Assessment of future prediction of urban growth and climate change in district Multan, Pakistan using CA-Markov method

This research utilized remote sensing (RS) technology to analyze and monitor land use land cover (LULC) and climate change. This work mainly involves three components: (i) investigating the historical trends of LULC and land surface temperature (LST) using Landsat data for 1990, 2005, and 2020. (ii)Evaluate the impact of climate change on urban temperature for increased heat stress in the Multan region and (iii) Future predictions of LULC and LST for 2035 and 2050 by the CA-Markov Chain model. Supervised classification techniques were employed to classify the images into various LULC classes. Historical trends of LULC revealed that over the study period, the built-up area in Multan has increased by almost 30,435 Ha (8.34%), which would continue to reach40003 ha (10.96%) from 1990 to 2020 at the cost of vegetation loss, which is countlessly decreasing and will get from 283,145 Ha (-77.57%) in 2035 to264443 Ha (-71.4%) till 2050. Further, the findings about LST revealed that over the region, the average LST values have increased from 1.2 degrees C over the last two decades and will additionally include 1.8 degrees C until 2050. This study underscores the importance of RS technology in understanding past LULC changes and predicting future land transformations and temperature variations. The results contribute valuable insights for policymakers, land managers, and urban planners to make informed decisions for sustainable LULC and climate adaptation strategies in the face of growing urbanization and climate change challenges.

Automated summary

This research utilized remote sensing technology to analyze historical trends and predict future changes in land use, land cover, and climate. The findings reveal an increasing built-up area, decreasing vegetation, and rising land surface temperature in the Multan region. This study underscores the importance of remote sensing technology in understanding past and predicting future land transformations and temperature variations, providing valuable insights for decision makers in sustainable land use and climate adaptation.