A 36-year geospatial analysis of urbanization dynamics and surface urban heat island effect: Case study of the Bangkok Metropolitan Region

Abstract

This study examines the impact of urbanization on the Surface Urban Heat Island (SUHI) effect in the Bangkok Metropolitan Region (BMR) over a 36-year period, utilizing advanced machine learning techniques to assess changes in land use and land cover (LULC). The research addresses three key questions: (1) How have changes in LULC influenced the dynamics of the urban heat island (UHI) effect in the BMR? (2) What roles do green and blue infrastructure play in mitigating urban heat? (3) How effectively can machine learning models classify LULC changes and provide insights to support sustainable urban planning? The findings reveal a strong correlation between urban growth and increased land surface temperatures (LST), with parks and water bodies exhibiting lower LSTs, emphasizing the importance of green and blue infrastructure in mitigating urban heat. The SUHI effect, initially measured at 3 °C from 1988 to 1991, peaked at 4.8 °C between 2012 and 2019 before slightly declining to 4.1 °C in recent years due to urban greening initiatives. However, ongoing urban development continues to diminish green spaces and water bodies, underscoring the urgent need for sustainable urban planning. Economic factors, including the 1997 Asian Financial Crisis and land tax laws introduced in 2019, influenced land use patterns, further exacerbating the SUHI effect. The research highlights the necessity of integrated urban management and sustainable land use policies to enhance climate resilience in rapidly urbanizing regions like the BMR.

Keywords

Surface urban heat island (SUHI) / Urbanization / Machine learning

Declaration of generative AI and AI-assisted technologies in the writing process

During the preparation of this work, the authors used ChatGPT-4o to assist in improving the manuscript’s English language quality and to correct the syntax and structure of GEE. After employing this tool, the authors carefully reviewed and edited the content as necessary to ensure its accuracy, clarity, and adherence to scientific standards. The authors take full responsibility for the content of the publication and affirm that all insights, analyses, and conclusions drawn in this work are the result of their own intellectual and professional efforts.

CRediT authorship contribution statement

Nattapong Puttanapong: Writing – review & editing, Conceptualization. Nithima Nuengjumnong: Writing – review & editing. JoJinda SaeJung: Writing – review & editing. Sitthisak Moukomla: Writing – review & editing, Writing – original draft, Methodology, Formal analysis, Conceptualization.

Declaration of competing interests

The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.

Acknowledgements

The authors express their sincere gratitude to the anonymous reviewers for their insightful comments and constructive suggestions, which have significantly improved the quality of this manuscript. We also extend our gratitude to the Research Unit in Geospatial Research and Analytics for Climate and Environment (GRACE Lab), Thammasat University, for their support and resources that made this study possible.

Supplementary materials

Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.geosus.2025.100322.

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