KSCE Journal of Civil and Environmental Engineering Research (대한토목학회논문집)

Korean Society of Civil Engeneers (대한토목학회)
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The Use of Satellite Image for Uncertainty Analysis in Flood Inundation Mapping

홍수범람도 불확실성 해석을 위한 인공위성사진의 활용

  • 정영훈 (인하대학교 수자원시스템연구소) ;
  • 류광현 (농림수산식품부 새만금개발과) ;
  • 이충성 (한국수자원공사 기술지원센터) ;
  • 이승오 (홍익대학교 토목공학과)
  • Received : 2012.10.15
  • Accepted : 2013.01.15
  • Published : 2013.03.30
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Abstract

An flood inundation map is able to convey spatial distribution of inundation to a decision maker for flood risk management. A roughness coefficient with unclear values and a discharge obtained from the stage-discharge rating equation are key sources of uncertainty in flood inundation mapping by using a hydraulic model. Also, the uncertainty analysis needs an observation for the flood inundation, and satellite images is useful to obtain spatial distribution of flood. Accordingly, the objective of this study is to quantify uncertainty arising roughness and discharge in flood inundation mapping by using a hydraulic model and a satellite image. To perform this, flood inundations were simulated by HEC-RAS and terrain analysis, and ISODATA (Iterative Self-Organizing Data Analysis) was used to classify waterbody from Landsat 5TM imagery. The classified waterbody was used as an observation to calculate F-statistic (likelihood measure) in GLUE (Generalized Likelihood Uncertainty Estimation). The results from GLUE show that flood inundation areas are 74.59 $km^2$ for lower 5 % uncertainty bound and 151.95 $km^2$ for upper 95% uncertainty bound, respectively. The quantification of uncertainty in flood inundation mapping will play a significant role in realizing the efficient flood risk management.

정밀한 홍수 범람도는 홍수의 공간적 특성에 대한 정보를 의사 결정자나 설계자들에게 전달할 수 있다. 수리모형을 이용하여 홍수 범람도를 구축하는 과정에서 확실하게 정의되거나 측정되지 않은 조도계수와 수위유량관계식으로부터 얻은 유량은 불확실성을 일으키는 핵심 요인들이다. 또한, 홍수 범람도에 대한 불확실성 해석을 위해서는 관측 자료가 필요한데, 홍수 범람의 관측 자료는 인공위성영상을 이용하여 확보할 수가 있다. 따라서 본 연구의 목적은 수리모형과 인공위성자료를 이용하여 조도계수와 유량이 홍수범람도 제작에서 일으키는 불확실성을 정량적으로 산정하는 것이다. 미국 Illinois주 Metropolis시 주위의 Ohio 강에 대하여 HEC-RAS과 지형분석을 이용하여 홍수 범람를 모의하고 ISODATA(Iterative Self-Organizing Data Analysis)분류 방법으로 Landsat 5TM 위성 영상으로부터 수체를 추출하였다. 추출된 수체는 GLUE(Generalized Likelihood Uncertainty Estimation)에서 우도측정(F-통계량)을 계산하는데 관측 자료로 이용되었다. GLUE는 누적확률 5 %와 95 %에 각각 해당하는 74.59 $km^2$와 151.95 $km^2$의 홍수범람면적을 산정했다. 홍수 범람도 구축과정에서 발생하는 불확실성을 정량적으로 산정하는 것은 효율적인 홍수방어 계획을 실현화하는데 중요한 역할을 할 거라 사료된다.

