27 9 Vol. 27 No JOURNAL OF NATURAL RESOURCES Sep Landsat 7 ETM Google earth SPOT

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1 27 9 Vol. 27 No JOURNAL OF NATURAL RESOURCES Sep Landsat 7 ETM Google earth SPOT 4 ± TP79 X87 A Ridd 5 Vegetation-Impervious surface-soil model V-I-S Ward 6 V-I-S Phinn 7 V-I-S Wu 8 V-I-S libozju@ yahoo. com. cn

2 Lu 9 Lu 10 TM Michishita a Landsat ETM km km km Landsat 7 ETM + P119 /R39 ETM ETM + 30 m 30 m Landsat 7 Science Data User Handbook DN ETM Google earth

3 Fig. 1 1 Location of the study area 294 / SPOT 4 HRV 10 m

9 1593 12 3 4 22 23 Minimum Noise Fraction MNF ETM + MNF 6 2 3 93. 57% 1 3 MNF 3 Fig.

4 Minimum Noise Fraction MNF ETM + MNF % 1 3 MNF 3 Fig. 2 2 ETM + MNF Component images of ETM + images generated by MNF transformation 1 MNF Table 1 Eigen values of six components generated by MNF transformation MNF 1 MNF 2 MNF 3 MNF 4 MNF 5 MNF /% MNF

1594 27 4 Fig. 3 3 MNF 3 Feature space of the first three components generated by MNF transformation 2.

5 Fig. 3 3 MNF 3 Feature space of the first three components generated by MNF transformation Landsat ETM Fig. 4 Spectral reflectance of 4 endmembers at 6 bands ETM

6 Fig. 5 5 ETM + Land surface temperature generated by the inversion of ETM + thermal infrared images 6 Fig Fraction images of four endmembers

7 % a 7 b Fig. 7 7 Comparison of impervious surface images generated from different methods 70%

9 1597 60% ~ 70% 40% ~ 60% 40% 3. 3 RMSE 8 9 RMSE RMSE 8 RMSE Fig.

8 % ~ 70% 40% ~ 60% 40% 3. 3 RMSE 8 9 RMSE RMSE 8 RMSE Fig. 8 The frequency plot of RMSE 9 Fig. 9 RMSE The spatial distribution of RMSE

9 RMSE Google earth Google earth SPOT m 90 m ETM GIS 10 conversion KML Fig. 10 Accuracy evaluation of impervious surface estimation Google earth SPOT % ± Landsat ETM References 1 United Nations. World Urbanization Prospects The 2007 Revision Highlights M. New York United Nations Weng Q H. Remote Sensing of Impervious Surfaces M. London CRC Press Weng Q H. Thermal infrared remote sensing for urban climate and environmental studies Methods applications and trends J. ISPRS Journal of Photogrammetry and Remote Sensing

