Ocean Dynamics

, Volume 61, Issue 10, pp 1521–1540

Performance of intertidal topography video monitoring of a meso-tidal reflective beach in South Portugal

  • Michalis Ioannis Vousdoukas
  • Pedro Manuel Ferreira
  • Luis Pedro Almeida
  • Guillaume Dodet
  • Fotis Psaros
  • Umberto Andriolo
  • Rui Taborda
  • Ana Nobre Silva
  • Antonio Ruano
  • Óscar Manuel Ferreira
Article
Part of the following topical collections:
  1. Topical Collection on Multiparametric observation and analysis of the Sea

Abstract

This study discusses site-specific system optimization efforts related to the capability of a coastal video station to monitor intertidal topography. The system consists of two video cameras connected to a PC, and is operating at the meso-tidal, reflective Faro Beach (Algarve coast, S. Portugal). Measurements from the period February 4, 2009 to May 30, 2010 are discussed in this study. Shoreline detection was based on the processing of variance images, considering pixel intensity thresholds for feature extraction, provided by a specially trained artificial neural network (ANN). The obtained shoreline data return rate was 83%, with an average horizontal cross-shore root mean square error (RMSE) of 1.06 m. Several empirical parameterizations and ANN models were tested to estimate the elevations of shoreline contours, using wave and tidal data. Using a manually validated shoreline set, the lowest RMSE (0.18 m) for the vertical elevation was obtained using an ANN while empirical parameterizations based on the tidal elevation and wave run-up height resulted in an RMSE of 0.26 m. These errors were reduced to 0.22 m after applying 3-D data filtering and interpolation of the topographic information generated for each tidal cycle. Average beach-face slope tan(β) RMSE were around 0.02. Tests for a 5-month period of fully automated operation applying the ANN model resulted in an optimal, average, vertical elevation RMSE of 0.22 m, obtained using a one tidal cycle time window and a time-varying beach-face slope. The findings indicate that the use of an ANN in such systems has considerable potential, especially for sites where long-term field data allow efficient training.

Keywords

Video monitoring Coastal morphodynamics Artificial neural networks Coastal erosion Nearshore Remote sensing 

