Register      Login
Wildlife Research Wildlife Research Society
Ecology, management and conservation in natural and modified habitats
FOREWORD

Foreword to the Special Issue on ‘The rapidly expanding role of drones as a tool for wildlife research’

Aaron J. Wirsing https://orcid.org/0000-0001-8326-5394 A D , Aaron N. Johnston B and Jeremy J. Kiszka C
+ Author Affiliations
- Author Affiliations

A School of Environmental and Forest Sciences, University of Washington, Seattle, WA 98195, USA.

B U.S. Geological Survey, Northern Rocky Mountain Science Center, Bozeman, MT 59715, USA.

C Institute of Environment, Department of Biological Sciences, Florida International University, North Miami, FL 33818, USA.

D Corresponding author. Email: wirsinga@uw.edu

Wildlife Research 49(1) i-v https://doi.org/10.1071/WR22006
Submitted: 17 January 2022  Accepted: 25 January 2022   Published: 9 February 2022

Abstract

Drones have emerged as a popular wildlife research tool, but their use for many species and environments remains untested and research is needed on validation of sampling approaches that are optimised for unpiloted aircraft. Here, we present a foreword to a special issue that features studies pushing the taxonomic and innovation boundaries of drone research and thus helps address these knowledge and application gaps. We then conclude by highlighting future drone research ideas that are likely to push biology and conservation in exciting new directions.

Keywords: animal behavior, animal health, conflict, habitat characterisation, human–wildlife conflict, remotely piloted aircraft systems, unmanned/unpiloted aerial vehicles, unmanned/unpiloted aircraft systems, RPAS, UAS, UAV.


References

Aikens, E. O., Kauffman, M. J., Merkle, J. A., Dwinnell, S. P. H., Fralick, G. L., and Monteith, K. L. (2017). The greenscape shapes surfing of resource waves in a large migratory herbivore. Ecology Letters 20, 741–750.
The greenscape shapes surfing of resource waves in a large migratory herbivore.Crossref | GoogleScholarGoogle Scholar | 28444870PubMed |

Allan, B. M., Ierodiaconou, D., Hoskins, A. J., and Arnould, J. P. (2019). A rapid UAV method for assessing body condition in fur seals. Drones 3, 24.
A rapid UAV method for assessing body condition in fur seals.Crossref | GoogleScholarGoogle Scholar |

Allred, B. W., Bestelmeyer, B. T., Boyd, C. S., Brown, C., Davies, K. W., Duniway, M. C., Ellsworth, L. M., Erickson, T. A., Fuhlendorf, S. D., Griffiths, T. V., Jansen, V., Jones, M. O., Karl, J., Knight, A., Maestas, J. D., Maynard, J. J., McCord, S. E., Naugle, D. E., Starns, H. D., Twidwell, D., and Uden, D. R. (2021). Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty. Methods in Ecology and Evolution 12, 841–849.
Improving Landsat predictions of rangeland fractional cover with multitask learning and uncertainty.Crossref | GoogleScholarGoogle Scholar |

Alonzo, M., Andersen, H., Morton, D. C., and Cook, B. D. (2018). Quantifying boreal forest structure and composition using UAV structure from motion. Forests 9, 119.
Quantifying boreal forest structure and composition using UAV structure from motion.Crossref | GoogleScholarGoogle Scholar |

Altmann, J. (1974). Observational study of behavior: sampling methods. Behaviour 49, 227–267.
Observational study of behavior: sampling methods.Crossref | GoogleScholarGoogle Scholar | 4597405PubMed |

Aubert, C., Le Moguédec, G., Assio, C., Blatrix, R., Ahizi, M. N., Hedegbetan, G. C., Kpera, N. G., Lapeyre, V., Martin, D., Labbé, P., and Shirley, M. H. (2022). Evaluation of the use of drones to monitor a diverse crocodylian assemblage in West Africa. Wildlife Research 49, 11–23.
Evaluation of the use of drones to monitor a diverse crocodylian assemblage in West Africa.Crossref | GoogleScholarGoogle Scholar |

Bhatnagar, S., Gilla, L., Regan, S., Waldren, S., and Ghosh, B. (2021). A nested drone-satellite approach to monitoring the ecological conditions of wetlands. ISPRS Journal of Photogrammetry and Remote Sensing 174, 151–165.
A nested drone-satellite approach to monitoring the ecological conditions of wetlands.Crossref | GoogleScholarGoogle Scholar |

