Electronic Theses and Dissertations (Masters)
Permanent URI for this collectionhttps://hdl.handle.net/10539/38009
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Item A Geospatial Approach to Mapping Jacaranda Tree Distribution in Johannesburg, South Africa(University of the Witwatersrand, Johannesburg, 2023-11) Reddy, Rohini Chelsea; Fitchett, JenniferAccurate mapping of the spatial distribution of invasive species is vital for the implementation of effective monitoring and management strategies. In countries where resources are scarce and costly, citizen science provides a cost-effective and accurate alternative for large-scale data collection. Citizen’s familiarity with their environment contributes to aspects such as accurate identification of features on the landscape. Advances in a geographic information system (GIS) together with open-sourced photography from Google Street View, provide accurate methods for in-field and remote validation of citizen science data for invasive mapping and assists with the creation and compilation of maps to visualize the spatial distribution of invasive plants upon the landscape. In this study, the first spatial distribution maps for invasive tree species, Jacaranda mimosofolia (common name: Jacaranda), are created for the City of Johannesburg (CoJ). Jacaranda trees are well-known by citizens in the CoJ for their district purple flowers which blanket the landscape during springtime. A combination approach using citizen science, GIS, and Google Street View for data collection, analysis, and creation of the first spatial distribution map of exact location and prevalence of Jacaranda trees within certain suburbs of the CoJ, is produced. A total of 8,931 ground-truthing geopoints together with extensive Google Street View validation for Jacaranda tree presence, formed the basis of accurate spatial distribution maps. The first research question of this study focused on the spatial distribution of Jacaranda trees in the CoJ and was answered as a total of 54 suburbs were confirmed as having a large presence of Jacaranda trees in the CoJ. Citizen science data collected a total of 488 geotags for possible Jacaranda tree presence in the CoJ, over a 75-day online survey collection period. Although citizen science data provided a lower spatial resolution compared to successful fieldwork and Google Street View approaches, citizen science data provided very high accuracy for the identification and geolocation of Jacaranda tree presence in the CoJ which answers the second research question based on the effectiveness of the geospatial approach towards citizen science, ground-truthing and Google Street View as data collection methods. Since the accuracy of citizen science resulted in 66% of collected geotags within the categories of ‘very high’, ‘high’ and ‘moderate’ accuracy ranges of between <7-24m from a confirmed Jacaranda tree, together with the accuracy of 8,931 in-field collected geolocation of Jacaranda trees and Google Street View’s accuracy and capability of collecting street view imagery – it is concluded that the combined approach of ground-truthing, citizen science and Google Street View contribute not only to effective data collection, but also towards the successful mapping of Jacaranda tree presence in the CoJ.Item Monitoring and evaluating urban land use land cover change using machine learning classification techniques: a case study of Polokwane municipality(University of the Witwatersrand, Johannesburg, 2023) Funani, Tshivhase; Mhangara, PaidaRemote sensing is one of the tools which is very important to produce Land use and land cover maps through the process of image classification. Image classification requires quality multispectral imagery and secondary data, a precise classification technique, and user experience skill. Remote sensing and GIS were used to identify and map land-use/land-cover in the study region. Big Data issues arise when classifying a huge number of satellite images and features, which is a very intensive process. This study primarily uses GEE to evaluate the two classifiers, Support Vector Machine, and gradient boosting, using multi-temporal Landsat-8 images, and to assess their performance while accounting for the impact of data dimension, sample size, and quality. Land use/Landcover (LULC) classification, accuracy assessment, and landscape metrics comprise this study. Gradient Tree Boost and SVM algorithms were used in 2008, 2013, 2017, and 2022. Google Earth Engine was used for supervised classification. The results of change detection showed that urbanization has occurred and most of the encroachments were on agricultural land. In this study, XG boost, and support vector machine (SVM)) were used and compared for image classification to oversight spatio-temporal land use changes in Polokwane Municipality. The Google Earth Engine has been utilized to pre-process the Landsat imagery, and then upload it for classification. Each classification method was evaluated using field observations and high-resolution Google Earth imagery. LULC changes were assessed, utilizing Geographic Information System (GIS) techniques, as well as the dynamics of change in LULCC were analysed using landscape matrix analysis over the last 15 years in four different periods: 2008–2013, 2018 and 2022. The results showed that XGBoost performed better than SVM both in overall accuracies and Kappa statistics as well as F-scores and the ratio of Z-score. The overall accuracy of gradient boosting in 2008 was 0.82, while SVM showed results of 0.82 overall accuracy and kappa statistics of 0.69. The average F-score for SVM in 2008 was from 0.58- 1.00, in 2013 an average of 0.86-0.97, and in 2022 it was 0.76. Z values were not statistically significant as all values were below the z score of 1.96. The ratios for the two classifiers were also taken to know which classifier performs the best. The results showed 212:212 which indicates that during 2008 SVM and XG boost performed the same way as they classified the same number of cases. During 2013 the ratio was 345:312 which shows that XGBoost performed better than SVM. The results of 2017 show 374:316 which shows that XGBoost performed better than SVM. Lastly, in 2022 the ratio was 298:277 which shows that XGBoost performed better than SVM. Overall zscores result show that XGBoost performs better than SVM. Overall, this study offers useful insight into LULC changes that might aid shareholders and decision makers in making informed decisions about controlling land use changes and urban growthItem 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, AmandaUnderstanding 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.