References

  1. Aarninkhof SGJ, Holman RA (1999) Monitoring the nearshore with video. Backscatter 10:8–11Google Scholar
  2. Aarninkhof SGJ, Ruessink BG (2004) Video observations and model predictions of depth-induced wave dissipation. IEEE Trans Geosci Remote 42(11):2612–2622CrossRefGoogle Scholar
  3. Aarninkhof SGJ, Turner IL, Dronkers TDT, Caljouw M, Nipiusc L (2003) A video-based technique for mapping intertidal beach bathymetry. Coast Eng 49:275–289CrossRefGoogle Scholar
  4. Almeida LP, Ferreira Ó, Vousdoukas M, Dodet G (2011a) Historical variation and trends in storminess along the Portuguese South Coast. Nat Hazard Earth Sys Sci. (manuscript accepted)Google Scholar
  5. Almeida LP, Vousdoukas MI, Ferreira ÓM, Rodrigues BA, Matias A (2011b) Thresholds for storm impacts on an exposed sandy coastal area in southern Portugal. Geomorphology. (manuscript accepted)Google Scholar
  6. Battjes JA (1974) Surf similarity. In: 14th Conference on Coastal Engineering. ASCE. pp 466–480Google Scholar
  7. Bay H, Ess A, Tuytelaars T, Gool LV (2008) SURF: speeded up robust features. Comput Vis Image Underst CVIU 110(3):346–359CrossRefGoogle Scholar
  8. Bouguet J-Y (2007) Camera Calibration Toolbox for Matlab. Available at: http://www.vision.caltech.edu/bouguetj/calib_doc/. Accessed Dec 2009
  9. Catálan PA, Haller MC (2008) Remote sensing of breaking wave phase speeds with application to non-linear depth inversions. Coast Eng 55(1):93–111CrossRefGoogle Scholar
  10. Chickadel CC, Holman RA, Freilich MH (2003) An optical technique for the measurement of longshore currents. J Geophys Res 108:33–64CrossRefGoogle Scholar
  11. Ciavola P, Taborda R, Ferreira O, Dias JA (1997) Field Measurements of Longshore Sand transport and control processes on a steep meso-tidal beach in Portugal. J Coast Res 13(4):1119–1129Google Scholar
  12. Costa M, Silva R, Vitorino J (2001) Contribuição para o estudo do clima de agitação marítima na costa portuguesa. In: 2as Jornadas Portuguesas Engenharia Costeira e Portuária, Sines, Portugal. International Navigation Association PIANCGoogle Scholar
  13. Dodet G, Bertin X, Taborda R (2010) Wave climate variability in the North-East Atlantic Ocean over the last six decades. Ocean Model 31(3–4):120–131CrossRefGoogle Scholar
  14. Douglas SL (1992) Estimating extreme values of run-up on beaches. J Waterw Port Coast Ocean Eng 118(2):220–224CrossRefGoogle Scholar
  15. Ferreira PM, Ruano AE (2000) Exploiting the separability of linear and non-linear parameters in radial basis function neural networks. In: IEEE Symposium 2000: Adaptive systems for signal processing, communications and control, Alberta, Canada. pp 321–326Google Scholar
  16. Ferreira PM, Faria EA, Ruano AE (2002) Neural network models in Greenhouse air temperature prediction. Neurocomputing 43(1–4):51–75CrossRefGoogle Scholar
  17. Ferreira Ó, Garcia T, Matias A, Taborda R, Dias JA (2006) An integrated method for the determination of set-back lines for coastal erosion hazards on sandy shores. Cont Shelf Res 26(9):1030–1044CrossRefGoogle Scholar
  18. Ferreira O, Vousdoukas MV, Ciavola P (2009) MICORE Review of Climate Change Impacts on Storm Occurrence. (Open access, Deliverable WP1.4). Available at: https://micore.eu/area.php?idarea=28. Accessed Dec 2009
  19. Fischler M, Bolles R (1981) RANdom SAmpling Consensus: a paradigm for model fitting with application to image analysis and automated cartography. Commun Assoc Comput Machinery 24:381–395CrossRefGoogle Scholar
  20. Folk RL (1980) Petrology of the sedimentary rocks. Hemphill Publishing Company, AustinGoogle Scholar
  21. Harley MD, Turner IL, Short AD, Ranasinghe R (2006) Assessing the accuracy and applicability of a multi-decadal beach survey dataset. Paper presented at the 30st International Conference on Coastal Engineering, San Diego, USAGoogle Scholar
  22. Hartley R, Zisserman A (2006) Multiple view geometry in computer vision. Cambridge University Press, CambridgeGoogle Scholar
  23. Heikkilä J, Silvén O (1997) A four-step camera calibration procedure with implicit image correction. In: IEEE computer society conference on computer vision and pattern recognition, San Juan, Puerto Rico. pp 1106–1112Google Scholar
  24. Holland KT, Raubenheimer B, Guza RT, Holman RA (1995) Run-up kinematics on a natural beach. J Geophys Res 100(C3):4985–4993CrossRefGoogle Scholar
  25. Holman RA (1986) Extreme value statistics for wave run-up on a natural beach. Coast Eng 9:527–544CrossRefGoogle Scholar
  26. Holman RA, Sallenger JAH (1985) Setup and swash on a natural beach. J Geophys Res 90(C1):945–953CrossRefGoogle Scholar
  27. Holman RA, Stanley J (2007) The history and technical capabilities of Argus. Coast Eng 54(6–7):477–491CrossRefGoogle Scholar
  28. Huber PJ (1981) Robust Statistics. Wiley, HobokenCrossRefGoogle Scholar
  29. Hunt IA (1959) Design of seawalls and breakwaters. J Waterw Har Div ASCE 85(WW3):123–152Google Scholar
  30. Kalnay E, Kanamitsu M, Kistler R, Collins W, Deaven D, Gandin L, Iredell M, Saha S, White G, Woollen J, Zhu Y, Chelliah M, Ebisuzaki W, Higgins W, Janowiak J, Mo KC, Ropelewski C, Wang J, Leetmaa A, Reynolds R, Roy Jenne R, Joseph D (1996) The NCEP/NCAR 40-year reanalysis project. Bull Am Meteorol Soc 77:437CrossRefGoogle Scholar
