The FWS is using airborne remote sensing technologies to enhance migratory bird surveys to 1) enhance safety of aircrews conducting surveys by allowing flight at higher altitudes, 2) improve the quality of population and habitat data collected by minimizing and quantifying error rates, and 3) increase bird survey efficiency and utility by creating consistent automated processes. The FWS is partnering with the Bureau of Ocean Energy Management (BOEM), U.S. Geological Survey (USGS), academic institutions, and private contractors to accomplish these objectives. Through this collaboration, sensor hardware and software necessary for very high-resolution (centimeter to sub-centimeter), broad field-of-view aerial imaging has been developed to support marine wildlife surveys. Cutting-edge artificial intelligence/machine learning algorithms have been developed to automatically detect seabirds in imagery, and work is underway to also automate the process of species classification. To achieve these results efficiently, in-flight processing software will filter images to only those likely to contain seabirds. From there, species will be delineated. Finally, FWS is collaborating with USGS scientists to develop statistical population estimation frameworks that accommodate novel machine learning processes and outputs. Overall, the efficiency gained by these technologies may inform diverse management decisions, such as the development of renewable energy in Outer Continental Shelf systems, population monitoring for species of concern, and the setting of harvest limits for species that utilize these habitats. These novel technologies will continue to be used by FWS in conjunction with other remote sensing products, such as satellite imagery and light detection and ranging (lidar) data, that characterize fish and wildlife habitats, waters, wetlands, and landscapes.
Imagery of common terns flying over the Atlantic Ocean as captured from aircraft surveys. High-resolution imagery is being used to survey migratory seabirds to improve understanding of their abundance and distribution.