Electronic Theses and Dissertations (Masters)
Permanent URI for this collectionhttps://hdl.handle.net/10539/38009
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Item Investigating the impact of the land reform policy on land use and land cover changes, in Ngaka Modiri Molema district of the North West province(University of the Witwatersrand, Johannesburg, 2024) Mmangoedi, Molebogeng Precious; Adam, ElhadiThe purpose of this study was to assess how land reform policies affected changes in land use and land cover in the province of North West's Ngaka Modiri Molema district municipality. The study employed remote sensing technologies to analyse changes in land use and land cover (LULC) resulting from the implementation of land reform programs between 1985 and 2015. The primary objective of the research was to systematically map Land Use and Land Cover types across five-year intervals spanning from 1985 to 2024, leveraging Landsat earth observation data in conjunction with a random forest classifier. These methodologies were employed to facilitate the identification of spatial patterns and trends associated with the implementation of land reform policies within the study area. Furthermore, the study utilized Landsat data and advanced change detection algorithms to quantitatively assess LULC changes over the specified timeframes. Through the application of spatial analysis techniques, the research aimed to elucidate the relationship between the implementation of land reform measures and corresponding shifts in LULC patterns across the research study area. The findings of the investigation indicated a noticeable expansion in built-up areas between the years 1985 and 2024 which was approximately 10.86%. This expansion was primarily attributed to the growth experienced by the municipality during this period. Additionally, more opportunities might have risen from the agricultural farming activities and also from the land reform policy being implemented. However, as the ownership changed due to land redistribution and more land was being acquired by black people through the land reform policy, agricultural farming decreased slightly throughout the years. The reduction was due to the factors that arose from inefficient policy implementation. The study also recommends that remote sensing techniques should be utilised to carry out studies to determine LULC changes that derive from land policies aiming at dealing with socio-economic factors and urbanisation. An incorporated agrarian reform sustainable programme has vast potential in cultivating the production of the projects, particularly if it involves packages in rural infrastructure, support services, and co-operatives. The major role of such an approach should be in the trainings conducted for the farmers, obtaining, and distributing agricultural resources and equipment to agrarian reform or beneficiaries of the land reform projects. Additionally, there should be an allowance for special grants which will be useful in supporting the government’s efforts.Item Spatiotemporal characteristics of surface water in Sua Pan, Botswana, using Earth Observation data: 1992–2022(University of the Witwatersrand, Johannesburg, 2024-10) Peplouw, Muchelene Tiara; Adam, Elhadi; Grab, StefanSurface water is a critical resource for sustaining both human and ecological health. However, climate change and human actions threaten its availability in semi-arid regions like Botswana. In addition, current research on monitoring and understanding surface water dynamics in Botswana lacks the application of remote sensing and machine learning. This highlights a crucial gap in knowledge that this study aims to address. This study investigates the spatiotemporal dynamics of land use/land cover (LULC) and surface water extent changes in Sua Pan, Botswana, from 1992 to 2022. Employing remote sensing, machine learning, and statistical techniques, the research offers valuable insights into the intricate relationships between land cover modifications, surface water variations, and climatic variables. Google Earth Engine (GEE) facilitated efficient analysis of Landsat imagery for LULC mapping. Random Forest (RF) effectively classified several land cover types within Sua Pan. To address the challenges of saline environments, a novel water index, the Saline Water Index (SWI), was developed specifically for Sua Pan. The McNemar statistical test compared the performance of SWI to established indices like the Modified Normalised Difference Water Index (MNDWI) and the Normalised Difference Salinity Index (NDSI). Surface water variations were analysed using homogeneity tests and the Mann-Kendall trend test. The relationships between hydro climatic data (rainfall, evapotranspiration, land surface temperature) retrieved from GEE and surface water area for both wet and dry seasons were evaluated using Pearson correlation coefficients and visualised by line and area graphs. Additionally, the influence of the El Niño Southern Oscillation (ENSO) on rainfall and surface water area was assessed using Analysis of Variance (ANOVA) to identify the specific ENSO phases that exert an influence. The findings demonstrate the effectiveness of GEE for LULC mapping with the RF algorithm, achieving moderate to high classification accuracy (65.2% - 90.69%) and