Spatio-temporal analysis of land use and land cover dynamics in the peri-urban interface of Agra city, India
摘要
This study examines changes in land use and land cover (LULC) in the peri-urban interface of Agra city, India. The rapid and often unregulated urban expansion in such transitional zones raises critical concerns for sustainable land management. Despite this, limited studies have focused on the long-term LULC dynamics and their driving forces in medium-sized Indian cities. Addressing this gap, the present study employs remote sensing and GIS techniques to analyze LULC changes over a 40-year period from 1981 to 2021. For identifying the land use land cover changes study utilizes Landsat-3 Multispectral Scanner (1981), Landsat-5 Thematic Mapper (2001), and Landsat 8 Operational Land Imager and Thermal Infra-Red Sensor (2021) imagery. Supervised classification technique was employed to categories for delineating land use and land cover types. Present study assessed the classification accuracy utilizing the kappa coefficient and total accuracy metrics. The results reveal a significant expansion in built-up areas by 382.13%, increasing from 20.87 sq. km in 1981 to 100.62 sq. km in 2021, indicating intensive urban growth in the peri-urban zone. Wasteland expanded by 16.50%, from 129.97 sq. km to 151.41 sq. km during 1981 to 2021. In contrast, agricultural land declined sharply by -25.84%, falling from 208.13 sq. km to 154.35 sq. km during 1981 to 2021. Forest covers and water bodies decreased by -61.08% and − 12.25% respectively. The highest rate of LULC change occurred between 2001 and 2021, corresponding with intensified population growth and infrastructure development. These transformations are primarily driven by population growth, urban development, and socio-economic shifts. The findings of this study offer insights on alterations in land use and land cover and their driving forces, which Agra district authorities can utilize to formulate sustainable development strategies. While the study provides valuable insights, it is not without limitations. Factors such as classification errors, moderate spatial resolution of satellite imagery, and seasonal variability may have introduced some uncertainty in the analysis.