DOI QR코드

DOI QR Code

Consideration of NDVI and Surface Temperature Calculation from Satellite Imagery in Urban Areas: A Case Study for Gumi, Korea

  • Bhang, Kon Joon (Dept. of Civil Engineering, Kumoh National Institute of Technology) ;
  • Lee, Jin-Duk (Dept. of Civil Engineering, Kumoh National Institute of Technology)
  • Received : 2017.01.25
  • Accepted : 2017.02.27
  • Published : 2017.02.28

Abstract

NDVI (Normalized Difference Vegetation Index) plays an important role in surface land cover classification and LST (Land Surface Temperature Extraction). Its characteristics do not full carry the information of the surface cover typically in urban areas even though it is widely used in analyses in urban areas as well as in vegetation. However, abnormal NDVI values are frequently found in urban areas. We, therefore, examined NDVI values on whether NDVI is appropriate for LST and whether there are considerations in NDVI analysis typically in urban areas because NDVI is strongly related to the surface emissivity calculation. For the study, we observed the influence of the surface settings (i.e., geometric shape and color) on NDVI values in urban area and transition features between three land cover types, vegetation, urban materials, and water. Interestingly, there were many abnormal NDVI values systematically derived by the surface settings and they might influence on NDVI and eventually LST. Also, there were distinguishable transitions based on the mixture of three surface materials. A transition scenario was described that there are three transition types of mixture (urban material-vegetation, urban material-water, and vegetation-water) based on the relationship of NDVI and LST even though they are widely distributed.

Keywords

References

  1. Bhang, K.J. and Park, S.S. (2009), Evaluation of the surface temperature variation with surface settings on the urban heat island in Seoul, Korea, using Landsat-7 ETM+ and SPOT, IEEE Geoscience and Remote Sensing Letters, Vol. 6, no. 4, 708-712. https://doi.org/10.1109/LGRS.2009.2023825
  2. Carlson, T.N., Gillies, R.R., and Perry, E.M., (1994), A method to make use of thermal infrared temperature and NDVI measurements to infer surface soil water content and fractional vegetation cover. Remote Sensing Reviews, Vol. 9, pp. 161-173. https://doi.org/10.1080/02757259409532220
  3. Gillies, R.R. and Carlson, T.N. (1995), Thermal remote sensing of surface soil water content with partial vegetation cover for incorporation into climate models. Journal of Applied Meteorology, Vol. 34, pp. 745-756. https://doi.org/10.1175/1520-0450(1995)034<0745:TRSOSS>2.0.CO;2
  4. Gallo, K.P., McNab, A.L., Karl, T.P., Brown, J.F., Hood, J.J, and Tarpley, J.D. (1993), The use of a vegetation index for assessment of the urban heat island effect, International Journal of Remote Sensing, Vol. 14, pp. 2223-2230. https://doi.org/10.1080/01431169308954031
  5. Gallo, K.P., Tarpley, J.D., McNab, A.L., and Karl, T.R. (1995), Assessment of urban heat islands: a satellite perspective, Atmospheric Research, Vol. 37, pp. 37-43. https://doi.org/10.1016/0169-8095(94)00066-M
  6. Jimenez-Munoz, J.C., Cristobal, J., Sobrino, J.A., Soria, G., Ninyerola, M., Pons, X., and Pons, X. (2004), Revision of the single-channel algorithm for land surface temperature retrieval from Landsat thermal-infrared data, IEEE Transactions on Geoscience and Remote Sensing, Vol. 47, pp. 339-349.
  7. Owen, T.W., Carlson, T.N., and Gillies, R.R. (1998), An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization, International Journal of Remote Sensing, Vol. 19, pp. 1663-1681. https://doi.org/10.1080/014311698215171
  8. Price, J.C. (1990), Using spatial context in satellite data to infer regional scale evapotranspiration, IEEE Transactions on Geosciences and Remote Sensing, Vol. 28, pp. 940-948. https://doi.org/10.1109/36.58983
  9. Sobrino, J.A., Jimenez-Munoz, J.C., and Paolini, L. (2004), Land surface temperature retrieval from LANDSAT TM 5, Remote Sensing of Environment, Vol. 90, pp. 434-440. https://doi.org/10.1016/j.rse.2004.02.003
  10. Southworth, J. (2004), An assessment of Landsat TM band 6 thermal data for analysing land cover in tropical dry forest regions, International Journal of Remote Sensing, Vol. 25, pp. 689-706. https://doi.org/10.1080/0143116031000139917
  11. Stagakis, S., Markos, N., Sykioti, O., and Kyparissis, A. (2010), Monitoring canopy biophysical and biochemical parameters in ecosystem scale using satellite hyperspectral imagery: an application on a Phlomis fruticosa Mediterranean ecosystem using multiangular CHRIS/PROBA observations. Remote Sensing of Environment. Vol. 114, pp. 977-994. https://doi.org/10.1016/j.rse.2009.12.006
  12. Valor, E. and Caselles, V. (1996), Mapping land surface emissivity from NDVI: application to European, Afri-can and South American areas. Remote Sensing of Environment, Vol. 57, 167-184. https://doi.org/10.1016/0034-4257(96)00039-9
  13. Weeks, J.R. (2010), Remote Sensing of Urban and Suburban Areas, Remote Sensing and Digital Image Processing, Springer, New York.
  14. Wu, C. and Murray, A.T. (2003), Estimating impervious surface distribution by spectral mixture analysis, Remote Sensing of Environment, Vol. 84, pp. 493-505. https://doi.org/10.1016/S0034-4257(02)00136-0
  15. Yue, W., Xu, Y., Tan, W., and Xu, L. (2007), The relationship between land surface temperature and NDVI with remote sensing: application to Shanghai Landsat 7 ETM+ data, International Journal of Remote Sensing, Vol. 28, pp. 3205-3226. https://doi.org/10.1080/01431160500306906
  16. Yuan, F. and Bauer, M.E. (2007), Comparison of impervious surface area and normaized difference vegetation index as indicators of surface urban heat island effects in Landsat imagery, Remote Sensing of Environment, Vol. 106, pp. 375-386. https://doi.org/10.1016/j.rse.2006.09.003