Remote sensing-based study on the relationship between land brightness temperature and vegetation abundance in Wuhan city

Proceedings of SPIE - The International Society for Optical Engineering

Volumn 7147, Issue , 2008, Pages

  Zhang, Chunling ^a   Yu, Hua ^a   Gong, Peng ^a   Ju, Weimin ^a   Pei, Huan ^a  

^a NANJING UNIVERSITY   (China)

Author keywords

Fractal analysis; Land brightness temperature; Spectral mixture model; Urban heat island; Vegetation abundance

Indexed keywords

FRACTAL ANALYSIS; HIGHER TEMPERATURES; IMAGE TEXTURES; LAND BRIGHTNESS TEMPERATURE; LAND USE/LAND COVERS; LAND-COVER TYPES; LANDSAT ENHANCED THEMATIC MAPPER PLUS (ETM+) IMAGES; LINEAR SPECTRAL MIXTURE ANALYSIS; SENSIBLE AND LATENT HEAT FLUXES; SPATIAL PATTERNS; SPECTRAL MIXTURE MODEL; URBAN AREAS; URBAN HEAT ISLAND; VEGETATION FRACTIONS;

FRACTAL DIMENSION; GEOGRAPHIC INFORMATION SYSTEMS; HEAT FLUX; IMAGE ANALYSIS; IMAGE RECONSTRUCTION; MIXTURES; OPTICAL TELESCOPES; PARTIAL DISCHARGES; REMOTE SENSING; TEMPERATURE; THERMAL EFFECTS; THERMAL POLLUTION; VEGETATION;

LANDFORMS;

EID: 62449107166     PISSN: 0277786X     EISSN: None     Source Type: Conference Proceeding    
DOI: 10.1117/12.813251     Document Type: Conference Paper

References (26)

1. S. Kato and Y. Yamaguchi, Estimation of storage heat flux in an urban area using ASTER data. Remote sensing of Environment. 110, 1-17(2007).
2. L. Zhang, Z. X. Xu and B. Q. Ruan, Effect of urban heat island on the air temperate and precipitation in Beijing region. Journal of natural resources. 21(5), 745-755 (2006).
3. P. K. Rao, Remote sensing of urban "heat islands" from an environmental satellite. Bulletin of the American meteorological society. 53, 647-648(1972).
4. R. Gluch, A. Q. Dale and C. L. Jeffrey, A multi-scale approach to urban thermal analysis. Remote sensing of environment. 104, 123-132 (2006).
5. D. R. Streutker, A remote sensing study of the urban heat island of Houston, Texas. International journal of remote sensing. 23, 2595-2608(2002).
6. D. R. Streutker, Satellite-measured growth of the urban heat island of Houston, Texas. Remote Sensing Environment. 85, 282-289(2003).
7. Y. T. Song, S. X. Yu and A. Lin, Spatial structure of the surface temperate in Shenzhen, China. Acta ecologica Sinica. 27(4), 1489-1498(2007).
8. Y. B. Yang, W.Z. Su and N. Jiang, Time-space character analysis of urban heat island effect in Nanjing city using remote sensing. Remote sensing technology and application. 21(6), 478-492(2006).
9. S. Kato and Y. Yamaguchi, Analysis of urban heat-island effect using ASTER and ETM+ Data: Separation of anthropogenic heat discharge and natural heat radiation from sensible heat flux. Remote sensing of Environment 99, 44-54(2005).
10. F. Yuan and E. B. Marvin, 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, 375-386 (2007).
11. L. X. Qian and S. Y. Ding, Influence of land cover change on land surface temperate in Zhujiang Delta. Acta Geographica Sinica. 60 (5), 761-770 (2005).
12. T. W. Owen, T. N. Carlson and R. R. Gillies, An assessment of satellite remotely-sensed land cover parameters in quantitatively describing the climatic effect of urbanization. International Journal of Remote Sensing, 19, 1663-1681(1998).
13. T. R. Oke, The energetic basis of the urban heat island. Quarterly Journal of the Royal Meteorological Society, 108, 1-24(1982).
14. W. Yang, L. Yang and J. W. Merchnat, An analysis of AVHRR/NDVI ecoclimatological relations in Nebraska, USA. International Journal of Remote Sensing, 18, 2161- 2180(1997).
15. M. F. Jasinski, Sensitivity of the Normalized Difference Vegetation Index to sub-pixel canopy cover, soil albedo, and pixel scale. Remote Sensing of Environment, 32, 169-187 (1990).
16. Y. C. Wei and Y. Z. Huang, The gain/biases value and their effect on the calculation results of planetary reflectance in Landsat-5 image. Geo-information science, 8(1), 110-114 (2006).
17. F. Ding and H.Q. Xu, Comparison of two new algorithms for retrieving land surface temperature from Landsat TM thermal band. Geo-information Science, 8(3), 124-130(2006).
18. Q. Xue, G. Y. Kuang and Z. Y. Li, Endmember extraction algorithms from hyper-spectral image based on the linear mixing model: an overview. Remote sensing technology and application. 19(3), 197-200 (2004).
19. C. Wu, Normalized spectral mixture analysis for monitoring urban composition using ETM+ imagery. Remote Sensing of Environment. 93(4), 480-492 (2004).
20. D.A. Robert, G.T. Batista, J.L.G. Pereira, E.K. Waller and B.W. Nelson, Change identification using multitemporal spectral mixture analysis: Application in eastern Amazonia. Environmental monitoring methods and application. 1998, 137-161.
21. S. R. Brown, Measuring the dimension of self-affine fractals: Examples of rough surfaces In: CC Barton , P R LaPointe ( Eds). Fractals in the Earth Sciences.77-87(1995).
22. Q. H. Weng, D. S. Lu and J. Schubring, Estimation of land surface temperature-vegetation abundance relationship for urban heat island studies. Remote Sensing of Environment. 89,467-483 (2004).
23. N. S. N. Lam, Description and measurement of Landsat TM images using fractals. Photogrammetric Engineering and Remote Sensing. 56,187-1951 (1990).
24. R. M. Smith, Comparing traditional methods for selecting class intervals on choropleth maps. Professional Geographer. 38 (1), 62- 67 (1986).
25. X. F. Zhang, Y. L. Wang, J. S. Wu, W. F. Li and Z. G. Li, Study on land surface temperate vegetation cover relationship in urban region: a case in Shenzhen city. Geographical research. 25(3), 369-379 (2006).
26. Q. Weng, Fractal analysis of satellite-detected urban heat island effect. Photogrammetric Engineering and Remote Sensing. 69,555-566(2003).