Performance of intertidal topography video monitoring of a meso-tidal reflective beach in South Portugal |
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Authors: | 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 |
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Institution: | 1.Faculdade de Ciências do Mar e do Ambiente,Universidade do Algarve,Faro,Portugal;2.Forschungszentrum Küste,Hannover,Germany;3.Centre of Intelligent Systems,Universidade do Algarve,Faro,Portugal;4.Faculty of Science, LATTEX, IDL,University of Lisbon,Lisbon,Portugal;5.Department of Marine Science,University of the Aegean,Mytilene,Greece;6.Dipartimento di Scienze della Terra, Facoltà di Ingegneria,Università di Ferrara,Ferrara,Italy;7.LNEC Estuaries and Coastal Zones Division,National Laboratory of Civil Engineering,Lisbon,Portugal |
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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. |
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