Electronic Theses and Dissertations (PhDs)
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Item Constructed Wetlands: Additional heterogeneous configurations on existing infrastructures in Silvertown, Alexandra Township, Johannesburg(University of the Witwatersrand, Johannesburg, 2024-09) Sepeng, Motshwaedi Collen; Thatcher, AndrewGreywater disposal is a service issue that has yet to be addressed fully in urban informal settlements of South Africa. Greywater exposure poses great health dangers if this wastewater is disposed of incorrectly. Silvertown proved to lack infrastructure for greywater disposal and showed prevailing evidence of physical exposure to disposed greywater. There are no formally defined manners of greywater disposal in Silvertown. Therefore, there is a need for infrastructural development to support safe greywater collection, disposal, and possible treatment. Heterogeneous Infrastructure Configuration (HIC) scholars have pointed out that empirical work still needs to be done to validate the theoretical arguments that infrastructural growth in the Global South does not emphasise uniformity and standardisation. To address the greywater disposal problem, constructed wetlands (CWs) were implemented in Silvertown (a section of an informal settlement) as a proposed solution for greywater disposal. This study investigated how the CWs integrated with existing infrastructure over three years. A participative, iterative design process was implemented to help create links between the residents and the existing heterogeneous infrastructure. In this research, HIC was used as a theoretical framework to help understand the dynamics that shape the engagement with the CWs by the residents of Silvertown and integration with existing infrastructure. Notably, in the absence of the provision of infrastructure and services, the people of Silvertown have actively constructed the environment around them to provide the required services and infrastructure. It was also evident that “people as infrastructure”, as Simone (2004) phrases it, emerged as a prominent process in the provision of services. A qualitative study which included semi-structured interviews, unstructured interviews, and observations collected over three years was used to collect data from Silvertown. The research findings showed that CWs made significant progress beyond just offering a solution for greywater disposal and the controlled/treated release of contaminated water through integration with related infrastructure and services. However, it was also noted that the effectiveness of the CWs was hindered by challenges such as vandalism and negligence. A collective effort was needed from the service providers and the residents to ensure the functionality and sustainability of planned infrastructures such as CWs in Silvertown.Item Multitemporal analysis of land cover and evaluation of landscape influences on vegetation dynamics using remote sensing data and machine learning in a karst environment : A case study of the Cradle Nature Reserve(University of the Witwatersrand, Johannesburg, 2024-10) Matyukira, Charles; Mhangara, PaidaMonitoring vegetation dynamics and ecosystem processes is crucial for understanding and managing natural environments, particularly in sensitive regions such as protected heritage sites in karst landscapes prone to land degradation. Geospatial science and technology, especially remote sensing, have revolutionised environmental monitoring by providing unparalleled insights into complex ecological processes and land cover dynamics. Despite their immense potential, these advanced technologies remain underutilised in sensitive ecological archaeological sites, particularly within South African contexts. The Cradle of Humankind, a protected World Heritage site, is confronting a myriad of environmental problems, such as land degradation, invasion by alien plants, bush thickening, loss of native vegetation cover, increased soil erosion, and habitat loss, threatening its ecological integrity, biodiversity and archaeological significance. Furthermore, there is a notable paucity of research focusing on land degradation and land cover changes in protected heritage sites in Africa and other parts of the world, even though geospatial technologies capable of accurately mapping and tracking these changes have proliferated. Addressing this research gap is critical for developing effective conservation strategies and ensuring the preservation of sensitive ecological and archaeological areas. This study aims to demonstrate the significant value of integrating geospatial technology and machine learning in environmental monitoring by identifying and addressing research gaps in the application of these technologies, particularly remote sensing, for vegetation mapping. Through a detailed case study of the Cradle Nature Reserve in South Africa, the study employs novel machine learning classification algorithms and advanced geospatial analytical methods to assess land cover change, landscape fragmentation, multitemporal analysis of vegetation trends, topographic influences on vegetation vigour, biomass estimation, and impacts of land cover change on evapotranspiration using multisource satellite, drone, and field geospatial datasets. The study utilised a systematic review and scientometric analysis using the Scopus database to investigate the application and trends of geospatial technologies in archaeology and cultural heritage in South Africa from 1990 to 2022, employing VOSviewer for visualising bibliometric data. Additionally, the study employed a systematic review and scientometric analysis using the Scopus database to examine the growth and trends in the application of remote sensing and machine learning techniques for vegetation mapping from 2000 to 2024, using VOSviewer for visualising relationships and trends in the collected data. The research also utilised Google Earth Engine (GEE) to download and preprocess satellite imagery, employing the XGBoost and Naïve Bayes classifiers for land cover classification and accuracy assessment, followed by applying landscape metrics for fragmentation analysis using QGIS. Further, Sentinel-2 multispectral imagery was processed using QGIS and ArcGIS software to compute the Enhanced Vegetation Index (EVI) and analyse vegetation dynamics through Principal Component Analysis (PCA) and multilinear regression, examining the relationships between EVI and climatic factors such as rainfall, temperature, and soil moisture. The study also utilised RGB drone imagery and various vegetation indices (BGVI, ExG, GBVI, GRBI, NGRDI, RBVI) combined with nDSM and chlorophyll concentration measurements to estimate above ground biomass (AGB) in riparian and non-riparian zones of the Cradle Nature Reserve. High-resolution satellite imagery from Sentinel-2A and the Shuttle Radar Topography Mission Digital Elevation Model (SRTM DEM) was used to calculate the Enhanced Vegetation Index (EVI) and various topographic indices such as the Topographic Position Index (TPI), Topographic Ruggedness Index (TRI), and Topographic Wetness Index (TWI), with the data analysed using QGIS and ArcGIS software to model the relationship between topography and vegetation vigour in the Cradle Nature Reserve. Finally, the study employed MODIS data and XGBoost classification to analyse the impacts of land use change on annual evapotranspiration (ET) in the Cradle Nature Reserve from 2000 to 2023, incorporating