Electronic Theses and Dissertations (Masters)

Permanent URI for this collectionhttps://hdl.handle.net/10539/38009

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A Geospatial Approach to Mapping Jacaranda Tree Distribution in Johannesburg, South Africa
(University of the Witwatersrand, Johannesburg, 2023-11) Reddy, Rohini Chelsea; Fitchett, Jennifer
Accurate mapping of the spatial distribution of invasive species is vital for the implementation of effective monitoring and management strategies. In countries where resources are scarce and costly, citizen science provides a cost-effective and accurate alternative for large-scale data collection. Citizen’s familiarity with their environment contributes to aspects such as accurate identification of features on the landscape. Advances in a geographic information system (GIS) together with open-sourced photography from Google Street View, provide accurate methods for in-field and remote validation of citizen science data for invasive mapping and assists with the creation and compilation of maps to visualize the spatial distribution of invasive plants upon the landscape. In this study, the first spatial distribution maps for invasive tree species, Jacaranda mimosofolia (common name: Jacaranda), are created for the City of Johannesburg (CoJ). Jacaranda trees are well-known by citizens in the CoJ for their district purple flowers which blanket the landscape during springtime. A combination approach using citizen science, GIS, and Google Street View for data collection, analysis, and creation of the first spatial distribution map of exact location and prevalence of Jacaranda trees within certain suburbs of the CoJ, is produced. A total of 8,931 ground-truthing geopoints together with extensive Google Street View validation for Jacaranda tree presence, formed the basis of accurate spatial distribution maps. The first research question of this study focused on the spatial distribution of Jacaranda trees in the CoJ and was answered as a total of 54 suburbs were confirmed as having a large presence of Jacaranda trees in the CoJ. Citizen science data collected a total of 488 geotags for possible Jacaranda tree presence in the CoJ, over a 75-day online survey collection period. Although citizen science data provided a lower spatial resolution compared to successful fieldwork and Google Street View approaches, citizen science data provided very high accuracy for the identification and geolocation of Jacaranda tree presence in the CoJ which answers the second research question based on the effectiveness of the geospatial approach towards citizen science, ground-truthing and Google Street View as data collection methods. Since the accuracy of citizen science resulted in 66% of collected geotags within the categories of ‘very high’, ‘high’ and ‘moderate’ accuracy ranges of between <7-24m from a confirmed Jacaranda tree, together with the accuracy of 8,931 in-field collected geolocation of Jacaranda trees and Google Street View’s accuracy and capability of collecting street view imagery – it is concluded that the combined approach of ground-truthing, citizen science and Google Street View contribute not only to effective data collection, but also towards the successful mapping of Jacaranda tree presence in the CoJ.
An Assessment of Beauty Waste Management Practices: A Case Study of Rustenburg Beauty Salons
(University of the Witwatersrand, Johannesburg, 2024) Knight, Jasper; Knight, Jasper
The beauty salon industry is one of the fastest growing industries and is a significant waste generator in South Africa. Waste that results from beauty salons is a thorny environmental issue because it spans from different waste types and sources. Futhermore it requires waste treatment and different disposal methods. In view of this, this study seeks to assess waste management practices of beauty salons in Rustenburg, South Africa, in order to identify the types of waste salons generate and to identify recommendations that can assist beauty salons to be environmentally sustainable by improving their waste management practices. The aim of the research is to understand how beauty salon waste is discarded and to what degree beauty salon personnel understand the impacts of waste on the environment. The objectives of this research are to (1) determine the total amount of waste produced by selected beauty salons in Rustenburg, (2) identify the waste management practices undertaken by the beauty salons, (3) explore the challenges the beauty salons face in relation to waste management, and (4) identify recommendations of how beauty salons in South Africa can further improve their waste management practices. This study employed a mixed methods design through quantifying the amount of waste the selected beauty salons generate over a two month period, and by interviewing salon personell on their views on salon waste and waste management practices. Fifteen salons were surveyed. Results were analyzed using thematic analysis. The results show that the all the beauty salons combined produce a total annual estimated waste of 4732.2 kg. Through interviews, the study identified waste management practices of the beauty salons to be primarily premised on discarding waste in dustbins for municipal collection, burning waste, or dumping waste in unregulated dumping sites when waste service delivery poses challenges. These three practices are the most common modes of waste disposal in the Rustenburg beauty salons. Issues of waste management facing beauty salons includes lack of waste facilities and lack of knowledge about waste management. The major recommendation from beauty salons and civil (professional) organisations was for government to provide beauty salons with better infrastructure for waste sorting, recycling, pick-up and disposal. Promotion of good practice and awareness campaigns were cited as recommendations to improve waste management practices in beauty salons
