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Enumerating White-Tailed Deer Using Unmanned Aerial Vehicles
Todd M. Preston,
Mark L. Wildhaber,
Nicholas S. Green,
Janice L. Albers,
Geoffrey P. Debenedetto,
Corresponding Author
Todd M. Preston
U.S. Geological Survey, Northern Rocky Mountain Science Center, 2327 University Way, Suite 2, Bozeman, MT, 59715 USA
E-mail: [email protected]
Search for more papers by this authorMark L. Wildhaber
U.S. Geological Survey, Columbia Environmental Research Center, 4200 E New Haven Rd, Columbia, MO, 65201 USA
Search for more papers by this authorNicholas S. Green
U.S. Geological Survey, Columbia Environmental Research Center, 4200 E New Haven Rd, Columbia, MO, 65201 USA
Search for more papers by this authorJanice L. Albers
U.S. Geological Survey, Columbia Environmental Research Center, 4200 E New Haven Rd, Columbia, MO, 65201 USA
Search for more papers by this authorGeoffrey P. Debenedetto
U.S. Geological Survey, Arizona Water Science Center, 2255 Gemini Drive, Flagstaff, AZ, 86001 USA
Search for more papers by this authorTodd M. Preston,
Mark L. Wildhaber,
Nicholas S. Green,
Janice L. Albers,
Geoffrey P. Debenedetto,
Corresponding Author
Todd M. Preston
U.S. Geological Survey, Northern Rocky Mountain Science Center, 2327 University Way, Suite 2, Bozeman, MT, 59715 USA
E-mail: [email protected]
Search for more papers by this authorMark L. Wildhaber
U.S. Geological Survey, Columbia Environmental Research Center, 4200 E New Haven Rd, Columbia, MO, 65201 USA
Search for more papers by this authorNicholas S. Green
U.S. Geological Survey, Columbia Environmental Research Center, 4200 E New Haven Rd, Columbia, MO, 65201 USA
Search for more papers by this authorJanice L. Albers
U.S. Geological Survey, Columbia Environmental Research Center, 4200 E New Haven Rd, Columbia, MO, 65201 USA
Search for more papers by this authorGeoffrey P. Debenedetto
U.S. Geological Survey, Arizona Water Science Center, 2255 Gemini Drive, Flagstaff, AZ, 86001 USA
Search for more papers by this authorABSTRACT
The white-tailed deer (Odocoileus virginianus) is an ecologically important species in forests of North America. Effective management of forests requires accurate, precise estimates of deer population abundance to plan and justify management actions. Spotlight surveys in combination with distance sampling are a common method of estimating deer population abundance; however, spotlight surveys are known to have serious drawbacks such as high costs and sampling biases. Therefore, we tested the effectiveness of enumerating deer from unmanned aerial vehicle (UAV) flights, conducted 1 and 6 March 2018, to develop population and density estimates in 2 United States National Parks: Harpers Ferry National Historic Park (HAFE) and Monocacy National Battlefield (MONO). Concurrent spotlight surveys at MONO enabled us to compare estimates obtained by the 2 methods. Deer density estimates by 4 observers of UAV-obtained thermal imagery from HAFE were 94.5 ± 3.9 deer/km2. Concurrent UAV and spotlight surveys at MONO found 19.7 ± 0.5 deer/km2 and 6.4 ± 4.9 deer/km2, respectively; suggesting that spotlight surveys may significantly underestimate deer densities. Despite the logistical challenges to UAV operation, our findings demonstrate that UAVs will become an invaluable tool for wildlife management as technology improves. © 2021 The Wildlife Society. This article has been contributed to by US Government employees and their work is in the public domain in the USA.
LITERATURE CITED
- Aiken, R., and A. Harris. 2011. Deer hunting in the United States: demographics and trends. U.S. Fish and Wildlife Service Report 2006–10. <http://digitalmedia.fws.gov/cdm/ref/collection/document/id/207>. Accessed 9 May 2015.
- Anderson, C. W., C. K. Nielsen, C. M. Hester, R. D. Hubbard, J. K. Stroud, and E. M. Schauber. 2013. Comparison of indirect and direct methods of distance sampling for estimating density of white-tailed deer. Wildlife Society Bulletin 37: 146–154.
- Barton, K. 2019. MuMIn: multi-model inference. R package version 1.43.15. <https://cran.r-project.org/web/packages/MuMIn/index.html>. Accessed 10 Jan 2019.
