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.

This study specifically examines the impact of different variables like extrinsic reward (Pay), intrinsic reward (Appreciation), training and expectancy on employee motivation and impact of employee motivation on job satisfaction and knowledge transfer. The general purpose of this study is to motivate employees. If employees are motivated, they will be satisfied by their jobs and if they are satisfied by their job then they will work hard to attain organizational goals and if goals are achieved then organization will get profit. The current research is descriptive in its nature. We select the sample from different sectors in Pakistan. We select the sample of 200 respondents from population for the collection of data and to check the response of different employees. For this purpose, we structured a self- administrated questionnaire and filled it from the right respondent for true results. We use Regression analysis in this study. This analysis is very important to check the confidence of the study from different respondents. Due to this analysis, we found that Pay and Appreciation are not directly effecting the employee motivation. Job satisfaction also has no direct link with employee motivation but training of employee, expectancy and knowledge transfer have direct relationship with employee motivation. These three variables help a lot to motivate an employee.

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