Optimizing surveys of fall‐staging geese using aerial imagery and automated counting

Abstract

Abstract Ocular aerial surveys allow efficient coverage of large areas and can be used to monitor abundance and distribution of wild populations. However, uncertainty around resulting population estimates can be large due to difficulty in visually identifying and counting animals from aircraft, as well as logistical challenges in estimating detection probabilities. Photographic aerial surveys can mitigate these challenges and can allow flight at higher altitudes to minimize disturbance of birds and improve safety for surveyors. We evaluated a photographic aerial survey that incorporated a systematic sampling design with automated photo capture and processing for fall‐staging geese at Izembek Lagoon, Alaska, in 2017–2019. Ocular aerial surveys have been completed at Izembek Lagoon for >40 years. For the new photo survey, we used a commercial system to automatically trigger cameras at preset points. We then applied a machine‐learning algorithm trained to automatically identify and count geese in our photos, manually corrected those counts, and quantified the algorithm's accuracy. We translated corrected counts into density and extrapolated mean density across the entire lagoon to estimate total population size for Pacific brant (Branta bernicla) and cackling geese (B. hutchinsii). The automated algorithm undercounted geese, but successfully identified the small subset of photos containing geese. Manual correction was therefore needed only for photos automatically identified as containing geese, allowing substantial reduction of workload. Manually‐corrected, photo‐based estimates of Pacific brant and cackling goose population sizes were larger and more precise than ocular estimates in all 3 years. To reduce costs with little penalty for variance around population estimates, the photographic survey design could be optimized by reducing the number of transects to ~67% of the current number while still manually correcting all photos in which the automated algorithm detected geese. Further years of both ocular and photo surveys would be needed to calibrate the photo estimates against the >40‐year timeseries of the ocular survey, after which the photo series could successfully guide management of Pacific brant. As technologies continue to advance, we expect photographic surveys with automated counting to be easily implemented and advantageous to many monitoring programs.

Keywords

• aerial survey
• Branta bernicla
• Branta hutchinsii
• image analysis
• object identification
• photographic survey