- Bates, S. E. 2006. National Capital Region Network Inventory and Monitoring Program: white-tailed deer density monitoring protocol version 1.1: distance and pellet-group surveys. Unpublished report. <http://science.nature.nps.gov/im/units/ncrn/publications.cfm?tab=4#products>. Accessed 16 Jun 2015.
- Bates, S. E. 2007. National Capital Region Network 2006 deer monitoring report. Natural Resource Technical Report NPS/NCRN/NRTR—2007/033. U.S. National Park Service, Fort Collins, Colorado, USA.
- Bissonette, J. A., C. A. Kassar, and L. J. Cook. 2008. Assessment of costs associated with deer–vehicle collisions: human death and injury, vehicle damage, and deer loss. Human–Wildlife Conflicts 2: 17–27.
- Boukoberine, M. N., Z. Zhou, and M. Benbouzid. 2019. A critical review on unmanned aerial vehicles power supply and energy management: solutions, strategies, and prospects. Applied Energy 255:113823.
- Buckland, S. T., D. R. Anderson, K. P. Burnham, J. L. Laake, D. L. Borchers, and L. Thomas. 2001. Introduction to distance sampling. Oxford University Press, New York, New York, USA.
- Buckland, S. T., E. A. Rexstad, T. A. Marques, and C. S. Oedekoven. 2015. Distance sampling: methods and applications. Springer International Publishing, New York, New York, USA.
- Burke, C., M. Rashman, S. Wich, A. Symons, C. Theron, and S. Longmore. 2018. Optimising observing strategies for monitoring animals use drone-mounted thermal infrared cameras. International Journal of Remote Sensing 40: 439–467.
- Burnham, K. P., and D. R. Anderson. 2002. Model selection and multimodel inference: a practical information-theoretical approach. Second edition. Springer-Verlag, New York, New York, USA.
- Carr, N. L., A. R. Rodgers, S. R. Kingston, P. N. Hettinga, L. M. Thompson, J. L. Renton, and P. J. Wilson. 2012. Comparative woodland caribou population surveys in Slate Islands Provincial Park, Ontario. Rangifer 20: 205–217.
- Chabot, D. 2009. Systematic evaluation of a stock unmanned aerial vehicle (UAV) system for small-scale wildlife survey applications. Thesis, McGill University, Montreal, Canada.
- Chrétien, L. P., J. Théau, and P. Ménard. 2015. Wildlife multispecies remote sensing using visible and thermal infrared imagery acquired from an unmanned aerial vehicle (UAV). International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 11: 241–248.
- Chrétien, L. P., J. Théau, and P. Ménard. 2016. Visible and thermal infrared remote sensing for the detection of white-tailed deer using an unmanned aerial system. Wildlife Society Bulletin 40: 181–191.
- Collier, B. A., S. S. Ditchkoff, C. R. Ruth, and J. B. Raglin. 2013. Spotlight surveys for white-tailed deer: monitoring panacea or exercise in futility? Journal of Wildlife Management 77: 165–171.
- Conover, M. R., W. C. Pitt, K. K. Kessler, T. J. DuBow, and W. A. Sandborn. 1995. Review of human injuries, illnesses, and economic losses caused by wildlife in the United States. Wildlife Society Bulletin 23: 407–414.
- Côté, S. D., T. P. Rooney, J. P. Tremblay, C. Dussault, and D. M. Waller. 2004. Ecological impacts of deer overabundance. Annual Review of Ecology, Evolution, and Systematics 35: 113–147.
- Drake, D., J. B. Paulin, P. D. Curtis, D. J. Decker, and G. J. San Julian. 2005. Assessment of negative economic impacts from deer in the northeastern United States. Journal of Extension 43. <https://www.joe.org/joe/2005february/rb5.php>. Accessed 6 Jun 2015.
- Franke, U., B. Goll, U. Hohmann, and M. Heurich. 2012. Aerial ungulate surveys with a combination of infrared and high-resolution natural colour images. Animal Biodiversity and Conservation 35: 2.
- Gonzalez, L. F., G. A. Montes, E. Puig, S. Johnson, K. Mengersen, and K. J. Gaston. 2016. Unmanned aerial vehicles (UAVs) and artificial intelligence revolutionizing wildlife monitoring and conservation. Sensors 16. <https://www.mdpi.com/1424-8220/16/1/97>. Accessed 15 Nov 2018.
- Green, N. S., M. L. Wildhaber, and J. L. Albers. 2021. Effectiveness of a distance sampling from roads program for white-tailed deer in the National Capital Region parks. Natural Resource Report. NPS/NCR/NRR–2021/2224. National Park Service. Fort Collins, Colorado.
