Browsing by Author "Mhangara, Paida"

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    Mapping and assessment of informal settlements using object-based image analysis, a case study of Mamelodi, Tshwane, South Africa
    (University of the Witwatersrand, Johannesburg, 2024) Mudau, Naledzani; Mhangara, Paida
    The social and environmental challenges faced by people living in informal settlements or slums are widely recognized by development agendas including United Nations Sustainable Development Goals, Agenda 2063 and National Development Plans. The study aims to investigate informal settlement dynamics and spatial characteristics to generate an understanding of housing informality and environmental conditions for designing innovative sustainable solutions. The study assessed the use of 12 spectral indices and textural measures, and object-based image analysis (OBIA) technique to detect informal settlements from WorldView 2 images. A growth indicator that uses informal settlement extent and impervious surface was developed and used to assess informal settlement growth patterns between 2005 to 2020. Unmanned aerial vehicle image products, and landscape metrics were used to assess the spatial characteristics and patterns of backyard shacks and free-standing informal settlement structures. In addition, a settlement surface ecological index was developed and used to assess the ecological conditions of informal settlements. Lastly, the assessment of the location characteristics of informal settlements was done using ancillary data. The results show that the use of built-up index, coastal blue index and first order statistics mean textural measures and OBIA technique detected informal settlements with producer and user accuracies of 95% and 82% respectively. The developed informal settlement growth assessment indicator shows that informal settlement in 2020 had a slightly lower density of impervious surfaces than in 2005. The Euclidean Nearest-Neighbour Distance, Aggregation Index and Cohesion Index show that backyard shacks are less connected, less dense, and more isolated than freestanding informal settlement structures. Some informal settlements have better surface ecological conditions than some of the formal settlements. A higher extent of informal settlements continued to develop closer to formal settlements, rivers and railway lines between 2015 and 2020. The information demonstrated in this study can be used by local authorities to better understand and manage informal settlement developments, prioritize settlement upgrade projects and improve the environmental conditions and resilience of informal settlements
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    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, Paida
    Remote 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 growth