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October 4, 2022
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June 2, 2023
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Abstract

The rapid urbanization coupled with tremendous growth in population and built-up areas in the cities has led to issues associated with global climate change impacts such as urban heat islands. This study is concerned with the spatial impact of the dynamics of urbanisation on urban heat island (UHI) in the Sunyani Municipality using Satellite Remote Sensing techniques. Sunyani municipal is a growing area in Ghana with increasing population and urbanisation. Many people in Ghana are moving to settle in Sunyani for different purposes. In this process, vegetated areas have been converted to urban areas and this has led to a reduction in the vegetation cover and increased the urban temperature in the municipality. Therefore, there is a need for a study on how the dynamics in the land use and land cover (LULC) in the municipal are contributing to urban heat. The main aim of this research is to identify the impact of LULC dynamics on UHI using multi-temporal Landsat images from 2001 to 2021 with a 5-year epoch in the Sunyani Municipal. Landsat images acquired over Sunyani and its environs for the period from 2001 to 2021 with a 5-year epoch, were download freely from the USGS Earth Explorer website. These images were pre-processed in QGIS 3.16 using the Semi-automatic classification plugin (SCP). The pre-processed images were classified using the Random Forest (RF) algorithm. The supervised classification was done using the semi-automatic classification plugin in QGIS 3.16. This study considered two (2) classes (urban and vegetation). The thermal infrared bands of the Landsat images were used to compute the land surface temperature (LST) for each year, starting from 2001 up to 2021, at a 5-year interval. The standard deviation and mean LST were extracted and used to extract all the UHIs in the municipality. Regression analyses were used to determine the impact of LULC dynamics on LST and UHIs in the Sunyani Municipality. The analysis yielded a strong impact with an R-squared value of 75% and 77% for UHI and LST respectively. The vegetated covers yielded a strong negative correlation with UHIs while Urbanisation yielded a strong positive correlation with the intensity of UHIs. On the other hand, Vegetation reduced the intensity of UHIs in the municipality. This gives a clear understanding of how dynamics in urbanisation and vegetation are affecting UHIs in the Sunyani Municipality. A Linear model was developed for LULC dynamics, LST and UHIs in the Sunyani municipality.  It was concluded that LULC dynamics have a strong impact on UHI. It is recommended that the environmental Agencies in the Municipality should ensure the protection of the vegetated areas as well as improve the green areas in the Municipality to minimize urban heat.

Keywords

Urban Heat Islands Land Surface Temperature Land Use Land Cover Confusion Matrix Random Forest

