Urbanization-induced rapid land use/land cover change (LULCC) modifies the thermal characteristics of a region at local scale and often creates urban heat island (UHI) effects. Delhi also experiences such enhancing UHI scenario. In present study, LULCC was linked with the land surface temperature (LST), and surface urban heat island intensity (SUHII) was measured at the sub-district level. Landsat imageries and MODIS LST product for 2001, 2009 and 2017 were main data sources. LULC classification and LST were derived using maximum likelihood classification and single-channel algorithm techniques, respectively. Proposed LULC classification scheme was built-up, current fallows land, bare land (or source landscape); and crop land, vegetation and water bodies (or sink landscape). SUHII was calculated using the contribution index of both source and sink landscapes and landscape index (LI). Results from LI also supported with temperature vegetation feature space index, transformed difference vegetation index, enhanced vegetation index, diurnal temperature range, population density and transect analysis. The result showed that sub-districts of North, North-East, East and West Delhi were more prone to SUHII due to the highly dense built-up area and industrial area. SUHII was comparatively low in the sub-districts of South and South-West Delhi because of less built-up area as well the presence of greenery. Not only built-up but also fallow land and barren land contributed significantly in some places, i.e. South and South-West Delhi. Besides, urban green space and green crop field reduced the LST sufficiently in some areas, i.e. Najafgarh and New Delhi. Again, this study could help to understand UHI in Delhi at sub-district level.
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Amiri, R., Weng, Q., Alimohammadi, A., & Alavipanah, S. K. (2009). Spatial-temporal dynamics of land surface temperature in relation to fractional vegetation cover and land use/cover in the Tabriz urban area, Iran. Remote Sensing of Environment, 113, 2606–2617. https://doi.org/10.1016/j.rse.2009.07.021.
Ayanlade, A., & Jegede, O. O. (2015). Evaluation of the intensity of the daytime surface urban heat island: How can remote sensing help? International Journal of Image and Data Fusion, 6, 348–365.
Bannari, A., Asalhi, H., Teillet, P. M. (2002). Transformed difference vegetation index (TDVI) for vegetation cover mapping. ieeexplore.ieee.org 5.
Bokaie, M., Zarkesh, M. K., Arasteh, P. D., & Hosseini, A. (2016). Assessment of Urban Heat Island based on the relationship between land surface temperature and land use/land cover in Tehran. Sustainable Cities and Society, 23, 94–104. https://doi.org/10.1016/j.scs.2016.03.009.
Chakraborti, S., Banerjee, A., Sannigrahi, S., Pramanik, S., Maiti, A., & Jha, S. (2019). Assessing the dynamic relationship among land use pattern and land surface temperature: A spatial regression approach. Asian Geographic, 1–24.
Chen, L.-D., Fu, B.-J., & Zhao, W.-W. (2006). Source-sink landscape theory and its ecological significance. Acta Ecologica Sinica, 26, 1444–1449.
Chen, X., & Zhang, Y. (2017). Impacts of urban surface characteristics on spatiotemporal pattern of land surface temperature in Kunming of China. Sustainable Cities and Society, 32, 87–99. https://doi.org/10.1016/j.scs.2017.03.013.
Cohen, J. (1960). A coefficient of agreement for nominal scales. Educational and Psychological Measurement, 20, 37–46.
Delhi Tourism. (2018). Seasons of Delhi [WWW Document]. URL http://www.delhitourism.gov.in/delhitourism/aboutus/seasons_of_delhi.jsp accessed 18 Apr 18.
Effat, H. A., & Hassan, O. A. K. (2014). Change detection of urban heat islands and some related parameters using multi-temporal Landsat images: A case study for Cairo city, Egypt. Urban Climate, 10, 171–188. https://doi.org/10.1016/j.uclim.2014.10.011.
Estoque, R. C., Murayama, Y., & Myint, S. W. (2017). Effects of landscape composition and pattern on land surface temperature: An urban heat island study in the megacities of Southeast Asia. Science of the Total Environment, 577, 349–359. https://doi.org/10.1016/j.scitotenv.2016.10.195.
Guo, G., Wu, Z., Xiao, R., Chen, Y., Liu, X., & Zhang, X. (2015). Impacts of urban biophysical composition on land surface temperature in urban heat island clusters. Landscape and Urban Planning, 135, 1–10. https://doi.org/10.1016/j.landurbplan.2014.11.007.
Hang, H. T., Rahman, A., Mallick, J. (2017). Satellite-derived land surface temperature and landscape characterization of National Capital Region (NCR), India using multispectral and thermal data. In Euro-Mediterranean conference for environmental integration. Berlin, Springer, pp. 1783–1785.
Huete, A., Didan, K., Miura, T., Rodriguez, E., Gao, X., & Ferreira, L. (2002). Overview of the radiometric and biophysical performance of the MODIS vegetation indices. Remote Sensing of Environment, 83, 195–213. https://doi.org/10.1016/S0034-4257(02)00096-2.
Joshi, M., Ghosal, A., (2018). Delhi tree felling: Death by a thousand cuts. The Indian Express.
Latif, M. S. (2014). Land surface temperature retrival of landsat-8 data using split window algorithm: A case study of Ranchi District. International Journal of Engineering Development and Research, 2, 3840–3849.
