Impact of Land Transition around Eastern Economic Corridor in Thailand in the context of SDG 11.3.1 using Urban Heat Islands, Nighttime Light Intensity and Machine Learning

Abstract

The Eastern Economic Corridor (EEC) in Thailand has experienced unprecedented industrialization and urbanization since 2017 driven by the Thailand 4.0 initiative. This has resulted in complex land transitions contributing to increase Urban Heat Island (UHI) and changes in Land Use Dynamics (LUD). The aim of this study is to use geospatial data analytics to examine LUD, its impact on UHI and the trend of Land Use Efficiency (LUE: SDG Indicator 11.3.1) from 1995 to 2023. We used Landsat data to analyse LUD using Naive Bayes (NB), Support Vector Machine (SVM), Classification and Regression Trees (CART), and Random Forest (RF) optimizing the classification accuracy. The optimal features combined with population data, were utilized to estimate LUE between 1995 and 2023 at 5-year intervals. Additionally, VIIRS satellite data was employed to map nighttime light intensity, providing insights into nocturnal activities. The findings indicate that built-up areas have increased from 21.17% to 32.39% over the past 28 years, revealing changing patterns of LUD. The LUD is disproportionate with respect to population growth, resulting in dynamic LUE values: 1 (1995–2000), 0.6 (2000–2005), 3.3 (2005–2010), 0.7 (2010–2015), 0.2 (2015–2020), and 1.4 (2020–2023). The study suggests that there has been a rise in UHI effects due to rapid urbanization and industrialization, evidenced by increase in temperature 12.8 °C–14.48 °C (minimum) and 38.52 °C–43.85 °C (Maximum) between 1995 and 2023. The results of this study can assist in directing urban development projects in Thailand's EEC region by providing insight into urban growth trends, LUE, and environmental implications.

Keywords

• Eastern economic corridor
• Machine learning
• Nighttime light data
• Sustainable cities
• Urban heat islands