School of Animal, Plant and Environmental Sciences (ETDs)
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Item The application of machine learning methods to satellite data for the management of invasive water hyacinth(University of the Witwatersrand, Johannesburg, 2023-06) Singh, Geethe; Reynolds, Chevonne; Byrne, Marcus; Rosman, BenjaminBiological invasions are responsible for some of the most devastating impacts on the world’s ecosystems, with freshwater ecosystems among the worst affected. Invasions threaten not only freshwater biodiversity, but also the provision of ecosystem services. Tackling the impact of invasive aquatic alien plant (IAAP) species in freshwater systems is an ongoing challenge. In the case of water hyacinth (Pontederia crassipes, previously Eichhorniae crassipes), the worst IAAP presents a long-standing management challenge that requires detailed and frequently updated information on its distribution, the context that influences its occurrence, and a systematic way to identify effective biocontrol release events. This is particularly urgent in South Africa, where freshwater resources are scarce and under increasing pressure. This research employs recent advances in machine learning (ML), remote sensing, and cloud computing to improve the chances of successful water hyacinth management. This is achieved by (i) mapping the occurrence of water hyacinth across a large extent, (ii) identifying the factors that are likely driving the occurrence of the weed at multiple scales, from a waterbody level to a national extent, and (iii) finally identifying periods for effective biocontrol release. Consequently, the capacity of these tools demonstrates their potential to facilitate wide-scale, consistent, automated, pre-emptive, data-driven, and evidence-based decision making for managing water hyacinth. The first chapter is a general introduction to the research problem and research questions. In the second chapter, the research combines a novel image thresholding method for water detection with an unsupervised method for aquatic vegetation detection and a supervised random forest model in a hierarchical way to localise and discriminate water hyacinth from other IAAP’s at a national extent. The value of this work is marked by the comparison of the user (87%) and producer accuracy (93%) of the introduced method with previous small-scale studies. As part of this chapter, the results also show the sensor-agnostic and temporally consistent capability of the introduced hierarchical approach to monitor water and aquatic vegetation using Sentinel-2 and Landsat-8 for long periods (from 2013 - present). Lastly, this work demonstrates encouraging results when using a Deep Neural Network (DNN) to directly detect aquatic vegetation and circumvents the need for accurate water extent data. The two chapters that follow (Chapter 3 and 4 described below) introduce an application each that build off the South African water hyacinth distribution and aquatic vegetation time series (derived in Chapter 2). The third chapter uses a species distribution model (SDM) that links climatic, socio-economic, ecological, and hydrological conditions to the presence/absence of water hyacinth throughout South Africa at a waterbody level. Thereafter, explainable AI (xAI) methods (specifically SHapley Additive exPlanations or SHAP) are applied to better understand the factors that are likely driving the occurrence of water hyacinth. The analyses of 82 variables (of 140 considered) show that the most common group of drivers primarily associated with the occurrence of water hyacinth in South Africa are climatically related (41.4%). This is followed by natural land cover categories (32.9%) and socio-economic variables (10.7%), which include artificial land-cover. The two least influential groups are hydrological variables (10.4%) including water seasonality, runoff, and flood risk, and ecological variables (4.7%) including riparian soil conditions and interspecies competition. These results suggest the importance of considering landscape context when prioritising the type (mechanical, biological, chemical, or integrated) of weed management to use. To enable the prioritisation of suitable biocontrol release dates, the fourth chapter forecasts 70-day open water proportion post-release as a reward for effective biocontrol. This enabled the simulation of the effect of synthetic biocontrol release events under a multiarmed bandit framework for the identification of two effective biocontrol release periods (late spring/early summer (mid-November) and late summer (late February to mid-March)). The latter release period was estimated to result in an 8-27% higher average open-water cover post-release compared to actual biocontrol release events during the study period (May 2018 - July 2020). Hartbeespoort Dam, South Africa, is considered as a case study for improving the pre-existing management strategy used during the biocontrol of water hyacinth. The novel frameworks introduced in this work go a long way in advancing IAAP species management in the age of both ongoing drives towards the adoption of artificial intelligence and sustainability for a better future. It goes beyond (i) traditional small-scale and infrequent mapping, (ii) standard SDMs, to now include the benefits of spatially explicit model explainability, and (iii) introduces a semi-automated and widely applicable method to explore potential biocontrol release events. The direct benefit of this work, or indirect benefits from derivative work outweighs both the low production costs or equivalent field and lab work. To improve the adoption of modern ML and Earth Observation (EO) tools for invasive species management, some of the developed tools are publicly accessible. In addition, a human-AI symbiosis that combines strengths and compensates for weaknesses is strongly recommended. For each application, directions are provided for future research based on the drawbacks and limitations of the introduced systems. These future efforts will likely increase the adoption of EO-derived products by water managers and improve the reliability of these products.Item Quantifying and Mapping Urban Ecosystem Services in Johannesburg, South Africa(University of the Witwatersrand, Johannesburg, 2024) Friemond, JosephModern cities face a wide range of challenges such as flooding and heat stress, which are driven by urbanisation and exacerbated by the impacts of climatic change. The ecosystem services provided by green spaces in cities have become a crucial element in addressing these challenges by supporting climate change mitigation and adaptation. The first step in maintaining and improving the supply of these services is their quantification and mapping. However, large knowledge gaps exist in South Africa and Johannesburg relating to the provision of urban ecosystem services. This study aimed to quantify the supply of three important urban ecosystem services (carbon storage, runoff retention and cooling) and map their distribution across the wards of Johannesburg. Carbon storage was quantified through field sampling of four urban forest types (roadside trees, parks, gardens and nature reserves) and the use of biomass equations. InVEST's urban flood risk mitigation model was used to quantify runoff retention, while cooling was quantified by deriving land surface temperatures from Landsat satellite imagery, which were then used as inputs for a cooling indicator. All three services were mapped across the wards of Johannesburg and then normalised for comparison. The results revealed that Johannesburg's urban forest stores 2.4 million tonnes of carbon, with significant differences in carbon storage between forest types. Johannesburg’s ecosystem services provide great value in mitigating urban challenges, retaining 20.9 million m3 of runoff during a 50 mm storm, and providing cooling services across most of the city. However, the supply of these services is unequal, with large spatial disparities between the northern and southern regions in the city. Numerous wards receive critically low supply of these services, making them vulnerable to the impacts of climatic change. The northern- central wards have optimal supply of all three services, highlighting synergies between services. Ultimately, these three services have immense value in the Johannesburg context and play key roles in supporting the city’s climate change mitigation and adaptation, through the multi-functional delivery of ecosystem services from urban green infrastructure. By mapping these services at the ward scale, our findings can be used to accurately inform authorities and decision makers of priority areas for intervention, as well as key areas for conservation