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Integrating seasonal optical and thermal infrared spectra to characterize urban impervious surfaces with extreme spectral complexity: a Shanghai case study

文献类型： 外文期刊

作者： Wang, Wei 1 ; Yao, Xinfeng 2 ; Ji, Minhe 1 ;

作者机构： 1.E China Normal Univ, Key Lab GISci, Educ Minist China, 500 Dongchuan Rd, Shanghai 200241, Peoples R China

2.Shanghai Acad Agr Sci, Agr Informat Inst Sci & Technol, 1000 Jinqi Rd, Shanghai 201403, Peoples R China

关键词： impervious surfaces;land cover decomposition method;seasonal change analysis;land surface temperature

期刊名称：JOURNAL OF APPLIED REMOTE SENSING （ 影响因子：1.53； 五年影响因子：1.565 ）

ISSN： 1931-3195

年卷期： 2016 年 10 卷

页码：

收录情况： SCI

摘要： Despite recent rapid advancement in remote sensing technology, accurate mapping of the urban landscape in China still faces a great challenge due to unusually high spectral complexity in many big cities. Much of this complication comes from severe spectral confusion of impervious surfaces with polluted water bodies and bright bare soils. This paper proposes a two-step land cover decomposition method, which combines optical and thermal spectra from different seasons to cope with the issue of urban spectral complexity. First, a linear spectral mixture analysis was employed to generate fraction images for three preliminary endmembers (high albedo, low albedo, and vegetation). Seasonal change analysis on land surface temperature induced from thermal infrared spectra and coarse component fractions obtained from the first step was then used to reduce the confusion between impervious surfaces and nonimpervious materials. This method was tested with two-date Landsat multispectral data in Shanghai, one of China's megacities. The results showed that the method was capable of consistently estimating impervious surfaces in highly complex urban environments with an accuracy of R-2 greater than 0.70 and both root mean square error and mean average error less than 0.20 for all test sites. This strategy seemed very promising for landscape mapping of complex urban areas. (C) The Authors. Published by SPIE under a Creative Commons Attribution 3.0 Unported License. Distribution or reproduction of this work in whole or in part requires full attribution of the original publication, including its DOI.

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