An integrated approach for detecting and monitoring the Campuloclinium macrocephalum (Less) DC using the MaxEnt and machine learning models in the Cradle Nature Reserve, South Africa
(University of the Witwatersrand, Johannesburg, 2024) Makobe, Benjamin; Mhangara, Paidamoyo
The invasion of ecosystems by invasive plants is considered as one of the major human- induced global environmental change. The uncontrolled expansion of invasive alien plants is gaining international attention, and remote sensing technology is adopted to accurately detect and monitor the spread of invasive plants locally and globally. The Greater Cradle nature reserve is a world heritage site and intense research site for archaeology and paleontology.It was accorded the world status by the United Nations Educational, Scientific and Cultural Organizations (UNESCO) in 1991 due to its variety of biodiversity present and carries information of significance about the evolution of mankind. The invasion of Campuloclinium macrocephalum (pompom weed) at the Cradle nature reserve is downgrading the world status accorded to the site, lowers the grazing capacity for game animals and replaces the native vegetation. This research study explored the capability of Sentinel-2A multispectral imagery in mapping the spatial distribution of pompom weed at the nature reserve between 2019 and 2024. The non-parametric classification models, support vector machine (SVM) and random forests (RF) were evaluated to accurately detect, and discriminate pompom weed against the co-existing land cover types. Additionally, the species distribution modelling MaxEnt Entropy was incorporated to model spatial distribution and pompom weed habitat suitability. The findings indicates that SVM yielded 44% and 50.7% spatial coverage of pompom weed at the nature reserve in 2019 and 2024, respectively. Whereas, the RF model indicates that the spatial coverage of pompom weed was 31.1% and 39.3% in 2019 and 2024, respectively. The MaxEnt model identified both soil and rainfall as the most important environmental factors in fostering the aggressive proliferation of pompom weed at nature reserves. The MaxEnt predictive model obtained an area under curve score of 0.94, indicating outstanding prediction model performance. SVM and RF models had classification accuracy above 75%, indicating that they could distinguish pompom weeds from existing land cover types. The preliminary results of this study call for attention in using predictive models in predicting current and future spatial distribution of invasive weeds, for effective eradication control and environmental management.
Are our Head Teachers okay? Decision-making processes during COVID-19 across South African independent schools
(University of the Witwatersrand, Johannesburg, 2023-10) Pahl, Julia; Evans, Mary
The COVID-19 pandemic significantly affected the education sphere globally and in South Africa. The impact on pupils and teachers has been investigated. However, the impact on Head Teachers in schools, particularly independent schools in South Africa, has received less attention. Head Teachers within independent South African Schools are the decision makers, leaders, and influencers who were placed in a key role during the COVID-19 pandemic. These Head Teachers were asked to lead, make decisions, and positively influence and manage those under their care while the educational landscape shifted. This shifting educational landscape placed Head Teachers within independent schools under increased pressure and stress. This study aimed to understand the technological and financial access and contexts of Head Teachers at independent schools within South Africa and explore their decision-making processes and the impact of their decisions on their well-being. An online survey was sent to independent Head Teachers, and four in-depth, one-on-one interviews were conducted. Head Teachers showed that their financial and technological contexts did not constrain their pandemic responses as the nature of their independent school allowed them to have access to adequate technological resources and alternative financial support sources. Key findings of this research were that Head Teachers were commonly influenced both positively and negatively by the complex communication networks and channels created and used during the pandemic and the complex decision-making drivers they had to navigate while leading during the pandemic. The decision drivers of well-being