Bedload transport analysis using image processing techniques

Ermilov, Alexander A; Fleit, Gábor; Conevski, Slaven; Guerrero, Massimo; Baranya, Sándor; Rüther, Nils

doi:10.1007/s11600-022-00791-x

Powiadomienia systemowe

Sesja wygasła!

Artykuł - szczegóły

Tytuł artykułu

Bedload transport analysis using image processing techniques

Autorzy

Ermilov Alexander A , Fleit Gábor , Conevski Slaven , Guerrero Massimo , Baranya Sándor , Rüther Nils

Wybrane pełne teksty z tego czasopisma

https://www.springer.com/journal/11600

Identyfikatory

DOI

10.1007/s11600-022-00791-x

Warianty tytułu

Języki publikacji

Abstrakty

Bedload transport is an important factor to describe the hydromorphological processes of fluvial systems. However, conventional bedload sampling methods have large uncertainty, making it harder to understand this notoriously complex phenomenon. In this study, a novel, image-based approach, the Video-based Bedload Tracker (VBT), is implemented to quantify gravel bedload transport by combining two different techniques: Statistical Background Model and Large-Scale Particle Image Velocimetry. For testing purposes, we use underwater videos, captured in a laboratory fume, with future field adaptation as an overall goal. VBT offers a full statistics of the individual velocity and grainsize data for the moving particles. The paper introduces the testing of the method which requires minimal preprocessing (a simple and quick 2D Gaussian filter) to retrieve and calculate bedload transport rate. A detailed sensitivity analysis is also carried out to introduce the parameters of the method, during which it was found that by simply relying on literature and the visual evaluation of the resulting segmented videos, it is simple to set them to the correct values. Practical aspects of the applicability of VBT in the field are also discussed and a statistical filter, accounting for the suspended sediment and air bubbles, is provided.

Słowa kluczowe

bedload transport image processing Statistical Background Model Large-Scale Particle Image Velocimetry Video-based Bedload Tracker

transport ładunków przetwarzanie obrazu

Wydawca

Instytut Geofizyki PAN
Springer

Czasopismo

Acta Geophysica

Rocznik

2022

Tom

Vol. 70, no 5

Strony

2341--2360

Opis fizyczny

Bibliogr. 59 poz.

Twórcy

autor

Ermilov Alexander A

ermilov.alexander@emk.bme.hu

Department of Hydraulic and Water Resources Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics, Budapest, Hungary

https://orcid.org/0000-0002-2650-5870

autor

Fleit Gábor

feit.gabor@emk.bme.hu

Department of Hydraulic and Water Resources Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics, Budapest, Hungary
Water Management Research Group of the Eötvös Lóránd Research Network, Budapest, Hungary

autor

Conevski Slaven

slaven.conevski@ntnu.no

Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway

autor

Guerrero Massimo

massimo.guerrero@unibo.it

Department of Civil, Chemical, Environmental, and Materials Engineering, University of Bologna, Bologna, Italy

autor

Baranya Sándor

baranya.sandor@emk.bme.hu

Department of Hydraulic and Water Resources Engineering, Faculty of Civil Engineering, Budapest University of Technology and Economics, Budapest, Hungary

autor

Rüther Nils

nils.ruther@ntnu.no

Department of Civil and Environmental Engineering, Norwegian University of Science and Technology, Trondheim, Norway

