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The impact of spatiotemporal patterns of land use land cover and land surface temperature on an urban cool island: a case study of Bengaluru

  • Nithya R. GovindEmail author
  • H. Ramesh
Article

Abstract

In most of the developing countries, man-made developments in the environment have led to the growing demand to contextualize the land use land cover (LULC) changes and land surface temperature (LST) variations. Due to the modification in the surface properties of the cities, a difference in energy balance between the cities and its nonurban surroundings is observed. The aim of this study is to analyze the spatial and temporal patterns of LULC and LST and its interrelationship in Bengaluru urban district, India, during the period from 1989 to 2017 using remote sensing data. Intensity analysis was performed for the interval to analyze the LULC change and identify the driving forces. The impact of LULC change on LST was assessed using hot spot analysis (Getis–Ord Gi* statistics). The results of this study show that (a) dominant LULC change experienced is the increase in urban area (approximately 40%) and the rate of land use change was faster in the time period 1989–2001 than 2001–2017; (b) the major transition witnessed is from barren and agricultural land to urban; (c) over the period of 28 years, LST patterns for different land use classes exhibit an increasing trend with an overall increase of approximately 6 °C and the mean LST of urban area increased by about 8 °C; (d) LST pattern change can be effectively analyzed using hot spot analysis; and (e) as the urban expansion occurs, the cold spots have increased, and it is mainly clustered in the urban area. It confirms the presence of an urban cool island effect in Bengaluru urban district. The findings of this work can be used as a scientific basis for the sustainable development and land use planning of the region in the future.

Keywords

Land use land cover Land surface temperature Intensity analysis Urban cool island Bengaluru 

