Electronic Theses and Dissertations (Masters)

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Browsing Electronic Theses and Dissertations (Masters) by SDG "SDG-15: Life on land"

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    Assessment of disposal methods of construction and demolition waste: A case study of south-eastern industrial and residential areas in Johannesburg, South Africa
    (University of the Witwatersrand, Johannesburg, 2024) Jager, Vasti de; Kubanza, Nzalalemba Serge
    In a world where all strive for further development, construction and demolition play alarge role in that process. The waste generated in construction and demolition projects is of great magnitude and needs to be dealt with and disposed of appropriately, however, is this truly the case? Gauteng is a province where landfills are easily accessible and a cheap disposal option. This study set out to assess disposal methods of construction and demolition waste in south-east Johannesburg, South Africa. Landfills and recycling were the prevalent disposal methods, and these were compared to other countries’ disposal methods. Policy and legislation regarding solid waste management were analysed and a gap between written documents and implementation was identified. The question of sustainability also played a role in the synthesis of the study
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    Exploring Spatio-Temporal Climate Dynamics over Central Southern Africa: A Cross Border Analysis
    (University of the Witwatersrand, Johannesburg, 2023-07) Welff, Megan; Fitchett, Jennifer; Esterhuysen, Amanda
    Understanding the diverse nature of climate dynamics in southern Africa is imperative in the face of climate change. Ground-based meteorological stations provide high-resolution climate data that can be used to investigate and analyse climate in detail. However, southern African countries monitor and manage meteorological stations independently which presents various challenges when attempting cross-border studies. While there are many meteorological-station-based climate studies conducted for South Africa or Botswana, there are few that combine meteorological datasets from both these countries to investigate climate dynamics across political boundaries. In this study, meteorological data from Botswana Meteorological Services and the South African Weather Service spanning 1912-2019 is pre-processed, cleaned and combined to produce a cross-border dataset. A total of 44 stations covers the Gauteng and North West provinces in South Africa and the southern, Kweneng, Kgatleng, South-east and Kgalagadi districts of Botswana. The combined cross-border dataset includes the average monthly summer, winter and annual rainfall (RS, RW and RA respectively) and the average monthly minimum and maximum summer, winter and annual temperatures (TSmin, TSmax, TWmin, TWmax, TAmin and TAmax respectively). From the linear regression analysis, an overall increasing trend for temperature is identified barring two stations (TSmin and TAmin for Mahalapye Met station, and TWmin for Vaalharts). Additionally, for rainfall there is a significant decreasing trend identified. Lastly, the spatial variability of the region is determined using an Inverse Distance Weighted interpolation in the GIS Software, ArcMap, to interpolate between stations. From this a west to east reduction in rainfall and a north-western to south-eastern decreasing temperature gradient is identified across the study region.
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    Monitoring and evaluating urban land use land cover change using machine learning classification techniques: a case study of Polokwane municipality
    (University of the Witwatersrand, Johannesburg, 2023) Funani, Tshivhase; Mhangara, Paida
    Remote sensing is one of the tools which is very important to produce Land use and land cover maps through the process of image classification. Image classification requires quality multispectral imagery and secondary data, a precise classification technique, and user experience skill. Remote sensing and GIS were used to identify and map land-use/land-cover in the study region. Big Data issues arise when classifying a huge number of satellite images and features, which is a very intensive process. This study primarily uses GEE to evaluate the two classifiers, Support Vector Machine, and gradient boosting, using multi-temporal Landsat-8 images, and to assess their performance while accounting for the impact of data dimension, sample size, and quality. Land use/Landcover (LULC) classification, accuracy assessment, and landscape metrics comprise this study. Gradient Tree Boost and SVM algorithms were used in 2008, 2013, 2017, and 2022. Google Earth Engine was used for supervised classification. The results of change detection showed that urbanization has occurred and most of the encroachments were on agricultural land. In this study, XG boost, and support vector machine (SVM)) were used and compared for image classification to oversight spatio-temporal land use changes in Polokwane Municipality. The Google Earth Engine has been utilized to pre-process the Landsat imagery, and then upload it for classification. Each classification method was evaluated using field observations and high-resolution Google Earth imagery. LULC changes were assessed, utilizing Geographic Information System (GIS) techniques, as well as the dynamics of change in LULCC were analysed using landscape matrix analysis over the last 15 years in four different periods: 2008–2013, 2018 and 2022. The results showed that XGBoost performed better than SVM both in overall accuracies and Kappa statistics as well as F-scores and the ratio of Z-score. The overall accuracy of gradient boosting in 2008 was 0.82, while SVM showed results of 0.82 overall accuracy and kappa statistics of 0.69. The average F-score for SVM in 2008 was from 0.58- 1.00, in 2013 an average of 0.86-0.97, and in 2022 it was 0.76. Z values were not statistically significant as all values were below the z score of 1.96. The ratios for the two classifiers were also taken to know which classifier performs the best. The results showed 212:212 which indicates that during 2008 SVM and XG boost performed the same way as they classified the same number of cases. During 2013 the ratio was 345:312 which shows that XGBoost performed better than SVM. The results of 2017 show 374:316 which shows that XGBoost performed better than SVM. Lastly, in 2022 the ratio was 298:277 which shows that XGBoost performed better than SVM. Overall zscores result show that XGBoost performs better than SVM. Overall, this study offers useful insight into LULC changes that might aid shareholders and decision makers in making informed decisions about controlling land use changes and urban growth