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 Responsible Editor: Pierre-Marie Poulain This article is part of the Topical Collection on Multiparametric observation and analysis of the Sea 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.

LiDAR Validation of a Video-Derived Beachface Topography on a Tidal Flat

“…These errors could therefore be overcome by elevating the cameras, as in previous works (e.g., McCarrol et al [70] (55 m), Bracs et al [65] (44 m), Sénéchal et al [32], Almar et al [38] (27 m), Vousdoukas et al [47] (20 m), Abessolo Ondoa et al [69] (15 m)). …”

“…Vousdoukas et al [47] obtained vertical RMS errors of 0.26 m using empirical formulations based on tidal and wave runup levels on the beach. Similarly, Uunk et al [36] mentioned elevation RMS errors between 0.28 and 0.34 m, the highest values being observed with a completely automated method.…”

“…Furthermore, the automatic extraction technique applied here, which undertakes a water/beach distinction from color band ratios and a shoreline shape recognition module [38], failed at low tide mainly because the water depth was too shallow, therefore limiting swash detection and promoting numerous rogue detections of the sandy bottom. Vousdoukas et al [47] mentioned that rogue detections could be observed under high wave conditions, therefore promoting some rogue automated detections. Erroneously identified shorelines in our study were rather the result of calm conditions during low tide.…”

SVMT: A MATLAB toolbox for stereo-vision motion tracking of motor reactivity

“…(1), I is the identity matrix, and C and R are the translation and rotation matrices of the camera's COV respectively [4,10]. The C translation matrix accounts for the camera COV position Cartesian coordinates (X cam , Y cam , Z cam ) in the world coordinate reference system and can be represented as:…”

“…The SIFT algorithm has been extensively validated and applied for computer vision [14] and numerous other applications including commercial image stitching software (http://www.kolor.com/panorama-software-autopanopro.html) and even coastal video monitoring [9,10], and has been proved very robust. The patterns used for the present application were selected after extensive testing, thus ensuring that they consistently produce a satisfactory amount of extracted keypoints (>10).…”