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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

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  • Chaoqing Huang

    (School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
    Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China)

  • Chao He

    (College of Resources and Environment, Yangtze University, Wuhan 430100, China)

  • Qian Wu

    (School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
    Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China)

  • MinhThu Nguyen

    (Vietnam Institute of Meteorology Hydrology and Climate Change, Ministry of Natural Resources and Environment, Hanoi City 100000, Vietnam)

  • Song Hong

    (School of Resource and Environmental Science, Wuhan University, Wuhan 430079, China
    Key Laboratory of Geographic Information System, Ministry of Education, Wuhan University, Wuhan 430079, China)

Abstract

Accurate classification of land cover data can facilitate the intensive use of urban land and provide scientific and reasonable data support for urban development. Rapid changes in land cover due to economic growth are occurring in the megacities of developing countries more and more. A land cover classification method with a high spatiotemporal resolution and low cost is needed to support sustainable urban development for continuous land monitoring. This study discusses better machine learning algorithms for land cover classification in Ho Chi Minh City. We used band combination 764 and band combination 543 of LANDSAT8-OLI image data to classify the land cover in Ho Chi Minh City by combining three machine learning algorithms: Back-Propagation Neural Network, Support Vector Machine, and Random Forest. We divided the land cover into six types and collected 2221 samples, 60% of which were used for training and 40% for validation. Our results show that using the band combination 764 combined with the Random Forest algorithm is the most appropriate, with an overall classification accuracy of 99.41% and a Kappa coefficient of 0.99. Moreover, it shows a more significant advantage regarding city-level land cover details than other classification products.

Suggested Citation

  • 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.
    • Handle: RePEc:gam:jsusta:v:15:y:2023:i:8:p:6798-:d:1126312
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    References listed on IDEAS

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