Application of Radar Technologies for Classifying Bird Flight Behavior at Dangermond Preserve, January 5-10, 2026

Seabirds are abundant in the U.S. west coast wind energy areas and some dynamic soaring species and several other groups are potentially vulnerable to collision with offshore wind (OSW) turbines. Regulatory permits and authorizations will be needed for OSW energy projects to be planned, constructed, and operated in ways to minimize environmental impacts. For protected species, proposed OSW projects will likely be required to generate collision risk models (CRMs) to estimate species-specific impacts and potential “take” anticipated over the permit term, and monitoring will be necessary to verify actual take once the project is implemented. CRMs require extensive species-specific metrics and are most sensitive to avoidance rate. We present a seabird flight classification framework that leverages multi-feature extraction from radar technologies coupled with a validation methodology using drones programmed to mimic natural flight kinematics. This effort leverages the SEABIRD (System for Environmental Assessment of Bird/Bat Interactions with Real-Time Detection) project that integrates multiple sensor technologies ─ marine biological radar, stereo thermal imaging, and blade-mounted impact sensors ─ into a common data collection and interpretation system that is capable of detecting, tracking, and identifying birds and bats over multiple scales (wind farm to individual turbine to blade), as well as in three-dimensional (3D) space. Our approach used drones to generate a ground-truthed dataset encompassing natural maneuvers including dynamic soaring (large-scale sinusoidal flight), transiting (lower speeds during commuting and higher speeds for feeding), foraging (hovering and turning), diving (higher-speed vertical descent for feeding and lower speeds for resting), aerial chasing, and fine-scale microscale avoidance (rapid, evasive shifts). By precisely controlling drone flight path and speed, we established a clean dataset that accurately reflects the kinematic space occupied by west coast seabirds across these behaviors. Multi-feature extraction techniques, e.g., sinuosity and the relationship between horizontal and vertical flight speed will be applied to data to increase the ability to classify flight behavior using radar technologies.