Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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The impact of land use and land cover changes on land surface temperature in the Yangon Urban Area, Myanmar

  • Yee, Khin Mar (Department Spatial Information Engineering, Pukyong National University) ;
  • Ahn, Hoyong (Department Spatial Information Engineering, Pukyong National University) ;
  • Shin, Dongyoon (Department Spatial Information Engineering, Pukyong National University) ;
  • Choi, Chuluong (Department Spatial Information Engineering, Pukyong National University)
  • Received : 2016.02.05
  • Accepted : 2016.02.29
  • Published : 2016.02.28
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Abstract

Yangon Mega City is densely populated and most urbanization area of Myanmar. Rapid urbanization is the main causes of Land Use and Land Cover (LULC) change and they impact on Land Surface Temperature (LST). The objectives of this study were to investigate on the LST with respect to LULC of Yangon Mega City. For this research, Landsat satellite images of 1996, 2006 and 2014 of Yangon Area were used. Supervised classification with the region of interest and calculated change detection. Ground check points used 348 points for accuracy assessment. The overall accuracy indicated 89.94 percent. The result of this paper, the vegetation area decreased from $1061.08sq\;km^2$ (24.5%) in 1996 to $483.53sq\;km^2$ (11.2%) in 2014 and built up area clearly increased from $485.33sq\;km^2$ (11.2%) in 1996 to $1435.72sq\;km^2$ (33.1%) in 2014. Although the land surface temperature was higher in built up area and bare land, lower value in cultivated land, vegetation and water area. The results of the image processing pointed out that land surface temperature increased from $23^{\circ}C$, $26^{\circ}C$ and $27^{\circ}C$ to $36^{\circ}C$, $42^{\circ}C$ and $43.3^{\circ}C$ for three periods. The findings of this paper revealed a notable changes of land use and land cover and land surface temperature for the future heat management of sustainable urban planning for Yangon Mega city. The relationship of regression experienced between LULC and LST can be found gradually stronger from 0.8323 in 1996, 0.8929 in 2006 and 0.9424 in 2014 respectively.

