Land Surface Temperature, Land Cover Spectral Indices, and Their Correlations, Nan Town Municipality and Adjacent Sub-Districts, Nan Province

Authors

  • Jiraporn Kulsoontornrat Major of Geographic Information Science, School of Information and Communication Technology, University of Phayao, Phayao, 56000
  • Phatcharaporn Sayamee Major of Geographic Information Science, School of Information and Communication Technology, University of Phayao, Phayao, 56000
  • Wipop Pangewangtong Major of Geographic Information Science, School of Information and Communication Technology, University of Phayao, Phayao, 56000
  • Nattapong Puangkaew Department of Geography, Faculty of Humanities and Social Sciences, Prince of Songkla University, Pattani Campus, Pattani, 94000

Keywords:

Land Surface Temperature, Land Cover Spectral Indices, Correlations, LST Variations, Landsat 8 (L2SP) imagery

Abstract

Land use and land cover changes exert a direct impact on land surface temperatures, significantly contributing to environmental and climate shifts in the region. In this study, Landsat 8 (L2SP) imagery was utilized for extracting land cover spectral indices and computing land surface temperature (LST) to analyze the relationships and explain the variation in land surface temperatures from 2014 to 2022 in the Nan municipality area and adjacent sub-districts. The results revealed a fluctuation in average land surface temperatures over 9 years. In the year of 2014, 2016, 2017, 2019, and 2021, land surface temperatures were lower than the average, whereas in 2015, 2018, 2020, and 2022, land surface temperatures were higher than the average. Correlation coefficients between land surface temperature and four land cover spectral indices, namely Normalized Difference Vegetation Index (NDVI), Normalized Difference Water Index (NDWI), Normalized Difference Built-Up Index (NDBI), and Modified Bare Soil Index (MBI), demonstrated moderate to very high relationships. Specifically, LST-NDVI exhibited a negative correlation, while LST and the other three land cover indices showed a positive correlation. Additionally, a simple linear regression equation reveals that NDVI can explain the decrease in land surface temperature, while NDWI, NDBI, and MBI can explain the increase in land surface temperature. The results obtained from this analysis indicate the impact of changes in land cover, influencing both the increase and decrease in land surface temperatures.

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Published

2024-02-06

Issue

Vol. 5 No. 1 (2024): January-April (2024)

Section

Research Articles