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

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  • Isabel Leidiany De Sousa Brandão

    (Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, NL 7500 AE Enschede, The Netherlands)

  • Chris M. Mannaerts

    (Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, NL 7500 AE Enschede, The Netherlands)

  • Wouter Verhoef

    (Department of Water Resources, Faculty of Geo-Information Science and Earth Observation, University of Twente, NL 7500 AE Enschede, The Netherlands)

  • Augusto César Fonseca Saraiva

    (Analytical Center Laboratory, Belém 66815-140, Brazil)

  • Rosildo S. Paiva

    (Institute of Biological Sciences, Federal University of Pará, Belém 66075-110, Brazil)

  • Elidiane V. Da Silva

    (Institute of Biological Sciences, Federal University of Pará, Belém 66075-110, Brazil)

Abstract

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 (R 2 = 0.95; RMSE = 0.40). The SA red-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.

Suggested Citation

  • Isabel Leidiany De Sousa Brandão & Chris M. Mannaerts & Wouter Verhoef & Augusto César Fonseca Saraiva & Rosildo S. Paiva & Elidiane V. Da Silva, 2017. "Using Synergy between Water Limnology and Satellite Imagery to Identify Algal Blooms Extent in a Brazilian Amazonian Reservoir," Sustainability, MDPI, vol. 9(12), pages 1-20, November.
    • Handle: RePEc:gam:jsusta:v:9:y:2017:i:12:p:2194-:d:120733
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    References listed on IDEAS

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    1. Tundisi, J.G. & Goldemberg, J. & Matsumura-Tundisi, T. & Saraiva, A.C.F., 2014. "How many more dams in the Amazon?," Energy Policy, Elsevier, vol. 74(C), pages 703-708.
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