A design‐based framework for estimating wildlife density from uncrewed aerial systems imagery

Abstract

Uncrewed aerial systems (UAS) are increasingly used to survey wildlife populations, yet, until recently, most efforts were focused on developing technology rather than statistically defensible estimation of wildlife abundance. Therefore, we developed and applied an integrated framework to estimate waterfowl density and abundance from airborne imagery collected using 2 UAS platforms: a multirotor (DJI Mavic 2 Pro) in 2018 and a fixed wing (WingtraOne Gen II) in 2023, across wetlands in the Middle Rio Grande Valley, New Mexico, USA. We trained deep learning models to detect waterfowl in high‐resolution aerial imagery and quantify the sampled ground area required for density estimation. We corrected detection counts using platform‐specific confusion matrices to account for false detections and missed individuals. We used image footprints to quantify sampled areas and aggregated bias‐corrected observations at the transect level for analysis using design‐based ratio estimators, with uncertainty quantified through transect‐level bootstrap resampling. The fixed‐wing surveys estimated >20,000 ducks (95% CI = 13,321–29,384) and the multirotor surveys estimated >5,000 ducks (95% CI = 3,348–8,471) within surveyed areas, with additional populations of geese and cranes detected across platforms. Precision‐effort analyses quantified relationships between sampling intensity and estimator uncertainty, revealing precision dependence on platform‐specific spatial coverage and transect replication. By explicitly integrating automated detection, classification calibration, spatial sampling, and design‐based estimation, this workflow converts UAS imagery into statistically defensible estimates of density for broad taxonomic groups of waterfowl (e.g., ducks, geese, and cranes) with quantified uncertainty. This framework provides a practical and adaptable approach for incorporating UAS‐based surveys into wildlife monitoring and supports improved decision‐making in ecological management and conservation.