Analysis on the Sedimentary Environment Change Induced by Typhoon in the Sacheoncheon, Gangneung using Multi-temporal Remote Sensing Data

태풍 루사에 의한 강릉 사천천 주변 퇴적 환경 변화: 다중 시기 원격탐사 자료를 이용한 정보 분석

  • Park, No-Wook (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources) ;
  • Jang, Dong-Ho (Research Institute for Military Science, Kongju National University) ;
  • Chi, Kwang-Hoon (Geoscience Information Center, Korea Institute of Geoscience and Mineral Resources)
  • 박노욱 (한국지질자원연구원 지질자원정보센터) ;
  • 장동호 (공주대학교 군사과학연구원) ;
  • 지광훈 (한국지질자원연구원 지질자원정보센터)
  • Published : 2006.02.28

Abstract

The objective of this paper is to extract and analyze the sediment environment change information in the Sachencheon, Gangneung, Korea that was seriously damaged as a result of typhoon Rusa aftermath early in September, 2002 using multi-temporal remote sensing data. For the extraction of change information, an unsupervised approach based on the automatic determination of thresholding values was applied. As the change detection results, turbidity changes right after typhoon Rusa, the decrease of wetlands, the increase of dry sand and channel width and changes of relative level in the stream due to seasonal variation were observed. Sedimentation in the cultivated areas and restoration works also affected the change near the Sacheoncheon. In addition to the change detection analysis, several environmental thematic maps including microtopographic map, distributions of estimated amount of flood deposits and flood hazard landform classification map were generated by using remote sensing and field survey data. In conclusion, multi-temporal remote sensing data can be effectively used for natural hazard analysis and damage information extraction and specific data processing techniques for high-resolution remote sensing data should also be developed.

이 논문에서는 2002년 9월 태풍 루사로 인해 많은 재해 피해를 입은 강원도 강릉시 사천천 유역을 대상으로 다중 시기 원격탐사 자료를 이용하여 퇴적 지질환경 변화 정보를 추출하고 분석을 수행하였다. 다중 시기 자료에 대해 자동 임계치 설정 기반 무감독 변화 탐지 기법을 적용하여 여러 시기 및 센서별 변화 정보를 추출하였다. 변화탐지 결과, 제외지에서는 태풍 루사 직후 하천 탁도 변화, 습지의 수계 혹은 퇴적물로의 변화 및 계절적인 유량 차이에 의한 하도 노출 여부 등으로 변화지역이 나타났다. 주변 농경지에서는 홍수 및 산사태 등으로 인한 토사의 퇴적, 농지 개간 등으로 인한 변화가, 기타 지역에서는 제방 공사 등으로 인한 변화가 두드러지게 나타났다. 노한 야외 조사와 원격탐사 자료를 이용하여 미지형 분류도, 범람원 지역 지표 퇴적량 분포도 및 수해 지형 분류도를 작성하였다. 결론적으로 다중시기 고해상도 원격탐사 자료가 재해로 인한 변화 정보 추출에 유용하게 활용될 수 있을 것으로 기대되며, 이를 위해 고해상도 자료에 적합한 자료처리 기법 개발이 병행되어야 할 것으로 판단된다.

