27 9 Vol. 27 No. 9 2012 9 JOURNAL OF NATURAL RESOURCES Sep. 2012 1 2 2 3 1 1. 310058 2. 310058 3. 310058 Landsat 7 ETM + 4 0. 003 6 Google earth SPOT 4 ± 0. 15 TP79 X87 A 1000-3037 2012 09-1590 - 11 20 33 2030 50 1 2 3-4 1995 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 2011-12- 20 2012-04- 25 40871158 51108405 1983- E-mail libozju@ yahoo. com. cn
9 1591 Lu 9 Lu 10 TM Michishita 11 20 a 21 12 13 14 15 16 17 Landsat ETM + 1 1. 1 18 20 80 1949 30 km 2 1995 102 km 2 19 6 3 068 km 2 20 1 1. 2 2000 5 4 Landsat 7 ETM + P119 /R39 ETM + 1 5 0. 5 ETM + 30 m 30 m Landsat 7 Science Data User Handbook DN 21 12 22 ETM + 2000 2 Google earth
1592 27 Fig. 1 1 Location of the study area 294 /289 1999 2 19 SPOT 4 HRV 10 m 2 2. 1 8-9 12-13 8-9 2. 2
9 1593 12 3 4 22 23 Minimum Noise Fraction MNF ETM + MNF 6 2 3 93. 57% 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 6 79. 76 31. 56 11. 03 4. 00 2. 66 1. 72 /% 61. 00 24. 14 8. 43 3. 06 2. 04 1. 33 3 MNF 3 4 4 4 4
1594 27 4 Fig. 3 3 MNF 3 Feature space of the first three components generated by MNF transformation 2. 3 5 Landsat ETM + 5 24 2. 4 4 6 Fig. 4 Spectral reflectance of 4 endmembers at 6 bands 3 3. 1 6 ETM + 6 4 6 6
9 1595 Fig. 5 5 ETM + Land surface temperature generated by the inversion of ETM + thermal infrared images 6 Fig. 6 3. 2 4 Fraction images of four endmembers
1596 27 70% 28 25 7 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 The frequency plot of RMSE 9 Fig. 9 RMSE The spatial distribution of RMSE
1598 27 0. 003 6 0. 02 8 8 RMSE 0. 018 Google earth 2000 2 Google earth 1999 2 SPOT 4 3 3 90 m 90 m ETM + 105 GIS 10 conversion KML Fig. 10 Accuracy evaluation of impervious surface estimation Google earth SPOT 4 10 96. 2% ± 0. 15 4 0. 15-0. 15 4 Landsat ETM + 1 2 References 1 United Nations. World Urbanization Prospects The 2007 Revision Highlights M. New York United Nations 2008 1-12. 2 Weng Q H. Remote Sensing of Impervious Surfaces M. London CRC Press 2008 12-49. 3 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 2009 64 335-344.
9 1599 4 Weng Q H. Remote sensing of impervious surfaces in the urban areas Requirements methods and trends J. Remote Sensing of Environment 2012 117 34-49. 5 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 1995 16 12 2165-2185. 6 Ward D Phinn S R Murray A T. Monitoring growth in rapidly urbanizing areas using remotely sensed data J. Professional Geographer 2000 52 3 371-386. 7 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 2002 23 20 4131-4153. 8 Wu C Murray A T. Estimating impervious surface distribution by spectral mixture analysis J. Remote Sensing of Environment 2003 84 4 493-505. 9 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 2004 70 9 1053-1062. 10 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 2011 66 798-808. 11 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 2012 117 3-18. 12. J. 2007 11 6 914-922. YUE Wen-ze WU Ci-fang. Urban impervious surface distribution estimation by spectral mixture analysis. Journal of Remote Sensing 2007 11 6 914-922. 13. J. 2007 12 5 875-881. 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 2007 12 5 875-881. 14. V-I-AP J. 2010 32 3 520-527. 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 2010 32 3 520-527. 15. J. 2010 17 10 119-124. LI Fu-xiang LI Xue-ming LI Hua-peng. Urban sprawl study based on spectral mixture analysis method. Urban Studies 2010 17 10 119-124. 16. J. 2011 15 2 394-400. WANG Hao WU Bing-fang LI Xiao-song et al. Extraction of impervious surface in Haihe Basin using remote sensing. Journal of Remote Sensing 2011 15 2 394-400. 17. CBERS-02B J. 2011 15 3 630-639. 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 2011 15 3 630-639. 18. D. 2008. LIU Yong. Multi-scale Study of Urban Growth and Landscape Change in Hangzhou Metropolitan Area. Hangzhou Zhejing University 2008. 19. M. 1997. YAO Shi-mou. Spatial Expansion of Large Cities in China. Hefei University of Science and Technology of China Press 1997. 20. J. 2002 26 1 58-65. FENG Jian ZHOU Yi-xing. The spatial redistribution and suburbanization of urban population in Hangzhou city. City Planning Review 2002 26 1 58-65. 21 USGS. Landsat 7 Science Data Users Handbook EB /OL. http landsathandbook. gsfc. nasa. gov / 1999-04-15. 22 Small C. The Landsat ETM + spectral mixing space J. Remote Sensing of Environment 2004 93 1 1-17. 23 Small C. Estimation of urban vegetation abundance by spectral mixture analysis J. International Journal of Remote Sensing 2001 22 8 1305-1334.
1600 27 24 Zhang M H Arnon K. TM6 J. 2001 56 4 456-465. 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 2001 56 4 456-465. 25 Lu D S Weng Q H. Use of impervious surface in urban land-use classification J. Remote Sensing of Environment 2006 102 2 146-160. 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 310058 China 2. Key Laboratory of Agricultural Remote Sensing and Information System in Zhejiang Province Hangzhou 310058 China 3. Institute of Agricultural Remote Sensing and Information Technology Zhejiang University Hangzhou 310058 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 0. 0036 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 - 0. 15 to + 0. 15. 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