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Fine-Scale Classification of Urban Land Use and Land Cover with PlanetScope Imagery and Machine Learning Strategies in the City of Cape Town, South Africa

Author

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  • Bosiu E. Lefulebe

    (Department of Geography, University of the Free State, Bloemfontein 9301, South Africa)

  • Adriaan Van der Walt

    (Department of Geography, University of the Free State, Bloemfontein 9301, South Africa
    Afromontane Research Unit (ARU), Faculty of Natural and Agricultural Sciences, University of the Free State, Bloemfontein 9301, South Africa)

  • Sifiso Xulu

    (Department of Geography, University of the Free State, Bloemfontein 9301, South Africa)

Abstract

Urban land use and land cover (LULC) change can be efficiently monitored with high-resolution satellite products for a variety of purposes, including sustainable planning. These, together with machine learning strategies, have great potential to detect even subtle changes with satisfactory accuracy. In this study, we used PlaneScope Imagery and machine learning strategies (Random Forests, Support Vector Machines, Naïve Bayes and K-Nearest Neighbour) to classify and detect LULC changes over the City of Cape Town between 2016 and 2021. Our results showed that K-Nearest Neighbour outperformed other classifiers by achieving the highest overall classification of accuracy (96.54% with 0.95 kappa), followed by Random Forests (94.8% with 0.92 kappa), Naïve Bayes (93.71% with 0.91 kappa) and Support Vector Machines classifiers with relatively low accuracy values (92.28% with 0.88 kappa). However, the performance of all classifiers was acceptable, exceeding the overall accuracy of more than 90%. Furthermore, the results of change detection from 2016 to 2021 showed that the high-resolution PlanetScope imagery could be used to track changes in LULC over a desired period accurately.

Suggested Citation

  • Bosiu E. Lefulebe & Adriaan Van der Walt & Sifiso Xulu, 2022. "Fine-Scale Classification of Urban Land Use and Land Cover with PlanetScope Imagery and Machine Learning Strategies in the City of Cape Town, South Africa," Sustainability, MDPI, vol. 14(15), pages 1-16, July.
    • Handle: RePEc:gam:jsusta:v:14:y:2022:i:15:p:9139-:d:872168
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    2. Bolund, Per & Hunhammar, Sven, 1999. "Ecosystem services in urban areas," Ecological Economics, Elsevier, vol. 29(2), pages 293-301, May.
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    Cited by:

    1. Ting Liu & Gang Cheng & Jie Yang, 2023. "Multi-Scale Recursive Identification of Urban Functional Areas Based on Multi-Source Data," Sustainability, MDPI, vol. 15(18), pages 1-24, September.
    2. Chaoqing Huang & Chao He & Qian Wu & MinhThu Nguyen & Song Hong, 2023. "Classification of the Land Cover of a Megacity in ASEAN Using Two Band Combinations and Three Machine Learning Algorithms: A Case Study in Ho Chi Minh City," Sustainability, MDPI, vol. 15(8), pages 1-27, April.

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