- ItemE-waste management, practices, knowledge, and behaviour: a case study of Stellenbosch University(2023-03) Jefthas, Tammy Lee; Williams, Samantha; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.ENGLISH ABSTRACT: Electronics and electrical equipment (EEE) have revolutionised modern life since the early 1990s and have penetrated every aspect of our lives. The rise of disposable income and urbanisation, coupled with advances in technology and shorter product lifespans, have resulted in higher consumption of EEE (Forti et al. 2020). Once these devices reach their end-of-life (EOL), they generate a waste stream called e-waste. Considering its hazardous nature and rapid growth in global quantities, e-waste poses a serious environmental threat worldwide. The global quantity of e-waste in 2019 was 53.6 million metric tons (Mt), equivalent to 350 cruise ships in weight. Global e-waste production is expected to reach 74.7 Mt by 2030, making it the fastest-growing domestic waste stream in the world. If it is not managed and disposed of correctly, e-waste can negatively impact the environment and human health. Consumers of EEE play an important role in reducing the rising global quantities and negative impacts of this waste stream. Only by educating and empowering consumers about e-waste and responsible disposal, can e-waste be effectively managed. Through a case study of Stellenbosch University in South Africa, this study presents a qualitative method for investigating and documenting e-waste management, knowledge, awareness, and practices. Structured interviews and online questionnaires were used to collect data for this study. This study reveals that Stellenbosch University has the potential to generate a large amount of e-waste, generating 6 678 kg in 2019, 2 714 kg in 2020, 4 847 kg in 2021 and 7 599 kg in 2022. Two e-waste management strategies implemented have been identified that focus on recycling, i.e., The Non-Asset Registered E-waste (recycling of e-waste generated by the University community) and The Stellenbosch Asset E-waste (recycling e-waste generated by the University). The results show low e-waste practices which were linked to low awareness of e-waste recycling programmes and facilities on campus. Furthermore, the survey population had a general understanding of what e-waste is and recognised that a global e-waste problem exists, however, they had limited knowledge about the hazardous materials found in e-waste and how this impacts the environment and human health. Mobile phones were the most frequently used electronic devices among the survey population, with an average possession lifespan of 2.3 years, and obsolete hardware and software were the most common reason for replacing them. As a result of a lack of information about e-waste recycling facilities, personal storage was identified as the preferred method for disposing of waste mobile phones (WMP). There was, however, a positive attitude towards e-waste recycling among the survey population as they were very willing to recycle their e-waste on campus if more information was provided. The results reveal that more awareness is needed among the survey population, particularly about hazardous materials found in e-waste and the e-waste recycling programme on campus. Through information and education, Stellenbosch University can contribute to increasing e-waste awareness, which may encourage the University community to recycle their e-waste more.
- ItemImproving the generalisabiility of a deep learning model for global forest classification through image normalisation, enhancement and augmentation(Stellenbosch : Stellenbosch University, 2022-12) Swaine, Michael; Munch, Zahn; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.ENGLISH ABSTRACT: Effectively managing global forest resources, under threat from climate change, deforestation and fragmentation, requires the efficient extraction of a global tree cover dataset. The purpose of this research was to identify image enhancement and data augmentation methods that would improve the generalisability of a deep learning model for the classification of global tree cover. In the first experiment we aimed to improve the accuracy of a deep learning model for global forest classification using Sentinel 2 optical data. We present several image enhancement methods widely used in natural image classification and biomedical imaging domains, including histogram equalisation (HE), contrast limited adaptive histogram equalisation (CLAHE) and global contrast normalisation (GCN), as pre-processing steps. The enhancement methods were compared with each other on a per biome basis, and both training and validation regions were selected to represent the heterogeneity within biomes. Selected images were captured within the local optimal foliage growing season and contained minimal or no clouds. A U-Net convolutional neural network model