RESUMO
The dynamic interplay between urbanization and its impacts on climate is a subject of recent concern, particularly in rapidly urbanizing cities of Pakistan. This research investigated the spatio-temporal effects of urban growth in terms of Land Use Land Cover changes on the thermal environment (Land Surface Temperature) of the Sialkot city, Pakistan using satellite data spanning four distinct time periods (1989, 2000, 2009 and 2020) and predicted changes for year 2030 by employing Cellular Automata Markov Chain Model. Satellite imagery (Landsat 5, 7 and 8) was processed, and maximum likelihood supervised classification was done to generate LULC maps for each of the aforementioned years. In addition to LULC classification, thermal bands of satellite data (for summer and winter) were processed to compute Land Surface Temperature (LST) of the city. The prediction of LULC changes and LST was done for year 2030 using Cellular Automata Markov Chain Model. The accuracy of classified and prediction maps was checked using Kappa Index. The LULC analysis revealed 4.14% increase in the built-up area and 3.43% decrease in vegetation cover of the city during 1989 to 2020. Both land covers are expected to change in the future (year 2030) by + 1.31% (built-up) and - 1.1% (vegetation). Furthermore, a declining trend in the barren land and water bodies was also observed over time. These LULC changes were found affecting the LST of study area. The transformation of vegetation cover into built-up area resulted in an increase in LST over time. A notable rise of 4.5 °C (summer) and 5.7 °C (winter) in the mean LST of Sialkot was observed during 1989 to 2020 and further increases are anticipated in year 2030. This study calls for attention of the policy makers to reduce human impact on the local climate of the city. The study will also help city developers in analyzing the urban population growth trend, finding suitable location to built new infrastructure by governmental authorities and how the rising temperature can affect energy demand and agriculture production of the city in future.
