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Estimation of river water depth using UAV-assisted RGB imagery and multiple linear regression analysis

무인기 지원 RGB 영상과 다중선형회귀분석을 이용한 하천 수심 추정

  • Moon, Hyeon-Tae (Department of Civil Engineering, University of Seoul) ;
  • Lee, Jung-Hwan (Department of Civil Engineering, University of Seoul) ;
  • Yuk, Ji-Moon (Department of Civil Engineering, University of Seoul) ;
  • Moon, Young-Il (Department of Civil Engineering, University of Seoul)
  • 문현태 (서울시립대학교 토목공학과) ;
  • 이정환 (서울시립대학교 토목공학과) ;
  • 육지문 (서울시립대학교 토목공학과) ;
  • 문영일 (서울시립대학교 토목공학과)
  • Received : 2020.06.10
  • Accepted : 2020.10.14
  • Published : 2020.12.31

Abstract

River cross-section measurement data is one of the most important input data in research related to hydraulic and hydrological modeling, such as flow calculation and flood forecasting warning methods for river management. However, the acquisition of accurate and continuous cross-section data of rivers leading to irregular geometric structure has significant limitations in terms of time and cost. In this regard, a primary objective of this study is to develop a methodology that is able to measure the spatial distribution of continuous river characteristics by minimizing the input of time, cost, and manpower. Therefore, in this study, we tried to examine the possibility and accuracy of continuous cross-section estimation by estimating the water depth for each cross-section through multiple linear regression analysis using RGB-based aerial images and actual data. As a result of comparing with the actual data, it was confirmed that the depth can be accurately estimated within about 2 m of water depth, which can capture spatially heterogeneous relationships, and this is expected to contribute to accurate and continuous river cross-section acquisition.

하천단면 계측자료는 하천관리를 위한 유량산정 및 홍수 예·경보 방안 등 수리·수문 모델링 관련 연구에서 가장 중요한 입력자료 중 하나이다. 그러나 불규칙한 기하학적 구조로 이어지는 하천의 정확하고 연속적인 단면자료의 취득은 시간과 비용적 측면에서 큰 제약이 따른다. 이러한 관점에서 본 연구의 목적은 연속적인 하천특성의 공간분포를 시간과 비용, 인력의 투입을 최소화하여 계측할 수 있는 방법론을 개발하는 것이다. 따라서 RGB기반 항공 이미지와 실측 자료를 이용한 다중 선형 회귀 분석을 통해 각 단면별로 수심을 추정하고 연속적인 단면 추정 가능성과 정확도를 검토하고자 하였다. 실측 자료와 비교검증을 통해 공간적으로 이질적인 관계를 포착할 수 있는 수심 약 2 m 내외에서 수심을 정확하게 추정할 수 있음을 확인하였으며 이를 통해 정확하고 연속적인 하천 단면 취득에 기여할 수 있을 것으로 기대한다.

