Estimation of Land Surface Temperature in Northeast, Thailand Using Multi – Temporal Satellite Imageries
Abstract
The objectives of this research were 1) to estimate land surface temperature in the Northeast during 2000 to 2020, 2) to study the relationship between surface temperature and land cover, and 3) to study the relationship between land surface temperature and populations. This research studied the data from Terra/Aqua - MODIS satellite images and Landsat 8 image data from OLI (Operational Land Imager)/TIRS (Thermal Infrared Sensor) satellites using remote sensing techniques to evaluate the Normalized Difference Built-up Index (NDBI) and Normalized difference vegetation index (NDVI). The relationship between land surface temperature and land cover and population density was then determined using a simple linear regression equation. The results of the estimation of land surface temperature in the Northeast from 2000 to 2020 in each province had higher land surface temperature in all provinces both in summer and winter. The highest was 43.55 degrees celsius in Nakhon Ratchasima province and Loei province had the lowest surface temperature at 16.71 degrees celsius, and by comparison, the difference between surface temperatures was 26.84 degrees celsius. It is found that the land surface temperature during summer is presumably higher than winter. Provinces with high temperature including Roi-Et, Maha Sarakham, Yasothorn, Bureeram and Surin. Meanwhile in Nong Khai, Chaiyaphum, Mukdaharn, Sakhon Nakorn and Buengkan are recorded as the provinces with lowest land surface temperature. The research also found that the relations between land surface temperature with land cover and its population; with the normalized difference vegetation index: NDVI and the normalized difference Built-up Index: NDBI have its high relation which are equal (R2) = 0.9318 and 0.897 chronologically. Additionally, the relation between land surface temperature and its population have resulted in (R2) = 0.6159 which is resulted in a median level. The relation between land surface temperature and its the density of population have resulted in (R2) = 0.0734 which is resulted in a low level. Keywords : estimation ; land surface temperature ; multi – temporal satellite imageriesReferences
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Metropolitan. A thematic paper submitted in partial fulfillment of the requirements. For the degree of Master of Science Graduate School of Geographic information System Technology Mahanakorn University of Technology. (in Thai).
National Research Council of Thailand (NRCT). (2016). Research Strategy (2012-2016): Northeastern Thailand.Retrieved January 19, 2021. From: http://www2.kku.ac.th/images/strategy-ne.pdf
National Statistical Office. (2020). Demography Population and Housing Branch. Retrieved January 20, 2021. From: http://statbbi.nso.go.th/staticreport/page/sector/th/01.aspx.
Phengphit,N., Karnchanasutham, S., Nualchawee, K. and Phattraporn Soytong. (2017). The Study of Correlation Between Land Surface Temperature with Urban and Building Area, A Case Study of Amphoe Mueang Rayong, Rayong Province, Thailand. Journal ofGeoinformation Technology of Burapha University Vol. 2 No. 3 January - June 2017. 27- 40. (in Thai).
Rotjanakusol, T., Sangpradid, S., Itsarawisut, J., and Laosuwan,T. (2020). Estimation of Land Surface Temperature by Derivative Analysis of MOD11A2 Product Data, MODIS System. Defence Technology Academic Journal. Volume 2 Number 6 September-December 2020. pp 76 - 85. (in Thai).
Ruthirako, P. (2015). Application of Geo-information Technology to Study Urban Heat Island Phenomenon.SDU Research Journal Humanities and Social Sciences. 9(3): Sep – Dec 2016. 147- 163. (in Thai)
Sanecharoen, W., Nakhapakorn,K., Mutchimwong,A., Jirakajohnkool, S. and Onchang,R. (2019). Assessment of Urban Heat Island Pattern in Bankok Metropolitan Area Using Time – Series of LANDSAT Thermal Infrared Data. Environment and Natural Resources Journal 2019; 17(4): 87 – 102
Songsom,V., Suteerasak,T., and Sanwang, P. (2020). The Relationship between Urban Heat Island and Tourism at Chiangmai City, Thailand Based on Remote Sensing. The Journal of KMUTNB., Vol. 30, No. 4, Oct.–Dec. 2020. 678 – 688. (in Thai)
Tawannahot, N. (2020). The New Isan: From Urbanisation to Globalisation. (2020). Retrieved January 20, 2021. From: https://web.tcdc.or.th/th/Articles/Detail/the-new-isan.
Thai Meteorological Department. (2019). Climate Variability and Changer. Retrieved January 19, 2021. From:
http://www.climate.tmd.go.th/content/file/1616
Wiriyasombut, H., & Traiyawong, P. (2017). The Relationships between NDVI and NDWI of Land Cover Changes in Hatyai district, Songkhla province. Proceedings of the 1st Conference of Humanity and Social
Sciences, Songkhla Rajabhat University "Wisdom Power for Sustainable Development" on 20th - 21st August 2018. 485 – 492.
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Published
2023-01-04
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