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Detection and Mapping of the Geomorphic Effects of Flooding Using UAV Photogrammetry

Pure and Applied Geophysics volume 175, pages 3223–3245 (2018)Cite this article

Abstract

In this paper, we present a novel technique for the objective detection of the geomorphological effects of flooding in riverbeds and floodplains using imagery acquired by unmanned aerial vehicles (UAVs, also known as drones) equipped with an panchromatic camera. The proposed method is based on the fusion of the two key data products of UAV photogrammetry, the digital elevation model (DEM), and the orthoimage, as well as derived qualitative information, which together serve as the basis for object-based segmentation and the supervised classification of fluvial forms. The orthoimage is used to calculate textural features, enabling detection of the structural properties of the image area and supporting the differentiation of features with similar spectral responses but different surface structures. The DEM is used to derive a flood depth model and the terrain ruggedness index, supporting the detection of bank erosion. All the newly derived information layers are merged with the orthoimage to form a multi-band data set, which is used for object-based segmentation and the supervised classification of key fluvial forms resulting from flooding, i.e., fresh and old gravel accumulations, sand accumulations, and bank erosion. The method was tested on the effects of a snowmelt flood that occurred in December 2015 in a montane stream in the Sumava Mountains, Czech Republic, Central Europe. A multi-rotor UAV was used to collect images of a 1-km-long and 200-m-wide stretch of meandering stream with fresh traces of fluvial activity. The performed segmentation and classification proved that the fusion of 2D and 3D data with the derived qualitative layers significantly enhanced the reliability of the fluvial form detection process. The assessment accuracy for all of the detected classes exceeded 90%. The proposed technique proved its potential for application in rapid mapping and detection of the geomorphological effects of flooding.

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References

  • Anderson, K., & Gaston, K. J. (2013). Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3), 138–146.

    Article  Google Scholar 

  • Baker, V. R., Kochel, R. C., & Patton, P. C. (1988). Flood Geomorphology. New York: Wiley.

    Google Scholar 

  • Blaschke, T. (2010). Object based image analysis for remote sensing. The ISPRS Journal of Photogrammetry and Remote Sensing, 65, 2–16.

    Article  Google Scholar 

  • Böhner, J., & Selige, T. (2006). Spatial prediction of soil attributes using terrain analysis and climate regionalisation. Gottinger Geographische Abhandlungen, 115, 13–28.

    Google Scholar 

  • Borrelli, P., Panagos, P., Langhammer, J., Apostol, B., & Schütt, B. (2016). Assessment of the cover changes and the soil loss potential in European forestland: First approach to derive indicators to capture the ecological impacts on soil-related forest ecosystems. Ecological Indicators, 60(January 2016), 1208–1220.

    Article  Google Scholar 

  • Bryant, R. G., & 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(3), 307–321.

    Article  Google Scholar 

  • Burnett, C., & Blaschke, T. (2003). A multi-scale segmentation/object relationship modelling methodology for landscape analysis. Ecological Modelling, 168, 233–249.

    Article  Google Scholar 

  • Casado, M. R., Gonzalez, R. B., Kriechbaumer, T., & Veal, A. (2015). Automated identification of river hydromorphological features using UAV high resolution aerial imagery. Sensors, 15(11), 27969–27989.

    Article  Google Scholar 

  • Caviedes-Voullième, D., Morales-Hernández, M., López-Marijuan, I., & García-Navarro, P. (2014). Reconstruction of 2D river beds by appropriate interpolation of 1D cross-sectional information for flood simulation. Environmental Modelling & Software, 61, 206–228.

    Article  Google Scholar 

  • CHMI. (2008). Precipitation and runoff database. Prague: CHMI.

    Google Scholar 

  • Clapuyt, F., Vanacker, V., & Van Oost, K. (2016). Reproducibility of UAV-based earth topography reconstructions based on structure-from-motion algorithms. Geomorphology, 260, 4–15. https://doi.org/10.1016/j.geomorph.2015.05.011.