statistical analysis and regression models to explore the relationship between ET and different land cover types. The research identified significant gaps in the application of advanced geospatial technologies, noting that only 12% of global research output is attributed to South African studies despite an increase in publications from 2018 to 2022. Key research gaps identified include the limited use of virtual and augmented reality applications, aerial photography, optical radar, and UAVs, which remain underexplored and present opportunities for future research. The analysis revealed a significant increase in research publications, particularly from 2018 to 2023, with the number of articles rising from 6 in 2018 to 48 in 2023, highlighting the critical role of advanced machine learning algorithms and remote sensing technologies in enhancing vegetation mapping accuracy and depth. China led in research output, followed by the United States and India. However, we identified significant research gaps, such as the need for more comprehensive studies on evapotranspiration and above-ground biomass estimation and the integration of climate change data with remote sensing technologies. The study showed that between 1990 and 2020, the Cradle Nature Reserve's landscape became much more fragmented. There was a 39% rise in bare ground or rock outcrop and a 26% and 12% decrease in native forest and natural grassland, respectively. This was mostly caused by human activities and the introduction of alien plant species. The analysis revealed that the EVI exhibited strong positive correlations with rainfall (r = 0.71), temperature (r = 0.62), and soil moisture (r = 0.76), highlighting the significant influence of these climatic factors on vegetation health. The PCA revealed that the first two principal components accounted for 90.08% of the data variability, underscoring the combined impact of these environmental factors on vegetation vigour. The riparian vegetation model had an R² value of 0.42 (adjusted R² = 0.34), which means that chlorophyll and BGVI were important factors. Conversely, the non-riparian model had a higher R-value of 0.66 (adjusted R² = 0.64), which meant that nDSM and RBVI were important predictors. This shows how important vegetation height, and stress indicators are in estimating biomass. The analysis revealed strong correlations between EVI and topographic indices, with EVI showing a significant negative correlation with TPI (R² = 0.95) and TRI (R² = 0.94) and a strong positive correlation with percentage slope gradient (R² = 0.85), highlighting how terrain features like sinkholes and depressions impact vegetation health. The research identified significant ecological changes in the Cradle Nature Reserve, with ET values showing substantial fluctuations across different land cover types. Notably, the mean ET increased from 47.115 mm in 2000 to 57.316 mm in 2020, reflecting overall improvement in vegetation health and water availability, while the Indigenous Forest area saw an increase in ET from 45.59 mm in 2000 to 59.89 mm in 2020, underscoring the impact of land use changes and climatic factors on the reserve's ecosystem. This study equips researchers and conservationists focused on the Cradle Nature Reserve with advanced tools to enhance the precision of monitoring ecosystem changes and detecting land degradation. These insights facilitate targeted conservation efforts and informed management strategies tailored to the reserve's unique ecological needs, contributing significantly to the global understanding of sustainable land management and conservation practices.Item Modelling to determine optimal-water availability scenarios under drought conditions in agricultural environments: A study of the Western Cape, South Afica(University of the Witwatersrand, Johannesburg, 2024) Ngwenya, Mthulisi; Simatele, Mulala DannyProlonged drought events have created persistent water shortages, and this has led to fierce competition amongst water users and a decline in agricultural production in agroecological zones (AEZs) of the Western Cape (WC) Province in South Africa. A common strategy to manage drought and the associated water shortages is based on crisis management (ex−ante). However, a risk-based approach seems to be in line with proactive and optimal water resources management. Therefore, considering the challenges and opportunities in the context of sub-Saharan Africa's policies and frameworks, how can drought risk management be most effectively and sustainably implemented the through monitoring and vulnerability risk assessment considering the case study of the agricultural environments in semi-arid areas. In this regard, the overarching aim of this study was to quantify and analyse the impacts of meteorological drought on sustainable water availability in the AEZs of the WC during the between from 1980 and 2050. This study used two forms of datasets: observational data and model simulations. Observed precipitation data (1980–2020) were gathered from the University of East Anglia’s Climate Research Unit gridded Time Series (CRU TS Version 4.07) https://www.cru.uea.ac.uk/cru/data/hrg database using Google Earth engine interface, while future precipitation datasets (2021–2050) under SSP5–8.5, SSP2–4.5 and SSP1–2.6 climate scenarios were generated by the sixth version of Model for Interdisciplinary Research on Climate, (MIROC6) https://esgf–node.llnl.gov/search/cmip6/ database. These datasets were processed using the Standardized Precipitation Index (SPI), Reliance (Rel), Resilience (Res) and Vulnerability (Vul) indicators and Sustainability Index (SI). The Mann-Kendall (M-K) test was used for trend analysis at a significance level of p < 0.05. The results revealed that under the influence of climate change there will be a decrease in expected precipitation between 2021 and 2050 across all districts, with the greatest decrease expected in sub-tropical cool sub humid zone (STCSH). The average Rel index across the study area in observed data was calculated to be 0.53, which changed to 0.49 in the SSP2−4.5 scenario, a decrease of 1.43%. There was no change in average Res index across the study area for both base period and under climate change scenario, calculated to be 0.42. The average Vul index across the study area in the base period was calculated to be 0.47, which changed to 0.51 under the influence of climate change, an increase of 7.08%. The study findings indicated a progressive reduction in water availability across climate change scenarios with SSP5–8.5 indicating statistically significant negative trends while both the SSP2–4.5 and SSP1–2.6 indicated mostly positive insignificant precipitation trends in the near term (2021–2050). The results further indicate that in arid climates, future SI, exhibited moderate water shortage (0.60< SI <0.80), while in semiarid climates future SI values, exhibited mild water shortages (0.62< SI <0.71); while in the humid climates future SI values projected normal to above normal water availability condition (0.52 < SI <0.58). Trend analysis results indicated non-insignificant positive vulnerability trends and significant negative trends for both reliability and resilience indicator. Overall, the results suggest that between 2021 and 2050 drought conditions will aggravate water scarcity in AEZs compared to observed drought condition (1980-2020). The results of this study can aid policymakers in mitigating drought related water