Lillesand, T., Kiefer, R. W., & Chipman, J. (2014). Remote sensing and image interpretation. London: Wiley.
Liu, H., & Weng, Q. H. (2009). Scaling effect on the relationship between landscape pattern and land surface temperature: A case study of Indianapolis, United States. Photogrammetric Engineering & Remote Sensing, 75, 291–304.
Mallick, J., Kant, Y., & Bharath, B. D. (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM+. The Journal of Indian Geophysical Union, 12, 131–140.
Mallick, J., & Rahman, A. (2012). Impact of population density on the surface temperature and micro-climate of Delhi. Current Science, 102, 1708–1713.
Mathew, A., Khandelwal, S., & Kaul, N. (2018). Analysis of diurnal surface temperature variations for the assessment of surface urban heat island effect over Indian cities. Energy and Buildings, 159, 271–295. https://doi.org/10.1016/j.enbuild.2017.10.062.
Mohan, M., & Kandya, A. (2015). Impact of urbanization and land-use/land-cover change on diurnal temperature range: A case study of tropical urban airshed of India using remote sensing data. Science of the Total Environment, 506, 453–465.
Mohan, M., Kikegawa, Y., Gurjar, B. R., Bhati, S., & Kolli, N. R. (2013). Assessment of urban heat island effect for different land use-land cover from micrometeorological measurements and remote sensing data for megacity Delhi. Theoretical and Applied Climatology, 112, 647–658. https://doi.org/10.1007/s00704-012-0758-z.
Mushore, T. D., Odindi, J., Dube, T., & Mutanga, O. (2017). Understanding the relationship between urban outdoor temperatures and indoor air-conditioning energy demand in Zimbabwe. Sustainable Cities and Society, 34, 97–108. https://doi.org/10.1016/j.scs.2017.06.007.
Peng, S., Piao, S., Ciais, P., Friedlingstein, P., Ottle, C., Bréon, F. M., et al. (2012). Surface urban heat island across 419 global big cities. Environmental Science and Technology, 46, 696–703. https://doi.org/10.1021/es2030438.
Pramanik, S., & Punia, M. (2019). Assessment of green space cooling effects in dense urban landscape: A case study of Delhi, India. Modeling Earth Systems and Environment, 5, 867–884. https://doi.org/10.1007/s40808-019-00573-3.
Quattrochi, D., & Luvall, J. (2004). Thermal remote sensing in land surface processing. Amsterdam: CRC Press.
Ramachandra, T. V., & Uttam, K. K. (2009). Land surface temperature with land cover dynamics: Multi-resolution, spatio-temporal data analysis of Greater Bangalore. International Journal of Geoinformatics, 5(3), 44.
Rao, P. K. (1972). Remote sensing of urban heat islands from an environmental satellite. Bulletin of the American Meteorological Society, 53, 647.
Singh, P., Kikon, N., & Verma, P. (2017). Impact of land use change and urbanization on urban heat island in Lucknow city, Central India: A remote sensing based estimate. Sustainable Cities and Society, 32, 100–114. https://doi.org/10.1016/j.scs.2017.02.018.
Sobrino, J. A., Jiménez-Muñoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 90, 434–440.
Song, Y., & Wu, C. (2016). Examining the impact of urban biophysical composition and neighboring environment on surface urban heat island effect. Advances in Space Research, 57, 96–109. https://doi.org/10.1016/j.asr.2015.10.036.
Sun, Q., Wu, Z., & Tan, J. (2012). The relationship between land surface temperature and land use/land cover in Guangzhou, China. Environmental Earth Sciences, 65, 1687–1694. https://doi.org/10.1007/s12665-011-1145-2.
Voogt, J. A., & Oke, T. R. (2003). Thermal remote sensing of urban climates. Remote Sensing of Environment, 86, 370–384. https://doi.org/10.1016/S0034-4257(03)00079-8.
Walawender, J. P., Hajto, M. J., Iwaniuk, P. (2012). A new ArcGIS toolset for automated mapping of land surface temperature with the use of LANDSAT satellite data. In Geoscience and remote sensing symposium (IGARSS), 2012 IEEE International. IEEE, pp. 4371–4374.
Xu, S. (2009). An approach to analyzing the intensity of the daytime surface urban heat island effect at a local scale. Environmental Monitoring and Assessment, 151, 289.
Zhang, Y., Odeh, I. O. A., & Han, C. (2009). Bi-temporal characterization of land surface temperature in relation to impervious surface area, NDVI and NDBI, using a sub-pixel image analysis. International Journal of Applied Earth Observation and Geoinformation, 11, 256–264.
The authors thank anonymous reviewer for constructive comments. This paper is a part of M.Phil. dissertation of SP submitted to the Jawaharlal Nehru University, New Delhi, India. SP would like to acknowledge UGC fellowship for financial assistance during the research.
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Pramanik, S., Punia, M. Land use/land cover change and surface urban heat island intensity: source–sink landscape-based study in Delhi, India. Environ Dev Sustain 22, 7331–7356 (2020). https://doi.org/10.1007/s10668-019-00515-0
- Land use/land cover change
- Surface urban heat island intensity (SUHII)
- Contribution index (CI)
- Landscape index (LI)