- Hardin, P. J., V. Lulla, R. R. Jensen, and J. R. Jensen. 2019. Small unmanned aerial systems (sUAS) for environmental remote sensing: challenges and opportunities revisited. GIScience and Remote Sensing 56: 309–322.
- Havens, K. J., and E. J. Sharp. 2005. Thermal imaging techniques to survey and monitor animals in the wild: a methodology. Academic Press, San Diego, California, USA.
- Hodgson, A., N. Kelley, and D. Peel. 2013. Unmanned aerial vehicles (UAVs) for surveying marine fauna: a dugong case study. PLoS ONE 8:e79556.
- Hodgson, J. C., S. M. Baylis, R. Mott, A. Herrod, and R. H. Clarke. 2016. Precision wildlife monitoring using unmanned aerial vehicles. Scientific Reports 6:22574.
- Hodgson, J. C., R. Mott, S. M. Baylis, T. T. Pham, S. Wotherspoon, A. D. Kilpatrick, R. R. Segaran, I. Reid, A. Terauds, and L. P. Koh. 2018. Drones count wildlife more accurately and precisely than humans. Methods in Ecology and Evolution 9: 1160–1167.
- Israel, M. 2011. A UAS-based roe deer fawn detection system. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences 38: 1–5.
- Israel, M., and A. Reinhard. 2017. Detecting nests of lapwing birds with the aid of a small unmanned aerial vehicle with thermal camera. International Conference on Unmanned Aircraft Systems. Miami, FL, USA.
- Jones, G. P. IV, L. G. Pearlstine, and H. F. Percival. 2006. An assessment of small unmanned aerial vehicles for wildlife research. Wildlife Society Bulletin 34: 750–758.
- Kaneko, K., and S. Nohara. 2014. Review of effective vegetation mapping using the UAV (unmanned aerial vehicle) method. Journal of Geographic Information Systems 6: 733–742.
- Kinzel, P., M. Bauer, C. Holmquist-Johnson, and T. Preston. 2015. Experimental flights with a small UAS for mapping emergent sandbars. Great Plains Research 25: 39–52.
- Kissell, R. E., and S. K. Nimmo. 2011. A technique to estimate white-tailed deer Odocoileus virginianus density using vertical looking infrared imagery. Wildlife Biology 17: 85–92.
- Linchant, J., J. Lisein, J. Semeki, P. Lejeune, and C. Vermeulen. 2015. Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges. Mammal Review 45: 239–252.
- Mancini, F., M. Dubbini, M. Gattelli, F. Stecchi, S. Fabbri, and G. Gabbianelli. 2013. Using unmanned aerial vehicles (UAV) for high-resolution reconstruction of topography: the structure from motion approach on coastal environments. Remote Sensing 5: 6880–6898.
- McCullough, D. R. 1982. Evaluation of night spotlighting as a deer study technique. Journal of Wildlife Management 46: 963–973.
- McShea, W. J., C. M. Stewart, L. Kearns, and S. Bates. 2011. Road bias for deer density estimates at 2 national parks in Maryland. Wildlife Society Bulletin 35: 177–184.
- Miller, D. L. 2017. Distance: distance sampling detection function and abundance estimation. R package version 0.9.7. <https://cran.r-project.org/web/packages/Distance/index.html>. Accessed 10 Jan 2019.
- Miller, D. L., E. Rexstad, L. Thomas, L. Marshall, and J. L. Laake. 2019. Distance sampling in R. Journal of Statistical Software 89: 1–28.
- National Park Service [NPS]. 2015a. Harpers Ferry National Park trees and shrubs. <https://www.nps.gov/hafe/learn/nature/treesandshrubs.htm>. Accessed 13 Feb 2020.
- National Park Service [NPS]. 2015b. Harpers Ferry National Park weather. <https://www.nps.gov/hafe/learn/nature/weather.htm>. Accessed 13 Feb 2020.
- National Park Service [NPS]. 2015c. Monocacy National Park trees. <https://www.nps.gov/mono/learn/nature/plants_trees.htm>. Accessed 13 Feb 2020.
- Nielsen, D. G., M. J. Dunlap, and K. V. Miller. 1982. Pre-rut rubbing by white-tailed bucks: nursery damage, social role, and management options. Wildlife Society Bulletin 10: 341–348.
- Potvin, F., and L. Breton. 2005. From the field: testing two aerial survey techniques on deer in fenced enclosures—visual double counts and thermal infrared sensing. Wildlife Society Bulletin 33: 317–325.
