Data from: Monitoring large and complex wildlife aggregations with drones

Recent advances in drone technology have rapidly led to their use for
monitoring and managing wildlife populations but a broad and generalised
framework for their application to complex wildlife aggregations is still
lacking. We present a generalised semi-automated approach where machine
learning can map targets of interest in drone imagery, supported by
predictive modelling for estimating wildlife aggregation populations. We
demonstrated this application on four large spatially complex breeding
waterbird colonies on floodplains, ranging from ~20,000 to ~250,000 birds,
providing estimates of bird nests. Our mapping and modelling approach was
applicable to all four colonies, without any modification, effectively
dealing with variation in nest size, shape, colour and density and
considerable background variation (vegetation, water, sand, soil etc.).
Our semi-automated approach was between 3 to 8 times faster than manually
counting nests from imagery at the same level of accuracy. This approach
is a significant improvement for monitoring large and complex aggregations
of wildlife, offering an innovative solution where ground counts are
costly, difficult or not possible. Our framework requires minimal
technical ability, is open-source (Google Earth Engine and R), and easy to
apply to other surveys