Bibliografia

1. Abraham D, Pratt TC, Sharp J (2010) Measuring bedload transport on the Missouri River using time sequenced bathymetric data. In: 2nd Joint Federal Interagency Conference, Las Vegas, NV
2. Adrian R (1991) Particle-imaging techniques for experimental fluid-mechanics. Annu Rev Fluid Mech 23(1991):261–304. https://doi.org/10.1146/annurev.fluid.23.1.261
3. Agudo JR, Luzi G, Han J, Hwang M, Lee J, Wierschem A (2017) Detection of particle motion using image processing with particular emphasis on rolling motion. Rev Sci Instrum 88(5):051805. https://doi.org/10.1063/1.4983054
4. Ancey C, Pascal I (2020) Estimating mean bedload transport rates and their uncertainty. J Geophys Res Earth Surf. https://doi.org/10.1029/2020JF005534
5. Ballio F, Pokrajac D, Radice A, Hosseini Sadabadi SA (2018) Lagrangian and Eulerian description of bed load transport. J Geophys Res Earth Surf 123:384–408. https://doi.org/10.1002/2016JF004087
6. Blanckaert K, Heyman JH, Rennie CD (2017) Measurments of bedload sediment transport with an acoustic doppler velocity profiler(ADVP). J Hydraul Eng 143(6):04017008
7. Bouwmans T, Baf F, Vachon B (2010) Statistical background modeling for foreground detection a survey.In: Handbook of pattern recognition and computer. World Scientific Publishing
8. Bouzerdoum A, Beghdadi A (2010). On the analysis of background subtraction techniques using Gaussian Mixture Models.In: Conference Paper in Acoustics, Speech, and Signal Processing, 1988. ICASSP-88., 1988 International Conference on April 2010. DOI: https://doi.org/10.1109/ICASSP.2010.5495760
9. Buscombe Daniel (2013) Transferable wavelet method for grain-size distribution from images of sediment surfaces and thin sections, and other natural granular patterns. Sedimentology 60(7):1709–1732. https://doi.org/10.1111/sed.12049
10. Conevski S, Guerrero M, Ruther N, Rennie C (2019) Laboratory investigation of apparent bedload velocity measured by ADCPs under different transport conditions. J Hydraul Eng 145:04019036. https://doi.org/10.1061/(ASCE)HY.1943-7900.0001632
11. Conevski S, Guerrero M, Rennie CD, Ruther N (2020) Towards an evaluation of bedload transport characteristics by using doppler and backscatter outputs from ADCPs. J Hydraul Res 59(5):703–723. https://doi.org/10.1080/00221686.2020.1818311
12. Csoma, J, Szigyártó, Z (1975) A matematikai statisztika alkalmazása a hidrológiában (in English: The application of mathematical statistics in hydrology). Book, pp.341–357; Vízügyi Dokumentációs és Tájékoztató Iroda, Budapest
13. Detert, M, Liekai, C, Ismail, A (2019). Airborne image velocimetry measurements at the hydropower plant Schiffmühle on Limmat river, Switzerland. Proc. In: 2nd Int. Symp. Exhib. Hydro-Environment Sensors Software, HydroSenSoft, pp. 211–217
14. Dinehart RL (2002) Bedform movement recorded by sequential single-beam surveys in tidal rivers. J Hydrol 258(1–4):25–39. https://doi.org/10.1016/S0022-1694(01)00558-3
15. Drake TG, Shreve RL, Dietrich WE, Whiting PJ, Leopold LB (1988) Bedload transport of fine gravel observed by motion-picture photography. J Fluid Mech 192(1988):193–217. https://doi.org/10.1017/S0022112088001831
16. Duffy, GP (2006). Bedform migration and associated sand transport on a banner bank: application of repetitive multibeam surveying and tidal current measurement to the estimation of sediment transport. Ph.D. thesis, The University of New Brunswick, Fredericton, Canada
17. Einstein, H (1937). Der Geschiebetrieb als Wahrscheinlichkeitproblem (Bedload transport as a probability problem) ((English translation by W. W. Sayre, in Sedimentation (Symposium to honor H. A. Einstein, edited by H. W. Shen), Fort Collins, Colorado, 1972, C1–C105). Zurich: ETHZ
18. Einstein, H (1950). The bed-load function for sediment transportation in open channel flows. Technical Bulletins No. 1026. Washington, DC: US Dept. of Agriculture
19. Engel P, Lau YL (1981) Bed Load Discharge Coefficient. Hydraul Div 107:1445–1454
