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

Mohd Waseem Naikoo (), Ahmad A. Bindajam (), Shahfahad (), Swapan Talukdar (), Asif (), Mohammad Tayyab (), Javed Mallick (), M. Ishtiaq () and Atiqur Rahman ()
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Mohd Waseem Naikoo: University of Kashmir, Department of Geography & Disaster Management
Ahmad A. Bindajam: King Khalid University, Department of Architecture and Planning, College of Engineering
Shahfahad: Jamia Millia Islamia, Department of Geography, Faculty of Sciences
Swapan Talukdar: Jamia Millia Islamia, Department of Geography, Faculty of Sciences
Asif: Jamia Millia Islamia, Department of Geography, Faculty of Sciences
Mohammad Tayyab: Jamia Millia Islamia, Department of Geography, Faculty of Sciences
Javed Mallick: King Khalid University, Department of Civil Engineering, College of Engineering
M. Ishtiaq: Jamia Millia Islamia, Department of Geography, Faculty of Sciences
Atiqur Rahman: Jamia Millia Islamia, Department of Geography, Faculty of Sciences

Environment, Development and Sustainability: A Multidisciplinary Approach to the Theory and Practice of Sustainable Development, 2025, vol. 27, issue 11, No 69, 27699-27732

Abstract: 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.

Keywords: Urban expansion; Optimised machine learning; Random Forest; Buffer analysis; Delhi NCR (search for similar items in EconPapers)
Date: 2025
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DOI: 10.1007/s10668-024-04859-0

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