Urban Heat Island Effect of Addis Ababa City: Implications of Urban Green Spaces for Climate Change Adaptation

  • Ermias TeferiEmail author
  • Hiwot Abraha
Part of the Climate Change Management book series (CCM)


Land use and land cover (LULC) change is one of the most visible results of human’s modification of the terrestrial ecosystem, and it has a significant impact on climate. Urbanization has been a major force of LULC throughout human history that has had a great impact on climate change. Urban areas generally have higher absorption of solar radiation and greater thermal capacity and conductivity, leading to a relatively higher temperature in the urban areas compared with the surrounding rural areas. Studies related to the impact of land use and land cover changes in Addis Ababa are few. This study therefore examined the influence of LULC change on urban climate in Addis Ababa city from 1986 to 2011 by retrieving land surface temperature (LST) from thermal infrared (TIR) data of Landsat TM satellite data. The results show that in Addis Ababa, the area for grass land, agricultural land, forest land and bare land declined by 43.32, 16.03, 9.74, and 9.65 km2, respectively, on the other hand, built-up areas was dramatically expanded from 100.13 km2 in 1986 to 180.13 km2 in 2011 that is almost two times from the base year. Changes in LULC were accompanied by changes in LST. The average LST in 1986 was 302 K (28.88 °C) and it increased to 304 K (30.88 °C) in 2011. The change in LST is mainly associated with changes in impervious surface. The urban-rural temperature differences between the urban core and its surrounding areas show a maximum difference of 15 K. This could lead to an intensified urban heat island effect in the urban areas. The relationship between LST and Normalized Difference Vegetation Index (NDVI) clearly shows that vegetation has great impact on reducing UHI effect. Thus, development of urban green spaces is one of the most promising climate change adaptation response in order to minimize the impact of elevated temperatures on human health and comfort.


Heat island effect Urban green spaces Climate change Adaptation Addis ababa 


  1. Anderson, J. R. (1976). A land use and land cover classification system for use with remote sensor data (Vol. 964). US Government Printing Office.Google Scholar
  2. 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(12), 2606–2617.CrossRefGoogle Scholar
  3. Ball, G. H., & Hall, D. J. (1965). ISODATA, a novel method of data analysis and pattern classification. Melno Park, CA: Stanford Research Institute.Google Scholar
  4. 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, 113, 893–903.CrossRefGoogle Scholar
  5. Chen, D., & Brutsaert, W. (1998). Satellite-sensed distribution and spatial patterns of vegetation parameters over a tallgrass prairie. Journal of the Atmospheric Sciences, 55(7), 1225–1238.Google Scholar
  6. 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. doi: 10.1016/j.rse.2005.11.016.
  7. Congalton, R. G., & Green, K. (2009). Assessing the accuracy of remotely sensed data: Principles and practices. CRC Press.Google Scholar
  8. Foody, G. M., Campbell, N. A., Trodd, N. M., & Wood, T. F. (1992). Derivation and applications of probabilistic measures of class membership from the maximum-likelihood classification. Photogrammetric Engineering and Remote Sensing, 58(9), 1335–1341.Google Scholar
  9. 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(1), 34–48.CrossRefGoogle Scholar
  10. IPCC, Climate Change. (2013). The physical science basis—working group I contribution to the fifth assessment report of the intergovernmental panel on climate change. New York: Cambridge University Press.Google Scholar
  11. Li, Z. L., & Becker, F. (1993). Feasibility of land surface temperature and emissivity determination from AVHRR data. Remote Sensing of Environment, 43, 67–85.Google Scholar
  12. Mannstein, H. (1987). Surface energy budget, surface temperature and thermal inertia. In Remote Sensing Applications in Meteorology and Climatology, (pp. 391–410). Springer Netherlands.Google Scholar
  13. Pongrácz, R., & Bartholy, J. (2006). Dezső, Zs. Remotely sensed thermal information applied to urban climate analysis. Advances in Space Research, 37, 2191–2196.CrossRefGoogle Scholar
  14. Robine, J.-M., Cheung, S. L. K., Le Roy, S., Van Oyen, H., Griffiths, C., Michel, J.-P., et al. (2008). Death toll exceeded 70,000 in Europe during the summer of 2003. Comptes Rendus Biologies, 331(2), 171–178.CrossRefGoogle Scholar
  15. Van de Griend, A., & Owe, M. (1993). On the relationship between thermal emissivity and the normalized difference vegetation index for natural surfaces. International Journal of Remote Sensing, 14, 1119–1131.CrossRefGoogle Scholar
  16. Weng, Q., & Lo, C. P. (2001). Spatial analysis of urban growth impacts on vegetative greenness with Landsat TM data. Geocarto International, 16(4), 17–25.CrossRefGoogle Scholar
  17. WHO. (2013). Protecting health from climate change: vulnerability and adaptation assessment. Geneva, Switzerland: World Health Organization.Google Scholar
  18. Wolch, J. R., Byrne, J., & Newell, J. P. (2014). Urban green space, public health, and environmental justice: The challenge of making cities ‘just green enough’. Landscape and Urban Planning, 125, 234–244.CrossRefGoogle Scholar
  19. Xiao, H., & Weng, Q. (2007). The impact of land use and land cover changes on land surface temperature in a karst area of China. Journal of Environmental Management, 85(1), 245–257.Google Scholar
  20. 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.Google Scholar
  21. Zha, Y., Gao, J., & Ni, S. (2003). Use of normalized difference built-up index in automatically mapping urban areas from TM imagery. International Journal of Remote Sensing, 24(3), 583–594.Google Scholar
  22. Zhao, H. M., & Chen, X. L. (2005). Use of normalized difference bareness index in quickly mapping bare areas from TM/ETM+. Geoscience and Remote Sensing Symposium, 3(25–29), 1666−1668.Google Scholar
  23. Zhou, X., & Wang, Y. C. (2011). Dynamics of land surface temperature in response to land-use/cover change. Geographical Research, 49(1), 23–36.CrossRefGoogle Scholar

Copyright information

© Springer International Publishing AG 2017

Authors and Affiliations

  1. 1.Center for Environment and Development StudiesAddis Ababa UniversityAddis AbabaEthiopia
  2. 2.Addis Ababa Environmental Protection AuthorityAddis AbabaEthiopia

Personalised recommendations