Spatio-temporal dynamics of urbanization and environmental sustainability: A predictive modelling approach to forecasting land use transitions in Vellore, India
This study employs advanced geospatial tools and predictive modeling techniques to systematically assess Land Use Land Cover dynamics and climate projections in Vellore District, Tamil Nadu, India. Utilising satellite imagery from 2017, 2020, and 2023, the analysis reveals substantial transformation...
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| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2025-09-01
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| Series: | Results in Engineering |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123025026416 |
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| Summary: | This study employs advanced geospatial tools and predictive modeling techniques to systematically assess Land Use Land Cover dynamics and climate projections in Vellore District, Tamil Nadu, India. Utilising satellite imagery from 2017, 2020, and 2023, the analysis reveals substantial transformations in land use patterns, with urban expansion increasing from 13.23 % to 17.14 %, alongside reductions in forest cover (66.18 % to 63.53 %) and agricultural land (16.48 % to 15.05 %). Demographic analysis shows that the district’s population has more than doubled since 1990, rising from 3.07 million to a projected 6.26 million by 2025, further intensifying land and resource pressure. Future simulations using Cellular Automata and Artificial Neural Networks project that, by 2050, urban areas will constitute 27.1 %, whereas forest and agricultural land will decline further to 48.91 % and 9.15 %, respectively. Climate modeling indicates a notable upward trend in temperature, with averages rising from 27.7 °C in 2018 to approximately 32 °C by 2100, accompanied by intensified precipitation variability and extreme weather events. These findings underscore the interconnected challenges of urbanisation and climate change, necessitating Nature-Based Solutions such as sustainable urban planning, strategic reforestation, climate-resilient agricultural practices, and integrated water resource management. The integration of Remote Sensing, Geographical Information System, and machine learning provides a scientifically rigorous framework for monitoring, analysing, and forecasting LULC and climate trends with precision. The study offers actionable insights for policymakers and urban planners to achieve balanced development while addressing climate resilience, aligning with the Sustainable Development Goals. |
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| ISSN: | 2590-1230 |