NEARSHORE BATHYMETRIC MAPPING USING DRONE-ACQUIRED VISUAL WAVETOP IMAGERY

Abstract

Nearshore bathymetric mapping is critical for naval operations, coastal management, and oceanographic modeling. However, traditional acoustic survey methods are costly, time-consuming, and dangerous in surf zones. This study investigates the use of unmanned aerial systems (UASs) to derive bathymetry from visual wavetop imagery at the morphologically dynamic Pajaro River mouth in Monterey Bay, California. Data was collected during closed (2021) and open (2025) river mouth conditions. This study uses the open source cBathy and cBathyCT toolboxes to output bathymetry. Imagery was processed to identify wave characteristics so that the wave dispersion relationship could be used to calculate depth. cBathyCT provided improved fidelity on wave crest identification using a machine learning algorithm and a nonlinear wave theory correction for surfzone depth estimation. Results were consistent with expected depth overestimation within the surfzone for cBathy when compared to cBathyCT results. The technique successfully identified complex bathymetric features including a trough seaward of the river mouth, validated through consistent wave breaking patterns. This methodology provides a foundation for future research in bathymetry derived from drone-acquired wavetop visual imagery.Distribution Statement A. Approved for public release: Distribution is unlimited.Lieutenant, United States Nav