Kappa coefficients (0.54 - 0.85). Surface water and bare area exhibited increasing trends (coefficients: 13.017 and 9.0609, respectively), whereas vegetation and salt hard pan showed decreasing trends (-16.786 and -5.3081, respectively). The newly developed SWI outperformed MNDWI and NDSI in detecting surface water, achieving the highest overall accuracy (94%) compared to MNDWI (64%) and NDSI (59%). The McNemar test confirmed no significant statistical difference between the SWI map and the validation dataset (p = 0.2673), while both MNDWI and NDSI maps showed significant differences (p < 0.0001). Utilising SWI, the study revealed that surface water was most prevalent in central and northeastern regions, with an average coverage of 33%. Seasonal homogeneity tests indicated a non-homogenous distribution of surface water area in wet seasons, with abrupt changes in 1994 and 2003. Conversely, dry seasons exhibited a homogenous distribution. The Mann-Kendall trend test identified a statistically significant (p-value = 0.01) but weak positive trend (tau = 0.329) for surface water areas in wet seasons. In contrast, the dry seasons displayed a non-significant (p-value = 0.734) and a very weak positive trend (tau = 0.043). Surface water area, rainfall, evapotranspiration, and temperature consistently increase during the wet seasons compared to the dry seasons. Notably, increased evapotranspiration significantly impacted surface water presence. ENSO exhibited no significant influence on either rainfall or surface water extent (p-value > 0.05 for both). These findings highlight the potential of earth observation data for real-time surface water monitoring in salt pans. The developed techniques offer valuable insights for policy decisions regarding environmental management and conservation efforts in Sua Pan. In addition, the study emphasises the importance of cost-effective approaches for water change assessment, particularly appropriate for under-resourced regions.Item Optically stimulated luminescence dating of Kalundu and Urewe tradition ceramics(University of the Witwatersrand, Johannesburg, 2024-03) Haupt, Rachel Xenia; Schoeman, Maria; Evans, MaryOptically stimulated luminescence (OSL) dating is a method of providing the direct age of artefacts. While radiocarbon and seriation dating provide indispensable insight into archaeological sites, the direct dating of artefacts is beneficial in entangled contexts. The Lydenburg Heads Site is significant to the beginning of the Early Farming Communities (EFCs) sequence within the Mpumalanga province. The site has been occupied multiple times, as can be seen from the presence of the two major ceramic traditions of the age, Urewe and Kalundu. The site was originally excavated and analysed by Evers (1982) in the 1970s, with a reanalysis of the ceramic assemblage by Whitelaw (1996) and organic residue analysis on the ceramics by Becher (2021). The use of OSL dating on twelve ceramic sherds allowed for new insights into the chronological intricacies within the study site. To determine the age of the ceramics, the OSL quartz dating technique was used. The adjustments to the technique involved the use of a less destructive means of sample extraction. A slightly altered version of the standard means of sample extraction was used to create a comparison and allow the dating of the ceramics to be reliable. The minimal destruction technique (MET) combined with the bulk sampling proved useful to the dating of the ceramics. The use of previously excavated ceramics meant that some aspects of age determination required estimation and analysis. The major obstacles from such were the water content, the depth of burial, and the lack of in situ soil samples. In light of the elements of ambiguity for the site, the OSL dating considered these variations and how they affected the age. The Urewe tradition ceramics were determined to be in 6th and 8th century AD. The finding creates the alignment with the range of the radiocarbon ages done within previous work and the assumptions made by Evers (1982) and Whitelaw (1996). The Kalundu tradition ceramics ages were determined to be between the 7th and 10th century AD, conflicting with previous assumptions on the occupation. The result is the possibility the ceramic assemblages could be considered to be contemporaneous. The work in this thesis has, in part, been presented at the Luminescence and Electron Spin Resonance Dating conference in Copenhagen (LED2023) and at the Association of Southern African Professional Archaeologists 2024 Biennial Meeting (ASAPA 2024).Item The Influence of Climate Change on the Speed of Movement of Tropical Cyclones in the South Indian Ocean(University of the Witwatersrand, Johannesburg, 2024-07) Mahomed, Aaliyah; Fitchett, JenniferRecent studies on the speed of movement of tropical cyclones indicate that anthropogenic warming has resulted in a 10% global decrease of tropical cyclone translation speeds over the period 1949-2016. The recent increase in high intensity storms could severely impact Southern Hemisphere regions which are considerably more vulnerable than their Northern Hemisphere counterparts. High intensity storms occurring at a lower speed would worsen the impacts of tropical cyclones resulting in prolonged periods of flooding, storm surges, and