2026-09-19T09:09:58Zhttps://digital.lib.washington.edu/server/oai/request

oai:digital.lib.washington.edu:1773/551812026-02-06T11:01:35Zcom_1773_4888col_1773_4905

Hegermiller, Christie Thomson, Jim LeClair, Malcolm James 2026-02-05T19:33:47Z 2026-02-05T19:33:47Z 2026-02-05 2025 LeClair_washington_0250O_29134.pdf https://hdl.handle.net/1773/55181 Thesis (Master's)--University of Washington, 2025 A machine learning algorithm is developed to detect breaking waves in optical remote sensing data collected under visually diverse conditions along a kilometer-scale beach in Duck, NC. Bore speeds are estimated from the breaking-wave detections and are compared with theoretical models using surveyed bathymetry. Bathymetry inversion from the derived bore speeds is then explored, revealing low but systematic bias within the surf zone. Despite this limitation, a qualitative analysis of the inverted bathymetry demonstrates that the method captures morphological change over the course of the experiment. This method shows promise as a robust, low-cost approach for measuring wave-breaking patterns and dynamics across large surf zones. The results highlight important considerations for the data resolution, quality, and processing needed to achieve robust measurements of breaking waves using optical remote sensing. application/pdf en_US CC BY-NC bathymetry inversion breaking waves machine learning remote sensing Remote sensing Physical oceanography Civil engineering Detecting Breaking Waves and Measuring Bore Speeds in Optical Surf Zone Imagery using Machine Learning Thesis Open Access open.access