20. Ermilov AA, Baranya S, Török GT (2020) Image-based bed material mapping of a large river. Water 2020(12):916. https://doi.org/10.3390/w12030916
21. Ermilov AA, Fleit G, Zsugyel M, Baranya S, Török GT (2019). Video based bedload transport analysis in gravel bed rivers. Geophysical Research Abstracts. Vol. 21, EGU2019–15071, 2019. EGU General Assembly
22. Fleit G, Baranya S (2019) An improved particle image velocimetry method for efficient flow analyses. Flow Meas Instrum 69:101619
23. Fujita I, Muste M, Kruger A (1998) Large-scale particle image velocimetry for flow analysis in hydraulic engineering applications. J Hydraul Res 36(3):397–414. https://doi.org/10.1080/00221689809498626
24. Gaweesh M, Van Rijn L (1994) Bed-load sampling in sand-bed rivers. J Hydraul Eng 120(12):164–1384. https://doi.org/10.1061/(ASCE)0733-9429(1994)120:12(1364)
25. Latosinski FG, Szupiany RN, Guerrero M, Amsler ML, Vionnet C (2017) The ADCP’s bottom track capability for bedload prediction: Evidence on method reliability from sandy river applications. Flow Meas Instrum 54:124–135
26. Hayman E, Eklundh J (2003) Statistical Background Subtraction for a Mobile Observer. In: Proceedings of the Ninth IEEE International Conference on Computer Vision (ICCV 2003) 2-Volume Set 0–7695–1950–4/03 $17.00
27. Honkanen M, Nobach H (2005) Background extraction from double-frame PIV images. Exp Fluids 38(3):348–362. https://doi.org/10.1007/s00348-004-0916-x
28. Horn BKP, Schunck BG (1981) Determining optical flow. Artif Intell 17(1–3):185–203. https://doi.org/10.1016/0004-3702(81)90024-2
29. Huang PX, Boom BJ, Fisher RB (2015) Hierarchical classification with reject option for live fish recognition. Mach Vis App 26(1):89–102. https://doi.org/10.1007/s00138-014-0641-2
30. Hubbell DW (1964). Apparatus and techniques for measuring bedload. USGS. United States Government Printing Office. https://doi.org/10.3133/wsp1748
31. Hubbell D (1987). Bed load sampling and analysis, in Sediment transport in gravel bed rivers. Edited by C. Thorne, J. Bathurst, and R. Hey, pp.89–106, John Wiley and Sons Ltd., Chichester
32. KaewTraKulPong P, Bowden R (2002) An improved adaptive background mixture model for real-time tracking with shadow detection. In: Remagnino P, Jones GA, Paragios N, Regazzoni CS (eds) Video-based surveillance systems. Springer, Boston, pp 135–144. https://doi.org/10.1007/978-1-4615-0913-4_11
33. Kostaschuk RA, Church MA, Luternauer JL (1989) Bedforms, bed material, and bedload transport in a salt-wedge estuary: fraser river. Br Columbia Can J Earth Sci 26:1440–1452
34. Langmann B, Ghobadi SE, Hartmann K, Loffeld O (2010). Multi-modal background subtraction using Gaussian mixture models. In: Paparoditis N, Pierrot-Deseilligny M, Mallet C, Tournaire . (Eds), IAPRS, Vol. XXXVIII, Part 3A – Saint-Mandé, France, Sep 1–3
35. Lee D-S (2005) Effective gaussian mixture learning for video background subtraction. IEEE Trans Pattern Anal Mach Intell 27(5):827–832
36. Liu Y, Métivier F, Lajeunesse É, Lancien P, Narteau C, Ye B, Meunier P (2008). Measuring bedload in gravel-bed mountain rivers: averaging methods and sampling strategies. In: Conference Paper in Geodinamica Acta–April 2008. DOI: https://doi.org/10.3166/ga.21.81-92
37. Dengsheng L, Mausel P, Brondizio E, Moran E (2004) Change detection techniques. Int J Remote Sens 25(12):2365–2401
38. MATLAB (2011). https://www.mathworks.com/help/vision/ref/vision.foregrounddetector-system-object.html
39. Muste M, Ho HC, Kim D (2011) Considerations on direct stream flow measurements using video imagery: outlook and research needs. J Hydro Environ Res 5(2011):289–300. https://doi.org/10.1016/j.jher.2010.11.002
40. Muste M, Fujita I, Hauet A (2008) Large scale particle image velocimetry for measurements in riverine environments. Spec Issue Hydrol Measure Water Resour Res 44(4):19. https://doi.org/10.1029/2008WR006950
41. Muste M, Baranya S, Tsubaki R, Kim D, Ho H, Tsai H, Law D (2016) Acoustic mapping velocimetry. Water Resour Res 52(5):4132–4150. https://doi.org/10.1002/2015WR018354
