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Land use/land cover change and land surface temperature of Ibadan and environs, Nigeria

Abstract

Rapid urbanization is having a considerable impact on various aspects of living, thereby altering the biophysical environment. This study adopted the use of remote sensing technique and geographical information system (GIS) to analyse the relationship between changing land use/land cover and land surface temperature in a rapidly urbanizing tropical city of Ibadan between 1984 and 2019. Landsat series TM, ETM+, and OLI satellite imageries of Ibadan region city for 1984, 2002, and 2019, respectively, were obtained from the US Geological Survey (USGS) Landsat series of Earth Observation satellites accessible on the Google earth engine (GEE) platform. Supervised classification was done using a random forest (RF) machine learning classifier in the GEE platform. Surface emissivity maps were obtained from the normalized difference vegetation index (NDVI) thresholds method and land cover information. The surface emissivity based on NDVI classes was used to retrieve land surface temperature (LST). The results showed an increase in urban cover from 341.72 km2 in 1984 to 520.58 km2 in 2019 with an average increase in land surface temperature from 17 °C to 38 °C, respectively. Temperature sampling in the north-south and west-east transect revealed that highly urbanized areas located at the city centre of Ibadan have the highest LST of about 38 °C. It dissipates to about 19 °C at the suburb that is less built up. A significant negative relationship exists between the health condition of vegetation (NDVI) and LST with a correlation coefficient of r = − 0.95. The study confirms the potential application of GIS and remote sensing for detecting urban growth as well as relates growth impact to LST, thereby suggesting that fitting strategies will be important for the sustainable management of the urban areas.

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References

  1. Abegunde, L., & Adedeji, O. (2015). Impact of landuse change on surface temperature in Ibadan, Nigeria. International Journal of Environmental, Chemical, Ecological, Geological and Geophysical Engineering, 9(3), 235–241.

  2. Adagbasa, G., Adelabu, S., & Okello, T. (2018). Assessment of short term inter-annual post fire vegetation recovery using land surface temperature (LST). South African Geographers, 1, 605–631.

  3. Adebayo, Y. R. (1987). Land-use approach to the spatial analysis of the urban ‘heat island’ in Ibadan, Nigeria. Weather, 42(9), 273–280.

  4. Adebayo, Y. R. (1991). “Heat island” in a humid tropical city and its relationship with potential evaporation. Theoretical and Applied Climatology, 43(3), 137–147.

  5. Akher, S. K., & Chattopadhyay, S. Impact of Urbanization on Land Surface Temperature-A Case Study of Kolkata New Town. The International Journal Of Engineering And Science (IJES), 6(1), 71–81.

  6. Ayoade, J. O. (2012). Introduction to building and urban climatology. Ibadan: Agbo Areo Publishers.

  7. Babalola, O. S., & Akinsanola, A. A. (2016). Change detection in land surface temperature and land use land cover over Lagos Metropolis, Nigeria. J. Remote Sens. GIS, 5(2).

  8. Becker, F., & Li, Z. (1990). Temperature independent spectral indices in thermal infrared bands. Remote Sensing of Environment, 32, 17–33.

  9. Cai, Y., Zhang, H., Zheng, P., & Pan, W. (2016). Quantifying the impact of land use/land cover changes on the urban heat island: A case study of the natural wetlands distribution area of Fuzhou City,China. Wetlands, 36, 285–298.

  10. Chander, G., Markham, B. L., & Helder, D. L. (2009). Summary of current radiometric calibration coefficients for Landsat MSS, TM, ETM+, and EO−1 ALI sensors. Remote Sensing of Environment, 893–903.

  11. Christensen JH, Hewitson B, Busuioc A, Chen A, Gao X, et al. (2007) Regional climate projections. In: Climate change 2007: The physical science basis. Contribution of Working Group I to the Fourth Assessment Report of the Intergovernmental Panel on Climate Change.

  12. Corbari C, Horeschi D, Ravazzani G, Mancini M (2008) Land surface temperature from remote sensing and from an energy water balance model for irrigation management. Irrigation for Mediterranean Agriculture: challenges and innovation for the next decades.

  13. Dontree, S. (2010). Relation of land surface temperature (LST) and land use/land cover (LULC) from remotely sensed data in Chiang Mai—Lamphun basin. In SEAGA conference.

  14. Dos Santos Taciana, O., Moura Geber, B. D. E. A., DA Silva Bernardo, B., Leidjane, M. M., Oliveira, D. E., & Machado Celia, C. C. (2013). Influence of urbanization on land surface temperature in Recife City. Eng. Agríc., Jaboticabal, 33(6), 12341244 nov./dez. 2013.

  15. Fashae, O. A., & Onafeso, O. D. (2011). Impact of climate change on sea level rise in Lagos, Nigeria. International Journal of Remote Sensing, 32(24), 9811–9819.

  16. Fashae, O. A., Olusola, A., & Adedeji, O. (2017). Geospatial analysis of changes in vegetation cover over Nigeria. Bulletin of Geography. Physical Geography Series, 13(1), 17–27.

  17. Fashae, O. A., Obateru, R. O., & Olusola, O. A. (2018). A simple distributed water balance model for an urbanized river basin using remote sensing and GIS techniques. Geocarto International, 1–31.

  18. Funk, C., Peterson, P., Landsfeld, M., Pedreros, D., Verdin, J., Shukla, S., et al. (2015). The climate hazards infrared precipitation with stations—A new environmental record for monitoring extremes. Scientific data, 2, 150066.

  19. Howard, L. (1988). The climate of London. London, UK: Cambridge University Press.

  20. Mallick, J., Kant, Y., & Bharath, B. D. (2008). Estimation of land surface temperature over Delhi using Landsat-7 ETM+. J Ind Geophys Union, 12(3), 131–140.