    Article  Google Scholar 

  • Conrad, O., Bechtel, B., Bock, M., Dietrich, H., Fischer, E., Gerlitz, L., et al. (2015). System for automated geoscientific analyses (SAGA) v. 2.1.4. Geoscientific Model Development, 8(7), 1991–2007.

    Article  Google Scholar 

  • Cook, A., & Merwade, V. (2009). Effect of topographic data, geometric configuration and modeling approach on flood inundation mapping. Journal of Hydrology, 377(1), 131–142.

    Article  Google Scholar 

  • Dietrich, J. T. (2016). Riverscape mapping with helicopter-based structure-from-motion photogrammetry. Geomorphology, 252, 144–157.

    Article  Google Scholar 

  • Eltner, A., Baumgart, P., Maas, H.-G., & Faust, D. (2015). Multi-temporal UAV data for automatic measurement of rill and interrill erosion on loess soil. Earth Surface Processes and Landforms, 40(6), 741–755.

    Article  Google Scholar 

  • Fawcett, T. (2006). An introduction to ROC analysis. Pattern Recognition Letters, 27(8), 861–874.

    Article  Google Scholar 

  • Feng, D. D. (2011). Biomedical information technology. Amsterdam: Elsevier.

    Google Scholar 

  • Feng, Q., Liu, J., & Gong, J. (2015). Urban flood mapping based on unmanned aerial vehicle remote sensing and random forest classifier—a case of Yuyao, China. Water, 7(4), 1437–1455.

    Article  Google Scholar 

  • Flener, C., Vaaja, M., Jaakkola, A., Krooks, A., Kaartinen, H., Kukko, A., … Alho, P. (2013). Seamless mapping of river channels at high resolution using mobile liDAR and UAV-photography. Remote Sensing, 5(12), 6382–6407.

  • Fonstad, M. A., Dietrich, J. T., Courville, B. C., Jensen, J. L., & Carbonneau, P. E. (2013). Topographic structure from motion: a new development in photogrammetric measurement. Earth Surface Processes and Landforms, 38(4), 421–430.

    Article  Google Scholar 

  • Foody, G. M. (2002). Status of land cover classification accuracy assessment. Remote Sensing of Environment, 80(1), 185–201.

    Article  Google Scholar 

  • Geerling, G. W., Vreeken-Buijs, M. J., Jesse, P., Ragas, A., & Smits, A. (2009). Mapping river floodplain ecotopes by segmentation of spectral (CASI) and structural (LiDAR) remote sensing data. River Research and Applications, 25(7), 795–813.

    Article  Google Scholar 

  • Hackney, C., & Clayton, A. (2015). 2.1. 7. Unmanned Aerial Vehicles (UAVs) and their application in geomorphic mapping. Retrieved from https://eprints.soton.ac.uk/376639/1/2.1.7_UAV.pdf.

  • Hamilton, S. K., Kellndorfer, J., Lehner, B., & Tobler, M. (2007). Remote sensing of floodplain geomorphology as a surrogate for biodiversity in a tropical river system (Madre de Dios, Peru). Geomorphology, 89(1), 23–38.

    Article  Google Scholar 

  • Haralick, R. M., Shanmugam, K., & Dinstein, I. (1973). Textural features for image classification. IEEE Transactions on Systems, Man, and Cybernetics, SMC, 3(6), 610–621.

    Article  Google Scholar 

  • Haralick, R. M., & Shapiro, L. G. (1985). Image segmentation techniques. Computer Vision, Graphics, and Image Processing, 29, 100–132.

    Article  Google Scholar 

  • Hartvich, F., & Jedlicka, J. (2008). Progressive increase of inputs in floodplain delineation based on the DEM: application and evaluation of the model in the catchment of the Opava River. AUC Geographica, 53(1–2), 87–104.

