Ocean Dynamics

, Volume 61, Issue 10, pp 1521–1540 | Cite as

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
Part of the following topical collections:
  1. Topical Collection on Multiparametric observation and analysis of the Sea


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.


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



The authors gratefully acknowledge the European Community Seventh Framework Programme funding under the research project MICORE (grant agreement no. 202798). We are indebted to the Restaurant ‘Paquete’ for allowing us to deploy the cameras on their rooftop and for supplying electric power and space for our equipment.


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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

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