scarcity and foster drought resilient communities.Item Assessing the Impacts of Urbanisation on Land Use Change in Zambia: A Study of Lusaka Urban District(University of the Witwatersrand, Johannesburg, 2024-10) Simooya, Steriah Monica; Kubanza, Nzalalemba Serge; Simatele, Mulala DannyUrbanisation is a multifaceted, transformative process and a significant global trend that has impacted societies, economies and the biophysical environment. The process of urbanisation results in various challenges as it comes with profound positive and negative effects especially for developing countries. Most countries face insurmountable urbanisation challenges as their governance processes, systems and institutions are ideally not designed to deal and cope with urbanisation processes. Lusaka urban district has been urbanising at a fast pace and, just like many developing cities in Sub-Saharan Africa, has faced various challenges. Urbanisation in Lusaka has led to shifts in urban land use, consequently posing both challenges and opportunities to urban residents. Hence, this study was an assessment of the impacts of urbanisation on land use change in Zambia, a study that was conducted in Lusaka urban district. The aim of the study was to assess the impacts of urbanisation and land use change on the urban poor and vulnerable people in Lusaka whose livelihoods have been historically dependent on land. The study further sought to establish how the urban poor and vulnerable people negotiate their rights to the city in socially and economically productive ways considering the government’s policy on the economic growth and development of the city. This study was guided by pragmatism, which is concerned with what works in solving the problem and, the solution to the problem. Pragmatism posits that the nature of knowledge is not static while knowledge generation is achieved using various methods. Mixed methods research approach was used to assess the impacts of urbanisation and land use change on urban residents in Lusaka district. Both qualitative and quantitative methods were used to collect, analyse, and interpret the study findings simultaneously. Remote sensing (from 1990-2020 for selected areas of Lusaka urban), document analysis, questionnaires, and semi-structured interviews were used as data collection tools. Probability sampling was used to come up with households while non-probability sampling was used for key respondents. A total of 922 households were drawn from the selected residential areas and 12 key respondents from ministries and agencies, Non-Governmental and Civil Society Organisations. Qualitative data were analysed using themes and regular patterns derived from the study’s naturally occurring and emerging themes to derive meaning and interpretation expressed using words and not numbers. To generate frequencies and percentages, quantitative data were analysed using excel and the Statistical Package for Social Sciences (SPSS). Remote sensed imagery was analysed using ArcGIS 10.5. Documents such as maps, and policy documents were analysed for interpretation and meaning as they provided information on land use trends, management, and the regulations guiding the use and management of land in Zambia. Theoretically, the study employed the Hoyt Sector model of urban growth and expansion to explain the outward expansion of Lusaka district along the major transportation arteries of the city. The Hoyt sector model explains land use patterns from independence (1964) up to recent times. Lusaka’s initial development was along the major transportation artery, the British South African railway line and later, the major roads of the city. The Hoyt model also helps in explaining the location of residential areas and why industries are found in defined areas plus the role of the city’s major transportation arteries. The critical urban theory was used to explain the rapid urbanisation of Lusaka city, the emerging shifts in urban land use, and the resultant impacts on urban citizens and their livelihoods. This theory does not conform to mainstream urbanisation theory that explains urbanisation in relation to urban population growth. It emphasises that urbanisation is multifaceted and dynamic, a continuous construction of urban knowledge made up of political, cultural, historical, environmental and economic organisation of any given city. Most importantly, this theory advocates for understanding and explaining of urbanisation in socially inclusive, sustainable and democratic ways. The study findings revealed that Lusaka’s urbanisation has been characterised by the expansion of the built-up area at the expense of other land use and land cover classes. This has resulted in the mushrooming and expansion of informal settlements, diminishing agricultural land, the conversion of grass, crop, and bare land into mixed urban land uses particularly settlements and commercial use. The changes in urban land use are driven by urban population growth, economic growth and development policies and processes, rural-urban migration and the consumerism behaviour characterising most urban residents. The findings further indicate that urbanisation has brought about opportunities and challenges for urban residents. Urbanisation has come with various economic opportunities such as the creation and improved access to various goods and services, employment opportunities, the global exchange and fusion of ideas, cultures, food, and entertainment. Negatively, urbanisation has exacerbated corruption, social injustice and inequality consequently affecting the urban poor who have historically depended on land for agriculture and livelihoods. It has also created the urban divide in urban areas where Lusaka is now composed of the haves and have not, the poor and affluent, informal and overcrowded settlements, and gated communities. Various forms of pollution are now rampant, there’s widespread environmental degradation resulting in environmental ills such as deforestation, climate change, and shortage of resources. These have presented insurmountable challenges for the achievement of sustainable urban development. Furthermore, the diminishing agricultural land is a huge challenge impacting urban food security and urban livelihoods. This is further making it difficult to achieve Sustainable Development Goals particularly SDG no.11 on sustainable and inclusive cities and Africa’s Agenda 2063. The study concludes that significant changes in land use have occurred to urban land in the Lusaka district attributed mainly to urbanisation processes and urban population increase. The changes have mainly been from bare, crop, and grassland to built-up for settlements and commercial purposes, and various ecosystem goods and services have been lost in the process. This has greatly affected the urban poor and vulnerable whose livelihoods depended on agriculture and as such, are struggling to cope with the developments. The study concludes that human settlements are a key driver of urban land use change in Lusaka district. The study recommends that policy formulation, implementation, monitoring, and evaluation be prioritised to sustainably develop the district and manage its land use. The study also recommends the need to involve all stakeholders in the entire process so that policies reflect their various needs. All these challenges pose as infringements to urban livelihoods that are particularly felt by the urban poor and vulnerable people