Analysis on Spatial Pattern of Urban Heat Island and Impervious Surface Using Linear Spectral Mixture Analysis


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Based on Landsat ETM+ data within the metropolitan area of Lanzhou, China, green vegetation(GV) and impervious surface was extracted by a constrained linear spectral mixture analysis (LSMA),together with single window algorithm to invert land surface temperature ,and the correlation analysis was then conducted to examine the relationship between urban heat island (UHI) effect and impervious surface. Four types of end members with high albedo, GV, soil and low albedo are selected to model complicated urban land cover, estimation accuracy is assessed using Root-Mean-Square (RMS)error and color aerial images, with the help of Mantel and Partial Mantel. Spatial relationship of land surface temperatures (LST), impervious surface and GV were analyzed. Results indicate that impervious surface distribution and GV can be derived from Landsat TM/ETM+ images with satisfactory precision. Impervious surface and GV were positively correlated with UHI, while LST has space dependence, it has high space dependence, and was higher correlated with impervious surface than GV.



Edited by:

Yuhang Yang, Xilong Qu, Yiping Luo and Aimin Yang




J. H. Pan et al., "Analysis on Spatial Pattern of Urban Heat Island and Impervious Surface Using Linear Spectral Mixture Analysis", Advanced Materials Research, Vol. 216, pp. 600-604, 2011

Online since:

March 2011




[1] Dousset, B., &Gourmelon, F. (2003). Satellite multi-sensor data analysis of urban surface temperatures and land cover. ISPRS Journal of Photogram-metry and Remote Sensing, 58, 43-54.

DOI: 10.1016/s0924-2716(03)00016-9

[2] Yuan F, Marvin E. Comparison of Impervious Surface Area and Normalized Difference Vegetation Index as Indicators of Surface Urban Heat Island Effects in Landsat Imagery[J]. Remote Sensing of Environment, 2007, 206(3): 375-386.

DOI: 10.1016/j.rse.2006.09.003

[3] Smith,M. O., Ustin, S. L., Adams, J. B., & Gillespie, A. R. (1990). Vegetation in Deserts: I. A regional measure of abundance from multispectral images. Remote Sensing of Environment, 31, 1−26.

DOI: 10.1016/0034-4257(90)90074-v

[4] Wu C, Murray A T. Estimating Impervious Surface Distribution by Spectral Mixture Analysis[J]. Remote Sensing of Environment, 2003, 84(4): 493-505.

DOI: 10.1016/s0034-4257(02)00136-0

[5] Xu H Q. A Study on Information Extraction of Water Body with the Modified Normalized Difference Water Index (MNDWI) [J]. Journal of Remote Sensing, 2005, 9(5): 589-595.

[6] Adams, J. B., Sabol, D. E., Kapos, V., Almeida Filho, R., Roberts, D. A., Smith,M. O., et al. (1995).

[7] Zhou C L, Xu H Q. A Spectral Mixture Analysis and Mapping of Impervious Surfaces in Built-up Land of Fuzhou City[J]. Journal of Image and Graphics, 2007, 12(5): 875-881.

[8] Z Qin, A Karmieli. A mono-window algorithm for retrieving land surface: temperature from TM data and its application to the Israel-Egypt border region[J]. International Journal of Remote Sensing, 2001, 22(18): 3719-3746.

DOI: 10.1080/01431160010006971

[9] Xinyuan Wu, William J. Mitsch. Spatial and temporal patterns of algae in newly constructed freshwater wetlands[J]. Wetlands, 1998, 18(1): 9-20.

DOI: 10.1007/bf03161438

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