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Open AccessArticle

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

1
Department of Computer Science, Khon Kaen University, Khon Kaen 40002, Thailand
2
Department of Engineering, University of Perugia, via Duranti 93, 06125 Perugia, Italy
*
Author to whom correspondence should be addressed.
Remote Sens. 2018, 10(5), 665; https://doi.org/10.3390/rs10050665
Received: 23 March 2018 / Revised: 16 April 2018 / Accepted: 21 April 2018 / Published: 24 April 2018
(This article belongs to the Special Issue Urban Heat Island Remote Sensing)
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. View Full-Text
Keywords: land surface temperature; surface urban heat island; Thailand; MODIS; 2D-Gaussian surface land surface temperature; surface urban heat island; Thailand; MODIS; 2D-Gaussian surface
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Keeratikasikorn, C.; Bonafoni, S. Satellite Images and Gaussian Parameterization for an Extensive Analysis of Urban Heat Islands in Thailand. Remote Sens. 2018, 10, 665.

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