USING STRUCTURE FROM MOTION (SFM) TECHNIQUE FOR THE CHARACTERISATION OF RIVERINE SYSTEMS - CASE STUDY IN THE HEADWATERS OF THE VOLGA RIVER
https://doi.org/10.24057/2071-9388-2017-10-3-31-43
Abstract
Digital terrain models (DTM) were produced with the structure from motion (SfM) technique, using data from high resolution terrestrial photography. In addition 360-degree spheres were created from ground taken photos. These spheres allow capturing the environment at this moment and coming back to the environment virtually later on. Also overlapping this virtual realty of the environment with model results can be used for distributing study results to a broad audience. On this basis hydraulic and morphological conditions were assessed and compared to field records. The proposed methods enable the creation of a detailed view on different riverine systems, i.e. from small to large rivers. This enables a morphodynamic characterisation which can be linked with the biological dataset of the monitoring project REFCOND_VOLGA. We propose that environmental intelligence gathering using ground-based as well as remote sensing observations can be applied increase the scope of scientific surveillance, and can lead to new opportunities to detect and quantify complex ecological interactions across a wide spectrum of scales.
About the Authors
P. Thumser
I AM HYDRO GmbH
Germany
Germany
co-founder and managing director, iamhydro.com,
Märtishofweg 2, 78112 St. Georgen
V. V. Kuzovlev
Tver State Technical University
Russian Federation
Russian Federation
candidate of technical sciences, associate professor, Chair of Nature Management and Ecology,
nab. Afanasiya Nikitina 22, 170026 Tver
K. Y. Zhenikov
Tver State Technical University
Russian Federation
Russian Federation
Chair of Nature Management and Ecology,
nab. Afanasiya Nikitina 22, 170026 Tver
Yu. N. Zhenikov
Tver State Technical University
Russian Federation
Russian Federation
Chair of Nature Management and Ecology,
nab. Afanasiya Nikitina 22, 170026 Tver
M. Boschi
droneproject.at
Austria
Austria
economics student at Leopold-Franzens University in Innsbruck (Austria);
managing director of droneproject.at, an Austrian-based unmanned aerial vehicle (UAV) company,
Schneeburggasse 225, 6020 Innsbruck
P. Boschi
droneproject.at
Austria
Austria
student of Applied Geosciences at Montanuniversiät Leoben,
Schneeburggasse 225, 6020 Innsbruck
M. Schletterer
University of Innsbruck, Institute of Ecology
Austria
Technikerstrasse 25, 6020 Innsbruck
Austria
Technikerstrasse 25, 6020 Innsbruck
References
1. Arya, S., Mount, D., Netanyahu, N., Silverman, R. and Wu, A. (1998). An optimal algo- rithm for approximate nearest neighbour searching fixed dimensions. Journal of the Association for Computing Machinery 45, pp. 891-923.
2. Astre, H. (2010). [online] SFMToolkit3. Available at: http://www.visual-experiments.com/demos/sfmtoolkit.
3. Bagheri, O., Ghodsian, M. and Saadatseresht, M. (2015). Reach Scale Application of UAV + SFM method in shallow rivers Hyperspatial Bathymetry. The International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences 40, 77.
4. Barker, J., Pasternack, G., Bratovich, P., Massa, D., Reedy, G. and Johnson, T. (2010). Method to Rapidly Collect Thousands of Velocity Observations to Validate Million-Element 2D Hydrodynamic Models. AGU Fall Meeting Abstracts 51.
