Harnessing an Integrated Machine Learning based Approach in Monitoring and Predicting Dynamic Spatiotemporal Land Use and Land Cover Changes. A case study in a Mekong city

Localized rapid urban expansion and global climate change have contributed to land use and land cover (LULC) dynamic modifications, which further links to changed land surface temperatures (LST). This study proposes an integrated approach of machine learning (ML) models in assessing decadal LULC changes and future prediction in a city in the Mekong region. To achieve an accurate LULC map object-based classification strategies were implemented using various ML techniques across observed years with four main land cover categories: built-up areas, water bodies, paddy fields/shrubs, and orchards, together with LST extraction. The findings reveal that Random Forest classifier works superior to other classifiers, achieving the best overall accuracy of 81%. There have been substantial land usage changes, with the percentage of developed areas rising from 8% in 2014 to approximately 12% in 2024. Urbanization is correlated with rising temperatures , while, vegetation, on the other hand, helps alleviate this heat by providing shade and cooling. With an overall accuracy of 85% in the patch-generating land use simulation (PLUS) model, by 2030, under the impacts of both natural and socio-economic drivers, an apparent increase in the proportion of built-up areas to 15% and a slight variation in other categories could be seen in line with planning objectives. The urban expansion could be clearly seen in the highly dense districts with an increase to 42% by 2030 from an initial stage of merely 27% in 2014. The primary forecast conversions in LULC observed were vegetated lands transforming into construction areas for urbanization, yet maintaining agricultural practices for food security. The integrated approach has proven its suitability in intricate land usage patterns evaluation and optimization.