and shared moral purpose were more dominantly considered than the decision driver of National Government Directives, as Head Teachers had to consider their complex and multi-dimensional environments when deciding which decision driver was to be prioritized. Head Teachers had to navigate two main tensions: would they prioritize outcomes such as assessment and reports during the pandemic or would they prioritize awareness and well-being of staff and learners, and would they make their decisions from a compliance standpoint where full compliance was key or from a standpoint where complying as much as possible or enough for deniability was chosen. These results also associated increased communication networks during the pandemic with increased stress and burn-out levels and therefore a decreased well-being of Head Teachers, and increased numbers of Head Teachers leaving the profession. However, a positive outcome of the pandemic was that schools, staff, learners, and parents were forced to increase their device and/or internet access and technological skills to maintain education during the lockdown and the changing educational environment. When these results were studied across the geographical landscape of independent South African schools it was clear that the findings on access, well-being and decision-making were linked to the context of the Head Teacher and their school and not to their geographic location. Yet using complex, multi-faceted communication networks and the resultant stress was a country-wide experience and that National Government Directives was the country-wide main decision-driver for independent South African Head Teachers. Overall, this study identified six key lessons for education within any future South African pandemics: the need for clear and concise instructions within legislation, standard operating procedures need to consider access and context, blended education should become a norm in schools, consistency within education should be maintained throughout, personal boundaries should be maintained throughout and networking between leaders should be increased.
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, Elhadi
The 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.
Assessing the effectiveness of wetlands in the Krugersdorp Game Reserve in attenuating pollution from mines on the West Rand, South Africa
(University of the Witwatersrand, Johannesburg, 2023) Sawuka, Noluthando Thulisile; Evans, Mary; Masindi Khuliso
In South Africa, 48% of the country’s wetlands are critically endangered because of anthropogenic activities. Wetlands are an important part of the landscape and play a critical role including but not limited to improving water quality, habitat provision, and water storage. This research aimed to assess the effectiveness of wetland systemsin attenuating pollution from water discharged from abandoned gold mines in the Krugersdorp Game Reserve (KGR), West Rand. Eight (8) water samples were collected in the study site. Physico-chemical parameters were measured in situ, and chemical parameters were measured in the lab. The measured physico–chemical parameters from the majority of the sampled wetlands exceeded at least one of the stipulated water quality legislations, which included the General Authorization Limit Section 21f and h, 2013; Unit for TWQGR; Mine Health and Safety Act; and WUL wastewater in terms of the recorded pH, total dissolved solids, and salinity variables. Overall, a decreasing trend in pH level was observed from wetlands sampled upstream of the KGR to wetlands sampled downstream of the KGR, with the highest recorded pH level (Alkalinity: 8.9) obtained from the sampled wetland that was closest to the adjacent mining site upstream of theKGR whilst the lowest recorded pH level (Acidity: 3.9) obtained from a wetland sampling point that was further from the adjoining mine and downstream in the KGR. A weak and positive correlation (r=0.040) was obtained between the measured total dissolved solids and pH levels from the sampled wetlands, indicating minimal spatial variability. However, a strong positive correlation (r=0.999, Correlation is significant at the 0.01 level) was obtained between the measured total dissolved solids and salinity from the sampled wetlands. At least one of the limits stipulated by the water quality legislation was exceeded in terms of the analysed inorganic constituents from the sampled wetlands. The dominant ions recorded in the wetlands in increasing order are F, K, Cl, Mg, Na, Ca, and SO4. Mn and Si were the dominant metal concentrations recorded in most wetlands, with the former also showing exceedances when compared to the stipulated water quality guidelines. The recorded data from the measured physico–chemical parameters and analysed chemical variables indicated poor water quality in wetlands sampled downstream of the KGR and upstream of the KGR. Stringent measures in water quality monitoring need to be implemented to mitigate the environmental impacts associated with wastewater discharge into the receiving environment.
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, Jennifer
Biometeorological 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.