winds. This would subsequently lead to a loss of lives, economic loss and infrastructural and agricultural damage. However, studies have challenged this slowdown, suggesting that the transition to the geo-stationary era, introduces heterogeneity to tropical cyclone data. Additionally, imprecise estimates of tropical cyclone frequency influences the average speed of tropical cyclones, thereby impacting trend analysis. Using tropical cyclone data from National Oceanic and Atmospheric Administration (NOAA) International Best Track Archive for Climate Stewardship (IBTrACS), this study explores the current translation speed debate for the South Indian Ocean, over the period 1991-2021. The results of this study indicate that the translation speed of tropical cyclones has increased at a rate of 0.06km/h/yr over the 30-year period (r = 0.06 p = 0.19). Whilst the translation speed debate remains at an aggregated global scale, a comprehensive understanding of the influence of climate change on tropical cyclones is crucial for generating forecasts as this enables vulnerable regions to plan and adjust to evolving tropical cyclones.Item Assessing the Validity of the Exclusion of Night-time Thermal Comfort in Tourism Climate Indices(University of the Witwatersrand, Johannesburg, 2024-09) Mnguni, Zandizoloyiso; Fitchett, JenniferBiometeorological indices are instruments that can be used to streamline complex climatic information for economic and other decision-making. Indices hold inherent assumptions where the use of an index is only reliable and valuable if those assumptions are true. The Holiday Climate Index (HCI) is presented as the improved version of the TCI, with a key difference being the removal of night-time thermal comfort due to the assumption that air conditioning is ubiquitous throughout Europe. This study investigated the validity of this exclusion of night-time thermal comfort in tourism climate indices, particularly for the HCI using the six European cities for which the index was developed – Barcelona, Stockholm, London, Istanbul, Paris and Rome. The assumption of ubiquitous air conditioning was investigated using Booking.com accommodation listings, the night-time economy and prevalence of night-time activities outside of each accommodation establishment, and whether tourists experienced adverse thermal comfort during the night through posted reviews. Without the air conditioning filter applied, the proportion of listings categorized as offering air conditioning ranged from 28.8% for Stockholm to 98.9% for Rome. With the filter applied, the proportions ranged from 96.4% for Stockholm and 99.0% for Paris. A total of 24,252 TripAdvisor reviews were also examined for both accommodation establishments and night-time tourist activities. The reviews were manually examined for the mention of weather, climate, night-time temperature and air conditioning. The findings of this study exhibit a range of night-time activities, many of which are outdoors, where tourists did comment on night-time thermal comfort. The research disproves the claim of the original authors, and it was found that air conditioning is not ubiquitous. Therefore, the assumption that the HCI is based on is problematic, and the index should be used with caution. Moreover, a similar approach in index validity testing should be performed prior to future studies seeking to apply indices.Item Modelling for Rainwater Harvesting Structures Using Geospatial Techniques(University of the Witwatersrand, Johannesburg, 2024-10) Makaringe, Precious Nkhensani; Atif, IqraClimate change poses a significant threat, leading to droughts, floods, and hindering sustainable development. Water scarcity is a growing concern, particularly in developing countries like South Africa, where limited freshwater resources are further strained by climate variability. This research explores the potential of rainwater harvesting (RWH) as a strategy to address water scarcity in such regions. This study aims to model potential rainwater harvesting sites in Lynwood Park, Pretoria, South Africa, utilising geospatial techniques. Object-Based Image Classification (OBIC) was employed to extract building footprints from high-resolution satellite imagery. Microsoft and Google building footprints were utilised to determine the suitable automated building footprints for Lynnwood Park. ArcGIS Pro software served as the primary platform for spatial data analysis and mapping potential RWH sites. Data integration included high-resolution satellite imagery, a Digital Elevation Model (DEM), building footprints, and rainfall data. Additionally, questionnaires were distributed to estimate population and water demand within the study area. The research demonstrates the efficacy of geospatial tools in identifying suitable locations for RWH systems. Indicating that steeper slopes in the southern region of Lynnwood Park have limited collection from large rooftops, while the flatter north offered greater potential. Rainfall graphs and PRWH results suggest that over half of Lynwood Park's annual water demand could be met through rooftop rainwater collection. However, factors such as system losses due to evaporation, inefficiencies in collection and storage, and variability in rooftop sizes across different buildings would need to be