The significance of Urban heat island (UHI) is to space heater than the urban adjacent area. This heat energy creates by urban people, house, shops, cars, buses, trains, industrial zone, etc. The UHI be an vital occurrence of urban environment in addition to gives direct and circumlocutory effect lying on urban population. In UHI calculating using landsat thermal band, Landsat TM, ETM+ and OLI satellite image (2000, 2008, 2017 Year) data processed to obtain a atmoshphire window method use to retrieve the land surface temperature (LST) using specifically band-6 (TM, ETM+) and band 10 (OLI) for data procurement and investigation. UHI consequence related with rising impermeable surface both spatially and temporally. This study aims to analyze the changes in LST with advent of the Kolkata Metropolitan Area (KMA). The result found that the (LST) is increasing sharply. The mean temperature of the area was 22.33°C in 2000 which became 23.68°C in 2008 and 23.79°C in 2017. The relation of built-up (NDBI) and LST is found positively correlated with a r value of 0.96 in 2000 and 0.78in 2017 and the relation with vegetation (NDVI) is negatively related and the r value is -0.98 in the years of 2000 and the r value is -0.97 in 2017, several heat zones are highlight and have been identified on the map of the KMA area. The addition of Geospatial technology be set up in the direction of valuable in monitor and analyze urban expansion pattern and in evaluation urbanization collision on surface temperature. Finally, the study suggests considering the possible micro-climatic changes in Kolkata metropolitan area and planning for the sustainable improvement.

Urban Heat Island (UHI), land surface temperature (LST), Normalized differential build up index (NDBI), Normalized differential vegetation index (NDVI).

[1]. T. V. Ramachandra & Bharath H. Aithal & M. V. Sowmyashree (2014). Urban structure in Kolkata: metrics and modelling through geo-informatics, Appl Geomat DOI 10.1007/s12518-014-0135-y, # Società Italiana di Fotogrammetria e Topografia (SIFET), pp.1-15.
[2]. Chakraborty, K. R. (1991). “Urbanisation Trend in the Calcutta Metroplitan District”, in Dasgupta, B. et al. (eds.) “Calcutta`s Urban Future- Agonies From The Past and Prospects”, Government of West Bengal, Calcutta, pp. 114.
[3]. Mallick J, Kant Y, Bharath B. 2008. Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J Indian Geophys Union. 12:131–140.
[4]. Ambinakudige S. 2011. Remote sensing of land cover’s effect on surface temperatures: a case study of the urban heat island in Bangalore, India. Appl GIS. 7:1–12.
[5]. Bajaja, DN, Inamdarb, AB, Vaibhava, V. 2012. Temporal variation of urban heat island using Landsat data: a case study of Ahmadabad, India, in: Aiming Smart Space Sensing. Presented at the 33rd Asian Conference on Remote Sensing; Thailand
[6]. Borthakur M, Nath BK. 2012. A study of changing urban landscape and heat island phenomenon in guwahati metropolitan area. Int J Sci Res. 2:1–6
[7]. Kumar KS, Bhaskar PU, Padmakumari K. 2012. Estimation of land surface temperature to study urban heat island effect using Landsat ETM+ image. Int J Eng Sci. 4:771–778
[8]. Sharma R, Ghosh A, Joshi PK. 2013. Spatio-temporal footprints of urbanisation in Surat, the diamond city of India (1990–2009). Environ Monit Assess. 185:3313–3325.
[9]. Hung T, Uchihama D, Ochi S, Yasuoka Y. 2006. Assessment with satellite data of the urban heat island effects in Asian mega cities. Int J Appl Earth Obs Geoinf. 8:34–48.
[10]. Pongracz R, Bartholy J, Dezso Z. 2006. Remotely sensed thermal information applied to urban climate analysis. Adv Space Res. 37:2191–2196.
[11]. Tran H, Uchihama D, Ochi S, Yasuoka Y. 2006. Assessment with satellite data of the urban heat island effects in Asian mega cities. Int J Appl Earth Obs Geoinf. 8:34–48.
