Electronic Theses and Dissertations (Masters)

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Browsing Electronic Theses and Dissertations (Masters) by SDG "SDG-9: Industry, innovation and infrastructure"

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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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    SBLS Lithic Technology and its Behavioural Implications
    (University of the Witwatersrand, Johannesburg, 2024) Bielderman, Sebastian Christopher; Wurz, Sarah
    In the Middle Stone Age (MSA) of the southern Cape in South Africa significant research has been undertaken to understand the behaviours linked to coastal adaptation as well as the exploitation of terrestrial resources, however but relatively little is understood on how lithic technology relates to human behaviour during certain MSA periods in this region. The Silty Black Soils (SBLS) layer at Klasies River main site (KRM), which is older than 110 000 years ago, falls within one of these lesser understood periods and has yielded lithic material in association with both faunal and shellfish remains and other important features such as hearths. Understanding the behaviours of the SBLS is significant in broadening our understanding of the MSA I/earlier MSA technologies. Through the analysis of the Chaî ne Opé ratoire (or production sequence), macro-fractures, and the Tip Cross-Sectional Area of the SBLS lithics, significant information on the manufacturing and utilisation behaviours has been inferred. The data gained from these analyses allow for widespread behavioural comparison between the SBLS, overlaying KRM layers, and other sites. Broadly speaking, the assemblage shares several technological signatures with the MSA I/Klasies River technology previously identified at KRM and on a technological attribute level widespread similarities are shared with several MIS 5 assemblages in South Africa; an example of this is the widespread use of locally available raw materials. There is, however, a key behavioural inference which clearly indicates that the SBLS is different to other assemblages both at KRM and in the broader MIS 5. The SBLS points and their TCSA values point towards significantly smaller points. This supports a different and varied hunting approach which is unique to KRM during this period at KRM