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

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.

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Acknowledgments

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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Correspondence to Michalis Ioannis Vousdoukas.

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Responsible Editor: Pierre-Marie Poulain

This article is part of the Topical Collection on Multiparametric observation and analysis of the Sea

Appendix—iterative intertidal topography update

Appendix—iterative intertidal topography update

Surface grids were generated from the 3D scattered points resulting from the several ‘contour lines’ acquired during the measuring window. An iterative procedure was applied to identify and extract outliers, so as to estimate the latest intertidal beach topography and to update a digital elevation model (DEM) of the study area. The procedure consisted of the following steps:

  1. 1.

    For each DEM update step, all contour lines were accumulated according to the given time window.

  2. 2.

    The elevations of the points cloud were compared with those of the existing (previously updated) DEM corresponding locations (provided after interpolation), and points with an absolute elevation difference greater than a dz thres,1 threshold value of 2 m were excluded.

  3. 3.

    The perimeter of the points cloud was defined considering a maximum 2 m separation distance.

  4. 4.

    The DEM sections found beyond and landward of those limits were sub-sampled with cross- and longshore resolutions of 5 and 10 m, respectively, and were added to the points cloud.

  5. 5.

    A grid was generated from all the available points and was smoothed with a 4 × 4 m, 2D convolution function.

  6. 6.

    The elevations of the original points were compared with those of the corresponding survey grid locations (provided after interpolation) and points with an absolute elevation difference greater than a dz thres,2 threshold value of 0.5 m were excluded.

  7. 7.

    The data gridding and filtering steps 5–6 were repeated until the number of points was constant (zero outliers detected), or until a pre-defined maximum number of iterations had taken place (set to 100 for the present case).

  8. 8.

    The final grid was generated applying a quadratic loess interpolation method, with a 4 m length-scale smoothing (e.g. Plant et al. 2002).

  9. 9.

    The Faro Beach DEM was updated according to the generated grid, excluding non-moving areas (e.g. buildings, walls).

Please note that the several thresholds used (dz thres,1 and dz thres,2) were empirical and were based on our observations at the study area. When the time window was no longer than one tidal cycle, the procedure followed the steps below: (1) a DEM was generated considering a 2-day time window according to steps 1–9; (2) steps 1–4 were applied for the actual time window; (3) the outlier extraction described in steps 6–7 took place based on the DEM generated during (1); (4) The DEM was updated following steps 8 and 9. In that case, shoreline contours obtained only during the retreating phase of the tide were considered in step 1, unless fewer than four were available. In such a case, the data during the rising phase of the following tide were also considered.

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Vousdoukas, M.I., Ferreira, P.M., Almeida, L.P. et al. Performance of intertidal topography video monitoring of a meso-tidal reflective beach in South Portugal. Ocean Dynamics 61, 1521–1540 (2011). https://doi.org/10.1007/s10236-011-0440-5

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  • DOI: https://doi.org/10.1007/s10236-011-0440-5

Keywords

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