Land use and land cover (LULC) change analysis is a critical instrument for studying urban growth across the world. Our objectives were to produce historical LULC maps during the 1988–2016 period for spatial and temporal analysis, forecast future LULC until 2040 by using the Markov model, and identify the impact of LULC on urbanization. Two scenes of Landsat-5 TM for 1988 and 2001 and one scene of Landsat-8 OLI for 2016 were processed and used. The Normalized Difference Vegetation Index (NDVI) model with precise class value ranges was applied to produce land cover maps with six classes of water, built-up, barren land, shrub and grassland, sparse vegetation, and dense vegetation. LULC maps for the years of 1988 and 2001 were used to develop an LULC transformation matrix. It was used to drive an LULC transformation probability matrix using a Markov model for future forecasting of LULC in 2014, 2027, and 2040. The accuracy of 2016 LULC classes was estimated by comparing it against Markov modeled classes. It was found that the areas for: (i) water decreased from 1.43% to 0.51%; (ii) built-up increased from 9.58% to 20.80%; (iii) barren land decreased from 29.50% to 13.40%; (iv) shrub and grass land decreased from 30.57% to 21.10%; (v) sparse vegetation increased from 18% to 20.10%; and (vi) dense vegetation increased from 10.57% to 24.10%. The variations in LULC classes could be noticed by 2040 as compared to 1988. This LULC variation revealed that the water could decrease to 5.32 km² from 25.37 km²; the built-up could increase to 625.16 km² from 168.29 km²; the barren land could decrease to 137.53 km² from 514.13 km²; the shrub and grassland could decrease to 297.68 km² from 539.46 km²; the sparse vegetation could decrease to 297.68 km² from 539.46 km²; and the dense vegetation could increase to 409.65 km² from 191.51 km². The LULC classification accuracy was 90.27% and 95.11% for 1988 and 2001, respectively. The co-efficient of determination (R²) was found to be 0.90 for 2016 LULC classes obtained from Landsat-8 and derived from a Markov model. For District Lahore, the LULC changes could be related to increasing population and intense migration trends, which had progressive impact on infrastructure development, industrial and economic growth, and detrimental effects on water resources.

Livability is a concept that assesses the quality of life and relative performance of different regions and communities, based on various qualitative and quantitative factors. The assessment of urban livability is a complex and multidimensional task, which is of utmost importance for informed and transparent policy and decision making. The present study aimed to develop a road map for cities’ ranking on the bases of their livability. We have used a case study of eight major cities of Punjab, Pakistan. Indicators of cities’ sustainability considered for the cities’ ranking were spatial planning and growth, individual wellbeing, urban economy, connectivity and infrastructure, quality of life, and the urban environment. In the first stage, after the consolidation of socio-economic, environmental, and other indicators, they are converted into dimension indices by taking geometric means. In the second stage, the Analytical Hierarchical Process (AHP) has been employed for assigning weights to each dimension into a composite index. The results of the study depict the disparities among cities through a multidimensional analysis. Lahore is ranked as first overall from the bottom. Thus, the study recommends that, despite the high standing of Lahore on the livability ladder, it must address the issue of connectivity and traffic congestion and per capita needs of the public infrastructure for a growing mega metropolitan city of over 10 million people. Similarly, Rawalpindi is ranked as the second-best city with regard to livability in Punjab. The main contributing factors for Rawalpindi city are ‘individual well-being’, ‘urban economy’, and ‘urban environment’. Moreover, the current study also suggests important policy implications for decision makers to highlight the areas that must be reconsidered for improvement in terms of the selected indicators and dimensions.