Urbanization in India is having a significant impact on the environment and human health, as built-up areas are expanding while vegetation and water bodies are declining. To mitigate the negative effects of urbanization, there is an urgent need to monitor and manage the growth of cities through the creation of smart cities. Accurately identifying and mapping urban expansion patterns is crucial to achieving this goal, but this is currently lacking in India. To address this gap, this study takes a comprehensive approach to examining the processes of urban expansion in English Bazar Municipality, Malda, India. The study employs the random forest (RF) method for land use and land cover (LULC) mapping for the years 2001, 2011, and 2021 and then uses the frequency approach, landscape homogeneity, and fragmentation to model urban expansion over the same time period. The study also employs several landscape matrices to quantify urban expansion. The results of the study reveal a substantial increase in built-up areas from 601.26 hectares in 2001 to 859.13 hectares in 2021, accompanied by a corresponding decline in vegetation, cropland, and open land. This land use transition process has also led to increased fragmentation of the landscape. The spatiotemporal landscape homogeneity analysis identifies two transition zones (zone 2 and zone 1) surrounding the stable zone, which capture the new, small, isolated built-up areas and show a progression of urban expansion from 2001 to 2021. Overall, the study provides valuable insights into the changes in urban expansion and fragmentation in English Bazar Municipality. To promote sustainable urban development and enhance the quality of life for residents, it is important for stakeholders and the government to prioritize the conservation and creation of green and blue spaces. This can be accomplished through the incorporation of green infrastructure, smart city principles, community engagement and education, and partnerships with local businesses.
Keywords: Land use change; Landscape homogeneity; Machine learning; Patch analysis; Remote sensing; Urbanization.
© 2023. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.