Analysis of the relationship among land surface temperature (LST), land use land cover (LULC), and normalized difference vegetation index (NDVI) with topographic elements in the lower Himalayan region

Waheed Ullah; Khalid Ahmad; Siddique Ullah; Adnan Ahmad Tahir; Muhammad Faisal Javed; Abdul Nazir; Arshad Mehmood Abbasi; Mubashir Aziz; Abdullah Mohamed

doi:10.1016/j.heliyon.2023.e13322

Analysis of the relationship among land surface temperature (LST), land use land cover (LULC), and normalized difference vegetation index (NDVI) with topographic elements in the lower Himalayan region

Heliyon. 2023 Feb 3;9(2):e13322. doi: 10.1016/j.heliyon.2023.e13322. eCollection 2023 Feb.

Authors

Waheed Ullah¹, Khalid Ahmad¹, Siddique Ullah², Adnan Ahmad Tahir¹, Muhammad Faisal Javed², Abdul Nazir¹, Arshad Mehmood Abbasi¹, Mubashir Aziz^{3

4}, Abdullah Mohamed⁵

Affiliations

¹ Department of Environmental Sciences, COMSATS University Islamabad, Abbottabad Campus, 22060, Pakistan.
² Department of Civil Engineering, COMSATS University Islamabad, Abbottabad Campus, Tobe Camp University Road Abbottabad 22060, Pakistan.
³ Department of Civil and Environmental Engineering, King Fahd University of Petroleum & Minerals, Dhahran 31261, Saudi Arabia.
⁴ Interdisciplinary Research Center for Construction and Building Materials, King Fahd, University of Petroleum and Minerals, Dhahran 31261, Saudi Arabia.
⁵ Research Centre, Future University in Egypt, New Cairo 11835, Egypt.

Abstract

Land Surface Temperature (LST) affects exchange of energy between earth surface and atmosphere which is important for studying environmental changes. However, research on the relationship between LST, Land Use Land Cover (LULC), and Normalized Difference Vegetation Index (NDVI) with topographic elements in the lower Himalayan region has not been done. Therefore, the present study explored the relationship between LST and NDVI, and LULC types with topographic elements in the lower Himalayan region of Pakistan. The study area was divided into North-South, West-East, North-West to South-East and North-East to South-East directions using ArcMap 3D analysis. The current study used Landsat 8 (OLI/TIRS) data from May 2021 for LULC and LST analysis in the study area. The LST data was obtained from the thermal band of Landsat 8 (TIRS), while the LULC of the study areas was classified using the Maximum Likelihood Classification (MLC) method utilizing Landsat 8 (OLI) data. TIRS collects data for two narrow spectral bands (B10 and B11) with spectral wavelength of 10.6 μm-12.51 μm in the thermal region formerly covered by one wide spectral band (B6) on Landsat 4-7. With 12-bit data products, TIRS data is available in radiometric, geometric, and terrain-corrected file format. The effect of elevation on LST was assessed using LST and elevation data obtained from the USGS website. The LST across LULC types with sunny and shady slopes was analyzed to assess the influence of slope directions. The relationship of LST with elevation and NDVI was examined using correlation analysis. The results indicated that LST decreased from North-South and South-East, while increasing from North-East and South-West directions. The correlation coefficient between LST and elevation was negative, with an R-value of -0.51. The NDVI findings with elevation showed that NDVI increases with an increase in elevation. Zonal analysis of LST for different LULC types showed that built-up and bare soil had the highest mean LST, which was 35.76 °C and 28.08 °C, respectively, followed by agriculture, vegetation, and water bodies. The mean LST difference between sunny and shady slopes was 1.02 °C. The correlation between NDVI and LST was negative for all LULC types except the water body. This study findings can be used to ensure sustainable urban development and minimize urban heat island effects by providing effective guidelines for urban planners, policymakers, and respective authorities in the Lower Himalayan region. The current thermal remote sensing findings can be used to model energy fluxes and surface processes in the study area.

Keywords: Land surface temperature; Land use land cover changes; Maximum likelihood classification; Normalized difference vegetation index.