Evaluation of atmospheric correction algorithms for salt lake water assessment: Accuracy, band-specific effects, and sensor consistency.

Atmospheric correction plays an important role in satellite monitoring of lake water quality. However, different atmospheric correction algorithms yield significantly different accuracy for inland lake waters beset by shallowness and turbidity. Finding a suitable algorithm for a specific lake is critical for quantitative satellite water-environmental monitoring.

Assessing the Effects of Land Use on Surface Water Quality in the Lower uMfolozi Floodplain System, South Africa.

This study investigated the impacts of cultivation on water and soil quality in the lower uMfolozi floodplain system in KwaZulu-Natal province, South Africa. We did this by assessing seasonal variations in purposefully selected water and soil properties in these two land-use systems. The observed values were statistically analysed by performing Student's paired -tests to determine seasonal trends in these variables.

Dust Deposition Impacts at a Liquidated Gold Mine Village: Gauteng Province in South Africa.

The windy season brings numerous community complaints for gold mining companies situated in the Witwatersrand due to windblown dust from partially rehabilitated tailings storage facilities (TSFs). For communities encroaching onto TSFs, windblown dust is perceived as a health hazard and an environmental challenge. In a study conducted in 2017 by the Lawyers for Human Rights, the community of a gold mine village perceived tailings storage facility 6 (TSF6) and other surrounding tailings storage facilities which are partially rehabilitated to be a health and socio-economic threat.

Intra-and-inter species biomass prediction in a plantation forest: testing the utility of high spatial resolution spaceborne multispectral RapidEye sensor and advanced machine learning algorithms.

The quantification of aboveground biomass using remote sensing is critical for better understanding the role of forests in carbon sequestration and for informed sustainable management. Although remote sensing techniques have been proven useful in assessing forest biomass in general, more is required to investigate their capabilities in predicting intra-and-inter species biomass which are mainly characterised by non-linear relationships. In this study, we tested two machine learning algorithms, Stochastic Gradient Boosting (SGB) and Random Forest (RF) regression trees to predict intra-and-inter species biomass using high resolution RapidEye reflectance bands as well as the derived vegetation indices in a commercial plantation.