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

Abstract

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 less than 0.05) negative correlation between chl-a classes and red band in all analyses, while the NIR band showed a positive correlation (0.0001; p less than 0.89) for April 2014 data set. The 2015 image classification yielded an overall accuracy of 83% and κ=0.43. The difference vegetation index showed a significant (p less than 0.003) positive correlation with chl-a concentrations for May 2015 and July 2016, with chl-a ranges of between 2.5 μg/L and 1219 μg/L. These correlations show that a class increase in chl-a (from low to high) is in response to an increase in greenness within the Vaal Dam. We have demonstrated the applicability of Landsat 8 OLI data for inland water quality assessment.

Retrospective analysis of Chlorophyll-a and its correlation with climate and hydrological variations in Mindu Dam, Morogoro, Tanzania

“…Previous studies, for example, Malahlela et al. (2018) used Landsat 8 Operational Land Imager (OLI) for estimating Chlorophyll-a concentrations.…”

“…(2016) in near-surface water. This work further followed time series analysis approach as in Malahlela et al. (2018), a study which was conducted in Vaal Dam in South Africa.…”

Mapping microhabitat structure and connectivity on a tropical inselberg using UAV remote sensing

“…It assumes that healthy photosynthetic cells contain active chlorophyll, which absorbs light in the visible range and reflects in the NIR range. Higher levels of NDVI indicate the presence of healthy photosynthetic pigments, both in plants and microphytic communities (Gamon et al, 1995; Malahlela et al, 2018), and NDVI values can also increase concerning moisture conditions (Karnieli et al, 1999). To compare differences in potential photosynthetic activity between different microhabitat types (rock, dry pools, grass, and evergreen trees), we randomly selected 20 replicates in 20×20cm areas within each habitat type present in Figure 4.…”

The Role of African Emerging Space Agencies in Earth Observation Capacity Building for Facilitating the Implementation and Monitoring of the African Development Agenda: The Case of African Earth Observation Program

“…and geospatial technologies in order to support mandates and policies for sustainable integrated water management and ensure access to a safe and adequate water supply. At a national scale, SANSA supports the national Department of Water and Sanitation (DWS) by providing seasonal small water bodies layer ( Figure 7) and water quality assessments based on Landsat 8 OLI [82,83]. The National Water Act No.…”