Image Based Surface Temperature Extraction and Trend Detection in an Urban Area of West Bengal, India

Sk Ziaul 1  and Swades Pal 1
  • 1 Department of Geography, University of Gour Banga, Malda 732103, West Bengal, India


Rapid urbanization and change of landuse/landcover results in changes of the thermal spectrum of a city even in small cities like English Bazaar Municipality (EBM) of Malda district. Monitoring the spatio-temporal surface temperature patterns is important, therefore, the present paper attempts to extract spatio-temporal surface temperature from thermal band of Landsat imageries and tries to validate it with factor based Land Surface Temperature (LST) models constructed based on six proxy temperature variables for selected time periods (1991, 2010 and 2014). Seasonal variation of temperature is also analyzed from the LST models over different time phases. Landsat TIRS based LST shows that in winter season, the minimum and maximum LST have raised up 2.32°C and 3.09°C in last 25 years. In pre monsoon season, the increase is much higher (2.80°C and 6.74°C) than in the winter period during the same time frame. In post monsoon season, exceptional situation happened due to high moisture availability caused by previous monsoon rainfall spell. Trend analysis revealed that the LST has been rising over time. Expansion and intensification of built up land as well as changing thermal properties of the urban heartland and rimland strongly control LST. Factor based surface temperature models have been prepared for the same period of times as done in case of LST modeling. In all seasons and selected time phases, correlation coefficient values between the extracted spatial LST model and factor based surface temperature model varies from 0.575 to 0.713 and these values are significant at 99% confidence level. So, thinking over ecological growth of urban is highly required for making the environment ambient for living.

If the inline PDF is not rendering correctly, you can download the PDF file here.

  • Alavipanah S., Wegmann, M., Qureshi, S., Weng, Q., Koellner, T. 2015. The Role of Vegetation in Mitigating Urban Land Surface Temperatures: A Case Study of Munich, Germany during the Warm Season, Sustainability, 7, 4689–4706; DOI: 10.3390/su7044689.

  • Buyantuyev, A., Wu, J. 2010. Urban heat islands and landscape heterogeneity: linking spatiotemporal variations in surface temperatures to land-cover and socioeconomic patterns. Landscape Ecology 25: 17–33. DOI: 10.1007/s10980-009-9402-4

  • Carver, S. J. 1991. Integrating multi-criteria evaluation with geographical information systems. International Journal of Geographical Information Systems 5 (3), 321–339. DOI: 10.1080/02693799108927858

  • Central Ground Water Board, Ministry of Water Resources, Government of India 2010. Ground Water Scenario of India 2009-10, 8–10.

  • Chen, X.L., Zhao, H. M., Li, P. X., Yin, Z. Y. 2006. Remote sensing image-based analysis of the relationship between urban heat is-land and land use/cover changes, Remote Sensing of Environment 104, 133–146. DOI: 10.1016/j.rse.2005.11.016

  • Choudhury, B. J. 1987. Relationships between vegetation indices, radiation absorption and net photosynthesis evaluated by a sensitivity analysis. Remote Sensing of the Environment 22, 209–233. DOI: 10.1016/0034-4257(87)90059-9

  • Dai, X., Zhongyang, G., Zhang, L., Li, D. 2010. Spatiotemporal exploratory analysis of urban surface temperature field in Shanghai, China. Stochastic Environmental Research & Risk Assessment 24, 247–257. DOI: 10.1007/s00477-009-0314-2

  • Davis, A. P., Traver, R. G., Hunt, W. F. 2010. Improving urban storm-water quality: Applying fundamental principles. J. Contemp. Water Res. Educ146, 3–10. DOI: 10.1111/j.1936-704x.2010.00387.x

  • DeBusk, K., Hunt, W. F., Hatch, U., Sydorovych, O. 2010. Watershed retrofit and management evaluation for urban stormwater management systems in North Carolina. J. Contemp. Water Res.Educ, 146, 64–74. DOI: 10.1111/j.1936-704x.2010.00392.x