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References

  1. Becker, F. and Z.L. Li, 1995. Surface temperature and emissivity at various scales: definition, measurement and related problems. Remote Sensing Reviews, 12(3-4): 225-253. https://doi.org/10.1080/02757259509532286
  2. Carlson, T.N., J.A. Augustine, and F.E. Boland, 1977. Potential application of satellite temperature measurements in the analysis of land use over urban areas, Bulletin of the American Meteorological Society, 58(12): 1301-1303.
  3. Carlson, T.N., R.R. Gillies, and E.M. Perry, 1994. A method to make use of thermal infrared temperature and NDVI measurements to infer surface water content and fractional vegetation cover, Remote Sensing Reviews, 9(1-2): 161-173. https://doi.org/10.1080/02757259409532220
  4. Chen, X.L., H.M. Zhao, P.-X. Li, and Z.Y. Yin, 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. https://doi.org/10.1016/j.rse.2005.11.016
  5. Chen, Y., J. Wang, and X. Li, 2002. A study on urban thermal field in summer based on satellite remote sensing. Remote Sensing for Land and Resources, 14(4):55-59.
  6. Chen, S. and C.Y. Jim, 2003. Quantitative assessment of the tree scape and cityscape of Nanjing, China. Landscape Ecology, 18(4): 395-412. https://doi.org/10.1023/A:1026146123459
  7. Cortes, C. and V. Vapnik, 1995. Support-vector networks. Machine Learning 20.3 (1995):273-297. https://doi.org/10.1007/BF00994018
  8. Dash P., F.M. Gottsche, F.S. Olesen, and H. Fischer, 2005. Separating surface emissivity and temperature using two-channel spectral indices and emissivity composites comparison with a vegetation fraction method. Remote Sensing of Environment, 96(1): 1-17. https://doi.org/10.1016/j.rse.2004.12.023
  9. IPCC, 2002. Managing the Risks of Extreme Events and Disasters to Advance Climate Change Adaptation. A Special Report of Working Groups I and II and the Intergovernmental Panel on Climate Change, Cambridge University Press. Cambridge, UK.
  10. Jin, M., R.E. Dickinson, D.L. Zhang, 2005. The footprint of urban areas on global climate as characterized by MODIS. Journal of climate, 18(10): 1551-1565. https://doi.org/10.1175/JCLI3334.1
  11. Jones, P.D., P.Y. Groisman, M. Coughlan, N. Plummer, W. Wang, and T.R. Karl, 1990. Assessment of urbanization effects in time series of surface air temperature over land. Nature, 347(6289): 169-172. https://doi.org/10.1038/347169a0
  12. McCarthy, M.P., M.J. Best, and R.A. Betts, 2010. Climate Change in cities due to global warming and urban effects. Geophysical Research Letters, 37(9): L09705. https://doi.org/10.1029/2010GL042845
  13. Lo, C.P., D.A. Quattrochi, J.C. Luvall, 1997. Application of high resolution thermal infrared remote sensing and GIS to assess the urban heat island effect. International Journal of Remote Sensing, 18(2), 287-304. https://doi.org/10.1080/014311697219079
  14. Mutizwa-Mangiza, N.D., B.C. Arimah, I. Jensen, E.A. Yemeru, and M.K. Kinyanjui, 2011. Cities and Climate Change: Global Report on Human Settlements 2011, Earthscan Ltd, London, UK,
  15. Owen, T.W, T.N. Carlson, and R.R. Gillies, 1998. An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization. International Journal of Remote Sensing, 19(9), 1663-1681. https://doi.org/10.1080/014311698215171
  16. Ramachandra T.V. and U. Kumar, 2010. Greater Bangalore: Emerging Urban Heat Island. GIS Development, 14(1): 86-104.
  17. Setturu B., KS. Rajan, and T.V. Ramachandra, 2013. Land Surface Temperature Responses to Land Use Land Cover Dynamics, Geoinformatic & Geostatistics: An Overview, 1(4).
  18. Su, W.Z., and Y.B. Yang, 2007. Study on urban spatial structure based on landscape ecology. Science Press, Beijing, China.
  19. Thinh, N. X., G. Arlt, B. Heber, J. Hennersdorf, and I. Lehmann, 2002. Evaluation of urban land-use structures with a view to sustainable development. Environmental Impact Assessment Review, 22(5): 475-492. https://doi.org/10.1016/S0195-9255(02)00023-9
  20. Tran, H., D. Uchihama, S. Ochi, and Y. Yasuoka, 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. https://doi.org/10.1016/j.jag.2005.05.003
  21. United Nation, 2010. World urbanization prospects: The 2009 revision population database. http://esa.un.org/unpd/wup/index.htm.
  22. USGS, 2010. Landsat 7 Science Data Users Handbook, NASA's Goddard Space Flight Center. Greenbelt, MD, USA.
  23. Vapnik, V. 1979. Estimation of Dependences Based on Empirical Data Nauka. Moscow:Nauka. (in Russian)
  24. Voogt. J.A., and T.R. Oke, 2003. Thermal remote sensing of urban climate. Remote Sensing of Environment, 86(3): 370-384. https://doi.org/10.1016/S0034-4257(03)00079-8
  25. Weng Q, 2001. A remote sensing? GIS evaluation of urban expansion and its impact on surface temperature in the Zhujiang Delta, China. International Journal of Remote Sensing, 22(10): 1999-2014. https://doi.org/10.1080/713860788
  26. Ye, H., K. Wang, S. Huang, F. Chen, Y. Xiong, and X. Zhao, 2010. Urbanisation effects on summer habitat comfort: A habitat comfort: A case study of three coastal cities in southeast China. International Journal of Sustainable Development and World Ecology, 17(4): 317-323. https://doi.org/10.1080/13504509.2010.493324