Geospatial technologies play an important role in understanding and monitoring of land cover and land use change which is critical in achieving Sustainable Development Goal (SDG) 11 and related goals. In this study, we assessed SDG Indicator 11.3.1, Ratio of Land Consumption Rate to Population Growth Rate (LCRPGR) and other urban growth trends of four cities in South Africa using Landsat 5 TM and SPOT 2&5 satellite images and census data collected in 1996, 2001 and 2011. The 2011 built-up areas were mapped using South Africa’s SPOT 5 Global Human Settlements Layer (GHSL) system whereas the 1996 and 2001 built-up areas were extracted from Landsat 5 and SPOT 2 satellite imagery using a kNN object-based image analysis technique that uses textural and radiometric features. We used the built-up area layer to calculate the land consumption per capita and total urban change for each city, both of which have been identified as being important explanatory indicators for the ratio of LCRPGR. The assessment shows that the two major cities, Johannesburg and Tshwane, recorded a decline in the ratio of LCRPGR between the periods 1996–2001 and 2001–2011. In contrast, the LCRPGR ratios for secondary cities, Polokwane and Rustenburg increased during the same periods. The results further show that Tshwane recorded an increase in land consumption per capita between 1996 and 2001 followed by a decrease between 2001 and 2011. Over the same time, Johannesburg experienced a gradual decrease in land consumption per capita. On the other hand, Polokwane and Rustenburg recorded a unique growth trend, in which the overall increase in LCRPGR was accompanied by a decrease in land consumption per capita. In terms of land consumption, Tshwane experienced the highest urban growth rate between 1996 and 2001, whereas Johannesburg and Polokwane experienced the highest urban growth rates between 2001 and 2011. The information derived in this study shows the significance of Indicator 11.3.1 in understanding the urbanization trends in cities of different sizes in South Africa and creates a baseline for nationwide assessment of SDG 11.3.1.

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.

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