Hyperspectral remote sensing monitoring of cyanobacteria blooms in a large South American reservoir: high- and medium-spatial resolution satellite algorithm simulation
A. Drozd A , P. de Tezanos Pinto B C F , V. Fernández A , M. Bazzalo A , F. Bordet D and G. Ibañez E
+ Author Affiliations
- Author Affiliations
A Comisión Administradora del Río Uruguay, Avenida Costanera Norte S/N, Paysandú C.C. 57097 – Uruguay.
B Instituto de Botánica Darwinion, Labardén 200, Acassuso, Buenos Aires, Argentina.
C Consejo Nacional de Investigaciones Científicas y Técnicas (CONICET), Godoy Cruz 2290 (C1425FQB), Buenos Aires, Argentina.
D Comisión Técnica Mixta, Salto Grande, Argentina, Leandro N. Alem 449, Capital Federal (C.C. 1003), Argentina.
E Comisión Nacional de Actividades Espaciales (CONAE), Belgrano 210, Oeste 10 (C.C. 5500), Mendoza, Argentina.
F Corresponding author. Email: ptezanos@darwin.edu.ar
Marine and Freshwater Research 71(5) 593-605 https://doi.org/10.1071/MF18429
Submitted: 14 November 2018 Accepted: 4 June 2019 Published: 2 September 2019
Abstract
We used hyperspectral remote sensing with the aim of establishing a monitoring program for cyanobacteria in a South American reservoir. We sampled at a wide temporal (2012–16; 10 seasons) and spatial (30 km) gradient, and retrieved 111 field hyperspectral signatures, chlorophyll-a, cyanobacteria densities and total suspended solids. The hyperspectral signatures for cyanobacteria-dominated situations (n = 75) were used to select the most suitable spectral bands in seven high- and medium-spatial resolution satellites (Sentinel 2, Landsat 5, 7 and 8, SPOT-4/5 and -6/7, WorldView 2), and for the development of chlorophyll and cyanobacteria cell abundance algorithms (λ550 – λ650 + λ800) ÷ (λ550 + λ650 + λ800). The best-performing chlorophyll algorithm was Sentinel 2 ((λ560 – λ660 + λ703) ÷ (λ560 + λ660 + λ703); R2 = 0.80), followed by WorldView 2 ((λ550 – λ660 + λ720) ÷ (λ550 + λ660 + λ720); R2 = 0.78), Landsat and the SPOT series ((λ550 – λ650 + λ800) ÷ (λ550 + λ650 + λ800); R2 = 0.67–0.74). When these models were run for cyanobacteria abundance, the coefficient of determination remained similar, but the root mean square error increased. This could affect the estimate of cyanobacteria cell abundance by ~20%, yet it still enable assessment of the alert level categories for risk assessment. The results of this study highlight the importance of the red and near-infrared region for identifying cyanobacteria in hypereutrophic waters, demonstrating coherence with field cyanobacteria abundance and enabling assessment of bloom distribution in this ecosystem.
Additional keywords: Dolichospermum, Microcystis, Salto Grande Reservoir.
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