5. Berni, J., Zarco-Tejada, P., Suarez, L. and Fereres, E. (2009). Thermal and Narrowband Multispectral Remote Sensing for Vegetation Monitoring From an Unmanned Aerial Vehicle. IEEE Transactions on Geoscience and Remote Sensing 47, pp. 722–738. doi:10.1109/TGRS.2008.2010457
6. Buffington, J. and Montgomery, D. (2013). Geomorphic classification of rivers. Casado, M., Gonzalez, R., Kriechbaumer, T. and Veal, A. (2015). Automated Identification of River Hydromorphological Features Using UAV High Resolution Aerial Imagery. Sensors 15, pp. 27969–27989. doi:10.3390/s151127969
7. Charlton, M., Large, A. and Fuller, I. (2003). Application of airborne LiDAR in river environments: the River Coquet, Northumberland, UK. Earth surface processes and landforms 28, 299–306.
8. Dietrich, J. (2015). Applications of structure-from-motion photogrammetry to fluvial geomorphology.
9. Eisenbeiss, H. and Zhang, L. (2006). Comparison of DSMs generated from mini UAV imagery and terrestrial laser scanner in a cultural heritage application. International Archives of Photogrammetry, Remote Sensing and Spatial Information Sciences XXXVI-5, pp. 90-96.
10. Everaerts, J. (2008). The use of unmanned aerial vehicles (UAVs) for remote sensing and mapping. The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 37, pp. 1187–1192.
11. Fischler, M. and, Bolles, R. (1987). Random Sample Consensus: a paradigm for model fitting with applications to image analysis and automated cartography. pp. 726-740 In: Martin, A.F., Oscar, F. (Eds.), Readings inComputerVision: Issues, Problems, Principles, and Paradigms. Morgan Kaufmann Publishers Inc., London.
12. Flener, C., Vaaja, M., Jaakkola, A., Krooks, A., Kaartinen, H., Kukko, A., Kasvi, E., Hyyppä, H., Hyyppä, J. and Alho, P. (2013). Seamless Mapping of River Channels at High Resolution Using Mobile LiDAR and UAV-Photography. Remote Sensing 5, pp. 6382–6407. doi:10.3390/rs5126382
13. Flynn, K. and Chapra, S. (2014). Remote Sensing of Submerged Aquatic Vegetation in a Shallow Non-Turbid River Using an Unmanned Aerial Vehicle. Remote Sensing 6, pp. 12815–12836. doi:10.3390/rs61212815
14. Fonstad, M., Dietrich, J., Courville, B., Jensen, J. and Carbonneau, P. (2013). Topographic structure from motion: a new development in photogrammetric measurement. Earth Surf. Process. Landforms 38, pp. 421–430. doi:10.1002/esp.3366
15. Furukawa,Y. and Ponce, J. (2007). Accurate,dense, androbust multi-viewstereopsis. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 17-22 June, Minneapolis, USA, 1-8.
16. Furukawa, Y., Curless, B., Seitz, M. and Szeliski, R. (2010). Clustering view for multi-view ste- reo. Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 13-18 June, San Francisco, USA, pp. 1434-1441.
17. Hering, D., Borja, A., Carstensen, J., Carvalho, L., Elliott, M., Feld, C., Heiskanen, A.-S., Johnson, R., Moe, J., Pont, D., Solheim, A. and de Bund, W. van (2010). The European Water Framework Directive at the age of 10: A critical review of the achievements with recommendations for the future. Science of The Total Environment 408, pp. 4007–4019. doi:10.1016/j.scitotenv.2010.05.031
18. Hugenholtz, C., Whitehead, K., Brown, O., Barchyn, T., Moorman, B., LeClair, A., Riddell, K. and Hamilton, T. (2013). Geomorphological mapping with a small unmanned aircraft system (sUAS): Feature detection and accuracy assessment of a photogrammetrically-derived digital terrain model. Geomorphology 194, pp. 16–24. doi:10.1016/j.geomorph.2013.03.023
19. Javernick, L., Brasington, J., Caruso, B. (2014). Modeling the topography of shallow braided rivers using Structure-from-Motion photogrammetry. Geomorphology 213, pp. 166–182. doi:10.1016/j.geomorph.2014.01.006
20. Kaneko, K. and Seiich Nohara (2014). Review of Effective Vegetation Mapping Using the UAV (Unmanned Aerial Vehicle) Method. Journal of Geographic Information System pp. 733–742.
