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Monitoring dynamics of urban expansion using time series Landsat imageries and machine learning in Delhi NCR

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Abstract

In recent decades, cities in developing countries have experienced rapid and unregulated urban expansion. Hence, this study is designed to examine the built-up growth in Delhi NCR using optimized machine learning (ML) techniques and Landsat datasets. The LULC classification and built-up area extraction is done using multiple optimized ML algorithms while landscape fragmentation analysis (LFA) and frequency approach (FA) were used for further analysis of built-up area. The study shows a substantial increase in built-up area (328%) while agricultural land witnessed a decline of about 5.8% during 1990–2018. The city-wise analysis of built-up expansion shows that all the cities of Delhi NCR have witnessed very fast built-up expansion except Rohtak. Moreover, analysis of FA shows that maximum built- up area is under frequency 5 (91,184 hectare) frequency 6 (90,536 hectare) while minimum area is under frequency (45,511 hectare) indicating that built-up expansion in Delhi NCR is becoming permanent with time. Further, the result of CCDM demonstrates high suitability of LFA and FA in analyzing the built-up growth in Delhi NCR. The study may be helpful in the formulation of urban management plans and policies by the town planners and policy makers to tackle the problems of urban expansion.

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Data availability

The datasets used and generated during this research are available from the corresponding author on reasonable request.

Code availability

The codes used in this research are available from the corresponding author on reasonable request.

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Acknowledgements

Authors thankfully acknowledge the Deanship of Scientific Research for proving administrative and financial supports. Funding for this research was given under award numbers RGP2/411/44 by the Deanship of Scientific Research; King Khalid University, Ministry of Education, Kingdom of Saudi Arabia.

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No funds, grants, or other support was received.

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Authors and Affiliations

  1. Department of Geography & Disaster Management, University of Kashmir, Srinagar, Jammu and Kashmir, 190006, India

    Mohd Waseem Naikoo

  2. Department of Geography, Faculty of Sciences, Jamia Millia Islamia, New Delhi, 110025, India

    Mohd Waseem Naikoo,  Shahfahad, Swapan Talukdar,  Asif, Mohammad Tayyab, M. Ishtiaq & Atiqur Rahman

  3. Department of Architecture and Planning, College of Engineering, King Khalid University, Abha, 61411, Saudi Arabia

    Ahmad A. Bindajam

  4. Department of Civil Engineering, College of Engineering, King Khalid University, Abha, 61421, Saudi Arabia

    Javed Mallick

  5. Department of Geography, Asutosh College, Kolkata, West Bengal, 700026, India

    Swapan Talukdar

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  1. Mohd Waseem Naikoo
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  2. Ahmad A. Bindajam
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  3. Shahfahad
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  5. Asif
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  7. Javed Mallick
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  8. M. Ishtiaq
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  9. Atiqur Rahman
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Contributions

Conceptualization: Mohd Waseem Naikoo, Swapan Talukdar, & Atiqur Rahman; Methodology: Mohd Waseem Naikoo, Swapan Talukdar, Ahmad A. Bindajam & Shahfahad; Formal analysis and investigation: Ahmad A. Bindajam, Mohammad Tayyab, Javed Mallick, & Asif; Writing - original draft preparation: Mohd Waseem Naikoo, Asif, & Shahfahad; Writing - review and editing: Javed Mallick, Asif, & Atiqur Rahman; Resources: Ahmad A. Bindajam, Javed Mallick & M. Ishtiaq; Supervision: Atiqur Rahman & M. Ishtiaq.

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Naikoo, M.W., Bindajam, A.A., Shahfahad et al. Monitoring dynamics of urban expansion using time series Landsat imageries and machine learning in Delhi NCR. Environ Dev Sustain (2024). https://doi.org/10.1007/s10668-024-04859-0

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