Prediction modeling of land surface temperature in relation to land cover dynamics and health risk perception analysis in barishal city of Bangladesh

Abstract Rapid urbanization has brought about rapid changes in land-use and land-cover (LULC) patterns, significantly affecting land surface temperatures (LST). This study uses GIS and remote sensing techniques to assess changes in LULC classes and their impacts on LST at the Barishal City Corporati...

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Main Authors: Irteja Hasan, Osman Goni, Zarin Tasnim Katha, Mainul Islam Rabby, Siam Hossain, Arpa Banik, Sabbir Hasan, Iftekhar Rahman
Format: Article
Language:English
Published: Nature Portfolio 2025-08-01
Series:Scientific Reports
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Online Access:https://doi.org/10.1038/s41598-025-14868-2
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author Irteja Hasan
Osman Goni
Zarin Tasnim Katha
Mainul Islam Rabby
Siam Hossain
Arpa Banik
Sabbir Hasan
Iftekhar Rahman
author_facet Irteja Hasan
Osman Goni
Zarin Tasnim Katha
Mainul Islam Rabby
Siam Hossain
Arpa Banik
Sabbir Hasan
Iftekhar Rahman
author_sort Irteja Hasan
collection DOAJ
description Abstract Rapid urbanization has brought about rapid changes in land-use and land-cover (LULC) patterns, significantly affecting land surface temperatures (LST). This study uses GIS and remote sensing techniques to assess changes in LULC classes and their impacts on LST at the Barishal City Corporation (BCC) of Bangladesh. As summer is the season with the highest temperature, the study considered the months from March to July from 1998–2024. The key findings of this study reveal a substantial increase in the mean land surface temperature, increasing by 5.75°C from 1998–2024, with the highest temperature reaching 42°C in 2024. This increase in temperature was linked to an 11.29% expansion in built-up areas and a reduction in vegetation (4.8%) and agricultural land (3.8%). The correlation analysis results support these findings, as the NDBI is positively correlated with the LST, indicating that built-up areas tend to increase surface temperatures. In contrast, the NDWI is negatively correlated with the LST, suggesting that water presence helps lower temperatures. The relationship between the NDVI and LST is predominantly negative in the absence of water bodies, whereas the presence of water bodies tends to result in a positive correlation. Most built-up areas presented the highest temperatures over the years, ranging from 34°C to 42°C. Future projections indicate that if the current trend of urban expansion remains, approximately 5.89% of the BCC area is projected to experience temperatures exceeding 38°C by 2033, increasing to 7.93% of the area by 2042. In terms of health impacts, the study identified common risks such as heat stroke, respiratory problems, heat exhaustion, dehydration, diarrhea, and skin rashes, among which heat exhaustion (66.93%) was most prominent. Furthermore, in urban areas, children, elderly people, women, outdoor workers, and people living in tin-shed houses are more vulnerable to high temperatures. This study will help city planners and future stakeholders control the urban heat island effect and understand the side effects of concentrated settlements in the coastal area of Bangladesh.
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spelling doaj-art-97cdf92ad76142fa96b32405a294b9162025-08-24T11:24:57ZengNature PortfolioScientific Reports2045-23222025-08-0115113210.1038/s41598-025-14868-2Prediction modeling of land surface temperature in relation to land cover dynamics and health risk perception analysis in barishal city of BangladeshIrteja Hasan0Osman Goni1Zarin Tasnim Katha2Mainul Islam Rabby3Siam Hossain4Arpa Banik5Sabbir Hasan6Iftekhar Rahman7Department of Coastal Studies and Disaster Management, Faculty of Bio-Sciences, University of BarishalDepartment of Coastal Studies and Disaster Management, Faculty of Bio-Sciences, University of BarishalDepartment of Coastal Studies and Disaster Management, Faculty of Bio-Sciences, University of BarishalDepartment of Coastal Studies and Disaster Management, Faculty of Bio-Sciences, University of BarishalDepartment of Coastal Studies and Disaster Management, Faculty of Bio-Sciences, University of BarishalDepartment of Coastal Studies and Disaster Management, Faculty of Bio-Sciences, University of BarishalDepartment of Coastal Studies and Disaster Management, Faculty of Bio-Sciences, University of BarishalDepartment of Soil and Environmental Sciences, Faculty of Bio-Sciences, University of BarishalAbstract Rapid urbanization has brought about rapid changes in land-use and land-cover (LULC) patterns, significantly affecting land surface temperatures (LST). This study uses GIS and remote sensing techniques to assess changes in LULC classes and their impacts on LST at the Barishal City Corporation (BCC) of Bangladesh. As summer is the season with the highest temperature, the study considered the months from March to July from 1998–2024. The key findings of this study reveal a substantial increase in the mean land surface temperature, increasing by 5.75°C from 1998–2024, with the highest temperature reaching 42°C in 2024. This increase in temperature was linked to an 11.29% expansion in built-up areas and a reduction in vegetation (4.8%) and agricultural land (3.8%). The correlation analysis results support these findings, as the NDBI is positively correlated with the LST, indicating that built-up areas tend to increase surface temperatures. In contrast, the NDWI is negatively correlated with the LST, suggesting that water presence helps lower temperatures. The relationship between the NDVI and LST is predominantly negative in the absence of water bodies, whereas the presence of water bodies tends to result in a positive correlation. Most built-up areas presented the highest temperatures over the years, ranging from 34°C to 42°C. Future projections indicate that if the current trend of urban expansion remains, approximately 5.89% of the BCC area is projected to experience temperatures exceeding 38°C by 2033, increasing to 7.93% of the area by 2042. In terms of health impacts, the study identified common risks such as heat stroke, respiratory problems, heat exhaustion, dehydration, diarrhea, and skin rashes, among which heat exhaustion (66.93%) was most prominent. Furthermore, in urban areas, children, elderly people, women, outdoor workers, and people living in tin-shed houses are more vulnerable to high temperatures. This study will help city planners and future stakeholders control the urban heat island effect and understand the side effects of concentrated settlements in the coastal area of Bangladesh.https://doi.org/10.1038/s41598-025-14868-2Land surface temperature(LST)Land use/coverNDVIPredictionHealth impactBangladesh
spellingShingle Irteja Hasan
Osman Goni
Zarin Tasnim Katha
Mainul Islam Rabby
Siam Hossain
Arpa Banik
Sabbir Hasan
Iftekhar Rahman
Prediction modeling of land surface temperature in relation to land cover dynamics and health risk perception analysis in barishal city of Bangladesh
Scientific Reports
Land surface temperature(LST)
Land use/cover
NDVI
Prediction
Health impact
Bangladesh
title Prediction modeling of land surface temperature in relation to land cover dynamics and health risk perception analysis in barishal city of Bangladesh
title_full Prediction modeling of land surface temperature in relation to land cover dynamics and health risk perception analysis in barishal city of Bangladesh
title_fullStr Prediction modeling of land surface temperature in relation to land cover dynamics and health risk perception analysis in barishal city of Bangladesh
title_full_unstemmed Prediction modeling of land surface temperature in relation to land cover dynamics and health risk perception analysis in barishal city of Bangladesh
title_short Prediction modeling of land surface temperature in relation to land cover dynamics and health risk perception analysis in barishal city of Bangladesh
title_sort prediction modeling of land surface temperature in relation to land cover dynamics and health risk perception analysis in barishal city of bangladesh
topic Land surface temperature(LST)
Land use/cover
NDVI
Prediction
Health impact
Bangladesh
url https://doi.org/10.1038/s41598-025-14868-2
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