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Chatterjee, S., Misra, R. B. and Alam, S. S. (1997). Prediction of software reliability using an autoregressive process, International Journal of System Science, 28(2), 211-216.

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Article

Temporal Analysis of Land Surface Temperature Dynamics and Urban Heat Islands in Chennai using Geo-Spatial Technology

1Research Scholar, Department of Geography, Presidency College, Chennai

2Assistant Professor, Department of Geography, Presidency College, Chennai

3Lecturer, Department of Geography, University of Madras, Guindy Campus, Chennai, India


Applied Ecology and Environmental Sciences. 2022, Vol. 10 No. 12, 828-836
DOI: 10.12691/aees-10-12-18
Copyright © 2022 Science and Education Publishing

Cite this paper:
P. Shanmugapriya, Shaik Mahamad, Manivel P. Temporal Analysis of Land Surface Temperature Dynamics and Urban Heat Islands in Chennai using Geo-Spatial Technology. Applied Ecology and Environmental Sciences. 2022; 10(12):828-836. doi: 10.12691/aees-10-12-18.

Correspondence to: Manivel  P, Lecturer, Department of Geography, University of Madras, Guindy Campus, Chennai, India. Email: drmanivelp@gmail.com

Abstract

This study examines Chennai's Urban Heat Islands (UHIs) using advanced thermal remote sensing and an Autoregressive Integrated Moving Average (ARIMA) model. The research predicts Land Surface Temperature (LST) and its relationship with factors influencing UHIs by analyzing historical observations, Normalized Difference Vegetation Index (NDVI), Normalized Difference Built-up Index (NDBI), and Land Use/Land Cover (LULC) changes between 2012 and February 2023. Analyzing satellite imagery from Landsat 7 ETM+ and Landsat 8 OLI alongside NASA's LST dataset reveals an increase in urban areas and decrease in vegetation, leading to rising LST. The ARIMA-based model forecasts a continual temperature rise from 2012 to March 2023, primarily due to urbanization. Recommendations include integrating LST, LULC, and vegetation indices in urban planning to mitigate UHI expansion. This research emphasizes the importance of predictive models in understanding and addressing UHI impacts, despite limitations posed by cloud cover in remote sensing data.

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