Implications of Decreasing Farm Size on Urbanization: A Case Study of Punjab Pakistan

Mukhtar, Umar; Zhong, Zhaohui; Beihai, Tian; Naseer, Muhammad Asad ur Rehman; Razzaq, Amar; Hina, Tayyaba

doi:10.5296/jsss.v5i2.12746

Cited by 6 publications

(3 citation statements)

References 8 publications

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“…Urbanization has been increasing in Punjab province for the past few decades. Because it is richer in industrialization than other provinces and has many educational and employment opportunities [111], the people prefer to move to Punjab. The rate of urbanization in Punjab is 36.71 percent [112].…”

Section: Climate Variability and Sustainabilitymentioning

confidence: 99%

Assessment of Climate Variability among Seasonal Trends Using In Situ Measurements: A Case Study of Punjab, Pakistan

et al. 2021

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This research assessed the changes in spatial patterns and the seasonal trends in temperature, precipitation, and relative humidity over 36 years (1979–2014) using Climate Forecast System Reanalysis (CFSR) datasets. The evaluation of climate deviations was the prime objective of this research. The augmented Dickey–Fuller Test (ADF) was used to scrutinize whether the data was either stationary or non-stationary. The results of the ADF test showed that all the datasets were found to be stationary at lag order 3. To observe undulations in the time series data, trend analyses were done using Sen’s slope (SS), Mann–Kendall (MK), and Cox and Stuart (CS) tests. For all the statistical analyses, we considered the 5% significance level (α = 0.05) and p < 0.05 to be statistically significant. We observed significant (p < 0.05) trends in spring (MAM) and autumn (SON) for minimum temperature (Tmin) in Punjab. We also noted a significant (p < 0.05) trend in precipitation during autumn (SON). Annually, all the variables showed a non-significant (p > 0.05) trend for Punjab, Pakistan, during the period 1979–2014. Climate variability, such as a decrease in precipitation, higher temperature, and relative humidity fluctuations, were the reasons for the imbalance in the sustainability of Punjab, Pakistan.

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Section: Climate Variability and Sustainabilitymentioning

confidence: 99%

Assessment of Climate Variability among Seasonal Trends Using In Situ Measurements: A Case Study of Punjab, Pakistan

et al. 2021

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“…km.). (Mukhtar et al, 2018) in Punjab Pakistan, the rate of urbanization is quite high, especially in Punjab Pakistan.…”

Section: Extent Of Urbanization Inmentioning

confidence: 99%

Appraisal of Urbanization and Its Impacts on the Albedo and Climate Change of the Punjab-Pakistan: A Spatio-Temporal Analysis

Amjad¹,

Shirazi

Sarwar

et al. 2023

PJS

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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 mostcritical aspects of human activities. The shift in the reflective properties of the surface is one of theparticular climatic consequences of urbanization which most researchers have overlooked (Albedo).Changes in urbanized land surface properties resulted in a global, regional, and local changes inbiochemical 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. InGoogle Earth Engine, the classification technique was utilized to detect urban areas and barren landcover 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 werecreated from 1990 to 2018 by applying the Inverse Distance Weighted method in a GIS system. Therehas been an apparent increase in the urban area of Punjab Province from 1990 to 2018. The urban areahas increased from 1183.5342 km2 to 8396.4618 km2 during the study period. Results from climaticmaps showed hotspots in the areas that have undergone urbanization. Overall, the accuracy of theclassification images was 81-88 percent, which was foremost to achieve the objectives of the research.

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“…Each city's impervious surface area and growth information can be used to update Punjab's impervious surfaces inventory. Faisalabad, Gujranwala, Sialkot, Sheikhupura, and Rawalpindi are identified to have high urban growth [59]. These cities cover the different sizes, climatic conditions, locations, and geography within the province.…”

Section: Introductionmentioning

confidence: 99%

Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier

2021

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Urbanization increases the amount of impervious surfaces, making accurate information on spatial and temporal expansion trends essential; the challenge is to develop a cost- and labor-effective technique that is compatible with the assessment of multiple geographical locations in developing countries. Several studies have identified the potential of remote sensing and multiple source information in impervious surface quantification. Therefore, this study aims to fuse datasets from the Sentinel 1 and 2 Satellites to map the impervious surfaces of nine Pakistani cities and estimate their growth rates from 2016 to 2020 utilizing the random forest algorithm. All bands in the optical and radar images were resampled to 10 m resolution, projected to same coordinate system and geometrically aligned to stack into a single product. The models were then trained, and classifications were validated with land cover samples from Google Earth’s high-resolution images. Overall accuracies of classified maps ranged from 85% to 98% with the resultant quantities showing a strong linear relationship (R-squared value of 0.998) with the Copernicus Global Land Services data. There was up to 9% increase in accuracy and up to 12 % increase in kappa coefficient from the fused data with respect to optical alone. A McNemar test confirmed the superiority of fused data. Finally, the cities had growth rates ranging from 0.5% to 2.5%, with an average of 1.8%. The information obtained can alert urban planners and environmentalists to assess impervious surface impacts in the cities.

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Implications of Decreasing Farm Size on Urbanization: A Case Study of Punjab Pakistan

Cited by 6 publications

References 8 publications

Assessment of Climate Variability among Seasonal Trends Using In Situ Measurements: A Case Study of Punjab, Pakistan

Assessment of Climate Variability among Seasonal Trends Using In Situ Measurements: A Case Study of Punjab, Pakistan

Appraisal of Urbanization and Its Impacts on the Albedo and Climate Change of the Punjab-Pakistan: A Spatio-Temporal Analysis

Impervious Surfaces Mapping at City Scale by Fusion of Radar and Optical Data through a Random Forest Classifier

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