  21. Kumar, K. S., Bhaskar, P. U., & Padmakumari, K. (2012). Estimation of land surface temperature to study urban heat island effect using LANDSAT ETM+ image. International Journal of Engineering, Science and Technology, 4(2), 771–778.

  22. Lili, T., Qin, Z., Li, W., Geng, J., Yang, L., Zhao, S., Zhan, W., & Wang, F. (2016). Surface urban heat island effect and its relationship with urban expansion in Nanjing, China. Journal of Applied Remote Sensing, 10(2), 026037. https://doi.org/10.1117/1.JRS.10.026037.

  23. Liu, L., & Zhang, Y. (2011). Urban heat island analysis using the Landsat TM data and ASTER data: A case study in Hong Kong. Remote Sensing, 3(7), 1535–1552.

  24. MacLachlan, A., Biggs, E., Roberts, G., & Boruff, B. (2017). Urbanisation-induced land cover temperature dynamics for sustainable future urban heat island mitigation. Urban Science, 1(4), 38.

  25. NASA (2009). Landsat 7 Science Data Users Handbook, Chapter 2. 11, 117–120.

  26. National Population Commission (NPC). (2011). Nigeria’s over 167 million Population: Implications and Challenges.

  27. Nwaerema, P., Vincent, O. N., Amadou, C., & Atuma, I. M. (2019). Spatial Assessment of Land Surface Temperature and Emissivity in the Tropical Littoral City of Port Harcourt, Nigeria. International Journal of Environment and Climate Change, 88–103.

  28. Ogunjobi, K. O., Adamu, Y., Akinsanola, A. A., & Orimoloye, I. R. (2018). Spatio-temporal analysis of land use dynamics and its potential indications on land surface temperature in Sokoto Metropolis, Nigeria. Royal Society Open Science, 5(12), 180,661.

  29. Oguntoyinbo, J. S. (1986). Some aspects of urban climates of tropical Africa. In Proceedings of the Conference on Urban Climatology and its Application With Special Regards to Tropical Areas.. WMO.

  30. Ojeh, V., Balogun, A., & Okhimamhe, A. (2016). Urban-rural temperature differences in Lagos. Climate, 4(2), 29.

  31. Oke, T. R. (1978). Boundary layer climates. Routledge.

  32. Polydoros, A., Mavrakou, T., & Cartalis, C. (2018). Quantifying the Trends in Land Surface Temperature and Surface Urban Heat Island Intensity in Mediterranean Cities in View of Smart Urbanization. Urban Science, 2(1), 16.

  33. Rongali, G., Keshari, A. K., Gosain, A., & Khosa, R. (2018). A Mono-Window Algorithm for Land Surface Temperature Estimation from Landsat 8 Thermal Infrared Sensor Data: A Case Study of the Beas River Basin, India. Pertanika Journal of Science and Technology, 26.

  34. Simwanda, M., Ranagalage, M., Estoque, R. C., & Murayama, Y. (2019). Spatial analysis of surface urban heat islands in four rapidly growing African Cities. Remote Sensing, 11(14), 1645.

  35. Sobrino, J. A., Jimenez-Munoz, J. C., & Paolini, L. (2004). Land surface temperature retrieval from LANDSAT TM 5. Remote Sensing of Environment, 434–440.

  36. Sultana, S., & Satyanarayana, A. N. V. (2018). Urban heat island intensity during winter over metropolitan cities of India using remote-sensing techniques: Impact of urbanization. International journal of remote sensing, 39(20), 6692–6730.

  37. United Nations. (2014). World Urbanization Prospects: The 2014 Revision. New York, NY, USA: United Nations.

  38. USGS (2009). SLC-off Gap-filled products e Gap-fill algorithm Methodology. U.S. Geological Survey Earth Resources observation System data Center. http://landsat.usgs.gov/documents/L7SLCGapFilledMethod.pdf.

  39. Vidal, A. (1991). Atmospheric and emissivity correction of land surface temperature measured from satellite using ground measurements or satellite data. Title Remote Sensing, 12(12), 2449–2460.

  40. Wang, C., Platnick, S., Zhang, Z., Meyer, K., and Yang, P. (2016). Retrieval of ice cloud properties using an optimal estimation algorithm and MODIS infrared observations: 1. Forward model, error analysis, and information content. Journal of Geophysical Research: Atmospheres, 121(10), 5809–5826.

  41. 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(4), 467–483.

  42. Yamashita. (1996). Detail Structure of Heat Island Phenomena from Moving Observations from Electric Trans Cars in Metropolitan Tokyo. Atmospheric Environment, 30(429), 435.

  43. Yang, J., & Wang, Y. Q. (2002). Estimation of land surface temperature using Landsat-7 ETM+ thermal infrared and weather station data. In Proceedings of Huangshan International Thermal Infrared Remote Sensing Workshop, July (pp. 14–17).

  44. Zaeemdar, S., & Baycan, T. (2017). Analysis of the Relationship between Urban Heat Island and Land Cover in Istanbul through Landsat 8 OLI. Journal of Earth Science & Climatic Change, 8. https://doi.org/10.4172/2157-7617.1000423.

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Correspondence to Olutoyin Adeola Fashae.

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Adeola Fashae, O., Gbenga Adagbasa, E., Oludapo Olusola, A. et al. Land use/land cover change and land surface temperature of Ibadan and environs, Nigeria. Environ Monit Assess 192, 109 (2020). https://doi.org/10.1007/s10661-019-8054-3

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Keywords

  • Land surface temperature
  • Land use/land cover
  • Urban heat island
  • NDVI
  • Random forest
  • Google earth engine