living in Lusaka urban district. The study contributes to the body of knowledge by providing insights into the impacts of urbanisation, land use change and management, urban population growth, urban food security, and urban livelihoods. These are all prerequisites to the achievement of SDGs particularly no.11 on sustainable cities and Africa’s Agenda 2063, the blueprint for the continent’s sustainable development. The study will provide insights that will help policy and decision makers and all concerned stakeholders in the re-planning of land use change in Lusaka district to allocate resources to where they are most needed. The study will help policy and decision makers to come up with environmentally sustainable land use and management policies that do not degrade the environment, expose and leave urban livelihoods vulnerable particularly the urban poor and vulnerable groups not just in Lusaka but in other Sub-Saharan African cities with similar but complex urban spatial landscapes.Item Mapping and monitoring the impacts of climate variability on rainfed agriculture in Semi-arid North Darfur, Sudan(University of the Witwatersrand, Johannesburg, 2024-02) Altoom, Mohammed Bashar Adam; Adam, ElhadiRainfed agriculture is vital to food security and income in most parts of the world. However, one-third of the population of developing countries population lives in the less favoured rainfed agricultural regions. Around 75-82% of the total cropland areas in the world are under rainfed agriculture and produce more than 60% of the globe’s cereal grains. However, rainfed agriculture is most prominent in some regions of Africa, such as Sub-Saharan Africa, where more than 95% of the cropland is rainfed. This crucial agriculture sector usually depends on the physical environment and, most importantly, the variability and distribution of rainfall. Therefore, rainfed farming is vulnerable to climate-related hazards, and the crop yield is unreliable and difficult to predict. For instance, the spatio-temporal variability of precipitation extreme events often subjects crops to short-term water deficits, causing crop losses. Sudan heavily depends on rainfed agriculture—about 90% of arable land dominates rainfed cultivation, contributing one-third of the country’s gross domestic product (GDP). Rainfed agriculture is the primary source of livelihood for 65% of the population. Unfortunately, agriculture in North Darfur of the west Sudan is characterised by environmental hazards, e.g., frequent droughts and unpredictable low, poorly distributed, and highly variable monthly/seasonal rainfall. Therefore, using various Earth observation data, this study aimed to monitor the impacts of rainfall variability on rainfed agriculture in North Darfur State in Sudan. Firstly, the study aimed to determine the feasibility of estimating rainfall variability across North Darfur State at daily, monthly and annual timescales using six satellite precipitation products (SPPs), i.e., the Tropical Rainfall Measuring Mission (TRMM) Multi-satellite Precipitation Analysis (TMPA), African Rainfall Climatology (ARC), and Climate Hazards Group Infrared Precipitation with Station Data (CHIRPS) were evaluated using four categorical indices, i.e., probability of detection (POD), probability of false alarm (POFA), bias in detection (BID) and Heidke skill score (HSS), and four continuous indices, i.e., Pearson correlation coefficient (r), root mean square error (RMSE), per cent bias (Pbias), and Nash-Sutcliffe model efficiency coefficient (NSE) against ground rain-gauge observations. The other SPPs were Integrated Multi-satellitE Retrievals for Global Precipitation Measurements (GPM) Final Run (GPMIMERG), Precipitation Estimation from Remote Sensing Information using Artificial Neural Networks-Climate Data Record (PERSIANN-CDR), and the Tropical Applications of Meteorology using SATellite and ground-based observations (TAMSAT). Results of the statistical analysis demonstrated that 1) at the daily timescale, the SPPs underestimate daily rainfall by 6.53–17.61%, and CHIRPS was the best for detecting rainy days, while PERSIANN-CDR performed poorly; 2) monthly and annual scales performed better than daily timescale, and TAMSAT and CHIRPS portrayed better performance than the ther SPPs. Secondly, the study assessed the capability of optical Earth Observation Data (EOD), i.e., Sentinel-2 multispectral dataset, to map crop types in the heterogeneous semi-arid environment of North Darfur using machine learning classifiers in Google Earth Engine (GEE) platform. Five datasets were compared against random forest (RF) and support vector machine (SVM) classification algorithms: (1) 10 Sentinel-2 bands (comprising visible, near-infrared and shortwave infrared bands), (2) Sentinel-2 (10 bands) + 8 vegetation indices, (3) visible bands and near-infrared bands only, (4) visible and shortwave infrared bands only, and (5) 8 vegetation indices. The eight vegetation indices were normalised difference vegetation index (NDVI), enhanced vegetation index (EVI), soil-adjusted vegetation index (SAVI), green normalised difference vegetation index (GNDVI, weighted difference vegetation index (WDVI), red edge NDVI (NDVIre), ratio-vegetation index (RVI) and normalised difference infrared index (NDII). Results showed that the RF algorithm produced the highest classification overall accuracy (OA), i.e., 97% and Kappa coefficient (κ), 0.96, using 10 Sentinel-2 bands dataset. Producer’s (PA) and user’s accuracies (UA) were in the range of 40-97% and 40-100%, respectively. Thirdly, the spatiotemporal trend of drought events and their impact on millet production in North Darfur from 1981 to 2020 was analyzed using standardized precipitation index (SPI) and reconnaissance drought index (RDI) by employing different timescales, i.e., 3- month (June-August), 6-month (June-November), and 9-month (June-February) timescales. Drought-yield relationships were assessed using Pearson correlation coefficients (r). Results indicated that RDI is more sensitive to rainfall variabilities than SPI in detecting drought trends. Results revealed that drought events affected North Darfur over broad spatial extents, particularly in 1989, 1990, 1992, 1999, and 2001—an extreme drought event was in 2003. Correlation analysis between the SPI and RDI and the standardized variable of crop yield (SVCY) for millet grain yield showed a strong agreement between them. Moderate to extreme reductions in millet crop yield occurred in 1992, 1999, 2001, and 2003, corresponding to the moderate to extreme drought indicated by RDI. Severe crop losses were in Kabkabiya and Umm Kadadda. Fourthly, this study aimed to map and monitor spatio-temporal dynamics of rainfed agriculture in North Darfur State from 1984 to 2019 using multitemporal Landsat observation data using random forest (RF) classification algorithm. Overall, Landsat Operational Landsat Imageries (OLI) outperformed Landsat Multispectral Scanner (MSS), Landsat Thematic Mapper (TM), Landsat Enhanced Thematic Mapper Plus (ETM+) in monitoring change in agricultural land and other land use land cover (LULC) classes. Overall accuracies ranged between 94.7% and 96.9%, while kappa statistics were greater than 0.90. Results showed that Goz land used for rainfed agriculture increased by 889,622.46 ha between 1994 and 999, while it decreased by 658,568.61 ha between 2004 and 2009. Rainfed cultivation of wadi lands expanded significantly by 580,515.03 