[12]. Imhoff ML, Zhang P, Wolfe RE, Bounoua L. 2010. Remote sensing of the urban heat island effect across biomes in the continental USA. Remote Sens Environ. 114:504–513.
[13]. Peng S, Piao S, Ciais P, Friedlingstein P, Ottle C, Bréon FM, Myneni RB. 2011. Surface urban heat island across 419 global big cities. Environ Sci Technol. 46:696–703.
[14]. Guo Z, Wang SD, Cheng MM, Shu Y. 2012. Assess the effect of different degrees of urbanization on land surface temperature using remote sensing images. Procedia Environ Sci. 13:935–942.
[15]. Mohan M, Kikegawa Y, Gurjar B, Bhati S, Kandya A, Ogawa K. 2012. Urban heat island assessment for a tropical urban airshed in India. Atmos Clim Sci. 2:127–138
[16]. Pandey P, Kumar D, Prakash A, Masih J, Singh M, Kumar S, Jain VK, Kumar K. 2012. A study of urban heat island and its association with particulate matter during winter months over Delhi. Sci Total Environ. 414:494–507.
[17]. Pichierri M, Bonafoni S, Biondi R. 2012. Satellite air temperature estimation for monitoring the canopy layer heat island of Milan. Remote Sens Environ. 127:130–138.
[18]. Nichol J. 2009. An emissivity modulation method for spatial enhancement of thermal satellite images in urban heat island analysis. Photogramm Eng Remote Sens. 75:547–556.
[19]. Mallick J, Rahman A, Singh CK. 2013. Modeling urban heat islands in heterogeneous land surface and its correlation with impervious surface area by using night-time ASTER satellite data in highly urbanizing city, Delhi-India. Adv Space Res. 52:639–655
[20]. Stathopoulou M, Cartalis C. 2009. Downscaling AVHRR land surface temperatures for improved surface urban heat island intensity estimation. Remote Sens Environ. 113:2592–2605.
[21]. Badarinath K, Chand TK, Madhavilatha K, Raghavaswamy V. 2005. Studies on urban heat islands using envisat AATSR data. J Indian Soc Remote Sens. 33:495–501.
[22]. Xu H. 2008. A new index for delineating built‐up land features in satellite imagery. Int J Remote Sens. 29(14):4269–4276. DOI:10.1080/01431160802039957.
[23]. Wang Z, Gang C, Li X, Chen Y, Li J. 2015. Application of a normalized difference impervious index (NDII) to extract urban impervious surface features based on Landsat TM images. Int J Remote Sens. 36(4):1055–1069.
[24]. Khan A, Chatterjee S, Akbari H, Bhatti SS, Dinda A, Mitra C, Hong H & Doan QV (2017): Step-wise Landclass Elimination Approach for extracting mixed-type built-up areas of Kolkata megacity, Geocarto International, DOI: 10.1080/10106049.2017.1408704.
[25]. Guindon B, Zhang Y, Dillabaugh C. 2004. Landsat urban mapping based on a combined spectral–spatial methodology. Remote Sens Environ. 92(2):218–232. DOI: 10.1016/j.rse.2004.06.015.
[26]. Bhatta B. 2009. Analysis of urban growth pattern using remote sensing and GIS: a case study of Kolkata, India. Int J Remote Sens. 30(18):4733–4746.
[27]. Griffiths P, Hostert P, Gruebner O, Sebastian VDL. 2010. Mapping megacity growth with multi-sensor data. Remote Sens Environ. 114(2):426–439. DOI: 10.1016/j.rse.2009.09.012.
[28]. Bouzekri S, Lasbet AA, Lachehab A. 2015. A new spectral index for extraction of built-up area using Landsat-8 data. J Indian Soc Remote Sens. 43(4):867–873.