  • Deosthali, V., 2000. Impact of rapid urban growth on heat and moisture islands in Pune City, India. Atmospheric Environment 34, 2745−2754. 10.1016/s1352-2310(99)00370-2

  • Ding, H., Shi, W. 2013. Land-use/land-cover change and its influence on surface temperature: A case study in Beijing city, Int. J. Remote Sens., 34, 5503–5517. DOI: 10.1080/01431161.2013.792966

  • Eastman, J. R. 1997. Idrisi for Windows, Version 2.0: Tutorial Exercises, Graduate School of Geography—Clark University, Worcester, MA.

  • Essa, W., Verbeiren, B., van der Kwast, J., van de Voorde, T., Batelaan, O. 2012. Evaluation of the DisTrad thermal sharpening methodology for urban areas. Int. J. Appl. Earth Obs. Geoinf. 19, 163–172. DOI: 10.1016/j.jag.2012.05.010

  • Gémes, O., Tobak, Z., Leeuwen, B. V. 2016. Satellite based analysis of surface urban heat island intensity, Journal of environmental geography 9 (1–2), 23–30. DOI: 10.1515/jengeo-2016-0004

  • Giridharan, R., Ganesan, S., Lau, S. S. Y. 2004. Daytime urban heat island effect in high-rise and high-density residential developments in Hong Kong. Energy and Buildings 36(6), 525−534. 10.1016/j.enbuild.2003.12.016

  • Grover, A., Singh, R. B., 2015. Analysis of Urban Heat Island (UHI) in Relation to Normalized Difference Vegetation Index (NDVI): A Comparative Study of Delhi and Mumbai, Environments2015, 2, 125−138.

  • Gulácsi, A., Kovács, F. 2015. Drought Monitoring with Spectral Indices Calculated from Modis Satellite Images in Hungary, Journal of Environmental Geography 8 (3–4), 11–20, DOI: 10.1515/jengeo-2015-0008.

  • Jalan, S., Sharma, K. 2014. Spatio-Temporal Assessment Of Land Use/Land Cover Dynamics And Urban Heat Island Of Jaipur City Using Satellite Data, The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Volume XL-8, 767–772.

  • James, M. M., Charles, N. M. 2014. Dynamism of Land use Changes on Surface Temperature in Kenya: A Case Study of Nairobi City, International Journal of Science and Research 3 (4), 38–41.

  • Kar, S., Pal, S. 2012. Changing Land use Pattern in Chatra Wetland of English Bazar Town: Rationale and Flaws. International Journal of Humanities and Social Sciences 2(2), 201–206.

  • Kawashima, S., Ishida, T., Minomura, M., Miwa, T. 2000. Relations between Surface Temperature and Air Temperature on a Local Scale during Winter Nights. Journal of Applied Meteorology 39, 1570–1779. DOI: 10.1175/1520-0450(2000)039<1570:rbstaa>;2

  • Keller, C. F. 2008. Global warming: a review of mostly settled issue. Stochastic Environmental Research & Risk Assessment, DOI: 10.1007/s00477-008-0253-3.

  • Khatun, S., Pal, S. 2016. Identification of Prospective Surface Water Available Zones with Multi Criteria Decision Approach in Kushkarani River Basin of Eastern India, Archives of Current Research International 4(4): 120, DOI:10.9734/ACRI/2016/27651.

  • Kibert, C. J. 2012 Sustainable Construction: Green Building Design and Delivery; 3rd ed.; John Wiley and Sons, Inc: Hoboken, NJ, USA, p. 236.