Chabot, D. (2018). Trends in drone research and applications as the Journal of Unmanned Vehicle Systems turns five. Journal of Unmanned Vehicle Systems 6, vi–xv.
Trends in drone research and applications as the Journal of Unmanned Vehicle Systems turns five.Crossref | GoogleScholarGoogle Scholar |

Chabot, D., and Bird, D. M. (2015). Wildlife research and management methods in the 21st century: where do unmanned aircraft fit in? Journal of Unmanned Vehicle Systems 3, 137–155.
Wildlife research and management methods in the 21st century: where do unmanned aircraft fit in?Crossref | GoogleScholarGoogle Scholar |

Christie, K. S., Gilbert, S. L., Brown, C. L., Hatfield, M., and Hanson, L. (2016). Unmanned aircraft systems in wildlife research: current and future applications of a transformative technology. Frontiers in Ecology and the Environment 14, 241–251.
Unmanned aircraft systems in wildlife research: current and future applications of a transformative technology.Crossref | GoogleScholarGoogle Scholar |

Corcoran, E., Winsen, M., Sudholz, A., and Hamilton, G. (2021). Automated detection of wildlife using drones: synthesis, opportunities and constraints. Methods in Ecology and Evolution 12, 1103–1114.
Automated detection of wildlife using drones: synthesis, opportunities and constraints.Crossref | GoogleScholarGoogle Scholar |

Ejrnæs, D. D., and Sprogis, K. R. (2022). Ontogenetic changes in energy expenditure and resting behavior of humpback whale mother–calf pairs examined using unmanned aerial vehicles. Wildlife Research 49, 34–45.
Ontogenetic changes in energy expenditure and resting behavior of humpback whale mother–calf pairs examined using unmanned aerial vehicles.Crossref | GoogleScholarGoogle Scholar |

Fiori, L., Doshi, A., Martinez. E., Orams, M. B., and Bollard-Breen, B. (2017). The use of unmanned aerial systems in marine mammal research. Remote Sensing 9, 54310.3390/rs9060543

Fust, P., and Loos, J. (2020). Development perspectives for the application of autonomous, unmanned aerial systems (UAS) in wildlife conservation. Biological Conservation 241, 108380.
Development perspectives for the application of autonomous, unmanned aerial systems (UAS) in wildlife conservation.Crossref | GoogleScholarGoogle Scholar |

Graves, T. A., Yarnall, M., Johnston, A., Chong, G., Cole, E. K., Janousek, W. M., and Cross, P. (2022). Eyes on the herd: quantifying elk aggregation from satellite, GPS, and UAS data. Ecological Applications , .

Gray, P. C., Bierlich, K. C., Mantell, S. A., Friedlaender, A. S., Goldbogen, J. A., and Johnston, D. W. (2019). Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry. Methods in Ecology and Evolution 10, 1490–1500.
Drones and convolutional neural networks facilitate automated and accurate cetacean species identification and photogrammetry.Crossref | GoogleScholarGoogle Scholar |

Howell, L. G., Clulow, J., Jordan, N. R., Beranek, C. T., Ryan, S. A., Roff, A., and Witt, R. R. (2022). Drone thermal imaging technology provides a cost-effective tool for landscape-scale monitoring of a cryptic forest-dwelling species across all population densities. Wildlife Research 49, 66–78.
Drone thermal imaging technology provides a cost-effective tool for landscape-scale monitoring of a cryptic forest-dwelling species across all population densities.Crossref | GoogleScholarGoogle Scholar |

Johnston, A. N., and Moskal, L. M. (2017). High-resolution habitat modeling with airborne LiDAR for red tree voles. The Journal of Wildlife Management 81, 58–72.
High-resolution habitat modeling with airborne LiDAR for red tree voles.Crossref | GoogleScholarGoogle Scholar |

Joyce, K. E., Duce, S., Leahy, S. M., Leon, J., and Maier, S. W. (2019). Principles and practice of acquiring drone-based image data in marine environments. Marine and Freshwater Research 70, 952–963.
Principles and practice of acquiring drone-based image data in marine environments.Crossref | GoogleScholarGoogle Scholar |

Kattenborn, T., Lopatin, J., Förster, M., Braun, A. C., and Fassnacht, F. E. (2019). UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data. Remote Sensing of Environment 227, 61–73.
UAV data as alternative to field sampling to map woody invasive species based on combined Sentinel-1 and Sentinel-2 data.Crossref | GoogleScholarGoogle Scholar |

Kiszka, J. J., and Heithaus, M. R. (2018). Using aerial surveys to investigate the distribution, abundance, and behavior of sharks and rays. In ‘Shark Research: Emerging Technologies and Applications for the Field and Laboratory’. (Eds C. Carrier, M. R. Heithaus, C. A. Simpfendorfer.) pp. 71–82. (CRC Press: Boca Raton, FL, USA.)