42. Papanicolaou A, Diplas P, Balakrishnan M, Dancey C (1999) Computer vision technique for tracking bed load movement. J Comput Civ Eng 13(2):71–79. https://doi.org/10.1061/(ASCE)0887-3801(1999)13:2(71)
43. Parker, G (2004). 1D sediment transport morphodynamics with applications to rivers and turbidity currents. St. Anthony Falls Laboratory, Mississippi River at 3rd Avenue SE
44. Power WP, Schoones JA (2002) Understanding Background Mixture Models for Foreground Segmentation. In: Proceedings Image and Vision Computing New Zealand 2002. University of Auckland, Auckland, New Zealand 26–28th November 2002
45. Radice A, Malavasi S, Ballio F (2006) Solid transport measurements through image processing. Exp Fluids 41(5):721–734. https://doi.org/10.1007/s00348-006-0195-9
46. Radice A, Sarkar S, Ballio F (2017) Image-based Lagrangian Particle tracking in bed-load experiments. J Visual Exp. https://doi.org/10.3791/55874
47. Recking A, Liébault F, Peteuil C, Jolimet T (2012) Testing bedload transport equations with consideration of time scales. Earth Surf Proc Land 37:774–789
48. Rennie CD, Villard PV (2004) Site specificity of bedload measurement using an ADCP. J Geophys Res. https://doi.org/10.1029/2003JF000106)
49. Sabzi AK, Hasanzadeh RPR, Chegini AHN (2018) An Image-based object tracking technique for identification of moving sediment grains on bed deposits. KSCE J Civil Eng 22(4):1170–1178. https://doi.org/10.1007/s12205-017-0576-z
50. Scofield TP, Belfort J, Churnside JH, Roddewig MR, Shaw JA, Whitaker BM (2021) Applying Gaussian Mixture Models to Detect Fish from Airborne LiDAR Measurements.In: 2021 IEEE Research and Applications of Photonics in Defense Conference (RAPID), 2021, pp. 1–2, doi: https://doi.org/10.1109/RAPID51799.2021.9521457
51. Stauffer C, Grimson WEL (1999) Adaptive background mixture models for real-time tracking. In: Proceedings. 1999 IEEE Computer Society Conference on Computer Vision and Pattern Recognition (Cat. No PR00149), 1999, pp. 246–252 Vol. 2, doi: https://doi.org/10.1109/CVPR.1999.784637
52. Tauro F, Piscopia R, Grimaldi S (2019) PTV-stream: a simplified particle tracking velocimetry framework for stream surface flow monitoring. CATENA 172(2019):378–386. https://doi.org/10.1016/j.catena.2018.09.009
53. Tsubaki R, Baranya S, Muste M, Toda Y (2018) Spatio-temporal patterns of sediment particle movement on 2D and 3D bedforms. Exp Fluids 59(93):14p
54. Willert CE, Gharib M (1991) Digital particle image velocimetry. Exp Fluid 10(1991):181–193. https://doi.org/10.1007/BF00190388
55. Yadav G, Maheshwari S, Agarwal A (2014) Contrast limited adaptive histogram equalization based enhancement for real time video system. In: 2014 International Conference on Advances in Computing, Communications and Informatics (ICACCI). DOI: https://doi.org/10.1109/ICACCI.2014.6968381
56. You H, Kim D, Muste M (2022) High-gradient pattern image velocimetry (HGPIV). Adv Water Resour 159(104092):13p
57. You H, Muste M, Kim D, Baranya S (2021) Considerations on acoustic mapping velocimetry (AMV) application for in-situ measurement of bedform dynamics. Front Water 140(3):715308
58. Zhang C, Liu X, Xu Q (2018) Mixed Gaussian Model Based Video Foreground Object Detection. CSAE '18. In: Proceedings of the 2nd International Conference on Computer Science and Application Engineering. October 2018 Article No.: 112Pages 1–5. https://doi.org/10.1145/3207677.3278023
59. Zitouni MS, Bhaskar H, Al-Mualla M (2016) Robust Background Modeling and Foreground Detection using Dynamic Textures. In: Proceedings of the 11th Joint Conference on Computer Vision, Imaging and Computer Graphics Theory and Applications (VISIGRAPP 2016) - Volume 4: VISAPP, pages 403–410. ISBN: 978–989–758–175–5. DOI: https://doi.org/10.5220/0005724204030410

Uwagi

Opracowanie rekordu ze środków MEiN, umowa nr SONP/SP/546092/2022 w ramach programu "Społeczna odpowiedzialność nauki" - moduł: Popularyzacja nauki i promocja sportu (2022-2023).

Typ dokumentu

Bibliografia

Identyfikator YADDA

bwmeta1.element.baztech-3ffe906d-af80-4e53-a817-1787e0f28346