Integrated Influencing Mechanism of Potential Drivers on Seasonal Variability of LST in Kolkata Municipal Corporation, India
Increasing land surface temperature (LST) is one of the major anthropogenic issues and is significantly threatening the urban areas of the world. Therefore, this study was designed to examine the spatial variations and patterns of LST during the different seasons in relation to influencing factors i...
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MDPI AG
2022-09-01
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| author | Dipankar Bera Nilanjana Das Chatterjee Faisal Mumtaz Santanu Dinda Subrata Ghosh Na Zhao Sudip Bera Aqil Tariq |
| author_facet | Dipankar Bera Nilanjana Das Chatterjee Faisal Mumtaz Santanu Dinda Subrata Ghosh Na Zhao Sudip Bera Aqil Tariq |
| author_sort | Dipankar Bera |
| collection | DOAJ |
| description | Increasing land surface temperature (LST) is one of the major anthropogenic issues and is significantly threatening the urban areas of the world. Therefore, this study was designed to examine the spatial variations and patterns of LST during the different seasons in relation to influencing factors in Kolkata Municipality Corporation (KMC), a city of India. The spatial distribution of LST was analyzed regarding the different surface types and used 25 influencing factors from 6 categories of variables to explain the variability of LST during the different seasons. All-subset regression and hierarchical partitioning analyses were used to estimate the explanatory potential and independent effects of influencing factors. The results show that high and low LST corresponded to the artificial lands and bodies of water for all seasons. In the individual category regression model, surface properties gave the highest explanatory rate for all seasons. The explanatory rates and the combination of influencing factors with their independent effects on the LST were changed for the different seasons. The explanatory rates of integration of all influencing factors were 89.4%, 81.4%, and 88.7% in the summer, transition, and winter season, respectively. With the decreasing of LST (summer to transition, then to winter) more influencing factors were required to explain the LST. In the integrated regression model, surface properties were the most important factor in summer and winter, and landscape configuration was the most important factor in the transition season. LST is not the result of single categories of influencing factors. Along with the effects of surface properties, socio-economic parameters, landscape compositions and configurations, topographic parameters and pollutant parameters mostly explained the variability of LST in the transition (11.22%) and summer season (15.22%), respectively. These findings can help to take management strategies to reduce urban LST based on local planning. |
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| spelling | doaj.art-14f3e930164541e48cbc4e3d9599912c2023-11-23T17:17:29ZengMDPI AGLand2073-445X2022-09-01119146110.3390/land11091461Integrated Influencing Mechanism of Potential Drivers on Seasonal Variability of LST in Kolkata Municipal Corporation, IndiaDipankar Bera0Nilanjana Das Chatterjee1Faisal Mumtaz2Santanu Dinda3Subrata Ghosh4Na Zhao5Sudip Bera6Aqil Tariq7Department of Geography, Vidyasagar University, Midnapore 721102, IndiaDepartment of Geography, Vidyasagar University, Midnapore 721102, IndiaState Key Laboratory of Remote Sensing Science, Aerospace Information Research Institute, Chinese Academy of Sciences, Beijing 100101, ChinaDepartment of Geography, Vidyasagar University, Midnapore 721102, IndiaDepartment of Geography, Vidyasagar University, Midnapore 721102, IndiaState Key Laboratory of Resources and Environmental Information System, Institute of Geographic Science and Natural Resources Research, Chinese Academy of Sciences, Beijing 100101, ChinaDepartment of Geography, Vidyasagar University, Midnapore 721102, IndiaState Key Laboratory of Information Engineering in Surveying, Mapping and Remote Sensing, Wuhan University, Wuhan 430072, ChinaIncreasing land surface temperature (LST) is one of the major anthropogenic issues and is significantly threatening the urban areas of the world. Therefore, this study was designed to examine the spatial variations and patterns of LST during the different seasons in relation to influencing factors in Kolkata Municipality Corporation (KMC), a city of India. The spatial distribution of LST was analyzed regarding the different surface types and used 25 influencing factors from 6 categories of variables to explain the variability of LST during the different seasons. All-subset regression and hierarchical partitioning analyses were used to estimate the explanatory potential and independent effects of influencing factors. The results show that high and low LST corresponded to the artificial lands and bodies of water for all seasons. In the individual category regression model, surface properties gave the highest explanatory rate for all seasons. The explanatory rates and the combination of influencing factors with their independent effects on the LST were changed for the different seasons. The explanatory rates of integration of all influencing factors were 89.4%, 81.4%, and 88.7% in the summer, transition, and winter season, respectively. With the decreasing of LST (summer to transition, then to winter) more influencing factors were required to explain the LST. In the integrated regression model, surface properties were the most important factor in summer and winter, and landscape configuration was the most important factor in the transition season. LST is not the result of single categories of influencing factors. Along with the effects of surface properties, socio-economic parameters, landscape compositions and configurations, topographic parameters and pollutant parameters mostly explained the variability of LST in the transition (11.22%) and summer season (15.22%), respectively. These findings can help to take management strategies to reduce urban LST based on local planning.https://www.mdpi.com/2073-445X/11/9/1461land surface temperatureinfluencing factorsall-subsets regressionhierarchical partitioning analysisurban management |
| spellingShingle | Dipankar Bera Nilanjana Das Chatterjee Faisal Mumtaz Santanu Dinda Subrata Ghosh Na Zhao Sudip Bera Aqil Tariq Integrated Influencing Mechanism of Potential Drivers on Seasonal Variability of LST in Kolkata Municipal Corporation, India Land land surface temperature influencing factors all-subsets regression hierarchical partitioning analysis urban management |
| title | Integrated Influencing Mechanism of Potential Drivers on Seasonal Variability of LST in Kolkata Municipal Corporation, India |
| title_full | Integrated Influencing Mechanism of Potential Drivers on Seasonal Variability of LST in Kolkata Municipal Corporation, India |
| title_fullStr | Integrated Influencing Mechanism of Potential Drivers on Seasonal Variability of LST in Kolkata Municipal Corporation, India |
| title_full_unstemmed | Integrated Influencing Mechanism of Potential Drivers on Seasonal Variability of LST in Kolkata Municipal Corporation, India |
| title_short | Integrated Influencing Mechanism of Potential Drivers on Seasonal Variability of LST in Kolkata Municipal Corporation, India |
| title_sort | integrated influencing mechanism of potential drivers on seasonal variability of lst in kolkata municipal corporation india |
| topic | land surface temperature influencing factors all-subsets regression hierarchical partitioning analysis urban management |
| url | https://www.mdpi.com/2073-445X/11/9/1461 |
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