Notes

References

  1. Aldwaik, S. Z., & Pontius, R. G. (2012). Intensity analysis to unify measurements of size and stationarity of land changes by interval, category, and transition. Landscape and Urban Planning, 106(1), 103–114.  https://doi.org/10.1016/j.landurbplan.2012.02.010.CrossRefGoogle Scholar
  2. Arnfield, A. J. (2003). Two decades of urban climate research: a review of turbulence, exchanges of energy and water, and the urban heat island. International Journal of Climatology, 23(1), 1–26.  https://doi.org/10.1002/joc.859.CrossRefGoogle Scholar
  3. Asuero, A. G., Sayago, A., & González, A. G. (2006). The correlation coefficient: an overview. Critical Reviews in Analytical Chemistry, 36(1), 41–59.  https://doi.org/10.1080/10408340500526766.CrossRefGoogle Scholar
  4. Babazadeh, M., & Kumar, P. (2015). Estimation of the urban Heat Island in local climate change and vulnerability assessment for air quality in Delhi. European Scientific Journal, 7881(June), 55–65.Google Scholar
  5. Balzter, H., Weng, Q., Sobrino, J., Smith, C., Rasul, A., Adamu, B., et al. (2017). A review on remote sensing of urban heat and cool islands. Land, 6(2), 38.  https://doi.org/10.3390/land6020038.CrossRefGoogle Scholar
  6. Bendib, A., Dridi, H., & Kalla, M. I. (2017). Contribution of Landsat 8 data for the estimation of land surface temperature in Batna city, eastern Algeria. Geocarto International, 32(5), 503–513.  https://doi.org/10.1080/10106049.2016.1156167.CrossRefGoogle Scholar
  7. Bhat, P. A., Shafiq, M. u., Mir, A. A., & Ahmed, P. (2017). Urban sprawl and its impact on landuse/land cover dynamics of Dehradun City, India. International Journal of Sustainable Built Environment, 6(2), 513–521.  https://doi.org/10.1016/j.ijsbe.2017.10.003.CrossRefGoogle Scholar
  8. Chakraborty, S. D., Kant, Y., & Mitra, D. (2015). Assessment of land surface temperature and heat fluxes over Delhi using remote sensing data. Journal of Environmental Management, 148, 143–152.  https://doi.org/10.1016/j.jenvman.2013.11.034.CrossRefGoogle Scholar
  9. Chaudhuri, G., & Mishra, N. B. (2016). Spatio-temporal dynamics of land cover and land surface temperature in Ganges-Brahmaputra delta: a comparative analysis between India and Bangladesh. Applied Geography, 68, 68–83.  https://doi.org/10.1016/j.apgeog.2016.01.002.CrossRefGoogle Scholar
  10. Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational Psychology Measurement, 20, 37–46.CrossRefGoogle Scholar
  11. Congalton, R. G. (1991). A review of assessing the accuracy of classifications of remotely sensed data. Remote Sensing of Environment., 37, 35–46.  https://doi.org/10.1016/0034-4257(91)90048-B.CrossRefGoogle Scholar
  12. Craglia, M., Haining, R., & Wiles, P. (2000). A comparative evaluation of approaches to urban crime pattern analysis. Urban Studies., 37(4), 711–729.CrossRefGoogle Scholar
  13. Devadas, M. D., & Rose, L. A. (2009). Urban factors and the intensity of Heat Island in the city of Chennai. In: Proc. of the seventh International Conf. on Urban Climate, p. 3–6.Google Scholar
  14. ESRI, (2017). How hot spot analysis (Getis-Ord Gi/) works? http://pro.arcgis.com/en/pro-app/tool-reference/spatial-statistics/h-how-hot-spot-analysis-getis-ord-gi-spatial-stati.htm. Accessed on 8th February 2017.
  15. Fan, C., Myint, S. W., Kaplan, S., Middel, A., Zheng, B., Rahman, A., et al. (2017). Understanding the impact of urbanization on surface urban heat islands—a longitudinal analysis of the oasis effect in subtropical desert cities. Remote Sensing, 9(7).  https://doi.org/10.3390/rs9070672.CrossRefGoogle Scholar
  16. Faris, A. A., & Reddy, Y. S. (2010). Estimation of urban heat island using Landsat-7 ETM+ 259 imagery at Chennai city—a case study. International Journal of Earth Sciences and Engineering., 3(3), 332–340.Google Scholar
  17. Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201.  https://doi.org/10.1016/S0034-4257(01)00295-4.CrossRefGoogle Scholar
  18. Franco, S., Mandla, V. R., Rao, K. R. M., Kumar, M. P., & Anand, P. C. (2015). Study of temperature profile on various land use and land cover for emerging heat island. Journal of Urban and Environmental Engineering, 9(1), 32–37.  https://doi.org/10.4090/juee.2015.v9n1.032037.CrossRefGoogle Scholar
  19. Frey, C. M., Rigo, G., & Parlow, E. (2009). Investigation of the daily urban cooling island (UCI) in two coastal cities in an arid environment: Dubai and Abu Dhabi (UAE). City, 81, 2.06.Google Scholar
  20. Ghosh, S., Shastri, H., Sadavarte, P., Barik, B., & Venkataraman, C. (2017). Flip flop of day-night and summer-winter surface urban heat island intensity in India. Scientific Reports, 7(1).  https://doi.org/10.1038/srep40178.
  21. Grover, A., & Singh, R. (2015). Analysis of urban heat island (UHI) in relation to normalized difference vegetation index (NDVI): a comparative study of Delhi and Mumbai. Environments, 2(4), 125–138.  https://doi.org/10.3390/environments2020125.CrossRefGoogle Scholar
