Monitoring Hanoi's Golf Courses Using Remote Sensing and Machine Learning for Sustainable Land Use Planning

Golf courses have increasingly contributed to the economic growth of Vietnamese cities like Hanoi. However, their environmental impacts, particularly regarding land use and resource management, remain a concern. This study utilizes Sentinel-2 and Landsat satellite imagery, combined with Geographic Information Systems (GIS), to monitor golf courses in Hanoi’s metropolitan area. By evaluating two detection methods—Normalized Difference Vegetation Index (NDVI) analysis and feature recognition—we identify the strengths and limitations of these approaches in urban settings. While NDVI is constrained by similar vegetation signatures in tropical climates, feature recognition captures distinct golf course characteristics. The findings contribute to sustainable urban land use planning and highlight the potential of advanced remote sensing technologies in environmental conservation.