High-Spatial Resolution Monitoring of Phycocyanin and Chlorophyll-a Using Airborne Hyperspectral Imagery
- 주제(키워드) Hyperspectral image , atmospheric correction , bio-optical algorithm , phycocyanin , chlorophyll-a
- 주제(기타) Remote Sensing
- 설명문(일반) [Pyo, Jong Cheol; Ligaray, Mayzonee; Kwon, Yong Sung; Cho, Kyung Hwa] Ulsan Natl Inst Sci & Technol, Sch Urban & Environm Engn, Ulsan 689798, South Korea; [Ahn, Myoung-Hwan] Ewha Womans Univ, Dept Atmospher Sci & Engn, Ewha Yeodae Gil 52, Seoul 03760, South Korea; [Kim, Kyunghyun] Natl Inst Environm Res, Yeongsan River Environm Res Ctr, Gwangju 61011, South Korea; [Lee, Hyuk; Kang, Taegu] Natl Inst Environm Res, Water Qual Assessment Res Div, Environm Res Complex, Incheon 22689, South Korea; [Cho, Seong Been] GEOSTROY Inc, Hwa Gok Lo 68-82, Seoul 07566, South Korea; [Park, Yongeun] Konkuk Univ, Sch Civil & Environm Engn, Neumgdong Ro 120, Seoul 05029, South Korea
- 등재 SCIE, SCOPUS
- 발행기관 MDPI
- 발행년도 2018
- URI http://www.dcollection.net/handler/ewha/000000156611
- 본문언어 영어
- Published As http://dx.doi.org/10.3390/rs10081180
초록/요약
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.
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