  22. Harris, N. L., Goldman, E., Gabris, C., Nordling, J., Minnemeyer, S., Ansari, S., Lippmann, M., Bennett, L., Raad, M., Hansen, M., & Potapov, P. (2017). Using spatial statistics to identify emerging hot spots of forest loss using spatial statistics to identify emerging hot spots of forest loss. Environmental Research Letters, 12.Google Scholar
  23. Huang, J., Pontius, R. G., Li, Q., & Zhang, Y. (2012). Use of intensity analysis to link patterns with processes of land change from 1986 to 2007 in a coastal watershed of southeast China. Applied Geography, 34, 371–384.  https://doi.org/10.1016/j.apgeog.2012.01.001.CrossRefGoogle Scholar
  24. Jalan, S., & Sharma, K. (2014). Spatio-temporal assessment of land use/land cover dynamics and urban heat island of Jaipur city using satellite data. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, XL-8(1), 767–772.  https://doi.org/10.5194/isprsarchives-XL-8-767-2014.CrossRefGoogle Scholar
  25. Jiménez-Muñoz, J. C., & Sobrino, J. A. (2003). A generalized single-channel method for retrieving land surface temperature from remote sensing data. Journal of Geophysical Research, 108, 4688–4695.CrossRefGoogle Scholar
  26. Kotharkar, R., & Surawar, M. (2015). Land use, land cover, and population density impact on the formation of canopy urban heat islands through traverse survey in the Nagpur urban area, India. Journal of Urban Planning and Development, 142(1), 04015003.  https://doi.org/10.1061/(asce)up.1943-5444.0000277.CrossRefGoogle Scholar
  27. Landsat, N.A.S.A. (7) (2011). Science data users handbook. 2011-03-11. http://landsathandbook.gsfc.nasa.gov/inst_cal/prog_sect8_2.html. Accessed on 12th December 2017.
  28. Landsat, N.A.S.A. (8) (2015). Science data users handbook. 2015-June. http://landsat.usgs.gov/l8handbook.php. Accessed on 12th December 2017.
  29. Li, S., Mo, H., & Dai, Y. (2011). Spatio-temporal pattern of urban cool island intensity and its eco-environmental response in Chang-Zhu-Tan urban agglomeration. Communications in Information Science Management and Engineering, 1(9), 1–6.Google Scholar
  30. Li, Z. L., Tang, B. H., Wu, H., Ren, H., Yan, G., Wan, Z., Trigo, I. F., & Sobrino, J. A. (2013). Satellite-derived land surface temperature: current status and perspectives. Remote Sensing of Environment, 131, 14–37.  https://doi.org/10.1016/j.rse.2012.12.008.CrossRefGoogle Scholar
  31. Li, B., Wang, W., Bai, L., Wang, W., & Chen, N. (2018). Effects of spatio-temporal landscape patterns on land surface temperature: a case study of Xi’an city, China. Environmental Monitoring and Assessment, 190(7), 419.  https://doi.org/10.1007/s10661-018-6787-z.CrossRefGoogle Scholar
  32. Liu, G., Zhang, Q., Li, G., & Doronzo, D. M. (2016). Response of land cover types to land surface temperature derived from Landsat-5 TM in Nanjing metropolitan region, China. Environmental Earth Sciences, 75(20), 1–12.  https://doi.org/10.1007/s12665-016-6202-4.CrossRefGoogle Scholar
  33. Manandhar, R., Odeh, I., & Pontius, R. G. (2010). Analysis of twenty years of categorical land transitions in the lower hunter of New South Wales, Australia. Agriculture, Ecosystems and Environment, 135, 336–346.CrossRefGoogle Scholar
  34. Mathew, A., Khandelwal, S., & Kaul, N. (2016). Spatial and temporal variations of urban heat island effect and the effect of percentage impervious surface area and elevation on land surface temperature: study of Chandigarh city, India. Sustainable Cities and Society, 26, 264–277.  https://doi.org/10.1016/j.scs.2016.06.018.CrossRefGoogle Scholar
  35. McCarville, D., Buenemann, M., Bleiweiss, M., & Barsi, J. (2011). Atmospheric correction of Landsat thermal infrared data: a calculator based on North American Regional Reanalysis (NARR) data (p. 12). In: Proc. of the American Society for Photogrammetry and Remote Sensing Conf.Google Scholar
  36. Nelson, T. A., & Boots, B. (2008). Detecting spatial hot spots in landscape ecology. Ecography, 31(5), 556–566.  https://doi.org/10.1111/j.0906-7590.2008.05548.x.CrossRefGoogle Scholar
  37. Ogawa, K., Gurjar, B. R., Kikegawa, Y., Mohan, M., Kandya, A., & Bhati, S. (2012). Urban heat island assessment for a tropical urban airshed in India. Atmospheric and Climate Sciences, 02(02), 127–138.  https://doi.org/10.4236/acs.2012.22014.CrossRefGoogle Scholar
  38. Ord, J. K., & Getis, A. (1995). Local spatial autocorrelation statistics: distributional issues and an application. Geographical Analysis, 27(4), 286–306.CrossRefGoogle Scholar
  39. Pal, S., & Ziaul, S. (2017). Detection of land use and land cover change and land surface temperature in English Bazar urban centre. Egyptian Journal of Remote Sensing and Space Science, 20(1), 125–145.  https://doi.org/10.1016/j.ejrs.2016.11.003.CrossRefGoogle Scholar