Keywords

References

  1. Abdallah, H., Bailly, J.S., Baghdadi, N.N., Saint-Geours, N., and Fabre, F. (2013). "Potential of space-borne LiDAR sensors for global bathymetry in coastal and inland waters." IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing, Vol. 6, No. 1, pp. 202-216. https://doi.org/10.1109/JSTARS.2012.2209864
  2. Ahn, K., Park, J., and Oh, C.Y. (2016). "Estimation of shallow water depth using drone and HD camera." Proceeding of the conference of Korea Society of Coastal and Ocean Engineering.
  3. Ferguson, R.I., Parsons, D.R., Lane, S.N., and Hardy, R.J. (2003). "Flow in meander bends with recirculation at the inner bank." Water Resources Research, Vol. 39, No. 11, pp. 1322-1335.
  4. Fujita, I., Muste, M., and Kruger, A. (1998). "Large-scale particle image velocimetry for flow analysis in hydraulic engineering applications." Journal of Hydraulic Research, Vol. 36, pp. 397-414. https://doi.org/10.1080/00221689809498626
  5. Guenther, G.C., Cunningham, A.G., LaRocque, P.E., and Reid, D.J. (2000). "Meeting the accuracy challenge in airborne lidar bathymetry." Proceedings of the EARSel-SIG-Workshop, LIDAR, Dresden, Germany, p. 23.
  6. Jakob, S., Zimmermann, R., and Gloaguen, R. (2017). "The need for accurate geometric and radiometric corrections of drone-borne hyperspectral data for mineral exploration: Mephysto-a toolbox for pre-processing drone-borne hyperspectral data." Remote Sensing, Vol. 9, No. 1, p. 88. https://doi.org/10.3390/rs9010088
  7. Jia, Y., and Wang, S.S. (1999). "Numerical model for channel flow and morphological change studies." Journal of Hydraulic Engineering, Vol. 125, No. 9, pp. 924-933. https://doi.org/10.1061/(ASCE)0733-9429(1999)125:9(924)
  8. Kalkwijk, J.T., and De Vriend, H.J. (1980). "Computation of the flow in shallow river bends." Journal of Hydraulic Research, Vol. 18, No. 4, pp. 327-342. https://doi.org/10.1080/00221688009499539
  9. Kim, J.S., Baek, D.H., Seo, I.W., and Shin, J.H. (2019). "Retrieving shallow stream bathymetry from UAV-assisted RGB imagery using a geospatial regression method." Geomorphology, Vol. 341, pp. 102-114. https://doi.org/10.1016/j.geomorph.2019.05.016
  10. Kuster, T. (2012). "The possibility of using the Landsat image archive for monitoring long time trends in coloured dissolved organic matter concentration in lake waters." Remote Sensing of Environment, Vol. 123, pp. 334-338. https://doi.org/10.1016/j.rse.2012.04.004
  11. Lee, C.J., Kim, W., Kim, C.Y., and Kim, D.G. (2005). "Velocity and discharge measurement using ADCP." Journal of Korea Water Resources Association, Vol. 38, No. 10, pp. 811-824. https://doi.org/10.3741/JKWRA.2005.38.10.811
  12. Lee, D.S., Lee, J.H., and Jang, S.Y. (2018). Development planning of river research optimization drone system for smart river management. Korea Institute of Civil Engineering and Building Technology, pp. 111-120.
  13. Legleiter, C.J., Roberts, D.A., and Lawrence, R.L. (2009). "Spectrally based remote sensing of river bathymetry." Earth Surface Processes and Landforms, Vol. 34, No. 8, pp. 1039-1059. https://doi.org/10.1002/esp.1787
  14. Lyzenga, D.R. (1981). "Remote sensing of bottom reflectance and water attenuation parameters in shallow water using aircraft and Landsat data." International Journal of Remote Sensing, Vol. 2, No. 1, pp. 71-82. https://doi.org/10.1080/01431168108948342
  15. Oh, C.Y., Ahn, K.M., Park, J.S., and Park, S.W. (2017). "Coastal shallow-water bathymetry survey through a drone and optical remote sensors." Journal of Korean Society of Coastal and Ocean Engineers, Vol. 29, No. 3, pp. 162-168. https://doi.org/10.9765/KSCOE.2017.29.3.162
  16. Pan, Z., Glennie, C., Legleiter, C., and Overstreet, B. (2015). "Estimation of water depths and turbidity from hyperspectral imagery using support vector regression." IEEE Transactions on Geoscience and Remote Sensing Letters, Vol. 12, No. 10, pp. 2165-2169. https://doi.org/10.1109/LGRS.2015.2453636
  17. Peddle, D.R., White, H.P., Soffer, R.J., Miller, J.R., and Ledrew, E.F. (2001). "Reflectance processing of remote sensing spectroradiometer data." Computers and Geosciences, Vol. 27, No. 2, pp. 203-213. https://doi.org/10.1016/S0098-3004(00)00096-0
  18. Srivastava, D,K., Weinrotter, M., Kofler, H., Agarwal, A.K., and Wintner, E. (2009). "Laser-assisted homogeneous charge ignition in a constant volume combustion chamber." Optics and Lasers in Engineering, Vol. 47, No. 6, pp. 680-685. https://doi.org/10.1016/j.optlaseng.2008.12.002
  19. Yang, G., Liu, J., Zhao, C., Li, Z., Huang, Y., Yu, H., Xu, B., Yang, X., Zhu, D., Zhang, X., and Zhang, R. (2017). "Unmanned aerial vehicle remote sensing for field-based crop phenotyping: Current status and perspectives." Frontiers in Plant Science, Vol. 8, p. 1111. https://doi.org/10.3389/fpls.2017.01111
  20. Yi, J.H., Ryu, K.H., Shin, C., Baek, W.D., and Jeong, W.M. (2016). "Bathymetry estimation using aerial imagery for shallow water region." Journal of The Korean Society of Hazard Mitigation, Vol. 16, No. 5, pp. 351-358. https://doi.org/10.9798/KOSHAM.2016.16.5.351
  21. Zinke, P., and Flender, C. (2013). "Experiences from the use of unmanned aerial vehicles (UAV) for river bathymetry modelling in norway." VANN, Vol. 48, pp. 351-360.