was trained for each enhancement per biome and used to perform inference on validation images for each of the corresponding biomes and enhancements. Random stratified samples were collated for all validation images per biome per enhancement for statistical analysis. Only GCN and CLAHE RGB returned higher means than the baseline dataset. The results showed that GCN most consistently improved classification results for tree cover across biomes, possibly due to the standardization of contrast levels of the training and validation images. In the absence of accurately annotated training data for tree segmentation, training a robust, deep learning model for global tree cover classification remains a challenge. As its first objective, experiment 2 evaluated basic data augmentation methods and prediction frameworks that might lead to achieving an accurate, global tree cover classification. A training dataset was artificially inflated using common geometric and colour data augmentation methods borrowed from the computer vision domain. Their effectiveness in improving the generalisability of a U-Net model for tree classification was tested. Both geometric and colour augmentations, when applied individually, showed improvements in model accuracy. When applied together, the combined augmentations showed only marginal improvements over the individually applied augmentations. The second objective was to test two approaches towards achieving a global tree classification. The first was a model per biome approach, whereby a model was trained with data derived only from the respective biome. The second involved training a single globally representative model with training data from all biomes combined. This resulted in higher MCC scores than the multi-model approach. The diversity in training data appeared to increase model robustness. Thus, it was found that training a single, globally representative model with a combination of colour and geometric augmentations led to an effective framework to infer a global tree classification
- ItemGeospatial modelling of relationships between select recreational user groups' social values and ecosystem services in the Cape Peninsula of South Africa.(Stellenbosch : Stellenbosch University, 2023-03) Tonkin, Curtley Wayne; Williams, Samantha; Mashimbye, Eric; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.ENGLISH ABSTRACT: Integrative ecosystem service (ES) assessments are crucial to completely assess the benefits of ES and to evaluate synergies and trade-offs among ES. Numerous ES studies have investigated biophysical ES assessments and economic valuation, although social values (SVs) remain under-represented. Integrated modelling of SV maps and biophysically modelled services (BpSs) provide an integrated approach to incorporating SV into ES assessments, through social-ecological hotspot mapping of ES and regression analysis. This study aimed to investigate the relationships between recreational users’ social values and ecosystem services in the Cape Peninsula of the Western Cape province in South Africa. The following four objectives were set to achieve the overall aim of the study: 1) review literature to determine the current discourses and state of research on ES determination; 2) investigate the types and spatial distribution of social values linked to ecosystems in the Cape Peninsula using a participatory mapping exercise; 3) evaluate and quantify the spatial distribution of biophysically modelled services in the Cape Peninsula and 4) investigate the relationships of social values and distribution of biophysical services within the Cape Peninsula. Social values for Ecosystem services (SolVES) was used to model 11 SV for the Cape Peninsula based on questionnaire results. The Integrated Valuation of Ecosystem Services and Tradeoffs (InVEST) tool was used to model four BpSs based on geospatial biophysical data. A hotspot analysis on cumulative SV and BpS layers was conducted using the Getis-Ord Gi* statistic, to produce hotspot and coldspot maps of SVs and BpS. A regression analysis using the Ordinary Least Squares (OLS) tool was done to determine the relationships between SVs and BpSs These findings of the study provided areas of potential trade-offs conflict where there is a disconnect between SVs and BpSs, and where SVs and BpSs overlap, but are possibly not complementary. The study also highlighted potential areas (where SVs and BpSs values overlap) for stakeholder engagement in ES conservation. The weak relationship between biological diversity and habitat quality indicated limited respondents’ recognition of habitat quality. These findings can be incorporated within the management plans of conservation decision-makers such as South African National Parks (SANParks) to improve sustainable and inclusive ES conservation and planning, and to ensure SVs are included in ES assessments for the Cape Peninsula.