21. Klein, L., Clayton, S., Alldredge, J. and Goodwin, P. (2007). Long-Term Monitoring and Evaluation of the Lower Red River Meadow Restoration Project, Idaho, USA. Restoration Ecology 15, pp. 223–239.
22. Lowe, D. (2004). Distinctive image features from scale-invariant keypoints. International Journal of Computer Vision 60, pp. 91-110.
23. Lucieer, A., Jong, S. de and Turner, D. (2013). Mapping landslide displacements using Structure from Motion (SfM) and image correlation of multi-temporal UAV photography. Progress inPhysical Geography 0309133313515293. doi:10.1177/0309133313515293
24. Mancini, F., Dubbini, M., Gattelli, M., Stecchi, F., Fabbri, S. and Gabbianelli, G. (2013). Using Unmanned Aerial Vehicles (UAV) for High-Resolution Reconstruction of Topography: The Structure from Motion Approach on Coastal Environments. Remote Sensing 5, pp. 6880–6898. doi:10.3390/rs5126880
25. Mathews, A. and Jensen, J. (2013). Visualizing and Quantifying Vineyard Canopy LAI Using an Unmanned Aerial Vehicle (UAV) Collected High Density Structure from Motion Point Cloud. Remote Sensing 5, pp. 2164–2183. doi:10.3390/rs5052164
26. Nex, F. and Remondino, F. (2013). UAV for 3D mapping applications: a review. Appl Geomat 6, 1–15. doi:10.1007/s12518-013-0120-x
27. Nocedal, J. and Wright, S. (1999). Numerical Optimization. Springer, New York.
28. Ouédraogo, M., Degré, A., Debouche, C. and Lisein, J., 2014. The evaluation of unmanned aerialsystem-based photogrammetry and terrestrial laser scanning to generate DEMs of agriculturalwatersheds. Geomorphology 214, pp. 339–355. doi:10.1016/j.geomorph.2014.02.016
29. Remondino, F., Barazzetti, L., Nex, F., Scaioni, M. and Sarazzi, D. (2011). UAV photogrammetry for mapping and 3d modeling–current status and future perspectives. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 38, C22.
30. Rice, S., Lancaster, J. and Kemp, P. (2010). Experimentation at the interface of fluvial geomorphology, stream ecology and hydraulic engineering and the development of an effective, interdisciplinary river science. Earth Surface Processes and Landforms 35, pp. 64–77.
31. Room, M. and Ahmad, A. (2014). Mapping of a river using close range photogrammetry technique and unmanned aerial vehicle system. IOP Conference Series: Earth and Environmental Science 18, 012061. doi:10.1088/1755-1315/18/1/012061
32. Schletterer, M., Füreder, L., Kuzovlev, V., Zhenikov, K. and Zhenikov, Y. (2016). REFCOND_VOLGA: a monitoring programme for water quality in the headwaters of the Volga River (Tver region, Russia). Revista Eletrônica de Gestão e Tecnologias Ambientais (GESTA) 4/1, pp. 18-40.
33. Smith, M., Carrivick, J. and Quincey, D. (2015). Structure from motion photogrammetry in physical geography. Progress in Physical Geography 0309133315615805. doi:10.1177/0309133315615805
34. Smith, M. and Vericat, D. (2015). From experimental plots to experimental landscapes: topography, erosion and deposition in sub-humid badlands from Structure-from-Motion photogrammetry. Earth Surf. Process. Landforms 40, pp. 1656–1671. doi:10.1002/esp.3747
35. Snavely, N., Seitz, S. and Szeliski, R. (2007). Modeling the World from Internet Photo Collections. Int J Comput Vis 80, pp. 189–210. doi:10.1007/s11263-007-0107-3
36. Snavely, N., Seitz, S. and Szeliski, R. (2006). Photo tourism: exploring photo collections in 3D, in: ACM Transactions on Graphics (TOG). ACM, pp. 835–846.