    Google Scholar 

  • Hervouet, A., Dunford, R., Piégay, H., Belletti, B., & Trémélo, M.-L. (2011). Analysis of post-flood recruitment patterns in braided-channel rivers at multiple scales based on an image series collected by unmanned aerial vehicles, ultra-light aerial vehicles, and satellites. GIScience and Remote Sensing, 48(1), 50–73.

    Article  Google Scholar 

  • Hirschmüller, H. (2011). Semi-global matching-motivation, developments and applications. Photogrammetric Week, 11, 173–184.

    Google Scholar 

  • Hooshyar, M., Kim, S., Wang, D., & Medeiros, S. C. (2015). Wet channel network extraction by integrating LiDAR intensity and elevation data. Water Resources Research, 51(12), 10029–10046.

    Article  Google Scholar 

  • Křížek, M. (2008). Erosion and accumulation flood landforms in Sázava River in spring 2006. AUC Geographica, 53(1–2), 163–181.

    Google Scholar 

  • Kumar, R. M., & Sreekumar, K. (2014). A survey on image feature descriptors. Computers & Electrical Engineering, 5, 7847–7850.

    Google Scholar 

  • Langhammer, J., Hartvich, F., Kliment, Z., Jeníček, M., Bernsteinová, J., Vlček, L., … Miřijovský, J. (2015). The impact of disturbance on the dynamics of fluvial processes in mountain landscapes. Silva Gabreta, 21(1), 105–116.

  • Langhammer, J., Lendzioch, T., Miřijovský, J., & Hartvich, F. (2017). UAV-based optical granulometry as tool for detecting changes in structure of flood depositions. Remote Sensing, 9(3), 240.

    Article  Google Scholar 

  • Langhammer, J., Su, Y., & Bernsteinová, J. (2015b). Runoff response to climate warming and forest disturbance in a mid-mountain basin. Water, 7, 3320–3342.

    Article  Google Scholar 

  • Langhammer, J., & Vilímek, V. (2008). Landscape changes as a factor affecting the course and consequences of extreme floods in the Otava river basin. Czech Republic. Environmental Monitoring and Assessment, 144(1–3), 53–66.

    Article  Google Scholar 

  • Lejot, J., Delacourt, C., Piégay, H., Fournier, T., Trémélo, M.-L., & Allemand, P. (2007). Very high spatial resolution imagery for channel bathymetry and topography from an unmanned mapping controlled platform. Earth Surface Processes and Landforms, 32(11), 1705–1725.

    Article  Google Scholar 

  • Lucieer, A., de Jong, S. M., & Turner, D. (2013). Mapping landslide displacements using structure from motion (SfM) and image correlation of multi-temporal UAV photography. Progress in Physical Geography, 38(1), 97–116.

    Article  Google Scholar 

  • Mäenpää, T., Turtinen, M., & Pietikäinen, M. (2003). Real-time surface inspection by texture. Real-Time Imaging, 9(5), 289–296.

    Article  Google Scholar 

  • Magilligan, F. J. (1992). Thresholds and the spatial variability of flood power during extreme floods. Geomorphology, 5(3), 373–390.

    Article  Google Scholar 

  • Magilligan, F. J., Phillips, J. D., James, L. A., & Gomez, B. (1998). Geomorphic and sedimentological controls on the effectiveness of an extreme flood. The Journal of Geology, 106(1), 87–96.

    Article  Google Scholar 

  • Mertes, L. A. K. (2002). Remote sensing of riverine landscapes. Freshwater Biology, 47(4), 799–816.

    Article  Google Scholar 

  • Miřijovský, J., & Langhammer, J. (2015). Multitemporal monitoring of the morphodynamics of a mid-mountain stream using UAS photogrammetry. Remote Sensing, 7(7), 8586–8609.

    Article  Google Scholar 

  • Miyamoto, E., & Merryman, T. (2005). Fast calculation of Haralick texture features. Human Computer Interaction Institute. Retrieved from https://www.inf.ethz.ch/personal/markusp/teaching/18-799B-CMU-spring05/material/eizan-tad.pdf.

