Modeling Spatio-temporal Assessment of Land Use Land Cover of Lahore and its impact on Land surface Temperature Using Multi-Spectral Remote Sensing Data

Abstract Urban sprawl, also widely known as urbanization, is one of the significant problems in the world. This research aims to assess and predict the urban growth and impact on Land Surface Temperature (LST) of Lahore as well as Land-Use and Land-cover (LULC) with a Cellular–Automata–Markov–Chain (CA–Markov–Chain). LULC and LST distributions were mapped using Landsat (5, 7, and 8) data from 1990, 2004, and 2018. Long–term changes to the landscape were simulated using a CA-Markov model at 14-year intervals from 2018 to 2046. Results indicate that the built-up area was increased from 342.54 (18.41%) km2 to 720.31 (38.71%) km2. Meanwhile, barren land, water and vegetation area was decreased from 728.63 (39.16%) km2 to 544.83 (29.28%) km2, from 64.85 (3.49%) km2 to 34.78 (1.87%) km2 and from 724.53 (38.94%) km2 to 560.63 (30.13%) km2 respectively. In addition, Urban Index, a non-vegetation index, accurately predicted LST, showing the maximum correlation R2 = 0.87 with respect to retrieved LST. According to CA–Markov Chain analysis, we can predict the growth of built-up area from 830.22 km2 to 955.53 km2 between 2032 and 2046, based on the development from 1990 to 2018. As Urban Index as the predictor anticipated that the LST 20–23°C, and 24–27°C, regions would all decline in coverage from 5.30–4.79% and 15.79–13.77% in 2032 and 2046, while the temperature 36–39°C regions would all grow in coverage from 15.60–17.21% of the city. These findings are significant for the planning and development division to ensure the long-term usage of land resources for urbanization expansion projects in the future.