Keywords

References

  1. Beven, K. J., and Binley, A. M. (1992) "The future of distributed models: model calibration and uncertainty prediction." Hydrol. Process., Vol. 6, pp. 279-298. https://doi.org/10.1002/hyp.3360060305
  2. Bales, J. D., and R. (2009) "Sources of uncertainty in flood inundation maps." Flood Risk Manage, Vol. 2, No. 2, pp. 139-147. https://doi.org/10.1111/j.1753-318X.2009.01029.x
  3. Calenda, G.C., Mancini, C.P., and Volpi, E. (2005) "Distribution of Extreme Peak Floods of the Tiber River from the XV Century." Advances in Water Resources, Vol. 28, No. 6, pp. 615-625. https://doi.org/10.1016/j.advwatres.2004.09.010
  4. Collins, M. J. (2009). "Evidence for changing flood risk in new England since the late 20th century." J. Am. Water Resour. Assoc., Vol. 45, No. 2, pp. 279-290. https://doi.org/10.1111/j.1752-1688.2008.00277.x
  5. Chow, V.T. (1959) Open-channel hydraulics. McGraw- Hill Book Co., New York, NY.
  6. ERDAS (1999) ERDAS Field Guide Fifth Edition. ERDAS Inc.
  7. Gianinetto, M., Villa, P., and Lechi, G. (2006) "Postflood damage evaluation using Landsat TM and ETM+ data integrated with DEM." IEEE Trans. Geosci. Remote Sens., Vol. 44, No. 1, pp. 236-243. https://doi.org/10.1109/TGRS.2005.859952
  8. Hurkmans, R. T. W. L., Terink, W., Uijlenhoet, R., Moors, E. J., Troch, P. A., and Verburg, P. H. (2009) "Effects of land use changes on streamflow generation in the Rhine basin." Water Resour. Res., Vol. 45, No. 6, W06405.
  9. Xu, Y., Xu, C., Gao, X., and Luo, Y. (2009) "Projected changes in temperature and precipitation extremes over the Yangtze River Basin of China in the 21st century." Quat. Int., Vol. 208, No. 1-2, pp. 44-52. https://doi.org/10.1016/j.quaint.2008.12.020
  10. Takeuchi, S., Konishi, T., Suga, Y., and Kishi, S. (1999) "Comparative study for flood detection using JERS-1 SAR and Landsat TM data" Proceedings of IGARSS'99, Hamburg, Germany, 28 June-2 July 1999 (Piscataway, NJ: IEEE), pp. 873-875.
  11. Wang, Y., Colby, J. D., and Mulcahy, K. A. (2002) "An efficient method for mapping flood extent in a coastal flood plain using Landsat TM and DEM data" International Journal of Remote Sensing, Vol. 23, No. 18, pp. 3681-3696. https://doi.org/10.1080/01431160110114484
  12. Merwade, V., Olivera, F., Arabi, M., and Edleman, S. (2008) "Uncertainty in flood inundation mapping—Current issues and future directions." J. Hydrol. Eng., Vol. 13, No. 7, pp. 608-620. https://doi.org/10.1061/(ASCE)1084-0699(2008)13:7(608)
  13. Moore, M.R. (2011) Development of a high-resolution 1D/2D coupled flood simulation of Charles City. Iowa, Master's thesis, University of Iowa, Iowa City, Iowa, USA.
  14. Pappenberger F., Beven K.J., Hunter N., Gouweleeuw B., Bates P., de Roo A., and Thielen, J. ( 2005) "Cascading model uncertainty from medium range weather forecasts (10 days) through a rainfall-runoff model to flood inundation predictions within the European Flood Forecasting System (EFFS)." Hydrol. Earth Syst. Sci. Vol. 9, pp. 381-393. https://doi.org/10.5194/hess-9-381-2005
  15. Pappenberger, F., Beven, K.J., Frodsham, K., Romanovicz, R., and Matgen, P. (2006) "Grasping the unavoidable subjectivity in calibration of flood inundation models: a vulnerability weighted approach." J. Hydrol. Vol. 333, pp. 275-287.
  16. Romanowicz, R. and Beven, K.J. (1998) "Dynamic real-time prediction of flood inundation probabilities." Hydrol. Sci. J. Vol. 43, pp. 181-196. https://doi.org/10.1080/02626669809492117
  17. Sanyal, J. and Lu, X.X. (2004) "Application of remote sensing in flood management with special reference to Monsoon Asia: a review." Natural Hazards, Vol. 33, pp. 283-301. https://doi.org/10.1023/B:NHAZ.0000037035.65105.95
  18. Song, C., Woodcock, C.E., Seto, K.C., Pax-Lenney, M., and Macomber, S.A. (2001) "Classification and change detection using Landsat TM data: When and how to correct atmospheric effects." Remote Sensing of Environment, Vol. 75, pp. 230-244. https://doi.org/10.1016/S0034-4257(00)00169-3
  19. Toyra, J, and Pietroniro, A. (2005) "Towards operational monitoring of a northern wetland using geomatics-based techniques." Remote Sensing of Environment, Vol. 97, pp. 174-191. https://doi.org/10.1016/j.rse.2005.03.012
  20. USGS Landsat Missions http://landsat.usgs.gov/about_landsat5.php