  • Morent, D., Stathatos, K., Lin, W.-C., & Berthold, M. R. (2011). Comprehensive PMML preprocessing in KNIME. In Proceedings of the 2011 workshop on predictive markup language modeling (pp. 28–31). San Diego, CA: ACM.

  • Papaioannou, G., Loukas, A., Vasiliades, L., & Aronica, G. T. (2016). Flood inundation mapping sensitivity to riverine spatial resolution and modelling approach. Natural Hazards, 83(1), 117–132.

    Article  Google Scholar 

  • Phillips, J. D. (2002). Geomorphic impacts of flash flooding in a forested headwater basin. Journal of Hydrology, 269(3), 236–250.

    Article  Google Scholar 

  • Poole, G. C., Stanford, J. A., Frissell, C. A., & Running, S. W. (2002). Three-dimensional mapping of geomorphic controls on flood-plain hydrology and connectivity from aerial photos. Geomorphology, 48(4), 329–347.

    Article  Google Scholar 

  • Porebski, A., Vandenbroucke, N., & Macaire, L. (2008). Haralick feature extraction from LBP images for color texture classification. In 2008 First Workshops on Image Processing Theory, Tools and Applications (pp. 1–8).

  • Powers, D. M. (2011). Evaluation: from precision, recall and F-measure to ROC, informedness, markedness and correlation. Retrieved from http://dspace2.flinders.edu.au/xmlui/handle/2328/27165.

  • Quackenbush, L. J. (2004). A review of techniques for extracting linear features from imagery. Photogrammetric Engineering & Remote Sensing, 70(12), 1383–1392.

    Article  Google Scholar 

  • Riley, S. J. (1999). Index that quantifies topographic heterogeneity. Intermountain Journal of Sciences: IJS, 5(1–4), 23–27.

    Google Scholar 

  • Sanders, B. F. (2007/2008). Evaluation of on-line DEMs for flood inundation modeling. Advances in Water Resources, 30(8), 1831–1843.

  • Sanyal, J., & Lu, X. X. (2004). Application of remote sensing in flood management with special reference to Monsoon Asia: A review. Natural Hazards, 33(2), 283–301.

    Article  Google Scholar 

  • Şerban, G., Rus, I., Vele, D., Breţcan, P., Alexe, M., & Petrea, D. (2016). Flood-prone area delimitation using UAV technology, in the areas hard-to-reach for classic aircrafts: case study in the north-east of Apuseni Mountains, Transylvania. Natural Hazards, 82(3), 1817–1832.

    Article  Google Scholar 

  • Smith, M. W., & Vericat, D. (2015). From experimental plots to experimental landscapes: topography, erosion and deposition in sub-humid badlands from structure-from-motion photogrammetry. Earth Surface Processes and Landforms, 40(12), 1656–1671.

    Article  Google Scholar 

  • Tamminga, A., Eaton, B., & Hugenholtz, C. H. (2015a). UAS-based remote sensing of fluvial change following an extreme flood event. Earth Surface Processes and Landforms, 40(11), 1464–1476.

    Article  Google Scholar 

  • Tamminga, A., Hugenholtz, C., Eaton, B., & Lapointe, M. (2015b). Hyperspatial remote sensing of channel reach morphology and hydraulic fish habitat using an unmanned aerial vehicle (UAV): A first assessment in the context of river research and management. River Research and Applications, 31(3), 379–391.

    Article  Google Scholar 

  • Thumser, P., Kuzovlev, V. V., Zhenikov, K. Y., Zhenikov, Y. N., Boschi, M., Boschi, P., et al. (2017). Using structure from motion (SfM) technique for the characterization of riverine systems—case study in the headwaters of the Volga River. Geography, Environment, Sustainability, 10(3), 31–43.

    Article  Google Scholar 

  • Tonkin, T. N., Midgley, N. G., Graham, D. J., & Labadz, J. C. (2014). The potential of small unmanned aircraft systems and structure-from-motion for topographic surveys: A test of emerging integrated approaches at Cwm Idwal. North Wales. Geomorphology, 226(Supplement C), 35–43.

