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Stream/Area:
-
Subject:
-
Paper ID:
IJSREM65137
Volume:
10 |
6 |
Jun 2026
Pages:
1-11
Publication Frequency:
Monthly (12 issues per year)
License:International Journal of Scientific Research in Engineering and Management follows the Creative Commons Attribution 4.0 (CC BY 4.0) License, allowing free use and distribution with proper attribution.
Geography / Geospatial Science
Anna University
Geography / Geospatial Science
Tagore Engineering College, Chennai -127.
Urban sprawl presents significant challenges due to the complex interactions among environmental variables and driving forces that continuously reshape landscape patterns and biodiversity. This study assesses and predicts urban sprawl in Chennai, India, using advanced geospatial modeling techniques. It evaluates land use/land cover (LULC) changes over a five-year period and identifies the major drivers of these changes using high-resolution geospatial datasets. The methodology includes the selection of the study area through Multi-Criteria Decision Analysis (MCDA), the development of multi-temporal land use maps, and the prediction of urban sprawl for 2021 using MOLUSCE (Modelling Land Use Change with Cellular Automata and GIS) and the Land Change Modeler (LCM). Additionally, the study examines several geospatial modeling approaches—such as operations research, system dynamics, agent-based modeling, and geospatial analysis—commonly used in rapidly urbanizing and peri-urban landscapes. Ten primary LULC classes (water, trees, flooded vegetation, crops, built-up area, bare ground, snow/ice, and rangeland) were mapped using ESA Sentinel-2 imagery at a 10-meter resolution for the years 2017–2021. The analysis identifies urbanization, population growth, socio-economic development, and climate change as key drivers of LULC transformations in the Chennai Metropolitan Region. The findings underscore the need for robust geo-based modeling frameworks and integrated landscape planning support systems to strengthen policy formulation, urban planning, and sustainable development efforts in the region.
KEYWORDS
Urban sprawl prediction models can help us effectively in quantifying future urban sprawl growth and also identify direction and pattern of urban sprawl growth. This approach can help in urban planning and good governance. Also, it helps in resource management as identifying urban sprawl growth can also provide information about quantifying increased demand of the resources correctly.
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Publication Date
6/17/2026
Peer Review Type
Double-Blind Peer Review
Review Rounds: 2
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The authors declare no conflict of interest related to this research work.
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