Electronic Theses and Dissertations (Masters)

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

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    Pandemics and Heritage: understanding the impact of the Covid-19 pandemic on Archaeotourism in South Africa
    (University of the Witwatersrand, Johannesburg, 2023) Eswaran, Nithya
    The project explores the impact of the Covid-19 pandemic on archaeotourism at three public visitation sites in South Africa. According to the 2021 UNESCO report on the impact of Covid- 19 on heritage tourism, visitors to Africa decreased by 71% in 2020. The drop significantly impacted the revenue of the continent (UNESCO 2021). This research focuses on assessing the effects of the COVID-19 pandemic locally by examining two UNESCO World Heritage sites: the Sterkfontein Caves in the Cradle of Humankind, Gauteng Province, and Main Caves in Giant’s Castle Game Reserve, KwaZulu-Natal Province. The third study site is the Origins Centre Museum at the University of Witwatersrand in Johannesburg. These sites are open to tourists for guided tours. Quantitative data from Kruger National Park is collated to analyse the pandemic's influence on public, nature-based sites for comparison to culture sites
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    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.
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    The holiday climate index: applicability and suitability for the South African context
    (University of the Witwatersrand, Johannesburg, 2024) Kristensen, Daniella; Fitchett, Jennifer
    Tourism is one of the largest economic sectors and continues to grow at a rapid pace. This sector is under threat by climate change, with Africa deemed to be most vulnerable to these changes. The projected climatic changes and increase in occurrence and intensity of extreme events over South Africa has an impact on overall tourism comfortability. Quantifying the climatic suitability of tourist destinations has been achieved through tourism climate indices. Some of these indices cover all tourism activities and some are specific to a tourism type (e.g., snow tourism). The Holiday Climate Index (HCI) was developed to determine climactic comfortability of beach and urban destinations and to address the limitations of previous indices. This study will provide the first determination of the appropriateness of the HCI for the South African context and calculations of the HCI for destinations across South Africa. The mean annual HCIurban and HCIbeach scores for the longest continuous period of each destination reveal that the majority of destinations demonstrate HCIurban and HCIbeach scores between 70 and 79 and are considered to have ‘very good’ climatic conditions for tourism. An exception is the HCIurban result for Durban which is scored as ‘good’. Generally, the highest HCI scores were calculated for Cape Town on the west coast, while the lowest HCI scores were calculated for Durban on the east coast. It was determined that McBoyle’s (2001) winter season peak distribution is applicable to seven of the 13 HCIurban and three of the five HCIbeach destinations. This indicates that the winter season is most suitable for tourism for most destinations. In comparing the results of destinations where both the HCIurban and HCIbeach are applicable, it was determined that all destinations, with the exception of Durban, have a minimal difference in the average annual HCI scores. Durban recorded a notable difference which demonstrated that the destination would be more suitable for beach tourism. The results of this study can be used to quantify the impacts of climate change on the tourism sector and assist tourism stakeholders in developing the capacity to adapt to the projected changes.
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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.