  40. Prasannakumar, V., Vijith, H., Charutha, R., & Geetha, N. (2011). Spatio-temporal clustering of road accidents: GIS based analysis and assessment. Procedia - Social and Behavioral Sciences, 21, 317–325.  https://doi.org/10.1016/j.sbspro.2011.07.020.CrossRefGoogle Scholar
  41. Qin, Z., Karnieli, A., & Berliner, P. (2001). A mono-window algorithm for retrieving land surface temperature from Landsat TM data and its application to the Israel-Egypt border region. International Journal of Remote Sensing, 22(18), 3719–3746.  https://doi.org/10.1080/01431160010006971.CrossRefGoogle Scholar
  42. Ramachandra, T. V., & Kumar, U. (2009). Land surface temperature with land cover dynamics: multi-resolution, spatio-temporal data analysis of Greater Bangalore, India. International Journal of Geoinformatics, 5(3), 43–53.Google Scholar
  43. Ramachandra, T. V., Aithal, B. H., Vinay, S., Joshi, N. V., Kumar, U., & Rao, V. K. (2013). Modelling urban revolution in Greater Bangalore, India. 30th Annual In-House Symposium on Space Science and Technology, ISRO-IISc Space Technology Cell (pp. 1–5). Bangalore: Indian Institute of Science.Google Scholar
  44. Rasul, A., Balzter, H., & Smith, C. (2015). Urban climate 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.  https://doi.org/10.1016/j.uclim.2015.09.001.CrossRefGoogle Scholar
  45. Rasul, A., Balzter, H., & Smith, C. (2017). Applying a normalized ratio scale technique to assess influences of urban expansion on land surface temperature of the semi-arid city of Erbil. International Journal of Remote Sensing, 38(13), 3960–3980.  https://doi.org/10.1080/01431161.2017.1312030.CrossRefGoogle Scholar
  46. Shi, Y., & Zhang, Y. (2018). Remote sensing retrieval of urban land surface temperature in hot-humid region. Urban Climate, 24, 299–310.  https://doi.org/10.1016/j.uclim.2017.01.001.CrossRefGoogle Scholar
  47. Smits, P. C., Dellepiane, S. G., & Schowengerdt, R. A. (1999). Quality assessment of image classification algorithms for land-cover mapping: a review and a proposal for a cost-based approach. International Journal of Remote Sensing, 20(8), 1461–1486.  https://doi.org/10.1080/014311699212560.CrossRefGoogle Scholar
  48. Sudhira, H. S., Ramachandra, T. V., & Subrahmanya, M. H. B. (2007). Bangalore. Cities, 24(5), 379–390.  https://doi.org/10.1016/j.cities.2007.04.003.CrossRefGoogle Scholar
  49. Tan, K. C., Lim, H. S., MatJafri, M. Z., & Abdullah, K. (2010). Landsat data to evaluate urban expansion and determine land use/land cover changes in Penang Island, Malaysia. Environmental Earth Sciences, 60(7), 1509–1521.  https://doi.org/10.1007/s12665-009-0286-z.CrossRefGoogle Scholar
  50. Tran, D. X., Pla, F., Latorre-Carmona, P., Myint, S. W., Caetano, M., & Kieu, H. V. (2017). Characterizing the relationship between land use land cover change and land surface temperature. ISPRS Journal of Photogrammetry and Remote Sensing, 124, 119–132.  https://doi.org/10.1016/j.isprsjprs.2017.01.001.CrossRefGoogle Scholar
  51. Valor, E., & Caselles, V. (1996). Mapping land surface emissivity from NDVI: application to European, African, and South American areas. Remote Sensing of Environment, 57(3), 167–184.  https://doi.org/10.1016/0034-4257(96)00039-9.CrossRefGoogle Scholar
  52. Wolf, T., & McGregor, G. (2013). The development of a heat wave vulnerability index for London, United Kingdom. Weather and Climate Extremes, 1, 59–68.  https://doi.org/10.1016/j.wace.2013.07.004.CrossRefGoogle Scholar
  53. Zhang, Y., Fu, Y., Kong, X., & Zhang, F. (2019). Prefecture-level city shrinkage on the regional dimension in China: spatiotemporal change and internal relations. Sustainable Cities and Society, 47(February), 101490.  https://doi.org/10.1016/j.scs.2019.101490.CrossRefGoogle Scholar
  54. Zhao, R., Chen, Y., Shi, P., Zhang, L., Pan, J., & Zhao, H. (2013). Land use and land cover change and driving mechanism in the arid inland river basin: a case study of Tarim River, Xinjiang, China. Environmental Earth Sciences, 68(2), 591–604.  https://doi.org/10.1007/s12665-012-1763-3.CrossRefGoogle Scholar
  55. Zhou, D. C., Zhao, S. Q., Liu, S. G., Zhang, L. X., & Zhu, C. (2014). Surface urban heat island in China’s 32 major cities: spatial pattern and drivers. Remote Sensing of Environment, 152, 51–61.CrossRefGoogle Scholar
  56. Ziaul, S., & Pal, S. (2018). Anthropogenic heat flux in English Bazar town and its surroundings in West Bengal, India. Remote Sensing Applications: Society and Environment, 11, 151–160.  https://doi.org/10.1016/j.rsase.2018.06.003.CrossRefGoogle Scholar

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Authors and Affiliations

  1. 1.Department of Applied Mechanics & HydraulicsNational Institute of Technology KarnatakaMangaloreIndia

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