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Evaluation of the Climate Change Impact on Urban Heat Island Based on Land Surface Temperature and Geospatial Indicators

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Abstract

Population pressure, infrastructural development, and economic growth are the reasons for increasing urbanization process although urban expansion. Land-use change, vegetation degradation, and also climate change are the results of urban heat island (UHI). Land surface temperature (LST) is an essential aspect of global climate change studies, calculating radiation budgets, heat balance studies, and also estimating the climate change scenario. In this study, investigate the UHI using thermal remote-sensing data. Satellite thermal data are used to calculate the thermal variation in Kolkata metropolitan and surrounding area. The remote-sensing technique is used to detect land use in 2020 using supervised classification, calculation of LST, and variation of mean LST from this region. The maximum and minimum temperatures are 33 °C and 18 °C individually. This study analyzes the LST distribution correlation with different spectral indicator. The relationship between NDVI and SAVI results shows negative correlation (R2 0.20 and 0.15, respectively) because of vegetation area effect on urban expansion. The urban heat island shows positive relation between LST and NDBI and UI because of urbanization and industrial development. The R2 values are show 0.61 and 0.27, respectively. The relationship between MNDWI and NDBal is showing 0.0003 and 0.04 individually. Kolkata metropolitan and surrounding urban areas are increased temperature due to urbanization, climate change and global warming and overuse of public vehicles. The UHI was increased due to anthropological activities. This study helps to identify the recent thermal variation of this area and build a proper management and planning for sustainable urban development.

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References

  • Altman DG (1991) Mathematics for kappa. Practical Statistics for Medical Research 1991:406–407

    Google Scholar 

  • Amiri R, Weng Q, Alimohammadi A, Alavipanah SK (2009) Spatial–temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area. Iran Remote Sensing of Environment 113(12):2606–2617

    Article  Google Scholar 

  • Avdan U & Jovanovska G. (2016). Algorithm for automated mapping of land surface temperature using LANDSAT 8 satellite data. Journal of Sensors, 2016.

  • Boakye E, Odai SN, Adjei KA, Annor FO (2008) Landsat images for assessment of the impact of land use and land cover changes on the Barekese catchment in Ghana. Eur J Sci Res 22(2):269–278

    Google Scholar 

  • Chandler TJ (1976). Urban climatology and its relevance to urban design.

  • Cheruto MC, Kauti MK, Kisangau DP, & Kariuki PC. (2016). Assessment of land use and land cover change using GIS and remote sensing techniques: a case study of Makueni County, Kenya. http://repository.seku.ac.ke/handle/123456789/3062

  • Congalton RG (1991) Remote sensing and geographic information system data integration: error sources and. Photogramm Eng Remote Sens 57(6):677–687

    Google Scholar 

  • Coppin P, Jonckheere I, Nackaerts K, Muys B, Lambin E (2004) Review ArticleDigital change detection methods in ecosystem monitoring: a review. Int J Remote Sens 25(9):1565–1596

    Article  Google Scholar 

  • Gašparović M, Zrinjski M, Gudelj M (2019) Automatic cost-effective method for land cover classification (ALCC). Comput Environ Urban Syst 76:1–10

    Article  Google Scholar 

  • Getis A & Ord JK. (2010). The analysis of spatial association by use of distance statistics. In Perspectives on spatial data analysis (pp. 127–145). Springer, Berlin, Heidelberg. https://doi.org/10.1007/978-3-642-01976-0_10

  • Gutman G, Huang C, Chander G, Noojipady P, Masek JG (2013) Assessment of the NASA–USGS global land survey (GLS) datasets. Remote Sens Environ 134:249–265

    Article  Google Scholar 

  • Hassan MM, Abedin AK, J, Paul BK & Southworth J. (2020) A quantitative framework for analyzing spatial dynamics of flood events: a case study of super cyclone amphan. Remote Sensing 12(20):3454. https://doi.org/10.3390/rs12203454

    Article  Google Scholar 

  • Herold M, Goldstein NC, Clarke KC (2003) The spatio-temporal form of urban growth: measurement, analysis and modeling. Remote Sens Environ 86(3):286–302

    Article  Google Scholar 

  • Holec J, Feranec J, Šťastný P, Szatmári D, Kopecká M, Garaj M (2020) Evolution and assessment of urban heat island between the years 1998 and 2016: case study of the cities Bratislava and Trnava in western Slovakia. Theoret Appl Climatol 141:979–997

    Article  Google Scholar 

  • Ishola KA, Okogbue EC, Adeyeri OE (2016) Dynamics of surface urban biophysical compositions and its impact on land surface thermal field. Model Earth Systems Environ 2(4):1–20