ha over the 2014–2019 period and decreased by 182,701.8 ha over the 1994–1999 period. Overall, this study enhances the understanding of spatio-temporal rainfall patterns and current drought trends, aiding in developing more effective policies and resource management strategies. Additionally, it offers crucial spatial data that is currently scarce due to ongoing conflicts, empowering decision-makers to establish sustainable land use monitoring systems. The methodologies used in this study have proved successful in mapping crop types in a fragmented highly heterogeneous fine agricultural semi-arid landscape; such mapping approaches can be applied in other environments with similar characteristics.Item Capability of multi-remote sensing satellite data in detecting and monitoring cyanobacteria and algal blooms in the Vaal dam, South Africa(University of the Witwatersrand, Johannesburg, 2024-03) Obaid, Altayeb Adam Alsafi; Adam, Elhadi M.I.; Ali, Khalid A.Vaal Dam is a large dam in South Africa. It is the primary source of potable water for the metropolitan and industrial areas of Gauteng province and other surrounding areas. The dam's surface area is about 320 km². It’s the second biggest dam in South Africa in terms of surface area, and it drains a catchment area of approximately 38,000 km². The dam's total capacity is about 2.603 × 10⁶ m³ (Haarhoff and Tempelhoff, 2007). The dam catchment area holds various anthropogenic activities, including major agricultural activities, mining, and some industrial activities (Obaid et al., 2023, Du Plessis, 2017), as well as many formal and informal settlements. The dam water is strongly affected by such activities, releasing chemical, physical, and biological contaminants and dissolved urban effluents, most of which enrich the nutrients that reach the dam water in some way. Water resources assessment and monitoring are crucial practices due to their direct contribution to the effective use of such resources. They require precise information about the water quantity and quality. Monitoring of inland water resources has been conducted using in-situ sampling and in-vitro measurement of the water quality constituents. However, these methods have limitations such as high cost, labor-intensive limited spatial and temporal coverage, and time consumption. Over the last few years, remote sensing has been examined for water quality monitoring as a cost- effective system. This research has tested satellite remote sensing to detect some water quality parameters in the Vaal Dam of South Africa. The main objective of this research is to examine the recent generation multispectral satellite sensors, Sentinel-2 MSI, and Landsat-8 OLI data to detect and assess chlorophyll-a and cyanobacteria in the Vaal Dam, South Africa to be used as a cost-effective monitoring tool. To achieve the objective, the research first aimed to understand how the spatial and temporal dynamics of land use, and land cover (LULC) impact algal growth in the dam reservoir. Land use land cover classification was conducted in the catchment area of the Vaal Dam using a pixel-based classification method. Landsat data for the period from 1986 to 2021 were classified using a random forest (RF) classifier in seven-year intervals (1986, 1993, 2000, 2007, 2014, and 2021). Applying the RF classifier revealed that overall classification accuracies (OA) ranged from 87% in the 2014 classified image to 95% in the 2007 image. The change-detection analysis revealed the continuous increase of the settlement class owing to the continuous population growth. A lot of anthropogenic activities associated with population growth have been recognized to release contaminants into the surrounding environment and might end up reaching the water resources causing significant deterioration. As a result, Vaal Dam encounters significant nutrient input from multiple sources within its catchment. This situation raised the frequency of the Harmful Algal Blooms (HABs) within the dam reservoir during recent years. The study also performed a time series analysis for the potential nutrients expected to be the enhancing factors for algal blooms in the Vaal Dam. Using chlorophyll−a (Chl−a) as a proxy of HABs, along with the concentrations of potential nutrients, statistical measures, and water quality data were applied to understand the trend of selected water quality parameters. These parameters were: Chl−a, total phosphorus (TP), nitrate and nitrite nitrogen NO₃NO₂_N), organic nitrogen (KJEL_N), ammonia nitrogen (NH₄_N), dissolved oxygen (DO) and the water temperature. The results reveal that the HAB productivity in the Vaal Dam is influenced by the levels of TP and KJEL_N, which exhibited a significant correlation with Chl−a concentrations. From the Long- term analysis of Chl−a and its driving factors, some very high values of Chl−a concentrations and its driving factors TP and KJEL_N were recorded in erratic individual dates which suggested some nutrients rich in wastes find their way to the dam. Another important notice was that the average Chl-a concentration significantly increased during the period of the study (1986 to 2023) it increased from 4.75 μg/L in the first decade (1990–2000) to 10.51 μg/L in the second decade (2000–2010) and reaching 16.7 μg/L in the last decade (2010–2020). Additionally, Chl−a data extracted from Landsat-8 satellite images was utilized to visualize the spatial distribution of HABs in the reservoir. The satellite data analysis during the last decade revealed that the spatial dynamics of HABs are influenced by the dam’s geometry and the levels of discharge from its two feeding rivers, with higher concentrations observed in meandering areas of the reservoir, and within zones of restricted water circulation. These spatial distribution patterns of HABs are associated with spatial variations of algal species in term of domination through the seasons of the year. The research also examined the utility of remote sensing techniques for mapping algal blooms using the current generation Sentinel-2 and Landsat-8 data. The effectiveness of some band ratio indices in the blue-green and red-near infrared wavelengths was tested. The results suggested that the blue-green band ratio of Landsat-8 [Rrs(560)/Rrs(443)], and red/NIR of Sentinel-2 [Rrs(705)/Rrs(665)] were found to be the best indices for Chl-a retrieval in the Vaal Dam. Results for the Landsat OLI dataset showed R² = 0.89; RMSE = 0.36 μg/L, P < 0.05, and the Sentinel MSI dataset revealed R² = 0.75; RMSE = 0.48 μg/L, P < 0.05 which is a high degree of accuracy. As the potential toxicity comes from the cyanobacterial bloom, the study examines different models to assess and map cyanobacteria concentration in the dam reservoir. Sentinel-2 and in-situ hyperspectral data have been used. None of the Sentinel-2 band ratios showed a significant correlation with the laboratory-measured values of the cyanobacteria. The in-situ measured Hyperspectra showed strong correlations between the band ratios Rrs(705)/Rrs(655) and Rrs(705)/Rrs(620), and the measured cyanobacteria (R² = 0.96 and R² = 0.95 respectively). Chlorophyll−a concentration was retrieved using band ratio indices in the red-NIR region. The strongest correlation was found between the retrieved Chl−a of band ratio Rrs(705)/Rrs(665) and the laboratory-measured Chl−a concentrations for both reflectance datasets. This correlation resulted in an R² value of 0.78 for Sentinel-2 reflectance data and