Abstract
The Triton Initiative has evaluated environmental technologies and methodologies, focusing on the detection and tracking of marine wildlife, since 2018. This study builds upon an initial flight trial of a tethered balloon system (TBS) and sensor package conducted on behalf of the Triton Initiative in 2022, and further investigates the capabilities of a tethered balloon system (TBS)for detecting and monitoring marine wildlife, primarily focusing on gray whales (Eschrichtius robustus) and various avian species. Over 55.7 h of aerial and surface footage were collected, yielding significant findings regarding the detection rates of marine mammals and seabirds. A total of 59 Gy whale, 100 avian, and 6 indistinguishable marine mammal targets were identified by the airborne TBS, while surface-based observations recorded 1,409 Gy whales, 1,342 avian targets, and several other marine mammals. When the airborne and surface cameras were operating simultaneously, 21% of airborne whale and 34% of airborne avian detections were captured with the airborne TBS camera and undetected with the surface-based camera. The TBS was most effective at altitudes between 50 and 200 m above ground, with variable-pitch scanning patterns providing superior detection of whale blows compared to fixed-pitch and loitering methods. Notably, instances of airborne detections not corroborated by surface observations underscore the benefits of combining aerial monitoring with traditional survey techniques. Additionally, the integration of machine-learning (ML) algorithms into image analysis for marine wildlife detection enhances our capacity for processing large datasets, paving the way for real-time wildlife monitoring, which is currently limited by the time associated with human review of imagery. Currently, ML algorithms require more training datasets to be created from varied aerial platforms operating in many conditions to improve detection accuracy before they are comparable in cost and processing time to human image review. In our study for concurrent observations, the percentage of blows only identified by a human analyst was greater than the percentage uniquely detected by the algorithm. Notably, more unique detections by the ML algorithm occurred during daylight, suggesting that sun artifacts may hinder human detection performance during high glare, thereby highlighting the added value of ML under these conditions. This research lays the groundwork for future studies in marine biodiversity monitoring, emphasizing the importance of innovative aerial surveillance technologies and advanced imaging methodologies in understanding species behavior and informing conservation strategies for sustainable marine energy, offshore wind development, and other marine resource management efforts.