    Article  Google Scholar 

  • Jennings NR (2000) On agent-based software engineering. Artif Intell 117(2):277–296

    Article  Google Scholar 

  • Jensen R, Mausel P, Dias N, Gonser R, Yang C, Everitt J, Fletcher R (2007) Spectral analysis of coastal vegetation and land cover using AISA+ hyperspectral data. Geocarto Int 22(1):17–28. https://doi.org/10.1080/10106040701204354

    Article  Google Scholar 

  • Kato S, Yamaguchi Y (2005) Analysis of urban heat-island effect using ASTER and ETM+ Data: separation of anthropogenic heat discharge and natural heat radiation from sensible heat flux. Remote Sens Environ 99(1–2):44–54. https://doi.org/10.1016/j.rse.2005.04.026

    Article  Google Scholar 

  • Kayet N, Pathak K, Chakrabarty A, Sahoo S (2016) Spatial impact of land use/land cover change on surface temperature distribution in Saranda Forest. Jharkhand Modeling Earth Systems Environ 2(3):127. https://doi.org/10.1007/s40808-016-0159-x

    Article  Google Scholar 

  • Kerr JT, Ostrovsky M (2003) From space to species: ecological applications for remote sensing. Trends Ecol Evol 18(6):299–305. https://doi.org/10.1016/S0169-5347(03)00071-5

    Article  Google Scholar 

  • Loveland TR, Dwyer JL (2012) Landsat: building a strong future. Remote Sens Environ 122:22–29

    Article  Google Scholar 

  • Lu D, Weng Q (2006) Use of impervious surface in urban land-use classification. Remote Sens Environ 102(1–2):146–160. https://doi.org/10.1016/j.rse.2006.02.010

    Article  Google Scholar 

  • McIntyre N.E, Knowles-Yánez K & Hope D. (2008). Urban ecology as an interdisciplinary field: differences in the use of “urban” between the social and natural sciences. In Urban Ecology (pp. 49–65). Springer, Boston

  • Mirzaei PA, Haghighat F (2010) Approaches to study urban heat island–abilities and limitations. Build Environ 45(10):2192–2201

    Article  Google Scholar 

  • Mundia CN & James MM. (2014). Dynamism of land use changes on surface temperature in Kenya: a case study of Nairobi City. http://repository.dkut.ac.ke:8080/xmlui/handle/123456789/1025

  • Neog R, Acharjee S, Hazarika J (2019) Evaluation of spatio-temporal pattern of surface urban heat island phenomena at Jorhat. India Arab J Geosci 12(10):316

    Article  Google Scholar 

  • Oke TR (1982) The energetic basis of the urban heat island. Q J R Meteorol Soc 108(455):1–24

    Google Scholar 

  • Owojori A & Xie H. (2005, March). Landsat image-based LULC changes of San Antonio, Texas using advanced atmospheric correction and object-oriented image analysis approaches. In 5th international symposium on remote sensing of urban areas, Tempe.

  • Pellikka PK, Lötjönen M, Siljander M, Lens L (2009) Airborne remote sensing of spatio-temporal change (1955–2004) in indigenous and exotic forest cover in the Taita Hills, Kenya. Int J Appl Earth Obs Geoinf 11(4):221–232. https://doi.org/10.1016/j.jag.2009.02.002

    Article  Google Scholar 

  • Quattrochi DA, Pelletier RE (1991) Remote sensing for analysis of landscapes: an introduction. Ecol Stud 82:51–76

    Article  Google Scholar 

  • Rasul A, Balzter H, Smith C (2015) Spatial variation of the daytime Surface Urban Cool Island during the dry season in Erbil, Iraqi Kurdistan, from Landsat 8. Urban Climate 14:176–186

    Article  Google Scholar 

  • Rogan J, Miller J, Wulder MA & Franklin SE. (2006). Integrating GIS and remotely sensed data for mapping forest disturbance and change. Understanding forest disturbance and spatial pattern: Remote sensing and GIS approaches, 133–172.