an R² value of 0.93 for in-situ hyperspectral data. A Semi-analytical algorithm for estimating the Chl−a and phycocyanin (PC) pigments has also been examined. The algorithm uses the ratio of the calculated Chl−a absorption at 665 and phycocyanin absorption at 620 nm to their specific absorption coefficients a∗ (655) and a∗ (620) to estimate the concentration of Chl−a and phycocyanin respectively. It resulted in a strong correlation with measured chlorophyll-a, R² = 0.95. The algorithm also strongly correlated with measured cyanobacteria using the absorption to specific absorption ratio at 620 nm (R² = 0.97). However, the estimated values of cyanobacteria using a Semi-analytical algorithm resulted in cyanobacterial concentration values a little bit higher compared to the measured ones, hence, some factors used by the model need to be adjusted to the Vaal Dam site for better estimations. This research revealed that using band ratio indices of Landsat-8 and Sentinel-2 data are valuable tools for mapping chlorophyll-a in the Vaal Dam, a key indicator of phytoplankton biomass. Furthermore, using the semi-analytical algorithm with hyperspectral data is key for estimating the cyanobacteria concentration in the dam water. Models developed in this research will significantly improve near-real-time and long-term chlorophyll-a monitoring of the Vaal Dam. It will effectively help researchers and environmental agencies monitor changes in algal biomass of the dam water to address public health issues related to water quality. It helps to identify areas of high nutrient input and assess the effectiveness of water quality management strategies. It is of prime importance that the developments within the catchment of the Vaal Dam be carefully considered as it is one of the primary sources of dam water. The research recommends implementing the existing regulatory policies for effluent dispersal within the catchment to protect ecosystem functioning and water resources from further deterioration in their quality. It also recommends regular monitoring to detect real-time changes in HABs using satellite remote sensing.Item A geographical analysis of the impacts of construction and demolition waste on wetland functionality in South Africa: a study of Gauteng province(University of the Witwatersrand, Johannesburg, 2024-09) Mangoro, Ngonidzashe; Kubanza, Nzalalemba Serge; Mulala, Danny SimateleThe purpose of this study was to investigate construction and demolition waste management processes in sub-Saharan Africa and how they affect wetland ecosystems, using South Africa as a case study. Construction and demolition (CDW) waste has become a massive urban environmental challenge on a global scale, but more so in developing countries found in sub-Saharan Africa. In the context of South Africa, construction and demolition waste is not a waste stream taken seriously by local and national authorities because it is ‘general waste that does not pose an immediate threat to the environment. This position is premised on the idea that construction and demolition waste is generally inert (chemically inactive) and therefore cannot cause an immediate environmental risk. In this study, it is argued that the environmental risk of waste goes beyond the embedded chemical constituencies because some waste streams can cause immediate environmental risk through their physical properties depending on the location of disposal. It is further argued that although CDW is generally inert, disposal in wetlands immediately disrupts the way wetland ecosystem’s function, causing several environmental risks. To mitigate the environmental threats posed by construction and demolition waste, this study proposes a change in the methodological approaches and strategies deployed to manage the waste stream, such as by introducing a hybrid of circular economy and industrial ecology to minimize or eliminate waste production. This study involved several data collection and analysis methods. Using a combination of qualitative and quantitative studies methods, data was collected with the goal to understand the perceptions of experts on how construction and demolition waste management in South Africa affects wetland ecosystems and what can be done to effectively manage the waste stream in the context of a developing country. Data informing this study were collected through semi-structured interviews and surveys in the province of Gauteng, specifically in the City of Johannesburg and City of Ekurhuleni Municipalities, where there is massive illegal dumping in wetlands for various reasons. Furthermore, apart from the use of semi-structured interviews and surveys, a digital elevation model was generated in ArcGIS Pro 10.1 software to measure the effects of construction and demolition waste on wetlands in the study area. The approach to this study using both qualitative and quantitative methods was crucial because it provided human perceptions which were accurately corroborated by GIS software. The study found that construction and demolition waste management in South Africa is affected by several challenges that lead to massive illegal dumping in critical ecological ecosystems such as wetlands. In a broad sense, the major challenge to sustainable construction and demolition waste management in South Africa is institutional failure at both the local and national levels. Local authorities such as municipalities are characterized by massive corruption, poor funding, and lack of strategic technologies among other things, while at the national level, there is massive interference with municipal affairs through bureaucratic delays in the disbursement of municipal funds. A combination of these and other factors leads to illegal dumping of construction and demolition waste across the Gauteng Province, particularly in wetlands in low-income areas. The data informing this study reveals that dumping construction and demolition waste in wetlands causes an immediate threat to the existence of wetlands through massive sedimentation with insoluble materials. It is ultimately found that construction and demolition waste destroy the ability of wetlands to offer ecosystem services such as flood attenuation, carbon sequestration, water filtration, and habitat provision, among other functions, leading to environmental events such as flooding. A combination of circular economy and industrial ecology can be one of the ways that can be deployed to effectively and sustainably manage construction and demolition waste in South Africa. The circular economy and its three principles of ‘reduce’, ‘recycle’, and ‘reuse’ has been successfully deployed in developed countries in the European Union, where recycling has topped 70% of the total construction waste generated. Industrial ecology with its analogy of industrial ecoparks has been deployed in the European Union with immense success, until more attention was directed to circular economy. With an increase in municipal funding and introduction of a construction waste information system, a combination of ‘circular economy’ and ‘industrial ecology’ can significantly help to reduce pressure on wetlands and the environment at large. Even though the methodological improvements suggested above could significantly reduce pressure on wetlands, the implementation could be faced with institutional challenges. Therefore, it is argued that urgent institutional transformation is required to make tangible changes in the field of construction