Modeling climate change impacts on coastal land use and vegetation dynamics

Coastal deltaic landscapes are increasingly vulnerable to the dual pressures of rapid urbanization and climate variability. Despite this, the long-term trajectories of land degradation in southern Indian coastal systems remain poorly documented. This study employs an integrated geospatial–climatic framework to evaluate multi-decadal Land Use and Land Cover (LULC) dynamics and future projections in the Nagapattinam District, Tamil Nadu. By integrating a Cellular Automata- Weights of Evidence (CA–WoE) model with Normalized Difference Vegetation Index (NDVI) analysis, demographic and climatic datasets, we provide a spatially explicit assessment of landscape transformations. The CA–WoE model yielded an overall accuracy of 83.4% and a kappa coefficient of 0.74, indicating strong agreement between simulated and observed datasets. Our findings reveal a critical shift: agricultural land is projected to decline from 731.79 km 2 in 2017 to 481.95 km 2 by 2070, while tree cover is expected to diminish from 77.15 to 11.45 km 2 . These trends are combined by rising thermal stress, with historical maximum temperatures increasing by 0.8 °C (2000–2024) and projected to rise by 2.3 °C by 2100. As the first comprehensive spatial analysis of this southern Indian delta, this research offers a pioneering framework for forecasting land-use transitions. The results provide empirical evidence for Sustainable Development Goals (SDGs 2, 13, 15) and underscore the urgent need for climate-resilient, nature-based solutions to mitigate land deprivation in vulnerable coastal systems globally.

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Publication Details

Journal
Scientific Reports
Published
2026-07-22
DOI
https://doi.org/10.1038/s41598-026-54321-6
Primary Topic
Land Use and Ecosystem Services
Type
article
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article

Modeling climate change impacts on coastal land use and vegetation dynamics

Mohamed Tharik, V. Sai Saraswathi, Kumaraguru Arumugam
Scientific Reports
Land Use and Ecosystem Services
article

Modeling climate change impacts on coastal land use and vegetation dynamics

Mohamed Tharik, V. Sai Saraswathi, Kumaraguru Arumugam
article en

Abstract

Coastal deltaic landscapes are increasingly vulnerable to the dual pressures of rapid urbanization and climate variability. Despite this, the long-term trajectories of land degradation in southern Indian coastal systems remain poorly documented. This study employs an integrated geospatial–climatic framework to evaluate multi-decadal Land Use and Land Cover (LULC) dynamics and future projections in the Nagapattinam District, Tamil Nadu. By integrating a Cellular Automata- Weights of Evidence (CA–WoE) model with Normalized Difference Vegetation Index (NDVI) analysis, demographic and climatic datasets, we provide a spatially explicit assessment of landscape transformations. The CA–WoE model yielded an overall accuracy of 83.4% and a kappa coefficient of 0.74, indicating strong agreement between simulated and observed datasets. Our findings reveal a critical shift: agricultural land is projected to decline from 731.79 km 2 in 2017 to 481.95 km 2 by 2070, while tree cover is expected to diminish from 77.15 to 11.45 km 2 . These trends are combined by rising thermal stress, with historical maximum temperatures increasing by 0.8 °C (2000–2024) and projected to rise by 2.3 °C by 2100. As the first comprehensive spatial analysis of this southern Indian delta, this research offers a pioneering framework for forecasting land-use transitions. The results provide empirical evidence for Sustainable Development Goals (SDGs 2, 13, 15) and underscore the urgent need for climate-resilient, nature-based solutions to mitigate land deprivation in vulnerable coastal systems globally.

Scientific Reports
India Diabetes Research Foundation (IN), Vellore Institute of Technology University (IN)
Openalex Percentile: Top 10%
Land Use and Ecosystem Services
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Modeling climate change impacts on coastal land use and vegetation dynamics — Mohamed Tharik, V. Sai Saraswathi, et al. · Scientific Reports (2026) | TGRS Research Map | TGRS