Using Landsat and in situ data to map turbidity as a proxy of cyanobacteria in a hypereutrophic Mediterranean reservoir |
| |
| Institution: | 1. National Center for Remote Sensing, National Council for Scientific Research (CNRS), P.O. Box 11-8281, Riad El Solh, 1107 2260, Beirut, Lebanon;2. Optical Sensing Group, Institute for Electromagnetic Sensing of the Environment, National Research Council of Italy, via Bassini 15, I-20133 Milano, Italy;3. Laboratory of Microorganisms and Food Irradiation, Lebanese Atomic Energy Commission-CNRS, P.O. Box 11-8281, Riad El Solh, Beirut 1107 2260, Lebanon;1. Department of Ecology and Resource Management, University of Venda, Thohoyandou 0950, South Africa;2. Department of Biological Sciences and Biotechnology, Botswana International University of Science and Technology, Palapye, Botswana;1. Laboratory of Phycology, Federal University of Santa Catarina, Center for Biological Sciences, Florianópolis, SC, 88040-900, Brazil;1. Department of Geography, University of Cincinnati, Cincinnati, OH, United States;2. U.S. Army Corps of Engineers, Great Lakes and Ohio River Division, Cincinnati, OH, United States;3. U.S. Army Corps of Engineers, ERDC, JALBTCX, Kiln, MS, United States;4. U.S. Army Corps of Engineers, Louisville District, Water Quality, Louisville, KY, United States;5. U.S. Environmental Protection Agency, Cincinnati, OH, United States;6. Kentucky Department of Environmental Protection, Division of Water, Frankfort, KY, United States;7. National Oceanic and Atmospheric Administration, National Ocean Service, Silver Spring, MD, United States;8. Department of Physics and Geosciences, Texas A&M Kingsville, Kingsville, TX, United States;9. State Key Laboratory of Desert and Oasis Ecology, Xinjiang Institute of Ecology and Geography, Chinese Academy of Sciences, Urumqi, China;10. School of Geographic Sciences, Key Laboratory of Geographic Information Science, East China Normal University, Shanghai, China |
| |
| Abstract: | Satellite remote estimates of phycocyanin (PC) have become valuable for monitoring the quality of inland waters affected by harmful cyanobacterial blooms. In this study, we developed an algorithm for mapping turbidity as a proxy of PC content through Landsat 8 Operational Land Imager (OLI) data and in situ measurements. The chosen study site is Karaoun Reservoir, in Lebanon, a hypereutrophic freshwater body where turbidity is mostly driven by cyanobacteria. Satellite images were corrected for atmospheric effects with the 6S (Second Simulation of the Satellite Signal in the Solar Spectrum) code which proved to be more accurate than the DOS (Dark Object Subtraction) approach with R = 0.98 and R = 0.5, respectively. A strong relationship was found between turbidity and PC measurements (R = 0.92, R2 = 0.86), as well as between turbidity and the ratio of band 5 to band 4 of the OLI (R = 0.88, R2 = 0.77). Results reveal a promising performance of the algorithm for predicting PC concentrations with high correlations determined through simple linear regression analysis for both the calibration (R = 0.92, R2 = 0.85) and validation (R = 0.88, R2 = 0.78) periods. An application of the approach to a set of historical Landsat images revealed a time series of cyanobacterial bloom occurrence with high variation in surface area at the study site. The algorithm is considered to be suitable for retrieving cyanobacteria in highly eutrophic waters dominated by cyanobacteria where turbidity is mostly a function of the latter. This approach will improve monitoring cyanobacterial blooms on a spatial and timely basis. |
| |
| Keywords: | |
| 本文献已被 ScienceDirect 等数据库收录！ |
|
