Mapping chlorophyll-a concentrations in a cyanobacteria- and algae-impacted Vaal Dam using Landsat 8 OLI data

Mapping chlorophyll-a (chl-a) is crucial for water quality management in turbid and productive case II water bodies, which are largely influenced by suspended sediment and phytoplankton. Recent developments in remote sensing technology offer new avenues for water quality assessment and chl-a detection for inland water bodies. In this study, the red to near-infrared (NIR-red) bands were tested for the Vaal Dam in South Africa to classify chl-a concentrations using Landsat 8 Operational Land Imager (OLI) data for 2014–2016 by means of stepwise logistic regression (SLR). The moderate-resolution imaging spectroradiometer (MODIS) data were also used for validating chl-a concentration classes. The chl-a concentrations were classified into low and high concentrations. The SLR applied on 2014 images yielded an overall accuracy of 80% and kappa coefficient (κ) of 0.74 on April 2014 data, while an overall accuracy of 65% and κ=0.30 were obtained for the May 2015 Landsat data. There was a significant (p