Over the last three decades, Tehsil Mardan has gone through an incredible expansion of the built-up layer. This study explored the land use land cover changes of Tehsil Mardan from 1990 to 2021 along with population dynamics by applying geographic information systems and remote sensing techniques. Landsat satellite images for the years 1990, 1995, 2000, 2010, 2015, and 2021 were used for land use land cover classification. Maximum likelihood Supervised algorithm and confusion matrix were applied for classification and accuracy assessment respectively. Classification results outlined that there is a substantial increase in the built-up layer from 37 km2 to 188 km2 and a significant decrease in bare land class from 437 km2 to 252 km2 from 1990 to 2021. The classification processes overall accuracy was 87.42% to 98.30%, and Kappa Coefficient was from 0.82 to 0.97. Population dynamics were also studied in the present study and found that the total population of tehsil Mardan was 502435, 864017, 1,403,002 in 1981, 1998, and 2017 respectively, which was further forecasted based on historical trends till 2027. Statistical analysis found a strong positive correlation (0.98) between built-up and population and a significant negative correlation (-0.91) between population and bare land. Based on the findings of this study, policymakers should be able to better plan future land use and associated possibilities while keeping environmental threats and opportunities in mind.
Previous Article in event
Unleashing the Catalytic Power of Boric Acid: Accelerating the Knoevenagel Condensation between Aldehydes and Active Methylene CompoundsPrevious Article in session
Next Article in event
Spatio-temporal Assessment of Land Use Land Cover Changes and population dynamics Using Geoinformatics: a case study of Mardan, Khyber Pakhtunkhwa, Pakistan
Published: 26 October 2023 by MDPI in The 4th International Electronic Conference on Applied Sciences session Energy, Environmental and Earth Science
Keywords: LULC Classification, GIS and RS, Change Detection, Mardan