1. Title, Journal and Authors
Title : Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea
Journal : Water Research
Authors : Yongeun Parka, JongCheol Pyoa, Yong Sung Kwona, YoonKyung Chab, Hyuk Leec, Taegu Kangd, Kyung Hwa Choa,*
a School of Urban and Environmental Engineering, Ulsan National Institute of Science and Technology, Ulsan, 689-798, Republic of Korea
b School of Environmental Engineering, University of Seoul, Dongdaemun-gu, Seoul, 130-743, Republic of Korea
c Water Quality Assessment Research Division, National Institute of Environmental Research, Environmental Research Complex, Incheon, Republic of Korea
d Yeongsan River Environmental Research Center, National Institute of Environmental Research, Gwangju, Republic of Korea
Understanding harmful algal blooms is important to protect aquatic ecosystems and human health. This study describes the time-space distributions of cyanobacterial blooms to identify the relations between blooms and environmental factors in the Baekje Reservoir. The two-year cyanobacterial cell data at one fixed station were analyzed to investigate temporal variations in the biomass. Remotely sensed distributions of the PC concentration based on four sampling campaigns were used to describe the relation between the spatial and temporal variation in the blooms and the hydrodynamic characteristics. An ANN model and a hydrodynamic model were implemented to estimate the PC concentrations using HSI data and simulate the hydrodynamics, respectively. The major conclusions from this study are as follows. The statistical test results showed that the variations in the cyanobacterial biomass depended significantly on variations in the water temperature (slope = 0.13, p-value < 0.01), total nitrogen (slope = −0.487, p-value < 0.01), and total phosphorus (slope = 20.7, p-value < 0.05), whereas the variation in the biomass was moderately dependent on the variation in the outflow (slope = −0.0097, p-value = 0.065). Water temperature was the main factor affecting variations in the PC concentrations for the three months from August to October and was significantly different for the three months (p-value < 0.01). Hydrodynamic parameters also had a partial effect on the variations in the PC concentrations in those three months.
3. Originality & Creativity
The model performance and the use of input bands can be compared with previous studies that have tried to quantify cyanobacterial blooms using machine-learning methods.
4. Application to research
This study provides useful tools to understand the spatial and temporal variations in cyanobacterial blooms and identify the hydrodynamic effect on the variation in the blooms. In addition, the results can be used as a basis to develop strategies for reducing bloom frequency and severity and guiding actions to reduce the impact of a bloom that is underway.
Jeongwoo Moon/ Intern student
Environmental Systems Engineering Lab.
School of Earth Sciences & Environmental Engineering
Gwangju Institute of Science and Technology
1 Oryong-dong Buk-gu Gwangju, 500-712, Korea
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