incorporated into more detailed models, as well as water quality analysis for rooftop harvested water in future studies. This study highlights the potential of RWH as a viable water security strategy in water-scarce regions. The findings contribute to the development of geospatial approaches for RWH implementation, promoting water security and sustainability in a changing climate.Item Assessing and comparing the performance of different machine learning regression algorithms in predicting Chlorophyll-a concentration in the Vaal Dam, Gauteng(University of the Witwatersrand, Johannesburg, 2024-03) Mahamuza, Phemelo Hope; Adam, ElhadiThe state of Vaal Dam is influenced by various land uses surrounding the Dam, including agricultural activities, mining operations, industrial enterprises, urban settlements, and nature reserves. Mining activities, farming practices, and sewage outflows from nearby villages led to access contamination within the Dam, increasing algal bloom levels. Sentinel-2 MSI data were utilized to forecast and comprehend the spatial pattern of Chlorophyll-a concentration, indicating algal bloom occurrence in the Vaal Dam. Targeting Sentinel-2 Level-1C, the image was preprocessed on the Google Earth Engine (GEE) with acquisition dates from 25 – 26 October 30, 2016, corresponding to the on-site data collection between October 26 and October 28, 2016. Due to limited resources, up-to-date data on the Vaal Dam could not be collected. However, since this study focuses on applying various machine learning regression models to predict chlorophyll-a levels in waterbodies, the dataset is used to test the models rather than reflect the current state of the Vaal Dam. The dataset, comprising 23 samples, was divided into 70% training and 30% test sets, allowing for comprehensive model evaluation. Band ratio reflectance values were extracted from the satellite image and correlated with in-field Chlorophyll-a values. The highest correlation coefficient values were utilized to train five machine-learning models employed in this study: Random Forest (RF), Support Vector Regression (SVR), Least Absolute Shrinkage and Selection Operator (LASSO), Ridge Regression, and Multilinear Regression (MLR). Each model underwent training with ten iterations each; the best learning iteration was then used to generate the final Chlorophyll-a predictive model. The predictive models were validated using the Sentinel-2 MSI satellite data and in-situ measurements using R2, RMSE, and MAPE. Among the five machine learning algorithms trained, RF performed the best, with an R2 of 0.86 and 0.95, an RMSE of 1.38 and 0.8, and MAPE of 15.09% and 10.92% for the training and testing sets, respectively, indicating its ability to handle small, non-linear datasets. SVR also demonstrated a fair performance, particularly in handling multicollinearity in the data points with an R2 of 0.68 and 0.87, an RMSE of 2.37 and 1.56, and MAPE of 18.13% and 19.28% for the training and testing sets, respectively. The spatial pattern of Chlorophyll-a concentrations, mapped from the RF model, indicated that high concentrations of Chlorophyll-a are along the Dam shorelines, suggesting a significant impact of land use activities on pollution levels. This study emphasizes the importance of selecting suitable machine learning algorithms tailored to the dataset's characteristics. RF and SVR demonstrated proficiency in handling nonlinearity, with RF displaying enhanced generalization and resistance to overfitting. Limited field data evenly distributed across the Dam and satellite overpass dates may affect result accuracy. Future research should align satellite pass dates with fieldwork dates and ensure an even distribution of in-field samples across the Dam to represent all land uses and concentration levels.Item GIS-Based Location-Allocation Modelling of School Accessibility in the Bojanala Platinum District Municipality, South Africa(University of the Witwatersrand, Johannesburg, 2024-09) Molefe, Kebarileng Christinah; Atif, IqraSchool accessibility modelling performs a crucial part in guaranteeing that educational institutions are physically and practically reachable by every student, irrespective of their abilities, disabilities, or socioeconomic status. Neglecting school accessibility perpetuates inequality, reinforces negative stereotypes, and isolates affected students. Therefore, the principal goal of this research was to evaluate the distribution of schools across the Bojanala Platinum District Municipality, focusing on their accessibility to local communities. The study employed an integrated approach, combining geostatistical techniques, location-allocation modelling, and multicriteria decision analysis. By considering both quantitative data and spatial relationships, these methodologies contributed to robust decision-making and informed policy recommendations. The study utilized population data and school-related information sourced from the Department of Education and the HUMDATA websites, both dated to the year 2020. The study examined the distribution of schools in the Bojanala Platinum District Municipality. It was discovered that the schools were clustered, with a concentration in the Rustenburg local municipality, followed by Madibeng. However, a significant inequality in school access