Satellite Images and Gaussian Parameterization for an Extensive Analysis of Urban Heat Islands in Thailand

Abstract

For the first time, an extensive study of the surface urban heat island (SUHI) in Thailand's six major cities is reported, using 728 MODIS (MODerate Resolution Imaging Spectroradiometer) images for each city. The SUHI analysis was performed at three timescales-diurnal, seasonal, and multiyear. The diurnal variation is represented by the four MODIS passages (10:00, 14:00, 22:00, and 02:00 local time) and the seasonal variation by summer and winter maps, with images covering a 14-year interval (2003-2016). Also, 126 Landsat scenes were processed to classify and map land cover changes for each city. To analyze and compare the SUHI patterns, a least-square Gaussian fitting method has been applied and the corresponding empirical metrics quantified. Such an approach represents, when applicable, an efficient quantitative tool to perform comparisons that a visual inspection of a great number of maps would not allow. Results point out that SUHI does not show significant seasonality differences, while SUHI in the daytime is a more evident phenomenon with respect to nighttime, mainly due to solar forcing and intense human activities and traffic. Across the 14 years, the biggest city, Bangkok, shows the highest SUHI maximum intensities during daytime, with values ranging between 4 • C and 6 • C; during nighttime, the intensities are rather similar for all the six cities, between 1 • C and 2 • C. However, these maximum intensities are not correlated with the urban growth over the years. For each city, the SUHI spatial extension represented by the Gaussian footprint is generally not affected by the urban area sprawl across the years, except for Bangkok and Chiang Mai, whose daytime SUHI footprints show a slight increase over the years. Orientation angle and central location of the fitted surface also provide information on the SUHI layout in relation to the land use of the urban texture.

Land Surface Temperature and Urban Density: Multiyear Modeling and Relationship Analysis Using MODIS and Landsat Data

“…The day-night different shape may be ascribed to different factors. As reported in [10], the area surrounding the city core is constituted by residential, commercial and public zones: during the daytime, the intense human activities and the combined traffic congestion increase the surface warming, enlarging the heat island footprint [25]; during nighttime, due to the reduced working activities, a mitigation of the heating intensity and its footprint For night-time, the fitting with the highest coefficient of determination is linear, with an r 2 value of approximately 0.95-0.96, whereas for daytime it is quadratic, with an r 2 value of approximately 0.98-0.99. The r 2 values prove the efficiency of the relationship determined for urban density-LST n , with the daytime one exhibiting a very high accuracy.…”

“…The day-night different shape may be ascribed to different factors. As reported in [10], the area surrounding the city core is constituted by residential, commercial and public zones: during the daytime, the intense human activities and the combined traffic congestion increase the surface warming, enlarging the heat island footprint [25]; during nighttime, due to the reduced working activities, a mitigation of the heating intensity and its footprint in such Bangkok zones is evident, as shown in [25]. The role of the anthropogenic heat release (e.g., air conditioning systems, energy use in transportation) as essential contributor to daytime urban warming due to intense human and economic activities is also confirmed in [6,36,37].…”

“…The dry northeast monsoon occurs between October and February, when the summer season starts. A previous work [25] highlighted the presence of a surface urban heat island (SUHI) pattern quite similar in winter and summer, more evident in daytime (mean peak of 6 • C) with respect to night-time (mean peak 1-2 • C).…”

Associated Determinants of Surface Urban Heat Islands across 1449 Cities in China

“…erefore, few regions remained due to the dense distribution of cities in certain well-developed regions with dense populations. Moreover, certain studies have used the Gaussian fitting method [32,43,44], but this approach is only effective when the left field of the LST fits the Gaussian distribution after extraction of the background LST. However, this condition does not always exist [41].…”

Relationship between Land Surface Temperature and Land Use in Nakhon Ratchasima City, Thailand

“…The topic has also been studied in Thailand, e.g. using remote sensing technology from satellite imagery to assess the UHI in large cities where large changes in land utilization have occurred, including Bangkok [41][42][43][44][45], Chiang Mai [43,46], Songkhla [43] and Mahasarakram [16]. However, Nakhon Ratchasima has not been studied, despite its rapid urbanization and major changes in land utilization.…”