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Extents of Predictors for Land Surface Temperature Using Multiple Regression Model.

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Land surface temperature (LST) is influenced by vegetation, built-up areas, and topography. Increased vegetation and slope decrease LST, while increased built-up areas significantly raise LST, impacting urban heat dynamics.

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Area of Science:

  • Earth and Environmental Sciences
  • Remote Sensing
  • Urban Climatology

Background:

  • Land surface temperature (LST) is crucial for understanding climate change, urban land use, and heat balance.
  • Accurate LST data is vital for developing effective climate models.
  • Landsat satellite data offers valuable remote sensing capabilities for analyzing land processes.

Purpose of the Study:

  • To determine LST in a specific region using Landsat 8 imagery from two distinct time periods.
  • To compare LST variations between the two time periods.
  • To investigate the influence of the Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and terrain slope on LST.

Main Methods:

  • Utilized Landsat 8 Operational Land Imager (OLI)/Thermal Infrared Sensor (TIRS) satellite images.
  • Analyzed LST data for two different time periods (November and March).
  • Employed multiple linear regression to model the relationship between LST and NDVI, NDBI, and slope.

Main Results:

  • Recorded maximum LST of 40.44°C in November and 42.44°C in March.
  • Minimum LST recorded was 20.78°C in November and 24.57°C in March.
  • Established inverse relationships between LST and NDVI (-6.369) and slope (-0.077), and a direct relationship with NDBI (+14.74).

Conclusions:

  • Higher vegetation cover and steeper slopes are associated with lower LST.
  • Increased built-up areas (NDBI) significantly correlate with higher LST.
  • These findings highlight the impact of land cover and topography on LST, crucial for urban planning and climate studies.