Use of visible spectrum sUAS photography for land cover classification at nest sites of a declining bird species (Falco sparverius)

• Main Authors: Matthew Kamm, J. Michael Reed
• Format: Article
• Language: English
• Published: Wiley 2019-09-01
• Series: Remote Sensing in Ecology and Conservation
• Subjects: Citizen science; ENVI; habitat selection; supervised classification; UAV; Unmanned aerial vehicles
• Online Access: https://doi.org/10.1002/rse2.104

Abstract 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). The growing number of commercial sUAS being flown by recreational users represents a potentially valuable source of data for documenting and studying wildlife habitat. 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. We investigated inter–observer repeatability of habitat classification, effectiveness of cross‐image classification and Gaussian filtering, and sensitivity to classification resolution. We photographed vegetation around nests from both 25 m and 50 m above takeoff elevation, and analyzed images via maximum likelihood supervised classification. 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 m2 minimum patch size for aggregation. There was inter‐subject variability in designating training samples, but high repeatability of supervised classification accuracy. 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. Mean classification accuracy metrics (kappa values) across different photo heights differed little, but, importantly, the rank order of images differed noticeably.