Land is the fundamental factor of production and economic development. It is the fundamental bases for fulfilling our various needs and requirements. Land cover refers to the physical and biophysical cover on the earth’s surface. Land use refers to the arrangements, management, and modification of land-cover type by anthropogenic activities. The main objective of this paper is to predict and analyse the present and future urban growth of Varanasi city and its periphery area, using the Landsat data. For this study freely available Landsat-5 TM and Landsat-8 OLI, 30-m resoultion, path 142, and row 42 acquired during the month of February 2005, 2014, and 2020, dowloaded from global land-cover facilty are used. for this study. A hybrid approach combining unsupervised and supervised classification techniques are employed to classify the images. Six major land-use/land-cover types including built-up land, water bodies, cropped area, fallow land, palntation, and sandbar are identified and delineated. The results show that built-up land and crop land occupied the maximum of 32.5 and 36.3% of the study area in 2020. These two together accounted for around 68.8% of the study area. The accuracy assessment results showed an overall accuracy of 90.35, 81.94, and 90.52% for the classified images and Kappa coefficient of 0.85, 0.78, and 0.87 for 2005, 2014, and 2020.

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Prediction of Land-Use Changes Based on Land Change Modeler (LCM) Using Remote Sensing: A Case Study of Varanasi City (U.P.), India

  • Ramesh Kumar Patel,
  • Vineet Kumar Rai,
  • Narender Verma,
  • Satya Prakash

摘要

Land is the fundamental factor of production and economic development. It is the fundamental bases for fulfilling our various needs and requirements. Land cover refers to the physical and biophysical cover on the earth’s surface. Land use refers to the arrangements, management, and modification of land-cover type by anthropogenic activities. The main objective of this paper is to predict and analyse the present and future urban growth of Varanasi city and its periphery area, using the Landsat data. For this study freely available Landsat-5 TM and Landsat-8 OLI, 30-m resoultion, path 142, and row 42 acquired during the month of February 2005, 2014, and 2020, dowloaded from global land-cover facilty are used. for this study. A hybrid approach combining unsupervised and supervised classification techniques are employed to classify the images. Six major land-use/land-cover types including built-up land, water bodies, cropped area, fallow land, palntation, and sandbar are identified and delineated. The results show that built-up land and crop land occupied the maximum of 32.5 and 36.3% of the study area in 2020. These two together accounted for around 68.8% of the study area. The accuracy assessment results showed an overall accuracy of 90.35, 81.94, and 90.52% for the classified images and Kappa coefficient of 0.85, 0.78, and 0.87 for 2005, 2014, and 2020.