was evident. Secondary school students faced the greatest vulnerability, as most accessible schools primarily served primary students. To address this, potential school sites were proposed across the district. The study emphasizes the need for effective interventions by educational administrators and policymakers to eliminate this inequality. This study recommends the establishment of new schools in underserved regions, strategically enhance existing schools, and maximize school accessibility for all residents. Adequate school provision promotes equity, reduces travel burdens, and strengthens community bonds.Item Detecting Disease in Citrus Trees using Multispectral UAV Data and Deep Learning Algorithm(University of the Witwatersrand, Johannesburg, 2024-06) Woolfson, Logan Stefan; Adam, ElhadiThere is a high prevalence, in South Africa, of fruit tree related diseases infesting lemon trees, subsequently affecting overall crop yield and quality. Ultimately, the income for the farmers is significantly diminished and limits the supply of nutritional food crops for the South African population, who already suffer from a high incidence of malnutrition. Currently, there are various methods utilized to detect diseases in fruit trees, however they pose limitations in terms of efficiency and accuracy. By employing the use of drones and machine learning methods, fruit tree diseases could be detected at an earlier stage of development and with a much higher level of accuracy. Consequently, the chances of remedying the trees before the disease spreads is greatly improved, and the supply of nutritious fruit within South Africa is increased. This research report’s aim is to investigate the effectiveness of a deep learning algorithm for detecting and classifying diseases in lemon orchards using multispectral drone imagery. This entails assessing the performance of a pretrained ResNet-101 model, fine-tuned with additional sample images, in accurately identifying and classifying diseased lemon trees, specifically those affected by Phytophthora root rot. The methodology involves the utilization of a pretrained ResNet-101 model, a deep learning architecture, and the retraining of its layers with an augmented dataset from multispectral aerial drone images of a lemon orchard. The model is fine-tuned to enhance its ability to discern subtle spectral variations indicative of disease presence. The selection of ResNet-101 is grounded in its proven success in image recognition tasks and transfer learning capabilities. The results obtained demonstrated an impressive accuracy of 80%. The deep learning algorithm exhibited notable performance in distinguishing root rot-affected lemon trees from their healthy counterparts. The findings indicate the promise of utilizing advanced deep learning methods for timely and effective disease detection in agricultural farmlands, facilitating orchard management.Item Study of the influence of gust fronts and topographical features in the development of severe thunderstorms across South Africa(University of the Witwatersrand, Johannesburg, 2024) Mofokeng, Puseletso Samuel; Engelbrecht, Francois A.; Bopape, Mary-Jane M.; Grab, Stefan W.South Africa experiences a variety of severe thunderstorms which occasionally leads to a large quantity of small-sized or large-sized hailstones, heavy rain and flash flooding, strong damaging straight-line winds, and/or even tornadoes. For the base period, June 2016 to June 2021, a significant percentage of these severe storms was triggered by topographic features. The Unified Model (UM) at 4 km horizontal grid resolution was used and found to be unable to predict topography-generated vertical wind shear and the associated severe thunderstorms. This inability of the model necessitated the development of a conceptual model by relating the rapid cooling of the cloud-top temperatures with high resolution topographic maps. This means, satellite images were used to deduce the connection of atmospheric fluids (gust fronts) with near linear, concave and/or downslope topographical features. Severe thunderstorms included those connected to the large amounts of vorticity advection (e.g. 500 hPa level), development of low-level mesoscale circulations within the synoptic settings and the resultant vertical wind shear in the lower tropospheric levels. Large amounts of negative vorticity advection are typical with strong horizontal shear and curvature; they are often correlated with trough axes that lean from the south-west to north-east. The usage of large amounts of negative vorticity advection transcends to whether discrete severe thunderstorms will be characterised by heavy rain and flash-flooding or hail with damaging winds. Moreover, the interaction of topography with gust fronts of the upwind thunderstorms linked with large amounts of negative vorticity advection is also investigated. The impacts of storms studied in this dissertation posed a major threat to property, livelihood, agriculture, human and animal lives or even immediate to residual economic loss. This research is aimed at improving the service level for the benefit of disaster management agencies and the public at large. An in-depth study of microscale events such as tornadoes and landspouts was also conducted to improve lead-time for their nowcasting.