Korean Journal of Remote Sensing (대한원격탐사학회지)

Korean Society of Remote Sensing (대한원격탐사학회)
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Impervious Surface Estimation Using Landsat-7 ETM+Image in An-sung Area

Landsat-7 ETM+영상을 이용한 안성지역의 불투수도 추정

  • Kim, Sung-Hoon (School of Civil and Environmental Engineering, Yonsei University) ;
  • Yun, Kong-Hyun (School of Civil and Environmental Engineering, Yonsei University) ;
  • Sohn, Hong-Gyoo (School of Civil and Environmental Engineering, Yonsei University) ;
  • Heo, Joon (School of Civil and Environmental Engineering, Yonsei University)
  • 김성훈 (연세대학교 사회환경시스템공학부) ;
  • 윤공현 (연세대학교 사회환경시스템공학부) ;
  • 손홍규 (연세대학교 사회환경시스템공학부) ;
  • 허준 (연세대학교 사회환경시스템공학부)
  • Published : 2007.12.30
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Abstract

As the Imperious surface is an important index for the estimation of urbanization and environmental change, the increase of impervious surfaces causes meteorological and hydrological changes like urban climate change, urban flood discharge increasing, urban flood frequency increasing, and urban flood modelling during the rainy season. In this study, the estimation of impervious surfaces is performed by using Landsat-7 ETM+ image in An-sung area. The construction of sampling data and checking data is used by IKONOS image. It transform to a tasselled cap and NDVI through the reflexibility rate of Landsat ETM+ image and analyze various variables that influence on impervious surface. Finally, the impervious surfaces map is accomplished by regression tree algorithm.

불투수도는 도시화, 환경변화를 추정하기 위한 중요한 지수로서 도시 기후 변화, 홍수기철 도시 범람의 증가, 홍수 모델링에 영향 등 도시의 홍수 기상학과 수문학적인 변화와 매우 밀접한 관계가 있다. 본 연구에서는 안성지역 일대를 대상으로 하여 Landsat ETM+ 영상을 이용한 불투수도 작성을 시도하였다. 학습 및 검수자료는 고해상도 영상인 IKONOS 영상을 이용하였으며, Landsat ETM+ 영상에 대한 위성반사율을 이용하여 tasseled cap과 NDVI로 전환하고 다양한 변수들이 불투수도에 미치는 영향을 분석하였다. 그리고 Regression Tree 알고리즘에 따라 불투수도 추정식을 개발하여 지도화하였다.

Keywords

References

  1. 안성시청, 2006. 일반현황, www.anseong.go.kr
  2. Cubist Program, 2007. Cubist Information, www.rulequest.com
  3. 사공호상, 2003. IKONOS 위성영상을 이용한 불투수지표면 분석방법에 관한 실증연구, 한국GIS학회, 11(4): 509-518
  4. 성효현, 박옥준, 2000. 토지피복 변화에 따른 식생지수(NDVI) 분포 및 변화에 관한 연구, 한국GIS학회, 8(2): 275-288
  5. 신정일, 김선화, 윤정숙, 김태근, 이규성, 2006. 도시지역의 수문학적 토지피복 분류를 위한 초분관영상의 분광혼합분석, 대한원격탐사학회, 22(6): 565-574 https://doi.org/10.7780/kjrs.2006.22.6.565
  6. 조홍래, 정종철, 2005. 분광혼합분석 기법을 이용한 탄천유역 불투수율 평가, 대한원격탐사학회, 21(6): 457-468 https://doi.org/10.7780/kjrs.2005.21.6.457
  7. Breiman, L., Friedman, J. H., Olshen, R. A., and Stone, C. J., 1984. Classification and Regression trees, The Wadsworth Statistics/ Probability Series. Wadsworth International group, California, USA
  8. Crist, E. P. and Cicone, R. C., 1984. Application of the Tasseled Cap Concept to Simulated Thematic Mapper Data, Photogrammetric Engineering and Remote Sensing, 50(3): 343-352
  9. Herold, N. D., Koeln, G., and Cunnigham, D., 2003. Mapping impervious surfaces and forest canopy using classification and regression tree analysis, ASPRS 2003 Annual Conference Proceedings, Alaska, USA
  10. Homer, C., Dewitz, J., Fry, J., Coan, M., Hossain, N., Larson, C., Herold, N,. McKerrow, A., VanDriel, J. N., and Wickham, J., 2007. Completion of the 2001 National land cover database for the conterminous United States, Photogrammetric Engineering & Remote Sensing, 2007: 337-341
  11. Huang, C. and Townshend, J. R. G., 2002. Derivation of a tasselled cap transformation based on Landdat-7 at-satellite reflectance, International Journal of Remote Sensing, 23(8): 1741-1748 https://doi.org/10.1080/01431160110106113
  12. Huete, A., Didan, K., Miura, T., Rodriguez, E. P., Gao, X., and Ferreira, L. G., 2002. Overview of the radiometric and biophysical performance of the MODIS Vegetation indices, Remote Sensing of Environment, 83: 195 213
  13. Hurd, J. D. and Civeo, D. L., 2004. Temporal characterization of impervious surfaces for the state of connecticut, ASPRS 2004 Annual Conference Proceedings, Colorado, USA
  14. Lee, S., 2006. Landsat ETM+ sub-pixel ananlysis of urban landscape using Fuzzy C-means clustering and differentiated impervious surface classed, ASPRS 2006 Annual Conference Proceedings, Reno, Nevada, USA
  15. Lu, D. and Weng, Q., 2006. Use of impervious surface in urban land-use classification, Remote Sensing of Environment, 102: 146-160 https://doi.org/10.1016/j.rse.2006.02.010
  16. Quinlan, J. R., 1992. Simplifying decision trees, International Journal of Man-machine Studies, 27: 221-234
  17. Vikhamar, D. and Kastdalen, L., 2005. Impervious surface mapping in Southern Norway, 31st International Symposium on Remote Sensing of Environment, St. Petersburg, Russia
  18. Wu, C. and Murray, A. T., 2003. Estimating impervious surface distribution by spectral mixture analysis, Remote Sensing of Environment, 84: 493-505 https://doi.org/10.1016/S0034-4257(02)00136-0
  19. Yang, L., Huang, C., Homer, C. G., Wylie, B. K., and Coan, M. J., 2003. An approach for mapping large-area impervious surfaces synergistic use of Landsat-7 ETM+ and high spatial resolution imagery, Canadian Journal of Remote Sensing, 29(2): 230-240 https://doi.org/10.5589/m02-098
  20. Yang, X. and Liu, Z., 2005. Use of satellite-derived landscape imperviousness index to characterize urban spatial growth, Computers, Environment and Urban Systems, 29: 524-540 https://doi.org/10.1016/j.compenvurbsys.2005.01.005