[29]. Qian J, Zhou Q, Hou Q. 2007. Comparison of pixel-based and object-oriented classification methods for extracting built-up areas in Aridzone. In: ISPRS Workshop on Updating Geo-spatial Databases with Imagery & The 5th ISPRS Workshop on DMGISs; pp. 163–171).
[30]. Cleve C, Kelly M, Kearns FR, Moritz M. 2008. Classification of the wildland–urban interface: a comparison of pixel- and object-based classifications using high-resolution aerial photography. Comput Environ Urban Syst. 32(4):317–326. DOI: 10.1016/j.compenvurbsys.2007.10.001.
[31]. Zha Y, Gao J, Ni S. 2003. Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. Int J Remote Sens. 24(3):583–594. DOI:10.1080/01431160304987.
[32]. Zhang Q, Pavlic G, Chen W, Fraser R, Leblanc S, Cihlar J. 2005. A semi-automatic segmentation procedure for feature extraction in remotely sensed imagery. Comput Geosci. 31(3):289–296. DOI: 10.1016/j.cageo.2004.10.003.
[33]. He C, Shi P, Xie D, Zhao Y. 2010. Improving the normalized difference built-up index to map urban built-up areas using a semiautomatic segmentation approach. Remote Sens Lett. 1(4):213–221. DOI:10.1080/01431161.2010.481681.
[34]. Bhatti SS, Tripathi NK. 2014. Built-up area extraction using Landsat 8 OLI imagery. GISci Remote Sens. 51(4):445–467. DOI:10.1080/15481603.2014.939539.
[35]. Lu N, Hernandez AJ, Ramsey RD. 2015. Land cover dynamics monitoring with Landsat data in Kunming, China: a cost-effective sampling and modelling scheme using Google Earth imagery and random forests. Geocarto Int. 30(2):186–201.
[36]. Ke Y, Im J, Lee J, Gong H, Ryu Y. 2015. Characteristics of Landsat 8 OLI-derived NDVI by comparison with multiple satellite sensors and in-situ observations. Remote Sens Environ. 164:298–313.
[37]. Piyoosh AK, Ghosh SK. 2017. Development of a modified bare soil and urban index for Landsat 8 satellite data. Geocarto Int: 1–20.
[38]. Ahamed B, 2013. “Simulating Land Cover Changes and Their Impacts on Land Surface Temperature in Dhaka, Bangladesh” www.mdpi.com/journal/remotesensing.
[39]. Bhattacharjee D, Hazra S 2014. Distribution of Land Surface Temperature over Built-up Area by Web-GIS Techniques, Indian Journal of Spatial Science Vol - 5.0 No. 2 Winter Issue 2014 pp. 70 – 76.
[40]. Nath SK, Adhikari MD, Devaraj N, Maiti SK. 2015. Seismic vulnerability and risk assessment of Kolkata City, India. Nat Hazards Earth Sys Sci. 15(6):1103–1121.
[41]. Chander, G., Markham, B.L., Helder, D.L. (2009). Summary of Current Radiometric Calibration Coefficients for Landsat MSS, TM, ETM+, and EO-1 ALI Sensors. (In Press, Remote Sensing of Environment, Manuscript Number: RSE-D-08-00684)https://ntrs.nasa.gov/search.jsp?R=20090027884 2018-05-09T11:26:42+00:00Z.
[42]. Voogt JA, Oke TR, 2003. Thermal remote sensing of urban climates, Remote Sensing of Environment, Volume 86, Issue 3, 15 August 2003, Pages 370 384.
[43]. Lin liu and Yuanzhi Zhang e, Urban Heat Island Analysis Using the Landsat TM Data and ASTER Data: A Case Study in Hong Kong, Remote Sensing, 2011, 3, pp1535-1552.
[44]. Wang Z, Gang C, Li X, Chen Y, Li J. 2015. Application of a normalized difference impervious index (NDII) to extract urban impervious surface features based on Landsat TM images. Int J Remote Sens. 36(4):1055–1069.