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

Chaoqing Huang, Chao He, Qian Wu, MinhThu Nguyen and Song Hong ()
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Chaoqing Huang: School of Resource and Environmental Science, 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
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

Sustainability, 2023, vol. 15, issue 8, 1-27

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.

Keywords: land cover; classification; LANDSAT8-OLI; machine learning; Ho Chi Minh City (search for similar items in EconPapers)
JEL-codes: O13 Q Q0 Q2 Q3 Q5 Q56 (search for similar items in EconPapers)
Date: 2023
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