Using Synergy between Water Limnology and Satellite Imagery to Identify Algal Blooms Extent in a Brazilian Amazonian Reservoir

Monitoring algal blooms from space is a very challenging task, which becomes particularly difficult when dealing with cyanobacteria blooms. Cyanobacteria are strategic organisms adapted to a wide variety of environmental conditions. In high concentrations, they form scum on the water surface, which...

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Bibliographic Details
Published in:Sustainability 2017-11, Vol.9 (12), p.2194
Main Authors: De Sousa Brandão, Isabel, Mannaerts, Chris, Verhoef, Wouter, Saraiva, Augusto, Paiva, Rosildo, Da Silva, Elidiane
Format: Article
Language:English
Subjects:
Algae
Algal blooms
Atmospheric correction
Biological effects
Chlorophyll
Cyanobacteria
Data processing
Ecological monitoring
Environmental conditions
Environmental monitoring
Eutrophication
Glint
Limnology
Microprocessors
Phytoplankton
Public health
Reflectance
Reservoirs
Satellite imagery
Satellites
Scum
Sustainability
Toxins
Weather
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Summary:Monitoring algal blooms from space is a very challenging task, which becomes particularly difficult when dealing with cyanobacteria blooms. Cyanobacteria are strategic organisms adapted to a wide variety of environmental conditions. In high concentrations, they form scum on the water surface, which is a concern for public health due to the production of toxins, as well as being a nuisance. Knowledge of the ecological role of these organisms is, therefore, essential when trying to estimate their extent from satellite-based data. We present a multidisciplinary approach, based on both the ecological and the optical perspective. This approach is applied in a Brazilian Amazonian reservoir using spatial and temporal scales. The ACOLITE processor is employed to perform atmospheric correction. Extent of the algal bloom is mapped with outputs such as Rayleigh reflectance atmospheric corrected images. Chlorophyll-a estimation is accomplished using a blue-green edge algorithm from the Ocean Biology Processing Group (OBPG), and shows reasonable results (R2 = 0.95; RMSE = 0.40). The SAred-NIR slope algorithm identifies the extent of the algal bloom at both the spatial and temporal scale. Unfortunately, the performance of these algorithms is most likely affected by weather conditions and glint effects. Therefore, this study recommends that cyanobacteria or phytoplankton studies in this area ensure that their ecological functioning is carefully considered when attempting to map occurrence using limited satellite imagery.
ISSN:2071-1050
2071-1050
DOI:10.3390/su9122194