  31. Kingston KS (2003) Applications of complex adaptive systems, approaches to coastal systems. Ph.D. thesis, University of Plymouth, PlymouthGoogle Scholar
  32. Lagarias JC, Reeds JA, Wright MH, Wright PE (1998) Convergence properties of the Nelder–Mead simplex method in low dimensions. SIAM J Optim 9(1):112–147CrossRefGoogle Scholar
  33. Lippmann TC, Holman RA (1989) Quantification of sand bar morphology: a video technique based on wave dissipation. J Geophys Res 94(C1):995–1011CrossRefGoogle Scholar
  34. Madsen AJ, Plant NG (2001) Intertidal beach slope predictions compared to field data. Mar Geol 173(1–4):121–139. doi:10.1016/s0025-3227(00)00168-7 CrossRefGoogle Scholar
  35. Marquardt DW (1963) An algorithm for least-squares estimation of nonlinear parameters. J Soc Ind Appl Math 11(2):431–441CrossRefGoogle Scholar
  36. Moody J, Darken CJ (1989) Fast learning in networks of locally-tuned processing units. Neural Comput 1(2):281–294CrossRefGoogle Scholar
  37. Pearre NS, Puleo JA (2009) Quantifying seasonal shoreline variability at Rehoboth Beach, Delaware, using automated imaging techniques. J Coast Res 25(4):900–914CrossRefGoogle Scholar
  38. Plant NG, Holman RA (1997) Intertidal beach profile estimation using video images. Mar Geol 140(1–2):1–24. doi:10.1016/s0025-3227(97)00019-4 CrossRefGoogle Scholar
  39. Plant NG, Holland KT, Puleo JA (2002) Analysis of the scale of errors in nearshore bathymetric data. Mar Geol 191(1–2):71–86. doi:10.1016/s0025-3227(02)00497-8 CrossRefGoogle Scholar
  40. Plant NG, Aarninkhof SGJ, Turner IL, Kingston KS (2007) The performance of shoreline detection models applied to video imagery. J Coast Res 23(3):658–670CrossRefGoogle Scholar
  41. Ruano AE, Ferreira PM, Cabrita C, Matos S (2002) Training neural networks and neuro-fuzzy systems: a unified view. In: 15th IFAC world congress, Barcelona, SpainGoogle Scholar
  42. Ruessink BG, van Enckevort IMJ, Kingston KS, Davidson MA (2000) Analysis of observed two- and three-dimensional nearshore bar behaviour. Mar Geol 169(1–2):161–183. doi:10.1016/s0025-3227(00)00060-8 CrossRefGoogle Scholar
  43. Siegle E, Huntley DA, Davidson MA (2007) Coupling video imaging and numerical modelling for the study of inlet morphodynamics. Mar Geol 236(3–4):143–163CrossRefGoogle Scholar
  44. Sjöberg J, Ljung L (1994) Overtraining, regularization, and searching for minimum with application to neural networks. Paper presented at the IFAC Symposium on Adaptive Systems in Control and Signal ProcessingGoogle Scholar
  45. Smith KR, Bryan KR (2007) Monitoring beach volume using a combination of intermittent profiling and video imagery. J Coast Res 23(4):892–898CrossRefGoogle Scholar
  46. Stockdon HF, Holman RA, Howd PA, Sallenger JAH (2006) Empirical parameterization of setup, swash, and runup. Coast Eng 53(7):573–588CrossRefGoogle Scholar
  47. Sunamura T (1984) Quantitative predictions of beach-face slopes. Geol Soc Amer Bull 95:242–245CrossRefGoogle Scholar
  48. Tolman HL (2002) User manual and system documentation of WAVEWATCH-III version 2.22. NOAA/NWS/NCEP/MMABGoogle Scholar
  49. Tucker MJ, Pitt EG (2001) Waves in Ocean Engineering, vol 5 (Elsevier Ocean Engineering Book Series). Elsevier, AmsterdamGoogle Scholar
  50. US Army Corps of Engineers (2002) Coastal Engineering Manual. Engineer Manual 1110-2-1100 (in 6 volumes). U.S. Army Corps of Engineers, WashingtonGoogle Scholar
  51. Uunk L, Wijnberg KM, Morelissen R (2010) Automated mapping of the intertidal beach bathymetry from video images. Coast Eng 57:461–469CrossRefGoogle Scholar
  52. van Dongeren A, Plant N, Cohen A, Roelvink D, Haller MC, Catalán P (2008) Beach Wizard: nearshore bathymetry estimation through assimilation of model computations and remote observations. Coast Eng 55(12):1016–1027CrossRefGoogle Scholar
  53. van Enckevort IMJ, Ruessink BG (2003a) Video observations of nearshore bar behaviour. Part 1: alongshore uniform variability. Cont Shelf Res 23(5):501–512. doi:10.1016/s0278-4343(02)00234-0 CrossRefGoogle Scholar
  54. van Enckevort IMJ, Ruessink BG (2003b) Video observations of nearshore bar behaviour. Part 2: alongshore non-uniform variability. Cont Shelf Res 23(5):513–532. doi:10.1016/s0278-4343(02)00235-2 CrossRefGoogle Scholar
  55. Vousdoukas MI, Velegrakis AF, Karambas T, Valais G, Zarkoyiannis S (2005) Morphodynamics of beachrock infected beaches: Vatera Beach, NE Mediterranean. In: Sanchez-Arcilla A (ed) In: 5th International Conference on Coastal Dynamics, Barcelona, SpainGoogle Scholar
  56. Vousdoukas MI, Velegrakis AF, Dimou K, Zervakis V, Conley DC (2009) Wave run-up observations in microtidal, sediment-starved pocket beaches of the Eastern Mediterranean. J Mar Syst 78:37–47CrossRefGoogle Scholar
  57. Vousdoukas MI, Pennucci G, Holman RA, Conley DC (2011) A semi automatic technique for Rapid Environmental Assessment in the coastal zone using Small Unmanned Aerial Vehicles (SUAV). J Coastal Res SI64Google Scholar
  58. Wolf PR (1974) Elements of photogrammetry (with air photo interpretation and remote sensing). McGraw-Hill, St. LouisGoogle Scholar
  59. Wright LD, Short AD (1984) Morphodynamic variability of surf zones and beaches: a synthesis. Mar Geol 56(1–4):93–118CrossRefGoogle Scholar

