A Novel Automated Method for Point-Based Change Detection Increases Estimation Accuracy of Breeding Pairs in Waterbird Colonies

Abstract. The number of breeding pairs is an essential indicator for assessing waterbird colony status. Accurate estimates require distinguishing stationary adults (likely to be breeders on nests) from nonstationary adults (likely to be loafing or foraging breeders or non-breeders). High-resolution multi-temporal aerial imagery obtained from manned aircraft, satellites, or unmanned aircraft systems (UAS) can be analyzed to determine bird movements and derive estimates of the number of stationary adults, which corresponds to the number of active nests, and thereby breeding pairs. Automated detection of stationary objects, such as nesting waterbirds, can be complicated by small positional changes of objects, either because of image co-registration errors or because the object slightly shifts position. A non-parametric, point-based approach was developed to distinguish stationary birds from moving birds using sequences of either two or three consecutive remotely-sensed images. This approach was tested with simulated data and during a case study of nesting American White Pelicans (Pelecanus erythrorhynchos). In both cases, the non-parametric point-based approach had higher accuracy than other established methods such as ground counts. Using two consecutive images had higher sensitivity (correct classification of stationary birds) while using three consecutive images had higher specificity (correct classification of nonstationary birds). This novel, multi-temporal nearest-neighbor method is most useful when positional shifts of stationary animals is low between consecutive images.