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
Assessment of the impact of load shedding on the households of Alexandra, Johannesburg, South Africa
(University of the Witwatersrand, Johannesburg, 2023) Mbatha, Cebolethu; Kubanza, Nzalalemba Serge
In South Africa, persistent challenges in the electricity sector have been noted. This study emphasizes that having access to electricity is insufficient; the reliability of its supply is crucial,especially given prolonged power outages faced by a significant portion of the population. In Alexandra Township at two residential areas, 16th Avenue and East Bank, the research used a mixed-method approach, involving questionnaires for 100 households and 20 local businesses, and semi-structured interviews with representatives from the local city authority. Results indicated substantial disruptions to daily lives and operations. These disruptions adversely affected critical social services, hindering operations in essential infrastructures like water supply systems, hospitals, education institutions, and telecommunication systems. The study identifies illegal electricity connections, infrastructure loss, and political interference as perceived major causes of successive power outages in Alexandra. It highlights the worsening nature of load shedding, making it a significant political issue in South Africa, reflecting hardships households and businesses face. The paper recommends governmental subsidies for alternative energy appliances and more favorable electricity tariff rates for households and small businesses to alleviate demand during peak periods. The findings offer valuable insights for policymakers and the South African electricity utility in analyzing trade-offs between negative welfare effects and costs of reducing power outages
Climate change and heritage tourism: threats to Makgabeng in a regional context, Limpopo South Africa
(2020-11) Mcpherson, Fazlin Ahdielah
The Makgabeng area is situated in the north-west corner of the Limpopo province in South Africa. The Makgabeng area is an emerging tourist destination with a variety of activities to offer. The area is rich in ancient rock art sites and, as a result, has great potential for the development of heritage tourism. Extensive research has been conducted on the rock art in this region. However, the impact of climate change on heritage tourism has not yet been explored. The local community of the Makgabeng area is developing a heritage tourism destination within the region and it is important to determine whether the initiative will be sustainable, especially in the context of climate change threats to the region. In a region such as Makgabeng where the primary attraction is natural heritage tourism rather than cultural, this then poses a severe threat to tourism within the region, especially since most of these attractions are outdoors. Hence, this research project is primarily aimed at determining climate change threats to heritage tourism in the Makgabeng region, South Africa. The research has employed a mixed-method approach consisting of interviews done with various stakeholders within the tourism industry and community members in the Makgabeng region. The other methods used are hard-copy and online questionnaires, TripAdvisor reviews, and the Tourism Climatic Index (TCI). What the research has found is that people do not know that Makgabeng exists, and for those who are aware of its existence they have never visited the region. this is because the area is not being marketed effectively. The TCI scores show that winter is the best time of the year for tourism. Consequently, stakeholders and community members should market the area with this in mind. However, tourists have said they enjoy the weather in the region all year round.
Commercial maize farmers’ adaptations to climate change in Sannieshof, North West Province, South Africa
(University of the Witwatersrand, Johannesburg, 2024) Dunn, Benjamin Graham; Knight, Jasper
Commercial agriculture is a critical industry for South Africa, both from an economic and a social perspective. Maize forms a vital part of the diet of millions of people across the country and the continent of Africa. It is also an important commercial export crop. It is, therefore, imperative that the industry can adapt to both climate and socioeconomic changes. This study aims to investigate the specific challenges faced by commercial maize farmers in the Sannieshof region, North West Province, South Africa, in relation to climate change and socioeconomic factors. This study undertook questionnaires with 21 commercial maize farmers in the region, followed up by field observations and photographs from one large commercial maize farm. Several socioeconomic factors were identified by participants as having negative impacts on farming operations, including government policies, crime, and load shedding. Climate change adaptation includes several dynamic management practices which vary between seasons, including adjustments in cultivar choice, sowing dates, sowing depth, and plant population density. Adopting conservation agriculture and precision agriculture techniques forms an important element used by the farmers to achieve long-term climate change adaptation. Going forward, farmers need to consider the impacts of both climate change and socioeconomic factors, both of which impact their agricultural operation and which can be conceptualised through a Water-Energy-Food nexus framework. Due to limited financial resources, farmers need to consider which drivers of change need to be prioritised in their decision-making, which ultimately may create differences in adaptation strategies adopted by different farmers
Detecting Disease in Citrus Trees using Multispectral UAV Data and Deep Learning Algorithm
(University of the Witwatersrand, Johannesburg, 2024-06) Woolfson, Logan Stefan; Adam, Elhadi
There 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.
