Journal of Agricultural Engineering
  • Year: 2026
  • Volume: 63
  • Issue: 1

Spatio-Temporal Dynamics of Land Use/Land Cover Change Using Remote Sensing and GIS Techniques in Ambegaon Taluka, Maharashtra, India

  • Author:
  • Vikesh Suryawanshi1, S. K. Dingre2, Vinayak Paradkar3,*, Adarsha Gopalakrishna Bhat1, Anand Gupta4, Rahul Dhadwad2, Piyusha Dalavi2
  • Total Page Count: 11
  • Page Number: 174 to 184

1Division of Agricultural Engineering, Indian Agricultural Research Institute, New Delhi, India

2Department of Irrigation and Drainage Engineering, Dr. A. S. College of Agricultural Engineering and Technology, Mahatma Phule Krishi Vidyapeeth, Rahuri, Maharashtra, India

3Chhatrapati Shivaji Maharaj Krishi Vidnyan Sankul, Kashti, Maharashtra, India

4Division of Agricultural Physics, Indian Agricultural Research Institute, New Delhi, India

*Corresponding Author’s E-mail Address: paradkarvd@gmail.com

Online Published on 24 April, 2026.

Abstract

Understanding land use/land cover (LULC) changes is a crucial step for the optimum utilization and development of land resources and to employ land usage schemes satisfactorily to meet the growing demands for human needs and welfare. This study analyzes the temporal dynamics of LULC alterations using remote sensing and geographical information system (GIS) techniques in Ambegaon taluka of Pune district in Maharashtra, India. The study employed the maximum likelihood classifier method to identify five LULC categories viz., agricultural land, settlement, water body, forest land, and barren land through supervised classification. It examined the changes in LULC over two periods, i.e., 2013-2016 and 2016-2019, influenced by both anthropogenic activities and natural factors. Satellite imageries of LANDSAT 8 for the years 2013, 2016 and 2019 were analyzed to create the LULC maps. The results indicated a significant shift in LULC patterns over the two periods. Specifically, agricultural land expanded by 22.73%, barren lands decreased by 11.34% and settlements increased by 16.10%. Additionally, there was a minor decrease of 0.09% in water bodies and a substantial reduction (23.48%) in forest lands. Population growth, rising food demand, economic pressures and incentives might have driven the conversion of barren and forest lands into agriculture, at the cost of deforestation, over-extraction of limited water resources, and the reduction of water bodies in the study area. This analysis could offer valuable insights into how land cover is distributed and composed, which is essential for making informed decisions in environmental management, urban planning, and resource allocation in the study area. This study can also aid in identifying vulnerable areas prone to landslides, such as Malin village incidence occurred in 2014, and enabling the implementation of sustainable land use management practices to mitigate risks.

Keywords

Change recognition, Expanding agricultural land, Increased settlements, Maximum likelihood classifier, Supervised classification