- R Core Team. 2018. R: a language and environment for statistical computing. Version 3.5.2. R Foundation for Statistical Computing, Vienna, Austria. ISBN 3-900051-07-0. <http://www.R-project.org>. Accessed 10 Jan 2019.
- Rawinski, T. J., 2008. Impacts of white-tailed deer overabundance in forest ecosystems: an overview. <http://www.northforkdeer.org/files/80704231.pdf>. Accessed 15 Nov 2018.
- Roberts, C. W., B. L. Pierce, A. W. Braden, R. R. Lopez, N. J. Silvy, P. A. Frank, and D. Ransom. 2006. Comparison of camera and road survey estimates for white-tailed deer. Journal of Wildlife Management 70: 263–267.
- Russell, F. L., D. B. Zippin, and N. L. Fowler. 2001. Effects of white-tailed deer (Odocoileus virginianus) on plants, plant populations and communities: a review. American Midland Naturalist 146: 1–26.
- Seymour, A. C., J. Dale, M. Hammill, P. N. Halpin, and D. W. Johnston. 2017. Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery. Scientific Reports 7:45127.
- Shao, W., R. Kawakami, R. Yoshihashi, S. You, H. Kawase, and T. Naemura. 2020. Cattle detection and counting in UAV images based on convolutional neural networks. International Journal of Remote Sensing 41: 31–52.
- Smith, W. P. 1991. Odocoileus virginianus. Mammalian Species 388: 1–13.
- Stewart, C. M., W. J. McShea, and B. P. Piccolo. 2007. The impact of white-tailed deer on agricultural landscapes in 3 National Historical Parks in Maryland. Journal of Wildlife Management 71: 5–6.
- Terletzky, P., R. D. Ramsey, and C. M. U. Neale. 2012. Spectral characteristics of domestic and wild mammals. GIScience & Remote Sensing 49: 597–608.
- Thomas, J. E., A. Banasik, J. P. Campbell, T. J. B. Carruthers, W. C. Dennison, M. Lehman, and M. Nortrup. 2011. Monocacy national battlefield natural resource condition assessment: National capital region. Natural Resource Report NPS/NCRN/NRR—2011/415. National Park Service, Fort Collins, Colorado, USA.
- Thomas, J. E., J. P. Campbell, S. D. Costanzo, W. C. Dennison, M. Lehman, D. Nisbet, M. Nortrup, and M. Parsons. 2013. Harpers Ferry National Historical Park natural resource condition assessment: National Capital Region. Natural Resource Report. NPS/CATO/NRR—2013/746. National Park Service. Fort Collins, Colorado, USA.
- Tremblay, J. P., I. Thibault, C. Dussault, J. Huot, and S. D. Côté. 2005. Long-term decline in white-tailed deer browse supply: can lichens and litterfall act as alternative food sources that preclude density-dependent feedbacks. Canadian Journal of Zoology 83: 1087–1096.
- U.S. Department of the Interior, U.S. Fish and Wildlife Service, and U.S. Department of Commerce, and U.S. Census Bureau. 2011. 2011 National survey of fishing, hunting, and wildlife-associated recreation. FHW/11-NAT(RV). <http://www.census.gov/prod/2012pubs/fhw11-nat.pdf>. Accessed 9 May 2015.
- Vermeulen, C., P. Lejeune, J. Lisein, P. Sawadogo, and P. Bouche. 2013. Unmanned aerial survey of elephants. PLoS ONE e54700.
- Waller, D. M., and W. S. Alverson. 1997. The white-tailed deer: a keystone herbivore. Wildlife Society Bulletin 25: 217–226.
- Wang, D., Q. Shao, and H. Yue. 2019. Surveying wild animals from satellites, manned aircraft, and unmanned aerial systems (UASs): a review. Remote Sensing 11: 1308.
- Ward, A. I., P. C. White, and C. H. Critchley. 2004. Roe deer Capreolus capreolus behaviour affects density estimates from distance sampling surveys. Mammal Review 34: 315–319.
- Watts, A. C., J. H. Perry, S. E. Smith, M. A. Burgess, B. E. Wilkinson, Z. Szantoi, P. G. Ifju, and H. F. Percival. 2010. Small unmanned aircraft systems for low-altitude aerial surveys. Journal of Wildlife Management 74: 1614–1619.
- Witczuk, J., S. Pagacz, A. Zmarz, and M. Cypel. 2018. Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests—preliminary results. International Journal of Remote Sensing 39: 5504–5521.
