The Relationship between Urban Heat Island and Tourism at Chiangmai City, Thailand Based on Remote Sensing
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Published:
Jun 23, 2020
Keywords:
Urban Heat Islands Tourist Remote Sensing Chiangmai City
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Abstract
The objectives of this research are: 1) to evaluate monthly urban heat island in Chiang Mai city, Thailand and 2) to assess the relationship between urban heat island and tourist number. The MODIS (MOD11A1) with 1,000 meters resolution were used and combined with the monthly tourist number from 2013 to 2017. The results show that the urban heat island in Chiangmai city occurs in every month which can be clearly observed in the urban and sub-urban areas during the midday of hot season in February to June and December to January (in some years). But the maximum urban heat island temperature is during August to October. There is a positive relationship between urban heat island and the tourist number in daytime and night time but with the higher correlation coefficient in the night time. However, the study of urban heat island and the tourist number assessment in spatial data can be used as information for urban planning to sustainably and effectively support tourism in the future.
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Information Technology Research Articles
The articles published are the opinion of the author only. The author is responsible for any legal consequences. That may arise from that article.
References
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[2] T. R. Oke, “City size and the urban heat island,” Atmos. Environ, vol. 7, no. 8, pp. 769–779, 1973.
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[5] Y. Abbassi, H. Ahmadikia, and E. Baniasadi, “Prediction of pollution dispersion under urban heat island circulation for different atmospheric stratification,” Building and Environment, vol. 168, no. 106374, pp. 1–19, 2019.
[6] J. Quan, Y. Chen, W. Zhan, J. Wang, J. Voogt, and M. Wang. “Multi-temporal trajectory of the urban heat island centroid in Beijing, China based on a Gaussian volume model,” Remote Sensing of Environment, vol. 149, pp. 33–46, 2014.
[7] Q. Weng, D. Lu, and J. Schubring, “Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies,” Remote Sensing of Environment, vol. 89, no. 4, pp. 467–483, 2004.
[8] N. Phengphit, S. Karnchanasutham, K. Nualchawee, and P. Soytong, “The study of correlation between land surface temperature with urban and building area, a case study of Amphoe Mueang Rayong, Rayong province, Thailand,” Journal of Geoinformation Technology of Burapha University, vol. 2, no. 3, pp. 27–40, 2017 (in Thai).
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[10] V. Miles and I. Esau, “Seasonal and spatial characteristics of urban heat islands (UHIs) in northern West Siberian cities,” Remote Sensing, vol. 9, no. 10, pp. 989–1004, 2017.
[11] W. Wang, K. Liu, R. Tang, and S. Wang, “Remote sensing image-based analysis of the urban heat island effect in Shenzhen, China,” Physics and Chemistry of the Earth, Parts A/B/C, vol. 110, pp. 168–175, 2019.
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[15] C. Keeratikasikorn and S. Bonafoni, “Satellite images and Gaussian parameterization for an extensive analysis of urban heat Islands in Thailand,” Remote Sensing of Environment, vol. 10, no. 5, pp. 1–19, 2018.
[16] C. Keeratikasikorn and S. Bonafoni, “Urban heat island analysis over the land use zoning plan of Bangkok by means of Landsat 8 imagery,” Remote Sensing of Environment, vol. 10, no. 3, pp. 440–453, 2018.
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[19] Ministry of Tourism and Sports. (2014). Domestic Tourism Statistics Q1-Q4 (Classify by region and province). Ministry of Tourism and Sports. Bangkok, Thailand [Online]. Available: https://www.mots.go.th/old/more_news.php?cid=531&filename=index
[20] Northern Meteorological Center. Climate of Chiang Mai Province. Northern Meteorological Center, Thailand [Online]. Available: http://www.cmmet.tmd.go.th/index1.php
[21] ESRI. (2011). ArcGIS Desktop: Release 10. Redlands. Environmental Systems Research Institute., CA. [Online]. Available: http://www.oalib.com/references/11136683
[22] A. Savitzky and M.J.E. Golay, “Smoothing and differentiation of data by simplified least squares procedures,” Analytical Chemistry, vol. 36, no. 8, pp. 1627–1639, 1964.
[23] J. Chen, P. Jönsson, M. Tamura, Z. Gu, B. Matsushita, and L. Eklundh, “A simple method for reconstructing a high-quality NDVI time-series data set based on the Savitzky-Golay filter,” Remote Sensing of Environment, vol. 91, no. 3–4, pp. 332–344, 2004.
[24] L. Ulsig, C. J. Nichol, K. F. Huemmrich, D. R. Landis, E. M. Middleton, A. I. Lyapustin, I. Mammarella, J. Levula, and A. Porcar-Castell, “Detecting inter-annual variations in the phenology of evergreen conifers using long-term MODIS vegetation index time series,” Remote Sensing, vol. 9, no. 49, pp. 1–21, 2017.
[25] J. Martin-Vide, P. Sarricolea, and M. C. Moreno-García, “On the definition of urban heat island intensity: The ‘rural’ reference,” Frontiers in Earth Science, vol. 3, pp. 2–4, 2015.
[26] H. Xie, H. Guan, and S. Ytuarte, “Heat island of San Antonio, Texas detected by MODIS/Aqua temperature product,” in Proceedings 20th Biennial Workshop on Aerial Photography. Videography, and High Resolution Digital Imagery for Resource Assessment, 2005, pp. 306–312.
[27] J. Zhang and L. Wu, “Modulation of the urban heat island by the tourism during the Chinese New Year holiday: A case study in Sanya City, Hainan province of China,” Science Bulletin, vol. 60, no. 17, pp. 1543–1546, 2015.
[28] Y. Cui, X. Xu, J. Dong, and Y. Qin, “Influence of urbanization factors on surface urban heat island intensity: A comparison of countries at different developmental phases,” Sustainability, vol. 8, no. 8, pp. 706–720, 2016.