and demolition waste management. It is recommended that there should be increased law enforcement to curb widespread illegal dumping in South Africa’s major cities. It is also recommended that, like in Europe, South Africa must introduce tailor-made legislation of policies for construction and demolition waste alone. Promulgation of dedicated legislation provides clear direction on how the waste stream is managed and who is responsible for specific roles. Furthermore, dedicated legislation can be a crucial tool to deliver sustainable construction and demolition waste management in South Africa because it can be used to encourage the use of recycled aggregates and limit the amount of illegal dumping or extraction of materials from the environment. Finally, dedicated construction and demolition waste legislation can be used to shift from the traditional view of pollution or contamination through toxicity, and so the value of this study is immediately apparent.Item Remote sensing-based assessment of mangrove forest changes and related regulatory frameworks for the sustainability and conservation of coastal ecosystems in Zanzibar Island, Tanzania-East Africa(University of the Witwatersrand, Johannesburg, 2024-10) Mohamed, Mohamed Khalfan; Adam, ElhadiMangroves are vital components of the world's coastal ecosystems, yet they face significant threats from storm surges, tidal waves, commercial aquaculture, and expanding human settlements. These challenges have heightened the need for accurate mangrove maps to gauge ecosystem degradation. However, mapping mangroves at species and community levels is challenging due to the inaccessibility of these environments. Remote sensing offers an efficient alternative to conventional field-based methods by enabling data collection in these challenging ecosystems. This study aimed to apply remote sensing techniques to map mangrove forest changes and species in two protected bays in Zanzibar, Tanzania. The thesis focuses on four key areas. First, it examines the history of mangrove management in Zanzibar, from colonial times (1890) to the present, highlighting policies, laws, and community involvement in conservation. The colonial authority implemented several land administration laws and regulations to protect mangrove forests. However, mangrove forests suffered significant degradation from 1930 to the end of World War II. The post-independence policy framework established the legal foundation for the introduction of community involvement in mangrove conservation. The legal foundation for introducing community participation in mangrove protection was established by post-independence policy structures such as the National Forest Conservation and Management Act of 1996. Nevertheless, sustainable mangrove use remains inadequate. Second, the study compared community perceptions of mangrove ecosystem services using chi-squared tests and one-way ANOVA. Household surveys showed that provisioning services (PS) were the most identified (84%). Supporting (SS), regulating (RS), and cultural services (CS) were rated by 46.2%, 45.4%, and 21.0%, respectively. Statistical analyses indicated significant differences in the awareness of RS (χ2 = 6.061, p = 0.014) and SS (χ2 = 6.006, p = 0.014) between Chwaka, Charawe, Ukongoroni, Unguja Ukuu, and Uzi wards. There were no significant differences in the identification of PS (χ2 = 1.510, p = 0.919) and CS (χ2 = 1.601, p = 0.901). The study found that residents’ occupations did not determine their reliance on mangrove ecosystem services (χ2 = 8.015; p = 0.1554). Third, changes in mangrove cover in Menai Bay and Chwaka Bay between 1973 and 2020 were analyzed using Landsat data. TerrSet geospatial software was used to classify land cover. The SEGMENTATION module grouped pixels based on spectral similarity, and the images segments were transformed into training sites and signature classes using the SEGTRAIN module. Finally, the segments were classified with the SEGCLASS module into a pixel-based land cover map. Separation of land cover classes was determined using the Jeffries–Matusita (J-M) distance and the transformed divergence (TD) index. For Chwaka Bay, overall classification accuracy ranged from 82.5% to 92.7%, while for Menai Bay, it ranged between 85.5% and 94.5%. Producer and user accuracies ranged from 72% to 100%, with kappa coefficients (κ) between 0.72 and 0.90. Menai Bay experienced a 6.8 ha yearly decline in mangrove cover between 1973 and 2020, while Chwaka Bay saw a 48.5 ha annual decrease. Fourth, the study aimed to map mangrove species in Menai Bay using metrics extracted from the Landsat 9 OLI-2 dataset, i.e., vegetation indices (VIs) and gray-level co-occurrence matrices (GLCMs). A critical step in this study was identifying the contribution of vegetation indices and texture features to classifying mangroves. Training data from very high-resolution (VHR) unmanned aerial vehicle (UAV) data covering parts of the study area helped identify five major mangrove species, i.e., Rhizophora mucronata, Ceriops tagal, Sonneratia alba, Avicennia marina, and Bruguira gymnorrhiza. Results showed that textural features attained overall classification accuracy of 68.29% (kappa = 0.62) and 67.07% (kappa = 0.60) for random forest (RF) and support vector machine (SVM), respectively. Vegetation indices (VIs) recorded overall accuracy of 72.64% (kappa = 0.67) and 67.78% (kappa = 0.61) for RF and SVM. Overall, this study demonstrates the potential of remote sensing technologies for mapping mangrove forest changes and species in challenging environments like Zanzibar’s protected bays. By integrating historical policy analysis with modern geospatial techniques, the research highlights the significant role of both legal frameworks and community involvement in mangrove conservation. The community surveys underscore the varying perceptions of mangrove ecosystem services across different wards, with provisioning services being the most recognized. These findings underscore the importance of advancing remote sensing applications and refining conservation strategies to ensure the sustainability of mangrove ecosystems. Additionally, the analysis of long-term changes in mangrove cover from 1973 to 2020 reveals a concerning decline, particularly in Chwaka Bay. Lastly, the study’s classification of mangrove species using Landsat 9 OLI-2 data, vegetation indices, and texture metrics achieved notable accuracy, emphasizing the value of remote sensing in distinguishing species-level characteristics.Item Integrating Sentinel-1/2 and machine learning models for mapping fruit tree species in heterogeneous landscapes of Limpopo(University of the Witwatersrand, Johannesburg, 2024-10) Chabalala, Yingisani Winny; Adam, ElhadiFrom ancient times to this century, Africa has relied chiefly on agriculture for survival. Crop type maps are crucial for agricultural management, sustainable farming systems, and realizing food security. Agronomists, agricultural extension officers, policymakers, and the government rely on crop type spatial distribution information to make informed decisions and optimize resource allocation for sustainable agricultural management. Attaining food security for all is an urgent need in Africa. However, the farming landscapes predominately comprise fragmented smallholder heterogeneous farms. The farming systems include intercropping and cultivating different crops that require different management strategies. This