37. Snavely, N., Seitz, S. and Szeliski, R. (2008). Modelling the world from internet photo collections. International Journal of Computer Vision 80, pp. 189-210
38. ïStumpf, A., Malet, J., Allemand, P., Pierrot-Deseilligny and M., Skupinski, G. (2015). Groundbased multi-view photogrammetry for the monitoring of landslide deformation and erosion. Geomorphology 231, pp. 130–145. doi:10.1016/j.geomorph.2014.10.039
39. Szeliski, R. and Kang, S. (1994). Recovering 3-D shape and motion from image streams using nonlinear least squares. Journal of Visual Communication and Image Representation 5, pp. 10-28.
40. Tamminga, A., Eaton, B. and Hugenholtz, C. (2015). UAS-based remote sensing of fluvial change following an extreme flood event. Earth Surf. Process. Landforms 40, pp. 1464–1476. doi:10.1002/esp.3728
41. Tamminga, A., Hugenholtz, C., Eaton, B. and Lapointe, M., 2015. 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 Res. Applic. 31, pp. 379–391. doi:10.1002/rra.2743
42. Thumser, P., Haas, C., Tuhtan, J. and Schneider, M. (2015). HYDRONES: Hydrosystem Drone Surveying. E - proceedings of the 36th IAHR World Congress 28 June – 3 July, 2015, The Hague, the Netherlands.
43. Westoby, M., Brasington, L., Glasser, N., Hambrey, M. and Reynolds, J. (2012). Structurefrom-Motion photogrammetry: A low-cost, effective tool for geosience applications, in: Geomorphology 179, pp. 300-314.
44. Wheaton, J., Brasington, J., Darby, S. and Sear, D.A. (2010). Accounting for uncertainty in DEMs from repeat topographic surveys: improved sediment budgets. Earth Surface Processes and Landforms 35, pp. 136–156.
45. Whitehead, K., Hugenholtz, C., Myshak, S., Brown, O., LeClair, A., Tamminga, A., Barchyn, T., Moorman, B. and Eaton, B. (2014). Remote sensing of the environment with small unmanned aircraft systems (UASs), part 2: scientific and commercial applications. J. Unmanned Veh. Sys.02, pp. 86–102. doi:10.1139/juvs-2014-0007
46. Woodget, A. (2015). Quantifying physical river habitat parameters using hyperspatial resolution UAS imagery and SfM-photogrammetry (Doctoral Thesis). University of Worcester.
47. Woodget, A., Carbonneau, P., Visser, F. and Maddock, I. (2015). Quantifying submerged fluvial topography using hyperspatial resolution UAS imagery and structure from motion photogrammetry. Earth Surf. Process. Landforms 40, pp. 47–64. doi:10.1002/esp.3613
48. Wu, C. (2007). SiftGPU: A GPU implementation of Scale Invariant Feature Transform (SIFT), [online] Available at: http://cs.unc.edu/~ccwu/siftgpu/
49. ïWu, C. (2011). VisualSFM: A Visual Structure from Motion System, [online] Available at: http://ccwu.me/vsfm/
50. Wu, C., Sameer Agarwal, S., Brian Curless, B. and Seitz, S. (2011). Multicore Bundle Adjustment,in: 2011 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), IEEE, pp. 3057-3064.
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For citations:
Thumser P., Kuzovlev V.V., Zhenikov K.Y., Zhenikov Yu.N., Boschi M., Boschi P., Schletterer M. USING STRUCTURE FROM MOTION (SFM) TECHNIQUE FOR THE CHARACTERISATION OF RIVERINE SYSTEMS - CASE STUDY IN THE HEADWATERS OF THE VOLGA RIVER. GEOGRAPHY, ENVIRONMENT, SUSTAINABILITY. 2017;10(3):31-43. https://doi.org/10.24057/2071-9388-2017-10-3-31-43
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