These platforms with onboard sensors allow research that previously required manned flights, such as surveys of seabirds (Hodgson et al.
Small unmanned aerial systems (sUAS, “drones”), are gaining popularity as tools in ecological research. Mean classification accuracy metrics (kappa values) across different photo heights differed little, but, importantly, the rank order of images differed noticeably. Gaussian filtering reduced classification accuracy, while coarser classification resolution out-performed finer resolution due to “speckling noise.” Image self-classification significantly outperformed cross-image classification.
There was inter-subject variability in designating training samples, but high repeatability of supervised classification accuracy. Our results indicate that commercial off-the-shelf sUAS photography can distinguish between grass, herbaceous, woody, bare ground, and human-modified cover classes with good (kappa > 0.6) or strong (kappa > 0.8) accuracy using a 0.25 m 2 minimum patch size for aggregation. We photographed vegetation around nests from both 25 m and 50 m above takeoff elevation, and analyzed images via maximum likelihood supervised classification. We investigated inter–observer repeatability of habitat classification, effectiveness of cross-image classification and Gaussian filtering, and sensitivity to classification resolution. We used a commercially available quadcopter sUAS with a visible spectrum camera to classify habitat for American Kestrels ( Falco sparverius Aves), as well as to evaluate aspects of image processing and postprocessing relevant to a simple habitat analysis using citizen science photography. The growing number of commercial sUAS being flown by recreational users represents a potentially valuable source of data for documenting and studying wildlife habitat.
Photography with small unmanned aircraft systems (sUAS) offers opportunities for researchers to better understand habitat selection in wildlife, especially for species that select habitat from an aerial perspective (e.g., many bird species).
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