  • Rosenfield GH, Fitzpatrick-Lins K (1986) A coefficient of agreement as a measure of thematic classification accuracy. Photogr Eng Remote Sens 52(2):223–227

    Google Scholar 

  • Roy DP, Wulder MA, Loveland TR, Woodcock CE, Allen RG, Anderson MC, Zhu Z (2014) Landsat-8: science and product vision for terrestrial global change research. Remote Sens Environ 145:154–172

    Article  Google Scholar 

  • San-Cristobal P, Pacheco-Alvarez D, Richardson C, Ring AM, Vazquez N, Rafiqi FH, Gamba G (2009) Angiotensin II signaling increases activity of the renal Na-Cl cotransporter through a WNK4-SPAK-dependent pathway. Proc Natl Acad Sci 106(11):4384–4389

    Article  CAS  Google Scholar 

  • Sarrat C, Lemonsu A, Masson V, Guedalia D (2006) Impact of urban heat island on regional atmospheric pollution. Atmos Environ 40(10):1743–1758. https://doi.org/10.1016/j.atmosenv.2005.11.037

    Article  CAS  Google Scholar 

  • Scarano M, Sobrino JA (2015) On the relationship between the sky view factor and the land surface temperature derived by Landsat-8 images in Bari. Italy Int J Remote Sens 36(19–20):4820–4835

    Article  Google Scholar 

  • Shoshany M, Aminov R, Goldreich Y (1994) The extraction of roof tops from thermal imagery for analyzing the urban heat island structure. Geocarto Int 9(4):61–69. https://doi.org/10.1080/10106049409354469

    Article  Google Scholar 

  • Veena K, Parammasivam KM, Venkatesh TN (2020) Urban Heat Island studies: current status in India and a comparison with the International studies. J Earth Syst Sci 129(1):1–15

    Article  Google Scholar 

  • Veettil BK, Grondona AEB (2018) Vegetation changes and formation of small-scale urban heat islands in three populated districts of Kerala State. India Acta Geophysica 66(5):1063–1072

    Article  Google Scholar 

  • Voogt JA, Oke TR (2003) Thermal remote sensing of urban climates. Remote Sens Environ 86(3):370–384. https://doi.org/10.1016/S0034-4257(03)00079-8

    Article  Google Scholar 

  • Weng Q, Lu D, Schubring J (2004) Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies. Remote Sens Environ 89(4):467–483. https://doi.org/10.1016/j.rse.2003.11.005

    Article  Google Scholar 

  • Xu H (2006) Modification of normalized difference water index (NDWI) to enhance open water features in remotely sensed imagery. Int J Remote Sens 27(14):3025–3033

    Article  Google Scholar 

  • Yao R, Wang L, Huang X, Gong W, Xia X (2019) Greening in rural areas increases the surface urban heat island intensity. Geophys Res Lett 46(4):2204–2212. https://doi.org/10.1029/2018GL081816

    Article  Google Scholar 

  • Yu X, Guo X, Wu Z (2014) Land surface temperature retrieval from Landsat 8 TIRS—comparison between radiative transfer equation-based method, split window algorithm and single channel method. Remote Sensing 6(10):9829–9852

    Article  Google Scholar 

  • Zemba AA (2010) Analysis of urban surface biophysical descriptors and land surface temperature variations in Jimeta City, Nigeria. Global Journal of Human Social Science 10(1):19–25

    Google Scholar 

  • Zhang Q, Schaaf C, Seto KC (2013) The vegetation adjusted NTL urban index: a new approach to reduce saturation and increase variation in nighttime luminosity. Remote Sens Environ 129:32–41

    Article  Google Scholar 

  • Zheng B, Myint SW, Fan C (2014) Spatial configuration of anthropogenic land cover impacts on urban warming. Landsc Urban Plan 130:104–111

    Article  Google Scholar 

  • Zhou W, Huang G, Cadenasso ML (2011) Does spatial configuration matter? Understanding the effects of land cover pattern on land surface temperature in urban landscapes. Landsc Urban Plan 102(1):54–63

    Article  Google Scholar 

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Acknowledgements

We would like to thank the Vidyasagar University to support this research. We are also thankful to the Local Government body for our field data collection and other necessary secondary data collection. We also express our gratitude to the United States Geological Survey Department for providing freely satellite data. Also, the authors would like to thank the editor and reviewers to their valuable comment for improvement of this manuscript.

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Authors and Affiliations

  1. Department of Remote Sensing and GIS, Vidyasagar University, Midnapore, India

    Bijay Halder & Jatisankar Bandyopadhyay

  2. Centre for Environmental Studies, Vidyasagar University, Midnapore, India

    Jatisankar Bandyopadhyay

  3. Department of Geography, University of Calcutta, Kolkata, India

    Papiya Banik

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  1. Bijay Halder
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  2. Jatisankar Bandyopadhyay
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Correspondence to Bijay Halder.

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Halder, B., Bandyopadhyay, J. & Banik, P. Evaluation of the Climate Change Impact on Urban Heat Island Based on Land Surface Temperature and Geospatial Indicators. Int J Environ Res 15, 819–835 (2021). https://doi.org/10.1007/s41742-021-00356-8

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  • DOI: https://doi.org/10.1007/s41742-021-00356-8

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