  • Kuang, W., Dou, Y., Zhang, C., Chi, W., Liu, A., Liu, Y., Zhang, R., Liu, J. 2015a. Quantifying the heat flux regulation of metropolitan land use/land cover components by coupling remote sensing modeling with in situ measurement. J. Geophys. Res. Atmos. 120, 113–130. DOI: 10.1002/2014jd022249

  • Kuang, W., Liu, Y., Dou, Y., Chi, W., Chen, G., Gao, C., Yang, T., Liu, J., Zhang, R. 2015b. What are hot and what are not in an urban landscape: Quantifying and explaining the land surface temperature pattern in Beijing, China. Landsc. Ecol. 30, 357–373. DOI: 10.1007/s10980-014-0128-6

  • Landsat Project Science Office 2002. Landsat 7 Science Data User’s Handbook. URL:, Goddard Space Flight Center, NASA, Washington, DC (last date accessed: 10 September 2003).

  • Landsberg, H. E. 1981 The Urban Climate. New York: Academic Press.

  • Li, L., Tan, Y., Ying, S., Yu, Z., Li, Z., Lan, H. 2014. Impact of land cover and population density on land surface temperature: case study in Wuhan, China. Journal of AppliedRemote Sensing 8, DOI: 1117/1.JRS.8.084993.

  • Li, Y. Y., Zhang, H., Kainz, W. 2012. Monitoring patterns of urban heat islands of the fast-growing Shanghai metropolis, China: Using time-series of Landsat TM/ETM+ data. Int. J. Appl. EarthObs. Geoinf. 19, 127–138. DOI: 10.1016/j.jag.2012.05.001

  • Liu, L., Zhang, Y. 2011. Urban heat island analysis using the Landsat TM data and ASTER data: A Case study in Hong Kong. Remote Sens. 3, 1535–1552. DOI: 10.3390/rs3071535

  • Liu, Y., Hiyama, T., Yamaguchi, Y. 2006 Scaling of land surface temperature using satellite data: A case examination on ASTER and MODIS products over a heterogeneous terrain area. Remote Sensing of Environment, 105, 115–128. DOI: 10.1016/j.rse.2006.06.012

  • Malczewski, J. 2004 GIS-based land-use suitability analysis: a critical overview. Progr. Plann. 62 (1), 3–65. DOI: 10.1016/j.progress.2003.09.002

  • McFeeters, S. K. 1996. The use of normalized difference water index (NDWI) in the delineation of open water features. International Journal of Remote Sensing 17(7), 1425–1432. DOI: 10.1080/01431169608948714

  • McMillin, L.M. 1975 Estimation of sea surface temperature from two infrared window measurements with different absorptions. Journal of Geophysical Research 80, 5113–5117. DOI: 10.1029/jc080i036p05113

  • Monserud, R. A., Leemans, R. 1992. Comparing global vegetation maps with the Kappa statistic, Ecological Modelling 62, 275–293. DOI: 10.1016/0304-3800(92)90003-w

  • Neteler, M. 2010. Estimating daily land surface temperatures in mountainous environments by reconstructed MODIS LST Data. Remote Sensing 2, 333–351. DOI: 10.3390/rs1020333

  • Nichol, J. E., Hang, T. P. 2012. Temporal characteristics of thermal satellite images for urban heat stress and heat island mapping, ISPRS Journal of Photogrammetry and Remote Sensing 74, 153–162. DOI: 10.1016/j.isprsjprs.2012.09.007

  • Nieuwolt, S. 1966. The urban microclimate of Singapore. The Journal of Tropical Geography 22, 30−37.

  • Ogashawara, I., Bastos, V. S. B. 2012. A quantitative approach for analyzing the relationship between urban heat islands and land cover. Remote Sens.4, 3596–3618. DOI: 10.3390/rs4113596

  • Park, H. S. 1986. Features of the heat island in Seoul and its surrounding cities. Atmos. Environ. 20, 1859–1866. DOI: 10.1016/0004-6981(86)90326-4

  • Peng, S., Piao, S., Ciais, P., Friedlingstein, P., Ottle, C., Bréon, F., Nan, H., Zhou, L. 2012. Myneni, R. Surface Urban Heat Island Across 419 Global Big Cities. Environ. Sci. Technol. 46, 696–703. DOI: 10.1021/es2030438

  • Saaty, T.L. 1980. The Analytic Hierarchy Process, New York: McGraw Hill. International, Translated to Russian, Portuguese, and Chinese, Revised editions, Paperback (1996, 2000), Pittsburgh: RWS Publications.

