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

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.

Improving the efficiency and accuracy of evaluating aridland riparian habitat restoration using unmanned aerial vehicles

“…UAV surveys have a recognized potential of becoming a standard tool for evaluating vegetation response to restoration (Kamm & Reed, 2019). However, UAV‐based vegetation mapping still involves ground truthing.…”

Cumulative production potential of quarries to supply crushed stone factories in Russia: An overview

“…Various scientific missions are successively being met using satellite imagery information [4][5][6][7][8][9][10][11][12][13][14][15][16][17][18][19][20]. The basic and applied research school of the Siberian Federal University has effectively accomplished a next new project on determination of aggregate potential of all crushed stone factories in Russia using satellite data.…”

Thermal Imaging of Beach-Nesting Bird Habitat with Unmanned Aerial Vehicles: Considerations for Reducing Disturbance and Enhanced Image Accuracy

“…Within the context of wildlife research, UAVs have proven to be efficient tools for evaluating wildlife behavior [6,7], abundance [8][9][10][11][12][13][14][15], and habitat [16,17]. Habitat evaluation may be done using visible spectrum cameras to classify habitat by land cover type [16] or through analysis of light detection and ranging (LiDAR) data to examine not only spatial extent of habitat type, but also information on vertical components of the habitat, such as tree height [17]. One potential benefit of using UAVs in wildlife studies is the reduction in disturbances to wildlife caused by the presence of human observers [18][19][20] or other traditional survey methods such as manned aircraft [21].…”