A Vision-Based Method for Spatial and Temporal Tracking of Individual Whitecaps From Breaking Ocean Waves

Abstract

Whitecaps are formed at the ocean surface when breaking waves entrain sufficient quantities of air to produce an optically distinct signal at the water surface. Whitecaps drive energy transfer from the surface wave field to the upper ocean and the entrained air drives key bubble-mediated processes such as air-sea gas exchange and aerosol production flux. By exploiting the broadband scattering of light by the surface whitecaps, this study develops an algorithm for automated whitecap detection and tracking (AWDAT) from fixed image systems, in order to detect and track individual whitecaps. AWDAT introduces new image processing and computer vision techniques that handle temporal developments of whitecaps and complex behaviors of whitecap foam patches such as splitting and merging. We teach a learning-based model to filter tracked whitecaps based on the AWDAT-measured foam area, breaking speed, and direction time series. The algorithm is tested on three different image datasets to assess its performance with different camera systems in different geographic locations—the Adriatic Sea (AS), Black Sea (BS), and Yellow Sea (YS). Applications of AWDAT are demonstrated by aggregating whitecap statistics from geometric, kinematic, and dynamic measurements of individual breaking waves, which are then evaluated within the volume-time-integral (VTI) method and Phillips (1985) $\Lambda (c)$ spectral framework.