Article

Ocean Dynamics

, Volume 61, Issue 10, pp 1521-1540

First online:

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

  • Michalis Ioannis VousdoukasAffiliated withFaculdade de Ciências do Mar e do Ambiente, Universidade do AlgarveForschungszentrum Küste Email author 
  • , Pedro Manuel FerreiraAffiliated withCentre of Intelligent Systems, Universidade do Algarve
  • , Luis Pedro AlmeidaAffiliated withFaculdade de Ciências do Mar e do Ambiente, Universidade do Algarve
  • , Guillaume DodetAffiliated withFaculty of Science, LATTEX, IDL, University of LisbonLNEC Estuaries and Coastal Zones Division, National Laboratory of Civil Engineering
  • , Fotis PsarosAffiliated withDepartment of Marine Science, University of the Aegean
  • , Umberto AndrioloAffiliated withDipartimento di Scienze della Terra, Facoltà di Ingegneria, Università di Ferrara
  • , Rui TabordaAffiliated withFaculty of Science, LATTEX, IDL, University of Lisbon
  • , Ana Nobre SilvaAffiliated withFaculty of Science, LATTEX, IDL, University of Lisbon
  • , Antonio RuanoAffiliated withCentre of Intelligent Systems, Universidade do Algarve
    • , Óscar Manuel FerreiraAffiliated withFaculdade de Ciências do Mar e do Ambiente, Universidade do Algarve

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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