The impact of spatiotemporal patterns of land use land cover and land surface temperature on an urban cool island: a case study of Bengaluru
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.
KeywordsLand use land cover Land surface temperature Intensity analysis Urban cool island Bengaluru
- 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
- 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
- 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.
- 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
- 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
- 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
- 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.
- 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
- 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
- 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
- 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.
- Landsat, N.A.S.A. (8) (2015). Science data users handbook. 2015-June. http://landsat.usgs.gov/l8handbook.php. Accessed on 12th December 2017.
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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
- 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