Copyright information

© Springer-Verlag 2011

Authors and Affiliations

  • Michalis Ioannis Vousdoukas
    • 1
    • 2
  • Pedro Manuel Ferreira
    • 3
  • Luis Pedro Almeida
    • 1
  • Guillaume Dodet
    • 4
    • 7
  • Fotis Psaros
    • 5
  • Umberto Andriolo
    • 6
  • Rui Taborda
    • 4
  • Ana Nobre Silva
    • 4
  • Antonio Ruano
    • 3
  • Óscar Manuel Ferreira
    • 1
  1. 1.Faculdade de Ciências do Mar e do AmbienteUniversidade do AlgarveFaroPortugal
  2. 2.Forschungszentrum KüsteHannoverGermany
  3. 3.Centre of Intelligent SystemsUniversidade do AlgarveFaroPortugal
  4. 4.Faculty of Science, LATTEX, IDLUniversity of LisbonLisbonPortugal
  5. 5.Department of Marine ScienceUniversity of the AegeanMytileneGreece
  6. 6.Dipartimento di Scienze della Terra, Facoltà di IngegneriaUniversità di FerraraFerraraItaly
  7. 7.LNEC Estuaries and Coastal Zones DivisionNational Laboratory of Civil EngineeringLisbonPortugal

Personalised recommendations