Breaking Wave Height Estimation from Timex Images: Two Methods for Coastal Video Monitoring Systems

Abstract

The breaking wave height is a crucial parameter for coastal studies but direct measurements constitute a difficult task due to logistical and technical constraints. This paper presents two new practical methods for estimating the breaking wave height from digital images collected by shore-based video monitoring systems. Both methods use time-exposure (Timex) images and exploit the cross-shore length ( L H s ) of the typical time-averaged signature of breaking wave foam. The first method ( H s b , v ) combines L H s and a series of video-derived parameters with the beach profile elevation to obtain the breaking wave height through an empirical formulation. The second method ( H s b , v 24 ) is based on the empirical finding that L H s can be associated with the local water depth at breaking, thus it can be used to estimate the breaking wave height without the requirement of local bathymetry. Both methods were applied and verified against field data collected at the Portuguese Atlantic coast over two days using video acquired by an online-streaming surfcam. Furthermore, H s b , v 24 was applied on coastal images acquired at four additional field sites during distinct hydrodynamic conditions, and the results were compared to a series of different wave sources. Achievements suggest that H s b , v method represents a good alternative to numerical hydrodynamic modeling when local bathymetry is available. In fact, the differences against modeled breaking wave height, ranging from 1 to 3 m at the case study, returned a root-mean-square-error of 0.2 m. The H s b , v 24 method, when applied on video data collected at five sites, assessed a normalized root-mean-square-error of 18% on average, for dataset of about 900 records and breaking wave height ranging between 0.1 and 3.8 m. These differences demonstrate the potential of H s b , v 24 in estimating breaking wave height merely using Timex images, with the main advantage of not requiring the beach profile. Both methods can be easily implemented as cost-effective tools for hydrodynamic applications in the operational coastal video systems worldwide. In addition, the methods have the potential to be coupled to the numerous other Timex applications for morphodynamic studies.

Raindrop-Aware GAN: Unsupervised Learning for Raindrop-Contaminated Coastal Video Enhancement

“…It shows that the best reconstruction performance can be obtained when the coastal video is used for fine-tuning even in the same model architecture of Raindrop-aware GAN. By creating timestack image and visually assessment it, we can confirm that the performance of the proposed method is the best and it also has high applicability in studying nearshore wave dynamics with video remote sensing, in particular data preparation step, such as breaking wave height estimation from coastal video [31,32], video sensing of nearshore bathymetry evolution [33,34], nearshore wave transform with video imagery [35], shoreline response and resilience through video monitoring [36][37][38][39], wave run-up prediction [40,41], and nearshore wave tracking through coastal video [42,43].…”

Sea State from Single Optical Images: A Methodology to Derive Wind-Generated Ocean Waves from Cameras, Drones and Satellites

“…In the surf zone, there have been several successful attempts to use stereoscopic imaging to derive the sea surface using the wave-breaking induced foam texture for homologous points [8][9][10]. Wave heights were also derived from shore-based single cameras [11,12], using the optical signature of the wave roller and camera geometries. The consideration of the link between optical signal and the actual wave energy has generally remained outside the scope of nearshore studies using optical methods, with the exception of a few applications ( [4,7,13] which were dedicated to bathymetry inversion.…”

Shoreline Response to a Sandy Nourishment in a Wave-Dominated Coast Using Video Monitoring

“…The measures were negatively affected by rain drops and dust on the lens, presence of mist and low visibility on the beach (Figure 9). In addition, the fully automated methodology returned a poor description of breaking pattern when rip currents were present on the nearshore, as already pointed out by Andriolo et al [58]. Therefore, an automated algorithm to discard the low-quality images will be developed.…”

“…To improve the morphodynamic analysis, the Hs b,v24 method [58] was tested, to estimate the wave height using Timex image (Figure 9). The method is based on the findings that the cross-shore length of the typical time-averaged signature of breaking wave foam on Timex, can be empirically associated with the local water depth at breaking, thus to breaking wave height [58].…”

“…To improve the morphodynamic analysis, the , method [58] was tested, to estimate the wave height using Timex image (Figure 9). The method is based on the findings that the cross-shore length of the typical time-averaged signature of breaking wave foam on Timex, can be empirically associated with the local water depth at breaking, thus to breaking wave height [58]. A total amount of 35 days, covered by about 3000 rectified Timex images, were processed to sample the wave breaking bright pixel pattern over the nearshore bar and eventually applying , method to retrieve the estimation of breaking wave height for each 10-min Timex.…”