Assessment of land use/land cover changes and its effect on land surface temperature using remote sensing techniques in Southern Punjab, Pakistan

Land surface temperature (LST) is defined as a phenomenon which shows that microclimate of an urban system gets heated much faster than its surrounding rural climates. The expansion of buildings has a noteworthy influence on land use/land cover (LULC) due to conversion of vegetation land into commercial and residential areas and their associated infrastructure by which LST is accelerated. The objective of the research was to study the impact of changes in LULC on LST of Southern Punjab (Pakistan) through remote sensing (RS) data. Landsat images of 30-year duration (1987, 1997, 2007 and 2017) were employed for identifying vegetation indices and LST in the study region. These images also helped to work out normalized difference water index (NDWI) and normalized difference built-up index (NDBI) maps. There was an increase from 29620 (3.63 %) to 88038 ha (10.8 %) in built-up area over the 30 years. LST values were found in the range 12-42 degrees C, 11-44 degrees C, 11-45 degrees C and 11-47 degrees C in the years 1987, 1997, 2007 and 2017, respectively. Regression coefficients (R-2) 0.81, 0.78, 0.84 and 0.76 were observed between NDVI and LST in the corresponding years respectively. Our study showed that NDVI and NDWI were negatively correlated with less LST; however, NDBI showed positive correlation with high LST. Our study gives critical information of LULC and LST and will be a helpful tool for policy makers for developing effective policies in managing land resources.

Automated summary

This research examines the impact of changes in land use/land cover on land surface temperature (LST) in Southern Punjab, Pakistan, using remote sensing data. The study finds that the expansion of buildings and conversion of vegetation land into commercial and residential areas has accelerated the increase of LST. The study also shows a negative correlation between NDVI and NDWI with lower LST, and a positive correlation between NDBI and higher LST.