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Impacts of Land Use/land Cover Changes on Surface Urban Heat Islands: A Case Study of Coimbatore, India


Affiliations
1 Department of Civil Engineering, National Institute of Technology, Tiruchirappalli, India
2 Department of Applied Mechanics and Hydraulics, National Institute of Technology, Surathkal, Karnataka, India
3 Department of Civil Engineering, Anna University Regional Campus, Tirunelveli, India
4 Department of Geography, Bharathidasan University, Tiruchirappalli, India
     

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Urban Heat Island (UHI) is a major urban environmental issue throughout the world. UHI is a climatic phenomenon where anthropogenic modification leads to increased air temperature in urban areas when compared to that of the surrounding rural areas. Over urbanisation leads to an increase in UHI, resulting in the decrease of human health and a healthy environment. Remote sensing plays a major role in mapping the UHI as it can sense the top of the atmosphere radiances. From brightness, temperatures can be derived using Planck’s constant. In this study, UHI of Coimbatore was determined by using the single channel algorithm during winter season. Landsat data of TM, ETM+ and OLI/TIRS were used. Thus, LST helps to identify the increase in heat due to expansion urban areas. Supervised classification with maximum likelihood technique was used to classify the imageries into five landuse classes.Based on this study, the result emphasies that the land use changes was observed to be 14.55 per cent, where as vegetation reduction was 11.6 per cent.Thus, by correlating all these scenario from the year 1990 to 2015 with a five-year interval, the rapid development that took place in the Coimbatore region led to decrease in vegetation and increase in built-up land and temperature. This study reveals that there was an increase of 3.8°C in land surface temperature during in the study periods. Also, the result indicates that there is a strong linearly negative correlation between land surface temperature and vegetation.

Keywords

GIS, LST, Landsat, Urbanisation and UHI.
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  • Chen, X. L., Zhao, H. M., Li, P. X., & Yin, Z. Y. (2006), Remote sensing image-based analysis of the relationship between urban heat island and land use/cover changes, Remote Sensing of Environment, 104(2), 133-146.
  • Feizizadeh, B., Blaschke, T., (2012), Thermal remote sensing for examining the relationship between urban land surface temperature and land use/cover in Tabriz city, Iran. In: Geoscience and Remote Sensing Symposium (IGARSS), 2012 IEEE International, pp. 2229–2232.
  • Hu, Y.S., &Jia, G.S., (2010), Influence of land use change on urban heat island derived from multi-sensor data, International Journal of Climatology, 30, pp. 1382-1395.
  • Hung, T., Uchihama, D., Ochi, S., & Yasuoka, Y. (2006), Assessment with satellite data of the urban heat island effects in Asian mega cities. International Journal of Applied Earth Observation and Geoinformation, 8, 34–48.
  • Imhoff, M. L., Zhang, P., Wolfe, R. E., & Bounoua, L. (2010), Remote sensing of the urban heat island effect across biomes in the continental USA, Remote Sensing of environment, 114(3), 504-513.
  • 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 and Environment, 2(3), 1-10.
  • Li, J. J., Wang, X. R., Wang, X. J., Ma, W. C., & Zhang, H. (2009), Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan area, China, Ecological Complexity, 6(4), 413-420.
  • Mallick, J., Rahman, A., & Singh, C. K. (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, Advances in Space Research, 52(4), 639-655.
  • Renssen H, Goosse H, Fichefet T, Brovkin V, Driesschaert E, Wolk F (2005), Simulating the Holocene climate evolution at northern high latitudes using a coupled atmosphere-sea ice-ocean-vegetation model, ClimDyn 24(1):23–43
  • Sekertekin, A., Kutoglu, S.H., & Kaya, S., (2016), Evaluation of spatio-temporal variability in Land Surface Temperature: A case study of Zonguldak, Turkey, Environmental Monitoring and Assessment, 188, pp. 1-15.
  • Singh, R.B., Grover, A., & Zhan, J.Y., (2014), Inter-seasonal variations of surface temperature in the urbanised environment of Delhi using landsat thermal data, Energies, 7, pp. 1811-1828.
  • Snyder, W. C.,Wan, Z., Zhang, Y., & Feng, Y. Z. (1998), Classification-based emissivity for land surface temperature measurement from space, International Journal of Remote Sensing, 19(14), 2753-2774.
  • Sobrino, J.A., Oltra-Carrio´, R., Soria, G., Jime´nez-Mu˜oz, J.C., Franch, B., Hidalgo, V., Paganini, M. (2013), Evaluation of the surface urban heat island effect in the city of Madrid by thermal remote sensing, International Journal of Remote Sensing, 1–16.
  • Stathopoulou, M., Cartalis, C. (2007), Daytime urban heat islands from Landsat ETM+ and Corine land cover data: an application to major cities in Greece, Sol. Energy 81 (3), 358–368.
  • Sui, D.Z., Zeng, H., (2001), Modeling the dynamics of landscape structure in Asia’s emerging desakota regions: a case study in Shenzhen, Landsc. Urban Plan, 53:37–52.
  • Wang, F., Qin, Z.H., Song, C.Y., Tu, L.L., Karnieli, A., & Zhao, S.H., (2015), An improved mono-window algorithm for land surface temperature retrieval from landsat 8 thermal infrared sensor data. Remote Sensing, 7, pp. 4268-4289.
  • Weng Q, Lu D, Jacquelyn S (2004), Estimation of land surface temperature–vegetation abundance relationship for urban heat island studies, Rem Sens Environ, 89:467–483
  • 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(10):1999–2014.
  • Yuan, F., & Bauer, M. E. (2007), Comparison of impervious surface area and normalized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery, Remote sensing of Environment, 106 (3), 375-386.
  • Zhang, J., Wang, Y., & Li, Y. (2006), A C++ program for retrieving land surface temperature from the data of Landsat TM/ETM+ band 6, Computers & Geosciences, 32(10), 1796–1805. doi:10.1016/j. cageo.2006.05.001