  • Schwarz, N., Lautenbach, S., Seppelt, R. 2011. Exploring indicators for quantifying surface urban heat islands of European cities with MODIS land surface temperatures, Remote Sens. Environ. 115, 3175–3186. DOI: 10.1016/j.rse.2011.07.003

  • Sharma, R., Joshi, P.K. 2013. Monitoring urban landscape dynamics over Delhi (India) using remote sensing (1998–2011) inputs, J. Indian Soc. Remote Sens. 41, 641–650. 10.1007/s12524-012-0248-x

  • Singh, R. B., Grover, A., Zhan, J. 2014 Inter-seasonal variations of surface temperature in the urbanized environment of Delhi using Landsat thermal data. Energies 7, 1811–1828. DOI: 10.3390/en7031811

  • Stathopoulou, M., Cartalis, C. 2009. Downscaling AVHRR land surface temperatures for improved surface Urban Heat Island intensity estimation. Remote Sensing of Environment 113, 2592–2605. DOI: 10.1016/j.rse.2009.07.017

  • Townshend, J. R., Justice, C. O. 1986 Analysis of the dynamics of African vegetation using the normalized difference vegetation index. International Journal of Remote Sensing, 7(11), 1435–1445. DOI: 10.1080/01431168608948946

  • UNFPA 2007. The state of world population 2007: Unleashing the potential of urban growth. United Nations Population Fund, United Nations Publications.

  • Wan, Z., Dozier, J. A. 1996. Generalized split-window algorithm for retrieving land-surface temperature from space. IEEE Trans Geosci Remote Sens, 34, 892-905. DOI: 10.1109/36.508406

  • Weng, Q. 2001. A remote sensing-GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. International Journal of Remote Sensing, 22, 1999–2014.

  • Weng, Q., Lu, D., Schubring, J. 2004 Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies, Remote Sensing of Environment 89, 467–483. DOI: 10.1016/j.rse.2003.11.005

  • Weng, Q., Yang, S. 2004. Managing the adverse thermal effects of urban development in a densely populated Chinese city. J. Environ. Manag. 70, 145–156. DOI: 10.1016/j.jenvman.2003.11.006

  • Xiao, R. B., Ouyang, Z. Y., Zheng, H., Li, W. F., Schienke, E. W., Wang, X. K. 2007. Spatial patterns of impervious surfaces and their impact on land surface temperature in Beijing, China. J. Environ. Sci. 19, 250–256. DOI: 10.1016/s1001-0742(07)60041-2

  • Xiong, Y., Huang, S., Chen, F., Ye, H., Wang, C., Zhu, C. 2012. The impacts of rapid urbanization on the thermal environment: A re-mote sensing study of Guangzhou, South China. Remote Sens. 4, 2033–2056. DOI: 10.3390/rs4072033

  • Yuan, F., Bauer, M. E. 2007. Comparison of impervious surface area and normalized difference vegetation index as indicators of surface heat island effects in Landsat imagery. Remote Sensing of Environment 106, 375–386. DOI: 10.1016/j.rse.2006.09.003

  • Zha, Y., Gao, J., Ni, S. 2003. Use of normalized difference built-up in-dex in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing 24(3), 583–594. DOI: 10.1080/01431160304987

  • Zhang, H., Qi, Z. F., Ye, X.Y., Cai, Y. B., Ma, W. C. 2013. Analysis of land use/land cover change, population shift, and their effects on spatiotemporal patterns of urban heat islands in metropolitan Shanghai, China. Appl. Geogr. 44, 121–133. DOI: 10.1016/j.apgeog.2013.07.021


Journal + Issues