Article Details

References

  1. Abduwasit, G. (2010). Calculating surface temperature using Landsat Thermal Imagery, Macelwane Hall 324 3507 Laclede Ave, St Louis, MO 63103, pp. 1-9.
  2. Adusu, D., Anaafo, D., Abugre, S. and Bunyamin, A-R (2021). Effects of urban growth on historical land use/land cover changes in the Sunyani Municipality, Ghana. Journal of Energy and Natural Resource Management, pp. 16-27, https://doi.org/10.26796/jenrm.v7i2.182.
  3. Allen, M.R., Dube, O.P., Solecki, W., Aragón-Durand, F., Cramer, W., Humphreys, S., Kainuma, M., Kala, J., Mahowald, N., Mulugetta, Y., Perez, R., Wairiu, M. and Zickfeld, K. (2018). Framing and Context. In: Global Warming of 1.5°C. An IPCC Special Report on the impacts of global warming of 1.5°C above pre-industrial levels and related global greenhouse gas emission pathways, in the context of strengthening the global response to the threat of climate change, sustainable development, and efforts to eradicate poverty [Masson-Delmotte, V., P. Zhai, H.-O. Pörtner, D. Roberts, J. Skea, P.R. Shukla, A. Pirani, W. Moufouma-Okia, C. Péan, R. Pidcock, S. Connors, J.B.R. Matthews, Y. Chen, X. Zhou, M.I. Gomis, E. Lonnoy, T. Maycock, M. Tignor, and T. Waterfield (eds.)]. Cambridge University Press, Cambridge, UK and New York, NY, USA, pp. 49-92, https://doi.org/10.1017/9781009157940.003.
  4. GMet (2022). Regional Weather – Bono Region. Ghana Meteorological Agency. https://www.meteo.gov.gh/gmet/
  5. regional-weather-bono-region/
  6. Berkowitz, S. A., & Anderson, G. F. (2012). Planned readmissions: A potential solution. Archives of Internal Medicine, 172(3), pp. 269–270. https://doi.org/10.1001/archinternmed.2011.606.
  7. Chen, X.-L.,Zhao, H.-M., Li, P.-X. and 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) pp. 133–146.
  8. Deosthali, V. (2000). Impact of rapid urban growth on heat and moisture islands in Pune City, India. Atmospheric Environment, 34(17), pp. 2745-2754.
  9. Du, P. , Li, X.,Cao, W., Luo, Y. and Zhang, H. (2010). Monitoring urban land cover and vegetation change by multitemporal remote sensing information. Mining Science and Technology (China), 20(6), pp. 922–932.
  10. Emmanuel, R. (2003). Assessment of the impact of land cover changes on urban bioclimate: the case of Colombo, Sri Lanka. Architectural Science Review, 46(2) 151–158.
  11. Ghana Statisical Service. (2021). 2021 Population and housing census. Ghana Statistical Service. https://census2021.statsghana.gov.gh/
  12. Giridharan, R., Ganesan, S., & Lau, S. S. Y. (2004). Daytime urban heat island effect in high-rise and high-density residential developments in Hong Kong. Energy and buildings, 36(6), pp. 525-534.
  13. Grover, A., & Singh, R. B. (2015). Analysis of urban heat island (UHI) in relation to normalized difference vegetation index (NDVI): A comparative study of Delhi and Mumbai. Environments, 2(2), pp. 125-138.
  14. Hosseini, S. M. , Falahatkar, S. and Soffianian, A. R. (2011). The relationship between land cover changes and spatial temporal dynamics of land surface temperature. Indian Journal of Science and Technology. 4(2), pp. 76–81.
  15. Horning, N. (2010). Random Forests : An algorithm for image classification and generation of continuous fields data sets.
  16. Kim, Y. H., & Baik, J. J. (2002). Maximum urban heat island intensity in Seoul. Journal of Applied Meteorology, 41 (6), pp. 651-659.
  17. Nieuwolt, S. (1966). Urban microclimate of Singapore. Journal of Tropical Geography, 22(JUN), 30-37.
  18. Neteler, M. (2010). Estimating daily land surface temperatures in mountainous environments by reconstructed MODIS LST data. Remote sensing, 2(1), pp. 333-351.
  19. Ogashawara, I., & Bastos, V. D. S. B. (2012). A quantitative approach for analyzing the relationship between urban heat islands and land cover. Remote Sensing, 4(11), pp. 3596-3618.
  20. Oppong Peprah, P.,Ayidana, E. H. (2014). Sunyani municipality District Analytical Report. Sunyani. Senanayake, Welivitiya, W. and Nadeeka, P. (2013). Remote sensing-based analysis of urban heat islands with vegetation cover in Colombo city, Sri Lanka using Landsat-7 ETM+ data. Urban Climate, 5, pp. 19–35.
  21. Souch, C., & Grimmond, S. (2016). Applied climatology: urban climate. Progress in Physical Geography. https://doi.org/10.1191/0309133306pp484pr.
  22. USGS, (2016). Landsat 8 (L8) Data Users Handbook LSDS1574 Version 2.0, pp. 21-61. Wang, M., Cheng, Y., Long, X., & Yang, B. (2016). On-orbit geometric calibration approach for high-resolution geostationary optical satellite GaoFen-4. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences - ISPRS Archives, 2016- January(July), pp. 389–394. https://doi.org/10.5194/isprsarchives-XLI-B1-389-2016.
  23. Weng, Q.(2009). Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends. ISPRS Journal of Photogrammetry and Remote Sensing, 64(4), pp. 335–344.
  24. Xian, G. and Crane, M.(2006). An analysis of urban thermal characteristics and associated land cover in Tampa Bay and Las Vegas using Landsat satellite data. Remote Sensing of Environment, 104(2), pp. 147–156.
  25. Zhong, B. L. (1996). Urban heat island effect of Shenzhen city. Meteorol. Monthly, 22(5), pp. 23-24.