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The research is aimed at evaluating the process of urbanization and it's spatio-temporal impacts on albedo and the changing climate of the Punjab. Climate change in urban areas has become a public focal point, as urban development in our present-day environment is one of the most critical aspects of human activities. The shift in the reflective properties of the surface is one of the particular climatic consequences of urbanization which most researchers have overlooked (Albedo). Changes in urbanized land surface properties resulted in a global, regional, and local change s in biochemical cycles and environment. This analysis aims to identify the urban land use of the 1990-2018 study area (decadal) and evaluate Albedo and Climatic Variables' trends over the selected region. This study will provide a complete code of classification in Google Earth Engine for researchers. In Google Earth Engine, the classification technique was utilized to detect urban areas and barren land cover types by applying coding over four images of 1990, 2000, 2010, and 2018 of Punjab Province. The Albedo maps were created in the same way for the years 2000 and 2018. Climatic maps were created from 1990 to 2018 by applying the Inverse Distance Weighted method in a GIS system. There has been an apparent increase in the urban area of Punjab Province from 1990 to 2018. The urban area has increased from 1183.5342 km2 to 8396.4618 km2 during the study period. Results from climatic maps showed hotspots in the areas that have undergone urbanization. Overall, the accuracy of the classification images was 81-88 percent, which was foremost to achieve the objectives of the research. [ABSTRACT FROM AUTHOR]

ABSTRACT: In Pakistan, wheat is the principal food crop contributing 2.8 GDP in country's economy. For wheat production different factors are responsible, one of them is climate. This study has been carried out in Bahawalnagar, lying in arid zone of Punjab, to investigate the impact of year to year changes in meteorological parameters like rainfall and temperature on wheat production during the wheat cropping season i.e. November-April. This study focused to find out the impact of variation in temperature and rainfall on wheat production. Multiple linear regression model was applied to check out the relationship between the dependent(production) and independent(rainfall, temperature, area) variables. A significant regression equation was found with R2 of.946, thus explaining 94% variance in the response variable. The model was also tested through ANOVA and the significance of p-value was.000. Results concluded a strong denial of relationship of temperature and rainfall variability with wheat production in Bahawalnagar, however a strong relationship between area under cultivation and wheat production nwas identified. [ABSTRACT FROM AUTHOR]