Remote sensing of water quality indicators associated with mining activities : the case study of Mooi River in Carletonville, Gauteng Province, South Africa

Abstract: The mining sector is an important source of revenue for the South African economy; however, mining can have a detrimental impact on water quality. Therefore, efficient assessment and monitoring are needed to protect water bodies in mining-related environments. While remote sensing has proven to be an effective monitoring tool in various sectors, efforts must be intensified to apply it in the mining sector in order to combat the impact of mining pollution on water resources. Remote sensing techniques have been successfully used to estimate water quality parameters of inland waters, however, applications focussing on mining environments are rare. There is, therefore, a need to test the capabilities of the technology in mining areas in order to design an efficient water quality monitoring system that will allow relevant authorities to implement mitigation plans and sustain ecosystem services derived from the water bodies. This dissertation has investigated the capabilities of remote sensing in detecting and monitoring water quality parameters in a mining environment along the Mooi River, South Africa. The first objective of the dissertation sought to investigate the performances of raw hyperspectral data and simulated multispectral datasets in quantifying various water quality parameters. Seventy-eight water samples were collected from the study area. Reflectance measurements were taken from each sample using a field-spectroradiometer. The all-subsets regression technique and a support vector machine (SVM) were used to explore the relationships between 17 water quality parameters and hyperspectral datasets, as well as four simulated multispectral datasets (i.e. Landsat Operational Land Imager, Sentinel-2 Multispectral Instrument, WorldView-3 and SPOT 6). The results revealed the usefulness of combining hyperspectral and simulated datasets with different algorithms for effective water quality monitoring. Water quality parameters were estimated with high accuracy using a support vector machine (SVM), compared to the all-subsets regression approach for both datasets (raw hyperspectral and simulated). The second objective explored the accuracy of actual multispectral datasets in detecting water quality in the same river and field data utilised in the first objective mentioned above. The all-subsets regression technique that lists all possible models was applied to estimate the laboratory-measured parameters using reflectance values derived from the individual bands of Landsat OLI, Sentinel-2 MSI, ASTER and SPOT 6 data as explanatory variables. The results demonstrated the potential of multispectral reflectance data in water quality measurements...M.Sc. (Environmental Management

Dynamics of Undertory Shortleaf Pine (Pinus Echinata Mill.) And Hardwood after Thinning Shortleaf Pine Forests in the Southeastern United States

The shortleaf pine (Pinus echinata Mill.) population is consistently declining in southeastern United States. Shortleaf pine forests are thinned frequently to improve the growth and development of residual stands. But, the effect of thinning on growth and development of understory woody-plants in long term has not been extensively studied. We assessed the effects of thinning, overstory shortleaf pine characteristics, climatic, and topographic factors on shortleaf pine regeneration applying various predictive modeling techniques. We applied decision tree models to predict shortleaf pine regeneration. We also developed, evaluated, and compared the performance of three other predictive models to predict shortleaf pine regeneration. We used understory shortleaf pine data that were collected from shortleaf pine forests of Arkansas and Oklahoma spanning a period of 25 years following thinning and hardwood control treatments. The shortleaf pine densities have declined in every subsequent measurement since the first measurement of understory trees in 1996. Thinning treatments played a significant role on the understory shortleaf pine density. The decision tree model using the Gini criteria as the splitting rule to predict the shortleaf pine regeneration had a low misclassification rate of 7.6 percent. The decision tree model can be an efficient tool to make shortleaf pine stand management decisions. The best performing logistic regression model showed precipitation, plot age, site index, and overstory thinning were the significant inputs affecting shortleaf pine regeneration with validation misclassification rate of 8 percent. The best performing artificial neural network model predicted the shortleaf pine regeneration with validation misclassification rate of 7.6 percent, and cumulative lift of 5, 2.5 and 1.66 at depth of 20, 40 and 60 respectively. An artificial neural network model performed best to predict the shortleaf pine regeneration. Poor shortleaf pine regeneration performance over decades in study sites suggests the future of shortleaf pine dominated forests is questionable unless further regular silvicultural treatments are applied. We recommend continual hardwood removal every 10-15 years to obtain the satisfactory understory shortleaf pine regeneration in shortleaf pine forests of Arkansas and Oklahoma.Natural Resources and Ecology Managemen