Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review

Mulero‐Pázmány, Margarita; Jenni-Eiermann, Susanne; Strebel, Nicolas; Sattler, Thomas; Negro, Juan J.; Tablado, Zulima

doi:10.1371/journal.pone.0178448

Cited by 234 publications

(244 citation statements)

References 44 publications

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“…), consistent with findings by other researchers that flights by electric UAV 20–45 m from focal species are unlikely to provoke strong behavioral responses (Mulero‐Pázmány et al. ). American Kestrels specifically are resilient to disturbances at the nest site, including capture and handling (Smallwood ).…”

Section: Methodssupporting

confidence: 92%

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

Kamm

Reed

2019

Remote Sens Ecol Conserv

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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.

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Section: Methodssupporting

confidence: 92%

“…At study sites where kestrels were known to be present, UAV flights elicited either no reaction or an alert reaction ( sensu Mulero‐Pázmány et al. ), consistent with findings by other researchers that flights by electric UAV 20–45 m from focal species are unlikely to provoke strong behavioral responses (Mulero‐Pázmány et al. ).…”

Section: Methodssupporting

confidence: 88%

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

Kamm

Reed

2019

Remote Sens Ecol Conserv

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“…Following initial exploration of the utility of UAVs in wildlife monitoring (Hodgson & Koh, ; Jones, Pearlstine, & Percival, ; Mulero‐Pazmany et al., ), a number of seabird colonies have been counted using this approach. This has typically involved the collection of images by UAV survey followed by manual image counting of the number of individuals, for example Black‐headed Gulls, Chroicocephalus ridibundus (Sardà‐Palomera et al., ) and Common Terns, Sterna hirundo (Chabot, Craik, & Bird, ), with 93%–96% accuracy compared to ground counts.…”

Section: Introductionmentioning

confidence: 99%

Can drones count gulls? Minimal disturbance and semiautomated image processing with an unmanned aerial vehicle for colony‐nesting seabirds

Rush

Clarke

Stone

et al. 2018

Ecology and Evolution

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Accurate counts of wild populations are essential to monitor change through time, but some techniques demand specialist surveyors and may result in unacceptable disturbance or inaccurate counts. Recent technological developments in unmanned aerial vehicles (UAVs) offer great potential for a range of survey and monitoring approaches. They literally offer a bird's‐eye view, but this increased power of observation presents the challenge of translating large amounts of imagery into accurate survey data. Seabirds, in particular, present the particular challenges of nesting in large, often inaccessible colonies that are difficult to view for ground observers, which are commonly susceptible to disturbance. We develop a protocol for carrying out UAV surveys of a breeding seabird colony (Lesser Black‐backed Gulls, Larus fuscus) and subsequent image processing to provide a semiautomated classification for counting the number of birds. Behavioral analysis of the gull colonies demonstrated that minimal disturbance occurred during UAV survey flights at an altitude of 15 m above ground level, which provided high‐resolution imagery for analysis. A protocol of best practice was developed using the expertise from both a UAV perspective and that of a dedicated observer. A GIS‐based semiautomated classification process successfully counted the gulls, with a mean agreement of 98% and a correlation of 99% with manual counts of imagery. We also propose a method to differentiate between the different gull species captured by our survey. Our UAV survey and analysis approach provide accurate counts (when comparing manual vs. semi‐automated counts taken from the UAV imagery) of a wild seabird population with minimal disturbance, with the potential to expand this to include species differentiation. The continued development of analytical and survey tools whilst minimizing the disturbance to wild populations is both key to unlocking the future of the rapid advances in UAV technology for ecological survey.

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“…Negative reactions to UASs have been documented for birds, reptiles, and mammals (Mulero‐Pázmány et al. ; Bevan et al. ).…”

Section: Equipment and Applicationmentioning

confidence: 99%

Use of Drones in Fishery Science

et al. 2019

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The use of unmanned aircraft systems (UASs), commonly referred to as drones, has rapidly expanded across many scientific disciplines. Like other fields, fisheries research would benefit significantly from broader use of this emerging technology but has lagged behind other disciplines. Like the implementation of satellite and aircraft‐based remote sensing technology in previous decades brought a greater understanding of large‐scale spatial patterns and processes, UAS technology has the potential to put those tools in the hands of individual researchers, allowing implementation at finer spatial and temporal scales for a fraction of the cost. Our goal is to provide a “how‐to” for fisheries researchers interested in using UAS technology. We outline the necessary steps for any UAS project from choosing the appropriate platform and sensors to data acquisition and analysis. We also present the current ways in which UASs are being used in a fisheries research context as well as potential future research directions.

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Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review

Cited by 234 publications

References 44 publications

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

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

Can drones count gulls? Minimal disturbance and semiautomated image processing with an unmanned aerial vehicle for colony‐nesting seabirds

Use of Drones in Fishery Science

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