results in within-class spectral similarities and intra-spectral variability due to similar canopy structures and different phenologies, which complicates the application of remote sensing in crop type mapping. The free availability of Copernicus products such as Sentinel 1 and 2 have high temporal, spectral, and spatial resolution suitable for mapping smallholder agriculture. Thus, this research aimed to integrate Sentinel-1/2 and machine learning models for mapping fruit tree species in heterogeneous landscapes of Limpopo. First, the research tested the applicability of sampling techniques and five mapping classifiers (i.e., Random Forest (RF), Support vector Machine (SVM), Adaptive Boosting (AdaBoost), Gradient Boosting (GB), and eXtreme Gradient Boosting (XGBoost) in mapping fruit trees and co-existing land use types. The original dataset was under-sampled randomly into two balanced datasets (i.e., Dataset 1 and Dataset 2) consisting of 100 and 150 sample points. Furthermore, the imbalanced ratio from the original dataset was reduced by applying different sampling strategies to extract four imbalanced datasets (i.e., at 40%, 50%, 60%, and 70%), which resulted in the formation of Dataset 3, Dataset 4, and Dataset 5, respectively. These samples, together with the original dataset (i.e., Dataset 7), were used as input to Sentinel‑2 (S2) data using adaptive boosting (AdaBoost), gradient boosting (GB), random forest (RF), support vector machine (SVM), and eXtreme gradient boost (XGBoost) machine learning algorithms. The results showed that reducing the amount of imbalanced ratio by randomly under-sampling the original imbalanced dataset could increase the classification accuracy to 71% using the SVM classifier and 60% of the original dataset. Individually, the majority of the crop types were classified with an F1 score of between 60% and 100%. Secondly, the research independently assessed the effectiveness of Sentinel-1 (S1) and Sentinel-2 (S2) data for fruit tree mapping using random forest (RF) and support vector machine (SVM) classifiers. Four models were tested using each sensor independently and fusing both sensors. From the fused model, features were ranked using the RF mean decrease accuracy (MDA) and forward variable selection (FVS) to identify optimal spectral windows to classify fruit trees. The best fruit tree map with an overall accuracy (OA) of 0.91.6% with a kappa coefficient of 0.91% was produced using the RF MDA and FVS model and SVM classifier. The application of SVM to S1, S2, S2 selected variables and S1S2 fusion independently produced OA = 27.64, Kappa coefficient = 0.13%; OA= 87%, Kappa coefficient = 86.89%; OA = 69.33, Kappa coefficient = 69. %; A = 87.01%, Kappa coefficient = 87%, respectively. The green (B3), SWIR_2 (B10), and vertical horizontal (VH) polarization bands were identified as the optimal spectral features for S2 and S1 data, respectively. The third part of the research identified the optimal growth window period in which fruit trees can be detected with high accuracy. Phenological metrics were extracted from 12 months (i.e., January to December) of Sentinel-2 (S2) data and were used to classify fruit trees using a random forest (RF) classifier in a Google Earth Engine environment. The results showed that fruit trees can be detected and mapped with high accuracy during winter months (i.e., April-July) with an overall accuracy (OA) of 84.89% and a kappa coefficient of 83%. The user accuracy ranged from 62 to 100%, while the producer accuracy ranged from 60 to 100%. The fruit trees were mostly differentiated from co-existing land use types using the short infrared and the red-edge bands. The fourth part of the thesis attempted to increase fruit tree classification accuracy by classifying optimal Sentinel-2 images acquired during the fruit trees' critical growth stages using a Deep Neural Network (DNN) model. This was achieved by applying phenological metrics derived from Sentinel-2 images acquired during optimal crop-growing seasons (i.e., flowering, fruiting, harvesting). The DNN models were optimized by tuning the hyperparameters to achieve the best classification results. The DNN produced an OA of 86.96%, 88.64%, 86.76%, and 87.25% for April, May, June, and July images, respectively. The results indicate the DNN models were robust and stable across the selected fruit growth periods. This research has shown that earth observation (EO) data such as Sentinel 1 and 2 can be used to map fruit trees in fragmented sub-tropical horticultural landscapes characterized by different environmental conditions and different crop cultivars operating under different management practices. The research results will assist agricultural stakeholders (i.e., farm managers, agronomists, agricultural extension officers, and policymakers) in allocating agricultural resources, devising effective agricultural management strategies, and attaining sustainable agriculture and food security.Item Assessing the inter-annual and inter-seasonal climate-induced variation in caseload of respiratory diseases(University of the Witwatersrand, Johannesburg, 2024-06) Motlogeloa, OgoneIn South Africa, acute upper respiratory diseases pose a significant public health challenge, influenced heavily by climatic factors. Recognizing the critical need for detailed seasonal analysis. This thesis delves into the inter-annual and inter-seasonal impacts of climate on disease caseloads, offering four pivotal contributions to health biometeorology. The first contribution refines the understanding of the acute upper respiratory disease season in South Africa, previously recognized as the winter months of May to September. This research provides a more granular analysis by pinpointing specific onset timings and fluctuations within the season that are crucial for optimizing healthcare responses, particularly in vaccination schedules. The second contribution is an in-depth analysis of climatic variables affecting acute upper respiratory disease prevalence. Utilizing Spearman's correlation analyses and the Distributed Lag Non-linear Model across Johannesburg, Cape Town, and Gqeberha, this study identifies negative correlations between temperature and disease cases, pinpointing significant risk thresholds most prevalent during the winter peak. The third contribution investigates the impact of extreme climate events (ECEs) over twelve years, elucidating how, while individual ECEs influence medical aid claims and disease incidence, it is the broader seasonal patterns that predominantly dictate acute upper respiratory disease prevalence. The fourth contribution offers a nuanced exploration of the climate-health nexus, demonstrating that routine weather variations play a more significant role in the peak transmission of acute upper respiratory viruses than extreme events. This thesis elucidates the substantial yet nuanced influence of climate on respiratory health in South Africa. By specifying the disease season with greater precision and clarifying the relationship between temperature variations and disease prevalence, the research provides essential data for health practitioners to plan targeted interventions. This study moves beyond the focus on extreme weather events to expose the subtler, yet more consistent, impact of seasonal climate shifts on health outcomes, enriching our understanding and serving as a vital reference for enhancing disease preparedness in an era marked by climatic uncertainty.