Keywords

References

  1. 박노욱, 지광훈, 이광재, 권병두, 2003, 다중시기 원격탐사 화상의 변화탐지를 위한 임계치 자동 추정. 대한원격탐사학회지, 19(6), 465-478
  2. 양동윤 외, 2003, 주요수계 하상퇴적물에 의한 홍수재해 예측기법. 자연재해방재기술개발사업 보고서, 298 p
  3. 장동호, 2005, 고해상도 위성영상을 이용한 홍수 전 ${\cdot}$ 후의 하도 내 퇴적환경 변화 탐지: 강릉 사천천 사례연구. 한국지형학회지, 12(3), 49-58
  4. 지광훈 외, 2005, 산사태 등 자연재해로 발생한 하천 퇴적 지질환경 변화 탐지. 과학기술부 원격탐사기술개발사업보고서, 226 p
  5. Borah, D.K. and Ashraf, M.S., 1990, Modeling storm run-off and sediment with seasonal variations. Transactions in Agriculture, 33(1), 105-110
  6. Bruzzone, L. and Prieto, D.F., 2000, Automatic analysis of the difference image for unsupervised change detection. IEEE Transactions on Geoscience and Remote Sensing, 38(3), 1171-1182 https://doi.org/10.1109/36.843009
  7. Bryant R.G. and Gilvear, D.J., 1999, Quantifying geomorphic and riparian land cover changes either side of a large flood event using airborne remote sensing: River Tay, Scotland. Geomorphology, 29, 307-321 https://doi.org/10.1016/S0169-555X(99)00023-9
  8. Dempster, A.P., Laird, N.M., and Rubin, D.B., 1977, Maximum likelihood from incomplete data via the EM algorithm. Journal of the Royal Statistical Society Series(B), 39(1), 1-38
  9. Duda, R.O., Hart, P.E., and Stork, D.G., 2000, Pattern, Classification. Wiley-Interscience, New York, USA, 654 p
  10. Durgin, P. B., 1985, Buring changes the erodibility of forest soils. Journal of Soil and Water Conservation, 36(4), 235-240
  11. Fukunaga, K., 1990, Introduction to Statistical Pattern Recognition. Academic Press, London, U.K., 592 p
  12. Gellis, A.C., Cheama, A., and Lalio, S.M, 2000, Developing a geomorphic approach for ranking watersheds for rehabilitation, Zuni Indian Reservation, New Mexico. Geomorphology, 37, 105-134 https://doi.org/10.1016/S0169-555X(00)00065-9
  13. Iain, B., 2005, Modelling future landscape change on coastal floodplains using rule-based GIS. Environmental Modelling & Software, In Press, Corrected Proof, 1-15
  14. James, P.M.S., Albert, J.K., Anna, C., and Bruce, W.N., 2005, Distributary channels and their impact on sediment dispersal. Marine Geology, 222-223, 75-94 https://doi.org/10.1016/j.margeo.2005.06.030
  15. Jerry, C.R., Vernon, L.F., Kenneth, J.O., and Carole, A.R., 2004, Sediment deposition in the flood plain of Stemple Creek Watershed northern California. Geomorphology, 61(1), 347-360 https://doi.org/10.1016/j.geomorph.2004.01.009
  16. Liew, M.W.V., 1997, Prediction of sediment yield on a large watershed in North Central China. Transaction of the ASCE, 41(3), 599-604
  17. Marouane, T., Robert, L., Francois, B., and Naira, C., 2005, Flood monitoring over the Mackenzic river basin using passive microwave data. Reomte Sensing of Environment, 98(15), 344-355 https://doi.org/10.1016/j.rse.2005.06.010
  18. Michael, D.W. and Keith, A.G., 2006, The effects watershed urbanization on the stream hydrology and riparian vegetation of Los Penasquitos Creek, California. Landscape and Urban Planning, 74(2), 125-138 https://doi.org/10.1016/j.landurbplan.2004.11.015
  19. Miller, G.T., 1993, Environmental Science, Sustaining the Earth, Belmont. Wadsworth Publishing Company, California, 478 p
  20. Paul. S., Bruce. C., and Greg. M., 2003, The coregistration, calibration, and interpretation of multiseason JERS-1 SAR data over South America. Remote Sensing of Environment, 87(15), 389-403 https://doi.org/10.1016/j.rse.2002.12.002
  21. Perrone, J. and Madramootoo, C. A., 1999, Sediment yield prediction using AGNPS. Journal of Soil and Water Conservation, 54(1), 415-419
  22. Sande, C.J., Jong, S.M., and Roo, A.P.J., 2003, A segmentation and classification approach of IKONOS-2 imagery for land cover mapping to assist flood risk and flood damage assessment. International Journal of Applied Earth Observation and Geoinformation, 4, 217-229 https://doi.org/10.1016/S0303-2434(03)00003-5
  23. Tonis, I.E., Stam, J.M.T., and Graaf, J., 2002, Morphological changes of the Haringvliet estuary after closure in 1970. Coastal Engineering, 44, 191-203 https://doi.org/10.1016/S0378-3839(01)00026-6
  24. Yamamoto, T., Hanaizumi, H., and Chino, S., 2001, A change detection method for remotely sensed multisepctral and multitemporal images using 3-D segmentation. IEEE Transactions on Geoscience and Remote Sensing, 39(5), 976-985 https://doi.org/10.1109/36.921415
  25. Zhan, X., Sohlberg, R.A., Townshend, J.R.G., DiMiceli, C., Carroll, M.L., Eastman, J.C., Hansen, M.C., and DeFries, R.S., 2002, Detection of land cover changes using MODIS 250m data. Remote Sensing of Environment, 83, 336-350 https://doi.org/10.1016/S0034-4257(02)00081-0