Landeo-Yauri, S. S., Castelblanco-Martínez, D. N., Hénaut, Y., Arreola, M. R., and Ramos, E. A. (2022). Behavioural and physiological responses of captive Antillean manatees to small aerial drones. Wildlife Research 49, 24–33.
Behavioural and physiological responses of captive Antillean manatees to small aerial drones.Crossref | GoogleScholarGoogle Scholar |

Linchant, J., Lisein, J., Semeki, J., Lejeune, P., and Vermeulen, C. (2015). Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges. Mammal Review 45, 239–252.
Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges.Crossref | GoogleScholarGoogle Scholar |

McMahon, M. C., Ditmer, M. A., and Forester, J. D. (2022). Comparing unmanned aerial systems with conventional methodology for surveying a wild white-tailed deer population. Wildlife Research 49, 54–65.
Comparing unmanned aerial systems with conventional methodology for surveying a wild white-tailed deer population.Crossref | GoogleScholarGoogle Scholar |

Nowak, M. M., Dziób, K., and Bogawski, P. (2018). Unmanned aerial vehicles (UAVs) in environmental biology: a review. European Journal of Ecology 4, 56–74.
Unmanned aerial vehicles (UAVs) in environmental biology: a review.Crossref | GoogleScholarGoogle Scholar |

Odzer, M. N., Brooks, A. M. L., Heithaus, M. R., and Whitman, E. R. (2022). Effects of environmental factors on the detection of subsurface green turtles in aerial drone surveys. Wildlife Research 49, 79–88.
Effects of environmental factors on the detection of subsurface green turtles in aerial drone surveys.Crossref | GoogleScholarGoogle Scholar |

Oleksyn, S., Tosetto, L., Raoult, V., and Williamson, J. E. (2021). Drone-based tracking of the fine-scale movement of a coastal stingray (Bathytoshia brevicaudata). Remote Sensing 13, 40.
Drone-based tracking of the fine-scale movement of a coastal stingray (Bathytoshia brevicaudata).Crossref | GoogleScholarGoogle Scholar |

Pirotta, V., Smith, A., Ostrowski, M., Russell, D., Jonsen, I. D., Grech, A., and Harcourt, R. (2017). An economical custom-built drone for assessing whale health. Frontiers in Marine Science 4, 425.
An economical custom-built drone for assessing whale health.Crossref | GoogleScholarGoogle Scholar |

Preece, J. (2016). Citizen science: new research challenges for human–computer interaction. International Journal of Human–Computer Interaction 32, 585–612.
Citizen science: new research challenges for human–computer interaction.Crossref | GoogleScholarGoogle Scholar |

Preston, T. P., Wildhaber, M. L., Green, N. S., Albers, J. L., and Debenedetto, G. P. (2021). Enumerating white-tailed deer using Unmanned Aerial Vehicles. Wildlife Society Bulletin 45, 97–108.
Enumerating white-tailed deer using Unmanned Aerial Vehicles.Crossref | GoogleScholarGoogle Scholar |

Räsänen, A., and Virtanen, T. (2019). Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes. Remote Sensing of Environment 230, 111207.
Data and resolution requirements in mapping vegetation in spatially heterogeneous landscapes.Crossref | GoogleScholarGoogle Scholar |

Rieucau, G., Kiszka, J. J., Castillo, J. C., Mourier, J., Boswell, K. M., and Heithaus, M. R. (2018). Using unmanned aerial vehicle (UAV) surveys and image analysis in the study of large surface‐associated marine species: a case study on reef sharks Carcharhinus melanopterus shoaling behaviour. Journal of Fish Biology 93, 119–127.
Using unmanned aerial vehicle (UAV) surveys and image analysis in the study of large surface‐associated marine species: a case study on reef sharks Carcharhinus melanopterus shoaling behaviour.Crossref | GoogleScholarGoogle Scholar | 29855056PubMed |

Rigge, M., Homer, C., Cleeves, L., Meyer, D. K., Bunde, B., Shi, H., Xian, G., Schell, S., and Bobo, M. (2020). Quantifying western US rangelands as fractional components with multi-resolution remote sensing and in situ data. Remote Sensing 12, 412.
Quantifying western US rangelands as fractional components with multi-resolution remote sensing and in situ data.Crossref | GoogleScholarGoogle Scholar |

