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High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery
- Title
- High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery
- Authors
- Pyo, Jong Cheol; Ligaray, Mayzonee; Kwon, Yong Sung; Ahn, Myoung-Hwan; Kim, Kyunghyun; Lee, Hyuk; Kang, Taegu; Cho, Seong Been; Park, Yongeun; Cho, Kyung Hwa
- Ewha Authors
- 안명환
- SCOPUS Author ID
- 안명환
- Issue Date
- 2018
- Journal Title
- REMOTE SENSING
- ISSN
- 2072-4292
- Citation
- REMOTE SENSING vol. 10, no. 8
- Keywords
- Hyperspectral image; atmospheric correction; bio-optical algorithm; phycocyanin; chlorophyll-a
- Publisher
- MDPI
- Indexed
- SCIE; SCOPUS
- Document Type
- Article
- Abstract
- Hyperspectral imagery (HSI) provides substantial information on optical features of water bodies that is usually applicable to water quality monitoring. However, it generates considerable uncertainties in assessments of spatial and temporal variation in water quality. Thus, this study explored the influence of different optical methods on the spatial distribution and concentration of phycocyanin (PC), chlorophyll-a (Chl-a), and total suspended solids (TSSs) and evaluated the dependence of algal distribution on flow velocity. Four ground-based and airborne monitoring campaigns were conducted to measure water surface reflectance. The actual concentrations of PC, Chl-a, and TSSs were also determined, while four bio-optical algorithms were calibrated to estimate the PC and Chl-a concentrations. Artificial neural network atmospheric correction achieved Nash-Sutcliffe Efficiency (NSE) values of 0.80 and 0.76 for the training and validation steps, respectively. Moderate resolution atmospheric transmission 6 (MODTRAN 6) showed an NSE value >0.8; whereas, atmospheric and topographic correction 4 (ATCOR 4) yielded a negative NSE value. The MODTRAN 6 correction led to the highest R-2 values and lowest root mean square error values for all algorithms in terms of PC and Chl-a. The PC:Chl-a distribution generated using HSI proved to be negatively dependent on flow velocity (p-value = 0.003) and successfully indicated cyanobacteria risk regions in the study area.
- DOI
- 10.3390/rs10081180
- Appears in Collections:
- 일반대학원 > 대기과학공학과 > Journal papers
- Files in This Item:
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High-Spatial Resolution Monitoring.pdf(12.09 MB)
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