Evaluating physico-chemical influences on cyanobacterial blooms using hyperspectral images in inland water, Korea. (1st December 2017)

• Record Type: Journal Article
• Authors: Park, Yongeun; Pyo, JongCheol; Kwon, Yong Sung; Cha, YoonKyung; Lee, Hyuk; Kang, Taegu; Cho, Kyung Hwa
• Is Part Of: Water research. Volume 126(2017)
• Page Start: 319
• Page End: 328
• Publication Date: 2017-12-01
• Subjects: Hyperspectral image -- Cyanobacteria -- Phycocyanin -- Hydrodynamics; Water -- Pollution -- Research -- Periodicals; 363.7394
• DOI: 10.1016/j.watres.2017.09.026
• Languages: English
• ISSNs: 0043-1354

Abstract

Abstract: Understanding harmful algal blooms is imperative to protect aquatic ecosystems and human health. This study describes the spatial and temporal distributions of cyanobacterial blooms to identify the relations between blooms and environmental factors in the Baekje Reservoir. Two-year cyanobacterial cell data at one fixed station and four remotely sensed distributions of phycocyanin (PC) concentrations based on hyperspectral images (HSIs) were used to describe the relation between the spatial and temporal variations in the blooms and the affecting factors. An artificial neural network model and a three-dimensional hydrodynamic model were implemented to estimate the PC concentrations using remotely sensed HSIs and simulate the hydrodynamics, respectively. 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. Overall, this study helps to describe spatial and temporal variations in cyanobacterial blooms and identify the factors affecting the variation in the blooms. This study may play an important role as a basis for developing strategies to reduce bloom frequency and severity. Graphical abstract: Image 1 Highlights: Factors affecting on cyanobacterial blooms were determined using two-year cell data. Hyperspectral images were used to describe the spatiotemporal variations in blooms. Effects of hydrodynamics on the spatiotemporal variations in blooms were assessed. Cyanobacterial biomass depended significantly on four environmental factors. Hydrodynamic parameters had a partial effect on the variations in the blooms.