Determining the spatial variations of evapotranspiration rates in a semiarid region
(University of the Witwatersrand, Johannesburg, 2024) Sorour, Wendy; Shoko, Cletah
Evapotranspiration (ET) is one of the biggest ways in which water is transferred from water resources into the atmosphere as water vapor and understanding its variations is important for water resource management. This study determined land use land cover (LULC)-based ET and the influence of climatic events in Western Cape. Landsat 8, Surface Energy Balance System, Support Vector Machine, humidity, wind speed, surface pressure, temperature, and sunshine hours were used, during El Nino in 2015-2016, normal year in 2019-2020, and La Nina in 2020-2021. Median ET was calculated for each LULC type to determine their effect on ET. Climatic events increased ET compared to the normal year and increasing temperatures and rainfall during EL Nino and La Nina years respectively were the main drivers. Water had the lowest ET, and agricultural land had the highest. The results of this study can be used to create better water resource management plans
Does public participation facilitate the development of a comprehensive social impact assessment process in South Africa? a study of the Carolina town community in Mpumalanga
(University of the Witwatersrand, Johannesburg, 2024) Gilfillan, John; Simatele, Mulala Danny
The inadequate integration of social and economic dimensions within impact assessments, particularly in the context of mining activities is particularly worrisome. The lack of a standardized and inclusive model, coupled with insufficient consideration of local knowledge, contributes to conflicts and dissatisfaction in mining communities. Public participation, crucial for a holistic assessment, faces hurdles like awareness gaps, language barriers, and inadequate community representation. These challenges hinder the achievement of sustainable outcomes in mining development projects. This study assesses the current state of public participation in Social Impact Assessment (SIA) processes within mining communities in Carolina. This study employed a qualitative approach. Purposive and snowball sampling techniques were used to identify the relevant participants for this study. Drawing on diverse perspectives from technocrats, community members, homeowners, and stakeholders, the research unveils insights into the effectiveness of public engagement between the Carolina community and mining operatives. After engaging with the participants ultimately a total of 45 participants were identified. Data collection tools were the use of the focused group interviews and semi- structured questionnaires. The findings reveal a predominant dissatisfaction among participants, with 70% expressing the ineffectiveness of public participation in SIA. The study identifies a lack of inclusivity, transparency, and communication in the current approach, as community members feel excluded from decision making processes. Motivations for engagement centre on the crucial need for job opportunities provided by mining operations, reflecting the community's economic aspirations. However, expectations extend beyond employment to include broader community development aspects, indicating a desire for positive impacts resulting from mining activities. Further exploration into the role of public participation in identifying community challenges highlights a significant lack of awareness and understanding among participants. The study underscores language barriers, geographical distance to meeting venues, and a perceived absence of tangible outcomes as significant challenges. Participants express frustration with the current state of public participation, emphasizing the need for transparency, genuine commitment, and accountability from mining companies. The research concludes with recommendations for a more inclusive, accessible, and communicative public participation process to address community challenges effectively. It calls for improved education, language inclusivity, and enhanced communication channels to foster meaningful engagement and ensure a more equitable and sustainable future for the Carolina community affected by mining activities
Estimating rooftop solar energy potential using spatial radiation models and thermal remote sensing: The case of Witwatersrand University
(University of the Witwatersrand, Johannesburg, 2023) Ndemera, Rudo Hilda; Adem, Ali K.; Adam, Elhadi
The main purpose of this research was to estimate the University of Witwatersrand building’s rooftop solar energy potential using the GIS-based solar Area Solar Radiation (ASR) analyst upward hemispherical view shed algorithm. The two major datasets used in this research for rooftop solar energy potential modelling are building footprint data and the Digital Surface Model. Building footprint data, specifically rooftop area was extracted using machine learning CNTK unified toolkit and deep neural networks. The data was presented as individual polygon shape files for each building. The high-resolution Digital Surface Model imagery was sourced from the Advanced Land Observation Satellite. Pre-processing of the imagery was done for atmospheric correction. The DSM was then used in the Area Solar Radiation model to create an upward view shed for every point on the study area which is essential for computing solar radiation maps. The efficiency of using this algorithm is that it considers the shading effects caused by surrounding topography and surrounding man-made features, alterations in the azimuth angle and the position of the sun. Apart from the incoming solar radiation reaching the rooftops, the elevation and orientation of the rooftop cells limit the solar panel tilt angle and intensity of the incoming solar radiation, respectively. These factors were used in setting the suitability criteria together with solar radiation for the identification of suitable rooftop cells in this research. The relationship between land surface temperature and solar radiation values was assessed to determine if it can be used as an indicator for solar panel efficiency. Results from this research indicate that the University of Witwatersrand receives high levels of incoming solar radiation and has a high solar energy rooftop generation capacity that can meet the energy demand on campus. To improve accuracy of the research results, a drone could have been used to measure insolation across the study area to improve the spatial resolution. However, this was not possible due to various restrictions.
