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. 2015 Aug 26;12(9):10391-417.
doi: 10.3390/ijerph120910391.

Estimation of Chlorophyll-a Concentration and the Trophic State of the Barra Bonita Hydroelectric Reservoir Using OLI/Landsat-8 Images

Fernanda Sayuri Yoshino Watanabe  1 , Enner Alcântara  2 , Thanan Walesza Pequeno Rodrigues  3 , Nilton Nobuhiro Imai  4 , Cláudio Clemente Faria Barbosa  5 , Luiz Henrique da Silva Rotta  6
Affiliations

Estimation of Chlorophyll-a Concentration and the Trophic State of the Barra Bonita Hydroelectric Reservoir Using OLI/Landsat-8 Images

Fernanda Sayuri Yoshino Watanabe et al. Int J Environ Res Public Health. .

Abstract

Reservoirs are artificial environments built by humans, and the impacts of these environments are not completely known. Retention time and high nutrient availability in the water increases the eutrophic level. Eutrophication is directly correlated to primary productivity by phytoplankton. These organisms have an important role in the environment. However, high concentrations of determined species can lead to public health problems. Species of cyanobacteria produce toxins that in determined concentrations can cause serious diseases in the liver and nervous system, which could lead to death. Phytoplankton has photoactive pigments that can be used to identify these toxins. Thus, remote sensing data is a viable alternative for mapping these pigments, and consequently, the trophic. Chlorophyll-a (Chl-a) is present in all phytoplankton species. Therefore, the aim of this work was to evaluate the performance of images of the sensor Operational Land Imager (OLI) onboard the Landsat-8 satellite in determining Chl-a concentrations and estimating the trophic level in a tropical reservoir. Empirical models were fitted using data from two field surveys conducted in May and October 2014 (Austral Autumn and Austral Spring, respectively). Models were applied in a temporal series of OLI images from May 2013 to October 2014. The estimated Chl-a concentration was used to classify the trophic level from a trophic state index that adopted the concentration of this pigment-like parameter. The models of Chl-a concentration showed reasonable results, but their performance was likely impaired by the atmospheric correction. Consequently, the trophic level classification also did not obtain better results.

Keywords: bio-optical models; case-2 waters; chlorophyll-a; multispectral image; remote sensing.

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Figures

Figure 1
Figure 1
Study area-BBHR, Tietê River, São Paulo State. (a) Sampling spot locations; (b) OLI/Landsat-8 image of October 13, 2014, colored composition RGB-432, and Barra Bonita reservoir localization inside of Brazil.
Figure 2
Figure 2
Plot of average monthly precipitation from January 2009 to December 2013 (5 years) associated to the Barra Bonita automatic station.
Figure 3
Figure 3
Rrs spectra related to the field surveys conducted in (a) 5–9 May 2014, and (b) 13–16 October 2014.
Figure 4
Figure 4
Rrs simulated for OLI/Landsat-8 bands of the spectra collected in (a) May 2014 and (b) October 2014.
Figure 5
Figure 5
2-Dimensional plot of the correlation coefficients (R) between bands ratio and Chl-a concentration.
Figure 6
Figure 6
Two-band models developed from the OLI bands simulated using field radiometric data, using (a) NIR-Red ratio, (b) NIR-Green ratio, and (c) NIR-Blue ratio.
Figure 7
Figure 7
Two-band models validation, using (a) polynomial NIR-Green ratio and (b) polynomial NIR-Blue ratio.
Figure 8
Figure 8
Validation of atmospheric correction. Optical closure of (a) FLAASH satellite Rrs versus simulated Rrs for OLI data at Blue, Green, Red, and NIR bands and (b) comparison of Rrs calculated for OLI imagery (dotted line) with simulated OLI Rrs (solid line) at the time of the overpass.
Figure 9
Figure 9
Maps of Chl-a concentration (mg·m−3) retrieved from OLI/Landsat-8 based on NIR-Green algorithm for (a) May 2013, (b) June 2013, (c) August 2013, (d) September 2013, (e) December 2013, (f) January 2014, (g) September 2014, and (h) October 2014.
Figure 10
Figure 10
Trophic state classification: from Secchi disk transparency and Chl-a concentration. Classification for May according to (a) Secchi and (b) Chl-a, and October (c) Secchi and (d) Chl-a.
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