged as a serious social issue. In addition, more food wastes are being produced due to population growth and increased income level. Since the London Convention has banned the ocean dumping of wastes, some other waste treatment methods for land disposal had to be developed and applied. At the same time, researches have been conducted to develop alternative energy sources from various types of wastes. As a result, anaerobic digestion as a waste treatment method has become an attractive solution. In this study has three objectives: first, to identify the physical properties of the mixture of livestock wastewater and food waste when combining food waste treatment with the conventional livestock manure treatment based on anaerobic mesophilic digestion; second, to find the ideal ratio of waste mixture that could maximize the collection efficiency of methane ($CH_4$) from the anaerobic digestion process; and third, to promote $CH_4$ production by comparing the biodegradability. As a result of comparing the reactors R1, R2, and R3, each containing a mixture of food waste and livestock manure at the ratio of 5:5, 7:3, and 3:7, respectively, R2 showed the optimum treatment efficiencies for the removal of Total Solids (TS) and Volatile Solids (VS), $CH_4$ production, and biodegradability.

• ALGAE
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• v.31 no.3
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• pp.205-217
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• 2016

Freshwater red tides due to dinoflagellates have caused spectacular and regular “summer reddening” in recent years in Baishihai Lake, a temperate, meromictic, meso- or oligotrophic, high-altitude, landslide-dammed, deep lake located at the eastern edge of Tibetan Plateau in China. Based on morphological and molecular analyses, the causative organism has been identified as a new woloszynskioid dinoflagellate, Tovellia diexiensis Q. Zhang et G. X. Liu sp. nov. The vegetative cells are 20-32 μm long and 16-24 μm wide. They have a hemispherical episome and a broadly rounded hyposome with a short characteristic antapical spine. Usually cells are bright red due to the presence of numerous red-pigmented bodies, which often masked the yellowish green discoid chloroplasts. The amphiesma of motile cells comprise mainly quadrilateral, pentagonal or hexagonal thin plates, arranged in 4-5 latitudinal series on the episome, 1 in the cingulum and 4 on the hyposome. Molecular phylogenies based on small subunit ribosomal DNA and large subunit ribosomal DNA (LSU) indicate T. diexiensis from Baishihai Lake to belong to the family Tovelliaceae, which was monophyletic in our LSU phylogenies. During the bloom-forming period in 2005, cell density of T. diexiensis reached 9.15 × 10⁵ cells L⁻¹. Astaxanthin and its diester were found to be the major pigments in T. diexiensis, resulting in a characteristic blood-red color of the water in Baishihai Lake.

• KIISE Transactions on Computing Practices
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• v.23 no.1
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• pp.74-79
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• 2017

The Geostationary Ocean Color Imager (GOCI), the world's first ocean color sensor operated in a geostationary orbit, can be utilized to mitigate damages by monitoring marine disasters in real time such as red tides, green algae, sargassum, cold pools, typhoons, and so on. In this paper, we described a methodology and procedure for processing GOCI data in order to maximize its utilization potential. The GOCI data processing procedure is divided into data reception, data processing, and data distribution. The kinds of GOCI data are classified as raw, level 1, and level 2. "Raw" refers to an unstructured data type immediately generated after reception by satellite communications. Level 1 is defined as a radiance data type of two dimensions, generated after radiometric and geometric corrections for raw data. Level 2 indicates an ocean color data type from level-1 data using ocean color algorithms.

• Journal of the Institute of Convergence Signal Processing
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• v.24 no.1
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• pp.27-33
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• 2023

Recently, water shortages have occurred due to climate change, and the need for water management of agricultural water has increased due to the occurrence of algal blooms in reservoirs. Existing algae prevention is operated by putting many people on site and misses the optimal spraying time due to movement through boats. In order to solve this problem, it is necessary to block contamination in advance and move within time to uniformly spray complex microorganisms uniformly. Control drones are used for pesticide spraying and can be applied to algae prevention work by utilizing control drones. In this paper, basic research for the establishment of a marine control system was conducted for application to the reservoir environment, and as one of the results, the characteristics of a drone nozzle, a core technology that can be used for control drones, were calculated. In particular, it was found that the existing agricultural control drones had a disadvantage that the concentration was non-uniform within the suggested spraying interval, and to compensate for this, nozzle positioning and nozzle spraying uniformity were calculated. Based on the experimental results, we develop a core algorithm for establishing an algal bloom monitoring system in the reservoir environment and propose a precision control technology that can be used for marine control work in the future.

• Journal of Korea Water Resources Association
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• v.53 no.4
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• pp.273-283
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• 2020

Quantitative forecasting of groundwater levels for the assessment of groundwater variation and vulnerability is very important. To achieve this purpose, various time series analysis and machine learning techniques have been used. In this study, we developed a prediction model based on LSTM (Long short term memory), one of the artificial neural network (ANN) algorithms, for predicting the daily groundwater level of 11 groundwater wells in Hankyung-myeon, Jeju Island. In general, the groundwater level in Jeju Island is highly autocorrelated with tides and reflected the effects of precipitation. In order to construct an input and output variables based on the characteristics of addressing data, the precipitation data of the corresponding period was added to the groundwater level data. The LSTM neural network was trained using the initial 365-day data showing the four seasons and the remaining data were used for verification to evaluate the fitness of the predictive model. The model was developed using Keras, a Python-based deep learning framework, and the NVIDIA CUDA architecture was implemented to enhance the learning speed. As a result of learning and verifying the groundwater level variation using the LSTM neural network, the coefficient of determination (R²) was 0.98 on average, indicating that the predictive model developed was very accurate.

• Weed & Turfgrass Science
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• v.6 no.3
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• pp.222-234
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• 2017

Green tides, which was mainly caused by Ulva spp., have been increasing in severity and frequency globally, and have negatively affected on marine ecosystems. This study was conducted to investigate effects of various physical and chemical factors on the death of Ulva australis (ULAUS) and to consider a practical measures useful for alleviating Ulva bloom. Soaking of ULAUS thalli in pure water for 8 hr didn't induce a death, but incubation in 1.0-1.5% salinity for 7 d inhibited sporulation by about 70%. Desiccation gave rise to a serious damage when more than 40-50% of initial fresh weight was lost. ULAUS growth was sensitive to temperature and seriously inhibited from more than $30^{\circ}C$. At $35^{\circ}C$, $40^{\circ}C$, $45^{\circ}C$ and $50^{\circ}C$, treatment time required for 90-95% death of ULAUS thalli was 1 d, 10 min, 30 sec, and 1 sec, repectively. ULAUS growth was seriously inhibited at lower than pH 6.0 and completely dead at pH 4.0. Several compounds for ULAUS control was selected and the chemcals causing a rapid death were oxidants such as hydrogen peroxide and sodium percarbonate. Taken together, our results suggest that low salinities, dryness, pH, high temp. and compounds could be selected for Ulva bloom control, and high temperature and compounds seems to be useful for a development of practical control methods.