Rutten, A., Casaer, J., Vogels, M. F., Addink, E. A., Vanden Borre, J., and Leirs, H. (2018). Assessing agricultural damage by wild boar using drones. Wildlife Society Bulletin 42, 568–576.
Assessing agricultural damage by wild boar using drones.Crossref | GoogleScholarGoogle Scholar |

Sankey, J. B., Sankey, T. T., Li, J., Ravi, S., Wang, G., Caster, J., and Kasprak, A. (2021). Quantifying plant-soil-nutrient dynamics in rangelands: fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland. Remote Sensing of Environment 253, 112223.
Quantifying plant-soil-nutrient dynamics in rangelands: fusion of UAV hyperspectral-LiDAR, UAV multispectral-photogrammetry, and ground-based LiDAR-digital photography in a shrub-encroached desert grassland.Crossref | GoogleScholarGoogle Scholar |

Saunders, D., Nguyen, H., Cowen, S., Magrath, M., Marsh, K., Bell, S., and Bobruk, J. (2022). Radio-tracking wildlife with drones: a viewshed analysis quantifying survey coverage across diverse landscapes. Wildlife Research 49, 1–10.
Radio-tracking wildlife with drones: a viewshed analysis quantifying survey coverage across diverse landscapes.Crossref | GoogleScholarGoogle Scholar |

Smith, J. A., and Pinter-Wollman, N. (2021). Observing the unwatchable: integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data. Journal of Animal Ecology 90, 62–75.
Observing the unwatchable: integrating automated sensing, naturalistic observations and animal social network analysis in the age of big data.Crossref | GoogleScholarGoogle Scholar |

Stewart, J. D., Durban, J. W., Fearnbach, H., Barrett‐Lennard, L. G., Casler, P. K., Ward, E. J., and Dapp, D. R. (2021a). Survival of the fattest: linking body condition to prey availability and survivorship of killer whales. Ecosphere 12, e03660.
Survival of the fattest: linking body condition to prey availability and survivorship of killer whales.Crossref | GoogleScholarGoogle Scholar |

Stewart, J. D., Durban, J. W., Knowlton, A. R., Lynn, M. S., Fearnbach, H., Barbaro, J., Perryman, W. L., Miller, C. A., and Moore, M. J. (2021b). Decreasing body lengths in North Atlantic right whales. Current Biology 31, 3174–3179.
Decreasing body lengths in North Atlantic right whales.Crossref | GoogleScholarGoogle Scholar | 34087102PubMed |

Straw, A. D. (2021). Review of methods for animal videography using camera systems that automatically move to follow the animal. Integrative and Comparative Biology 61, 917–925.
Review of methods for animal videography using camera systems that automatically move to follow the animal.Crossref | GoogleScholarGoogle Scholar | 34117754PubMed |

Sudholz, A., Denman, S., Pople, A., Brennan, M., Amos, M., and Hamilton, G. (2022). A comparison of manual and automated detection of rusa deer (Rusa timorensis) from RPAS-derived thermal imagery. Wildlife Research 49, 46–53.
A comparison of manual and automated detection of rusa deer (Rusa timorensis) from RPAS-derived thermal imagery.Crossref | GoogleScholarGoogle Scholar |

Torres, L. G., Nieukirk, S. L., Lemos, L., and Chandler, T. E. (2018). Drone up! Quantifying whale behavior from a new perspective improves observational capacity. Frontiers in Marine Science 5, 319.
Drone up! Quantifying whale behavior from a new perspective improves observational capacity.Crossref | GoogleScholarGoogle Scholar |

Torres, L. G., Barlow, D. R., Chandler, T. E., and Burnett, J. D. (2020). Insight into the kinematics of blue whale surface foraging through drone observations and prey data. PeerJ 8, e8906.
Insight into the kinematics of blue whale surface foraging through drone observations and prey data.Crossref | GoogleScholarGoogle Scholar | 32351781PubMed |

Wang, D., Shao, Q., and Yue, H. (2019). Surveying wild animals from satellites, manned aircraft and unmanned aerial systems (UASs): a review. Remote Sensing 11, 1308.
Surveying wild animals from satellites, manned aircraft and unmanned aerial systems (UASs): a review.Crossref | GoogleScholarGoogle Scholar |