Spatiotemporal dynamics of land use/land cover (LULC) changes and its impact on land surface temperature: A case study in New Town Kolkata, eastern India
{"title":"Spatiotemporal dynamics of land use/land cover (LULC) changes and its impact on land surface temperature: A case study in New Town Kolkata, eastern India","authors":"Bubun Mahata , Siba Sankar Sahu , Archishman Sardar , Rana Laxmikanta , Mukul Maity","doi":"10.1016/j.regsus.2024.100138","DOIUrl":null,"url":null,"abstract":"<div><p>Rapid urbanization creates complexity, results in dynamic changes in land and environment, and influences the land surface temperature (LST) in fast-developing cities. In this study, we examined the impact of land use/land cover (LULC) changes on LST and determined the intensity of urban heat island (UHI) in New Town Kolkata (a smart city), eastern India, from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands. This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis (Getis–Ord Gi* method) to examine the impact of LULC changes on urban thermal environment. This study noticed that the area percentage of built-up land increased rapidly from 21.91% to 45.63% during 1991–2021, with a maximum positive change in built-up land and a maximum negative change in sparse vegetation. The mean temperature significantly increased during the study period (1991–2021), from 16.31°C to 22.48°C in winter, 29.18°C to 34.61°C in summer, and 19.18°C to 27.11°C in autumn. The result showed that impervious surfaces contribute to higher LST, whereas vegetation helps decrease it. Poor ecological status has been found in built-up land, and excellent ecological status has been found in vegetation and water body. The hot spot and cold spot areas shifted their locations every decade due to random LULC changes. Even after New Town Kolkata became a smart city, high LST has been observed. Overall, this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment, and appropriate planning is needed to reduce LST. This study can help policy-makers create sustainable smart cities.</p></div>","PeriodicalId":34395,"journal":{"name":"Regional Sustainability","volume":"5 2","pages":"Article 100138"},"PeriodicalIF":0.0000,"publicationDate":"2024-06-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.sciencedirect.com/science/article/pii/S2666660X24000379/pdfft?md5=f08741b88c418d119b60a611f75cc407&pid=1-s2.0-S2666660X24000379-main.pdf","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"Regional Sustainability","FirstCategoryId":"95","ListUrlMain":"https://www.sciencedirect.com/science/article/pii/S2666660X24000379","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"Social Sciences","Score":null,"Total":0}
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Abstract
Rapid urbanization creates complexity, results in dynamic changes in land and environment, and influences the land surface temperature (LST) in fast-developing cities. In this study, we examined the impact of land use/land cover (LULC) changes on LST and determined the intensity of urban heat island (UHI) in New Town Kolkata (a smart city), eastern India, from 1991 to 2021 at 10-a intervals using various series of Landsat multi-spectral and thermal bands. This study used the maximum likelihood algorithm for image classification and other methods like the correlation analysis and hotspot analysis (Getis–Ord Gi* method) to examine the impact of LULC changes on urban thermal environment. This study noticed that the area percentage of built-up land increased rapidly from 21.91% to 45.63% during 1991–2021, with a maximum positive change in built-up land and a maximum negative change in sparse vegetation. The mean temperature significantly increased during the study period (1991–2021), from 16.31°C to 22.48°C in winter, 29.18°C to 34.61°C in summer, and 19.18°C to 27.11°C in autumn. The result showed that impervious surfaces contribute to higher LST, whereas vegetation helps decrease it. Poor ecological status has been found in built-up land, and excellent ecological status has been found in vegetation and water body. The hot spot and cold spot areas shifted their locations every decade due to random LULC changes. Even after New Town Kolkata became a smart city, high LST has been observed. Overall, this study indicated that urbanization and changes in LULC patterns can influence the urban thermal environment, and appropriate planning is needed to reduce LST. This study can help policy-makers create sustainable smart cities.