Evaluating the spatiotemporal changes of urban wetlands in Klip River wetland, South Africa
(University of the Witwatersrand, Johannesburg, 2023-09) Nxumalo, Nolwazi; Knight, Jasper; Adam, Elhadi
This study assesses the impacts of land use / land cover (LULC) change in an urban wetland over the past 30 years utilizing machine learning and satellite-based techniques. This study looked at LULC distributions in the Klip River wetland in Gauteng, South Africa. The aims and methods used in this study were: (1) to conduct a comprehensive analysis to map and evaluate the effects of LULC changes in the Klip River wetland spanning from 1990 to 2020, employing Landsat datasets at intervals of 10 years, and to quantify both spatial and temporal alterations in urban wetland area. (2) To predict the change in urban wetland area due to specific LULC changes for 2030 and 2040 using the MOLUSCE plugin in QGIS. This model is based on observed LULC including bare soil, built-up area, water, wetland, and other vegetation in the quaternary catchment C22A of the Klip River wetland, using multispectral satellite images obtained from Landsat 5 (1990), Landsat 7 (2000 and 2010) and Landsat 8 OLI (2020). (3) For the results of this study, thematic maps were classified using the Random Forest algorithm in Google Earth Engine. Change maps were produced using QGIS to determine the spatiotemporal changes within the study area. To simulate future LULC for 2030 and 2040, the MOLUSCE plugin in QGIS v2.8.18 was used. The overall accuracies achieved for the classified maps for 1990, 2000, 2010, and 2020 were 85.19%, 89.80%, 84.09%, and 88.12%, respectively. The results indicated a significant decrease in wetland area from 14.82% (6949.39 ha) in 1990 to 5.54% (2759.2 ha) in 2020. The major causes of these changes were the build-up area, which increased from 0.17% (80.36 ha) in 1990 to 45.96% (22 901 ha) in 2020—the projected years 2030 and 2040 achieved a kappa value of 0.71 and 0.61, respectively. The results indicate that built-up areas continue to increase annually, while wetlands will decrease. These LULC transformations posed a severe threat to the wetlands. Hence, proper management of wetland ecosystems is required, and if not implemented soon, the wetland ecosystem will be lost.
Examining the remaining Rock Art at Linton, Eastern Cape, and its relationship with the Linton Panel at the Iziko South African Museum in Cape Town
(University of the Witwatersrand, Johannesburg, 2023) Oster, Sandee Michelle; Pearce, David
The Linton panel has been the subject of great awe for many decades. It has been displayed in various exhibits worldwide and the subject of multiple research publications. However, its history and origin are not nearly as well understood as once believed, as a large part of its past has been omitted or forgotten. In this dissertation the images of not only the Linton panel are discussed, but those that remain in the shelter from whence it came are brought out of obscurity. How the panel came to be where it is today and the images’ relationship with the shelter and the remaining paintings are examined. Lastly, a forgotten piece of the shelter, a second panel, will be examined in greater detail than ever before: how it fell into relative obscurity and what its images tell us about the Linton shelter and its artists’ beliefs and purposes.
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.
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, Iqra
School 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.
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, Elhadi
The 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.