    Article  Google Scholar 

  • Tralli, D. M., Blom, R. G., Zlotnicki, V., Donnellan, A., & Evans, D. L. (2005). Satellite remote sensing of earthquake, volcano, flood, landslide and coastal inundation hazards. ISPRS Journal of Photogrammetry and Remote Sensing: Official Publication of the International Society for Photogrammetry and Remote Sensing, 59(4), 185–198.

    Article  Google Scholar 

  • Vijayalakshmi, B., & Subbiah Bharathi, V. (2011). A novel approach to texture classification using statistical feature. arXiv [cs.CV]. Retrieved from http://arxiv.org/abs/1111.2391.

  • Vlasák, T. (2003). Overview and classification of historical floods in the Otava river basin. Acta Universitatis Carolinae—Geographica, 38(2), 49–64.

    Google Scholar 

  • Westoby, M. J., Brasington, J., Glasser, N. F., Hambrey, M. J., & Reynolds, J. M. (2012). “Structure-from-Motion” photogrammetry: A low-cost, effective tool for geoscience applications. Geomorphology, 179, 300–314.

    Article  Google Scholar 

  • Wheaton, J. M., Brasington, J., Darby, S. E., & Sear, D. A. (2010). Accounting for uncertainty in DEMs from repeat topographic surveys: Improved sediment budgets. Earth Surface Processes and Landforms, 35(2), 136–156.

    Google Scholar 

  • Witek, M., Jeziorska, J., & Niedzielski, T. (2014). An experimental approach to verifying prognoses of floods using an unmanned aerial vehicle. Meteorology Hydrology and Water Management. Research and Operational Applications, 2(1), 3–11.

    Google Scholar 

  • Wohl, E. E. (2000). Geomorphic effects of floods. Inland flood hazards: Human, riparian, and aquatic communities (pp. 167–193). Cambridge, UK: Cambrige University Press.

    Google Scholar 

  • Wolman, M. G. (1971). Evaluating alternative techniques floodplain mapping. Water Resources Research, 7(6), 1383–1392.

    Article  Google Scholar 

  • Woodget, A. S., Austrums, R., Maddock, I. P., & Habit, E. (2017). Drones and digital photogrammetry: from classifications to continuums for monitoring river habitat and hydromorphology. Wiley Interdisciplinary Reviews: Water. https://doi.org/10.1002/wat2.1222.

    Article  Google Scholar 

  • Woodget, A. S., Carbonneau, P. E., Visser, F., & Maddock, I. P. (2015). Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth Surface Processes and Landforms, 40(1), 47–64.

    Article  Google Scholar 

  • Yuheng, S., & Hao, Y. (2017). Image Segmentation Algorithms Overview. arXiv preprint arXiv:1707.02051.

  • Zhang, Y. (2001). Texture-integrated classification of urban treed areas in high-resolution color-infrared imagery. Photogrammetric Engineering and Remote Sensing, 67(12), 1359–1366.

    Google Scholar 

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Acknowledgements

The research was supported by the EU COST Action 1306 project LD15130 “Impact of landscape disturbance on the stream and basin connectivity” and Czech Science Foundation project 13-32133S “Retention potential of headwater areas”.

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  1. Department of Physical Geography and Geoecology, Faculty of Science, Charles University in Prague, Albertov 6, 128 43, Prague 2, Czech Republic

    Jakub Langhammer & Tereza Vacková

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  1. Jakub Langhammer
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Correspondence to Jakub Langhammer.

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Langhammer, J., Vacková, T. Detection and Mapping of the Geomorphic Effects of Flooding Using UAV Photogrammetry. Pure Appl. Geophys. 175, 3223–3245 (2018). https://doi.org/10.1007/s00024-018-1874-1

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  • DOI: https://doi.org/10.1007/s00024-018-1874-1

Keywords

  • UAV
  • structure from motion
  • flood
  • accumulations
  • bank erosion
  • OBIA
  • classification
  • textural features