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Open AccessArticle

Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River

1
Water Quality Assessment Research Division, Water Environment Research Department, National Institute of Environmental Research, Incheon 22689, Korea
2
Water Environment Research Department, National Institute of Environmental Research, Incheon 22689, Korea
*
Author to whom correspondence should be addressed.
Sensors 2021, 21(2), 530; https://doi.org/10.3390/s21020530
Received: 3 December 2020 / Revised: 30 December 2020 / Accepted: 5 January 2021 / Published: 13 January 2021
(This article belongs to the Special Issue Remote Sensing of Water Quality and Water Environment)
Process-based modeling for predicting harmful cyanobacteria is affected by a variety of factors, including the initial conditions, boundary conditions (tributary inflows and atmosphere), and mechanisms related to cyanobacteria growth and death. While the initial conditions do not significantly affect long-term predictions, the initial cyanobacterial distribution in water is particularly important for short-term predictions. Point-based observation data have typically been used for cyanobacteria prediction of initial conditions. These initial conditions are determined through the linear interpolation of point-based observation data and may differ from the actual cyanobacteria distribution. This study presents an optimal method of applying hyperspectral images to establish the Environmental Fluid Dynamics Code-National Institute of Environment Research (EFDC-NIER) model initial conditions. Utilizing hyperspectral images to determine the EFDC-NIER model initial conditions involves four steps that are performed sequentially and automated in MATLAB. The EFDC-NIER model is established using three grid resolution cases for the Changnyeong-Haman weir section of the Nakdong River Basin, where Microcystis dominates during the summer (July to September). The effects of grid resolution on (1) water quality modeling and (2) initial conditions determined using cumulative distribution functions are evaluated. Additionally, the differences in Microcystis values are compared when applying initial conditions using hyperspectral images and point-based evaluation data. Hyperspectral images allow detailed initial conditions to be applied in the EFDC-NIER model based on the plane-unit cyanobacterial information observed in grids, which can reduce uncertainties in water quality (cyanobacteria) modeling. View Full-Text
Keywords: water quality modeling; hyperspectral image; cyanobacterial bloom; Phytoplankton functional group; environmental fluid dynamics code water quality modeling; hyperspectral image; cyanobacterial bloom; Phytoplankton functional group; environmental fluid dynamics code
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MDPI and ACS Style

Ahn, J.M.; Kim, B.; Jong, J.; Nam, G.; Park, L.J.; Park, S.; Kang, T.; Lee, J.-K.; Kim, J. Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River. Sensors 2021, 21, 530. https://doi.org/10.3390/s21020530

AMA Style

Ahn JM, Kim B, Jong J, Nam G, Park LJ, Park S, Kang T, Lee J-K, Kim J. Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River. Sensors. 2021; 21(2):530. https://doi.org/10.3390/s21020530

Chicago/Turabian Style

Ahn, Jung M.; Kim, Byungik; Jong, Jaehun; Nam, Gibeom; Park, Lan J.; Park, Sanghyun; Kang, Taegu; Lee, Jae-Kwan; Kim, Jungwook. 2021. "Predicting Cyanobacterial Blooms Using Hyperspectral Images in a Regulated River" Sensors 21, no. 2: 530. https://doi.org/10.3390/s21020530

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