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  • Impacts of Land Use/land Cover Changes on Surface Urban Heat Islands: A Case Study of Coimbatore, India

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Authors

S. Saravanan
Department of Civil Engineering, National Institute of Technology, Tiruchirappalli, India
K. S. S. Parthasarathy
Department of Applied Mechanics and Hydraulics, National Institute of Technology, Surathkal, Karnataka, India
D. Abijith
Department of Civil Engineering, Anna University Regional Campus, Tirunelveli, India
S. Sivaranjani
Department of Geography, Bharathidasan University, Tiruchirappalli, India

Abstract


Urban Heat Island (UHI) is a major urban environmental issue throughout the world. UHI is a climatic phenomenon where anthropogenic modification leads to increased air temperature in urban areas when compared to that of the surrounding rural areas. Over urbanisation leads to an increase in UHI, resulting in the decrease of human health and a healthy environment. Remote sensing plays a major role in mapping the UHI as it can sense the top of the atmosphere radiances. From brightness, temperatures can be derived using Planck’s constant. In this study, UHI of Coimbatore was determined by using the single channel algorithm during winter season. Landsat data of TM, ETM+ and OLI/TIRS were used. Thus, LST helps to identify the increase in heat due to expansion urban areas. Supervised classification with maximum likelihood technique was used to classify the imageries into five landuse classes.Based on this study, the result emphasies that the land use changes was observed to be 14.55 per cent, where as vegetation reduction was 11.6 per cent.Thus, by correlating all these scenario from the year 1990 to 2015 with a five-year interval, the rapid development that took place in the Coimbatore region led to decrease in vegetation and increase in built-up land and temperature. This study reveals that there was an increase of 3.8°C in land surface temperature during in the study periods. Also, the result indicates that there is a strong linearly negative correlation between land surface temperature and vegetation.

Keywords


GIS, LST, Landsat, Urbanisation and UHI.

References





DOI: https://doi.org/10.25175/jrd%2F2018%2Fv37%2Fi2%2F129683