Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Relationship assessment among land use and land cover and land surface temperature over downtown and suburban areas in Yangon City, 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 : 2016.08.05
  • Accepted : 2016.08.29
  • Published : 2016.08.31
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Abstract

Yangon city is experienced a rapid urban expansion over the last two decades due to accelerate with the socioeconomic development. This research work studied an investigation into the application of the integration of the Remote Sensing (RS) and Geographic Information System (GIS) for observing Land Use and Land Cover (LULC) patterns and evaluate its impact on Land Surface Temperature (LST) of the downtown, suburban 1 and suburban 2 of Yangon city. The main purpose of this paper was to examine and analyze the variation of the spatial distribution property of the LULC of urban spatial information related with the LST and Normalized Difference Vegetation Index (NDVI) using RS and GIS. This paper was observed on image processing of LULC classification, LST and NDVI were extracted from Landsat 8 Operational Land Imager (OLI) image data. Then, LULC pattern was linked with the variation of LST data of the Yangon area for the further connection of the correlation between surface temperature and urban structure. As a result, NDVI values were used to examine the relation between thermal behavior and condition of land cover categories. The spatial distribution of LST has been found mixed pattern and higher LST was located with the scatter pattern, which was related to certain LULC types within downtown, suburban 1 and 2. The result of this paper, LST and NDVI analysis exhibited a strong negative correlation without water bodies for all three portions of Yangon area. The strongest coefficient correlation was found downtown area (-0.8707) and followed suburban 1 (-0.7526) and suburban 2(-0.6923).

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References

  1. Artis, D.A. and W.H. Carnahan, 1982. Survey of emissivity variability in the thermography of urban areas, Remote sensing of Environment, 12(4): 313-329. https://doi.org/10.1016/0034-4257(82)90043-8
  2. Amiri, R., Q. Weng, A. Alimohammadi, and A.K. Alavipanah, 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. https://doi.org/10.1016/j.rse.2009.07.021
  3. Braimoh, A.K. and T. Onishi, 2007. Geostatistical techniques for incorporating spatial correlation into land use change models, International journal of Applied Earth Observation and Geoformation, 9(4): 438-446. https://doi.org/10.1016/j.jag.2007.02.005
  4. Carlson, N. T. and A.R. David, 1997. On the relation between NDVI, fractional vegetation cover, and leaf area index, Remote sensing of Environment, 62(3): 241-252. https://doi.org/10.1016/S0034-4257(97)00104-1
  5. Cortes, C. and V. Vapnik, 1995. Support-vector networks, Machine learning, 20(3): 273-297. https://doi.org/10.1007/BF00994018
  6. Dewan, A.M. and Y. Yamaguchi, 2009. Land use and land cover change in Grater Dhaka, Bangladesh: Using remote sensing to promote sustainable urbanization, Applied Geography, 29(3): 390-401. https://doi.org/10.1016/j.apgeog.2008.12.005
  7. Gallo, K.P. and T.W. Owen, 1998. Satellite -based adjustments for the urban heat island temperature bias, Journal of Apply Meteorology, 38(6): 806-813. https://doi.org/10.1175/1520-0450(1999)038<0806:SBAFTU>2.0.CO;2
  8. Hathout, S., 2002. The use of GIS for monitoring and predicting urban growth in East and West St Paul, Winnipeg, Manitoba, Canada, Journal of Environmental Management, 66(3): 229-238. https://doi.org/10.1016/S0301-4797(02)90596-7
  9. Huang, C., L.S. Davis, and J.R.G. Townshend, 2002. An assessment of support vector machines for land cover classification, International Journal of remote sensing, 23(4): 725-749. https://doi.org/10.1080/01431160110040323
  10. 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
  11. Myint, S.W. and L. Wang, 2006. Multicriteria decision approach for land use land cover change using Markov chain analysis and a cellular automata approach, Canadian of Remote Sensing, 32(6): 390-404. https://doi.org/10.5589/m06-032
  12. Li, F., Jackson, T. J., Kustas, W. P., Schmugge, T. J., French, A. N., Cosh, M. H., and R. Bindlish, 2004. Deriving land surface temperature from Landsat 5 and 7 during SMEX02/SMACEX, Remote sensing of environment, 92(4): 521-534. https://doi.org/10.1016/j.rse.2004.02.018
  13. Li, J., X.R. Wang, W.C. Ma, and H. Zhang, 2009. Remote sensing evaluation of urban heat island and its spatial pattern of the Shanghai metropolitan, China, Ecological Complexity, 6(4): 413-420. https://doi.org/10.1016/j.ecocom.2009.02.002
  14. Markham, B.L. and J.L. Barker, 1985. Spectral characterization of the Landsat Thematic Mapper sensors, International Journal of Remote Sensing, 6(5): 697-716. https://doi.org/10.1080/01431168508948492
  15. Sobrino, J.A. and M. Romaguera, 2004. Land Surface Temperature retrieval from MSG1-SEVIRI data, Remote Sensing of Environment, 92(2): 247-254. https://doi.org/10.1016/j.rse.2004.06.009
  16. Streutker, D.R., 2003. Satellite-measured growth of the urban heat island of Houston, Texas, Remote Sensing Environment, 85(3): 282-289. https://doi.org/10.1016/S0034-4257(03)00007-5
  17. Thinh, N. X., G. Arlt, B. Heber, J. Hennersdorf, and I. Lehmann, 2002. Evaluation of urban landuse 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
  18. Thome, A. C. G., 2012. Svm classifiers-concepts and applications to character recognition, INTECH Open Access Publisher.
  19. Turner, B.L., 1994. Local faces, global flowers: the role of land use and land use changes in Datong basin, China, Environmental Geology, 5(2): 1825-1837.
  20. Voogt, J.A. and T.R. Oke, 1998. Effects of urban surface geometry on remotely-sensed surface temperature, International Journal of Remote Sensing, 19(5): 895-920. https://doi.org/10.1080/014311698215784
  21. Weng, Q., 2003. Fractal analysis of satellite-detected urban heat island effect, Photogramm Engineering Remote Sensing, 69(5): 555-556. https://doi.org/10.14358/PERS.69.5.555
  22. Weng, Q., D. Lu, and J. Schubring, 2004. Relations between NDVI and tree productivity in the central Great Plains, International Journal of Remote Sensing of Environment, 25(16): 3127-3138 https://doi.org/10.1080/0143116032000160499
  23. Weng, Q., 2009. Thermal infrared remote sensing for urban climate and environmental studies: Methods, applications, and trends, ISPRS Journal of Photogrammetry Remote Sensing, 64(4): 335-344. https://doi.org/10.1016/j.isprsjprs.2009.03.007
  24. Xiaolu, Z. and W. YC, 2011. Dynamics of Land Surface Temperature in response to Landuse/cover Change, Geographical Research, 49(1): 23-36. https://doi.org/10.1111/j.1745-5871.2010.00686.x
  25. Xian, G. and M. Crane, 2005. Assessments of urban growth in the Tampa Bay watershed using remote sensing data, Remote Sensing of Environment, 97(2): 203-215. https://doi.org/10.1016/j.rse.2005.04.017