10 Weng Q H. Remote sensing of impervious surfaces in the urban areas Requirements methods and trends J. Remote Sensing of Environment Ridd M K. Exploring a V-I-S vegetation-impervious surface-soil model for urban ecosystem analysis through remote-sensing-comparative anatomy for cities J. International Journal of Remote Sensing Ward D Phinn S R Murray A T. Monitoring growth in rapidly urbanizing areas using remotely sensed data J. Professional Geographer Phinn S Stanford M Scarth P et al. Monitoring the composition and form of urban environments based on the vegetationimpervious surface-soil VIS model by sub-pixel analysis techniques J. International Journal of Remote Sensing Wu C Murray A T. Estimating impervious surface distribution by spectral mixture analysis J. Remote Sensing of Environment Lu D S Weng Q H. Spectral mixture analysis of the urban landscape in Indianapolis with Landsat ETM + imagery J. Photogrammetric Engineering and Remote Sensing Lu D S Li G Y Moran E et al. Mapping impervious surfaces with the integrated use of Landsat Thematic Mapper and radar data A case study in an urban-rural landscape in the Brazilian Amazon J. ISPRS Journal of Photogrammetry and Remote Sensing Michishita R Jiang Z B Xu B. Monitoring two decades of urbanization in the Poyang Lake area China through spectral unmixing J. Remote Sensing of Environment J YUE Wen-ze WU Ci-fang. Urban impervious surface distribution estimation by spectral mixture analysis. Journal of Remote Sensing J ZHOU Cun-lin XU Han-qiu. A spectral mixture analysis and mapping of impervious surfaces in built-up land of Fuzhou city. Journal of Image and Graphics V-I-AP J PAN Jing-hu LI Xiao-xue FENG Zhao-dong. Analysis of spatial and temporal patterns of impervious surfaces and vegetation covers in Lanzhou based on the V-I-AP model. Resources Science J LI Fu-xiang LI Xue-ming LI Hua-peng. Urban sprawl study based on spectral mixture analysis method. Urban Studies J WANG Hao WU Bing-fang LI Xiao-song et al. Extraction of impervious surface in Haihe Basin using remote sensing. Journal of Remote Sensing CBERS-02B J CHEN Feng QIU Quan-yi GUO Qing-hai et al. The availability of CBERS-02B multi-spectral data in estimating urban impervious surface. Journal of Remote Sensing D LIU Yong. Multi-scale Study of Urban Growth and Landscape Change in Hangzhou Metropolitan Area. Hangzhou Zhejing University M YAO Shi-mou. Spatial Expansion of Large Cities in China. Hefei University of Science and Technology of China Press J FENG Jian ZHOU Yi-xing. The spatial redistribution and suburbanization of urban population in Hangzhou city. City Planning Review USGS. Landsat 7 Science Data Users Handbook EB /OL. http landsathandbook. gsfc. nasa. gov / Small C. The Landsat ETM + spectral mixing space J. Remote Sensing of Environment Small C. Estimation of urban vegetation abundance by spectral mixture analysis J. International Journal of Remote Sensing

11 Zhang M H Arnon K. TM6 J QIN Zhi-hao Zhang M H Arnon K et al. Mono-window algorithm for retrieving land surface temperature from Landsat TM6 data. Acta Geographica Sinica Lu D S Weng Q H. Use of impervious surface in urban land-use classification J. Remote Sensing of Environment Estimating Urban Impervious Surface Based on Thermal Infrared Remote Sensing Data and a Spectral Mixture Analysis Model LI Bo 1 2 HUANG Jing-feng 2 3 WU Ci-fang 1 1. Institute of Southeast Land Management Zhejiang University Hangzhou China 2. Key Laboratory of Agricultural Remote Sensing and Information System in Zhejiang Province Hangzhou China 3. Institute of Agricultural Remote Sensing and Information Technology Zhejiang University Hangzhou China Abstract The rapid growth of impervious surface is one of the remarkable characteristics of urbanization. Rapid extraction of urban impervious surface using remote sensing has been a hotspot research at home and abroad for large-scale urban monitoring. This paper explored extraction of impervious surface information of Hangzhou from a Landsat 7 ETM + image based on the integration of a spectral mixture analysis model and land surface temperature generated by thermal infrared images. The linear combination of high albedo low albedo vegetation and soil fraction was used to characterize the different types of urban land. The land surface temperature was considered as a mask to remove the noise from low albedo fraction and soil fraction was used to remove the noise from high albedo fraction. The modified high albedo fraction and low albedo fraction were adopted to estimate impervious surface distribution of Hangzhou. The result showed that the average RMSE was in the study area. Impervious surface distribution estimated u- sing the above method and the interpretation from high resolution images in Google earth and SPOT 4 image was comparatively analyzed and the majority of differences between estimating values and interpreting values of samples were ranged from to There was a promising accuracy. The result indicated that the method was feasible and reliable to precisely estimate impervious surface based on thermal infrared remote sensing images and a spectral mixture analysis model. Key words remote sensing impervious surface spectral mixture analysis thermal infrared data

GLOBAL GEOLOGY Vol. 35 No. 4 Dec Landsat 5 TM Landsat 7 ETM TM ETM + P A doi 1

35 4 2016 12 GLOBAL GEOLOGY Vol. 35 No. 4 Dec. 2016 1004 5589 2016 04 1151 07 130026 Landsat 5 TM Landsat 7 ETM + 12 12 1 2 TM ETM + P468. 023 A doi 10. 3969 /j. issn. 1004-5589. 2016. 04. 028 Surface