- ItemApplying remote sensing and spatial analysis to investigateregime shifts in the Albany Thicket of the Baviaanskloof(Stellenbosch : Stellenbosch University, 2023-03) Matowanyika, Danai; De Klerk, Helen; Maciejewski, Kristine; Biggs, Reinette, 1979-; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.ENGLISH ABSTRACT: Human activities have greatly altered the environment, the scale of impact has led to a new geological epoch - the Anthropocene. Of particular concern in this new era is the possibility for regime shifts: large, persistent changes in the structure and function of ecosystems that can have substantial impacts on human well-being and livelihoods. The Baviaanskloof is a semi-arid ecosystem in Southern Africa that has been substantively transformed by human activities. The Baviaanskloof, is located in the unique Thicket biome, characterized by dense spekboom thicket vegetation (Portulacaria Afra). Remote sensing and geoinformatics tools were used to investigate possible regime shifts in the Baviaanskloof, with a focus on regions classified as Baviaanskloof Thicket biome. The regime shift being investigated is the potential shift from an intact Thicket to a pseudo-Savanna Thicket regime. The thesis combined remotely sensed Enhanced Vegetation Index (EVI) from the MODIS platform as a surrogate of biomass and rainfall data from local weather stations over the period 2000 – 2018, complemented with observations from a field visit in Baviaanskloof Heartland area (BHB) in 2018. The BHB has been an active site for ongoing spekboom rehabilitation initiatives. Analytical tools, Break Detection for Additive Seasonal and Trend (BFAST), Sequential t-Test Analysis for Regime Shifts (STARS) and Green-Brown were used to investigate potential regime shifts. Different tools analysed different aspects of biomass changes in the Baviaanskloof, generally showing persistent decreases. Green-Brown showed that majority of the Baviaanskloof did not experience significant annual changes, but experienced significant seasonal changes. Most of the significant changes were decreases in biomass, with 58% of the Bavianskloof experiencing seasonal decreasing trend.Within the Thicket biome specifically, 77% of the area experienced significant biomass decreases. Majority of the changes occurred in the more arid vegetation classes (Groot Arid Spekboomveld), a possible early warning indicator of what lies ahead for the biome. BFAST and STARS detected two major breaks points in the Thicket biome in 2004 (2001 for STARS) and 2011. BFAST showed a decreasing trend of biomass in between and after both break points. STARS also identified a period of relatively low biomass between 2001 and 2011, a period of relatively higher biomass between 2011 and 2016, and a drastic drop in biomass after 2016 in the Thicket biome. The magnitude of the second break points is bigger than that of the first break points for both models. Spatial mapping of breakpoints showed majority of the Thicket biome (87.91%) experienced at least two break points, mostly negative break points experienced in the drier Groot Arid Spekboomveld regions. The thesis also anaylsed the effect of rehabilitation efforts at specific sites within the BHB. The BHB spekboom plantings of 2010-2013 initially showed an increase in biomass, followed by a decreasing trend. This variations in biomass patterns show that the Baviaanskloof is a complex system with spatial and temporal dynamism. The geoinformatics and analytical tools used in this study provided meaningful insights into investigating possible regime shifts in the Baviaanskloof, and was able to analyse these spatially and detect significant break points. This points to the potential for these tools in detecting regime shifts.
- ItemCollaborative governance, social capital, and drought: a case study of Graaff-Reinet(2023-03) Light, Rebecca Ann; Donaldson, Ronnie; Stellenbosch University. Faculty of Arts and Social Sciences. Dept. of Geography and Environmental Studies.ENGLISH ABSTRACT: In the Eastern Cape Province lies the small, historical town of Graaff-Reinet: a resource-dependent community that has been experiencing the negative effects of a drought since 2015. Local disaster response has come predominantly from within the community in the form of forum developments and collective action, all made possible using the resource of existing networks between groups, individuals, and forums. This thesis examines the relationship between this resource (known as social capital), collective action and disaster resilience in small towns in order to better understand how social capital can be used in sustainable disaster management of recurring, slow-onset disasters. The paper makes use of an evaluation design method as complex issues with fundamental contextual elements are examined, gathering qualitative data in the form of snowball interviews with key community and forum leaders involved in relevant collective action and forum developments. A diagram model of the links between community forums and public entities was designed to show where examples of bridging, bonding, and linking social capital take place, followed by a discussion of which examples of collective action were effective and how they made use of these connections. The results show that in the event of a disaster where the livelihoods of a community are impacted, civil society can make use of existing trust, networks, and norms to manage these impacts, despite existing in a heterogeneous community. Additionally, the forums and groups founded responding to such events can be used to improve the long-term socio-economic standing of these communities.