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Abstract <p>Urbanization is a key factor influencing sustainable city development, as rapid urban expansion driven through rural–urban migration leads to substantial shifts in land use and land cover (LULC). This study assessed LULC changes and predicted future trends for 2033 in the Delhi-NCR region, using Remote Sensing, Machine Learning, and Markov Chain based on the datasets from 2003 to 2023. Satellite images were used to classify LULC with Maximum Likelihood Classifier (MLC) algorithm. The explanatory variables, such as distance from the road, forest,&#xa0;water body and built-up were generated using the Euclidean distance tool. Results showed a 10% increase in the built-up area, accompanied by a 9% decrease in agricultural land and a 0.96% reduction in forest cover over a 20 year period. Change detection (CD) showed a 78% increase in a built-up area, while agriculture, forest, and open land declined by 11%, 31% and 63%, respectively. Projections for 2033 suggest built-up areas increased by 28%, with agriculture and forest areas expected to decrease by 66.5% and 2.05%, respectively. The predicted LULC map 2023 was validated using the CA-ANN model and found a strong agreement with the actual LULC 2023 map (K = 0.86). The Shannon’s Entropy Index shows greater urban sprawl in the periphery (0.24, 0.35, and 0.36) as compared to urban core (0.67, 0.68, and 0.66) in 2003, 2013, and 2023 respectively. The study provides valuable insight for urban planners and policymakers to guide sustainable urban growth in line with SDG-11, focusing on enhancing management strategies for future urban development.</p> Graphical abstract <p></p>

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Forecasting urban expansion in Delhi-NCR: integrating remote sensing, machine learning, and Markov chain simulation for sustainable urban planning

  • 期刊论文

  • 发表日期：

    2025年3月17日

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  • Shadman Nahid,
  • Ram Pravesh Kumar,
  • Prasenjit Acharya,
  • Krishan Kumar,
  • Sanju Purohit

摘要

Abstract

Urbanization is a key factor influencing sustainable city development, as rapid urban expansion driven through rural–urban migration leads to substantial shifts in land use and land cover (LULC). This study assessed LULC changes and predicted future trends for 2033 in the Delhi-NCR region, using Remote Sensing, Machine Learning, and Markov Chain based on the datasets from 2003 to 2023. Satellite images were used to classify LULC with Maximum Likelihood Classifier (MLC) algorithm. The explanatory variables, such as distance from the road, forest, water body and built-up were generated using the Euclidean distance tool. Results showed a 10% increase in the built-up area, accompanied by a 9% decrease in agricultural land and a 0.96% reduction in forest cover over a 20 year period. Change detection (CD) showed a 78% increase in a built-up area, while agriculture, forest, and open land declined by 11%, 31% and 63%, respectively. Projections for 2033 suggest built-up areas increased by 28%, with agriculture and forest areas expected to decrease by 66.5% and 2.05%, respectively. The predicted LULC map 2023 was validated using the CA-ANN model and found a strong agreement with the actual LULC 2023 map (K = 0.86). The Shannon’s Entropy Index shows greater urban sprawl in the periphery (0.24, 0.35, and 0.36) as compared to urban core (0.67, 0.68, and 0.66) in 2003, 2013, and 2023 respectively. The study provides valuable insight for urban planners and policymakers to guide sustainable urban growth in line with SDG-11, focusing on enhancing management strategies for future urban development.

Graphical abstract