The use of drones for studying the behaviour of mammals

Capa

Citar

Texto integral

Acesso aberto Acesso aberto
Acesso é fechado Acesso está concedido
Acesso é fechado Somente assinantes

Resumo

Since the advent and wide use of unmanned aerial vehicles (UAVs), they have been increasingly useful in monitoring the abundance, distribution and behaviour of terrestrial and aquatic animals. At present, this technique is actively applied to mammal research. The diversity and relative availability of drones allows for a variety of research tasks to be achieved. The use of UAVs has its advantages and disadvantages, these being discussed in the present review. The study examines the advantages of using UAVs in comparison to other methods, identifies new research opportunities opened up by drones, and emphasizes the advantages of modern analytical tools. The technical limitations of UAVs and the problem of the negative impact of this technique on mammals are discussed. The need to minimize the disturbance of animals during such research is emphasized. In addition, the work summarizes the experience of using UAVs in the studies on Russia’s theriofauna.

Texto integral

Acesso é fechado

Sobre autores

E. Berezina

Saint Petersburg State University

Autor responsável pela correspondência
Email: herionnee@gmail.com
Rússia, Saint Petersburg

A. Gilyov

Saint Petersburg State University

Email: a.gilev@spbu.ru
Rússia, Saint Petersburg

K. Karenina

Saint Petersburg State University

Email: herionnee@gmail.com
Rússia, Saint Petersburg

Bibliografia

  1. Алтухов А.В., Козлов М.С., Кочнев А.А., Крюкова Н.В., Скурихин Л.Э., Чакилев М.В., Бурканов В.Н., 2020. Оценка численности моржа (Odobеnus rosmarus) методом аэрофотосъемки с квадрокоптера Фантом 4 ПРО в бухте Кенискин, Чукотка, в 2017 г. // Морские млекопитающие Голарктики: сборник научных трудов по материалам X международной конференции. Т. 2. С. 42–47.
  2. Беликов Р.А., Прасолова Е.А., Краснова В.В., 2018. Опыт применения дистанционно пилотируемых и привязных беспилотных летательных аппаратов для исследования беломорской и анадырской белухи. // Морские млекопитающие Голарктики: сборник научных трудов по материалам IX международной конференции. Т. 1. С. 50–58.
  3. Березина Е.А., 2021. Сенсорная латерализация в поведении сайгака (Saiga tatarica) и джейрана (Gazella subgutturosa) в природе // ВКР по направлению подготовки “Биология” основная образовательная программа магистратуры "Биологии", Санкт-Петербург, 69 с.
  4. Бычков А.Т., Миронова А.М., Долганов К.В., Анисимова Т.В., Фомин С.В., Белонович О.А., 2021. Наблюдения плотоядных косаток Orcinus orca в акватории лежбищ северного морского котика о-ва Беринга (Командорские острова) в 2020–2021 гг. // Сохранение биоразнообразия Камчатки и прилегающих морей. С. 176–179.
  5. Васильев Д.В., Бабий У.В., Кулемеев П.С., Груздев А.Р., 2021. Результаты учета берлог белого медведя на острове Врангеля в 2020-2021 гг. // Труды Мордовского государственного природного заповедника им. П.Г. Смидовича. № 29. С. 172– 183.
  6. Иванов К.М., Купчинский А.Б., Овдин М.Е., Петров Е.А., Сыроватский А.А., Шабанов Д.Е., 2022. Опыт применения БПЛА в экологических исследованиях популяции байкальской нерпы (Pusa sibirica Gm.) в период начала формирования береговых лежбищ // Международный научно-исследовательский журнал. Т. 8. № 122. C. 1–12.
  7. Костин А.С., 2019. Классификация гражданских беспилотных летательных аппаратов и сферы их применения // Системный анализ и логистика: журнал. Т. 1. № 19. С. 70–80.
  8. Ласкина Н.Б., Гаев Д.Н., Бурканов В.Н., 2020. Опыт применения квадрокоптера для учета численности сивуча (Еumеtopias jubatus) на Юго-Восточном лежбище острова Медный // Морские млекопитающие Голарктики: сборник научных трудов по материалам X международной конференции. Т. 2. С. 103–110.
  9. Медведев А.А., Алексеенко Н.А., Карпенко И.О., 2015. Мониторинг животного мира на особо охраняемых природных территориях с помощью беспилотных летательных аппаратов // Известия Самарского научного центра РАН. Т. 6. № 1. C. 304–309.
  10. Медведев Н.В., Дудакова Д.С., Дудаков М.О., Сипиля Т., 2017. Особенности поведения ладожской нерпы во время ее учетов с использованием беспилотного летательного аппарата (БПЛА) // Биоразнообразие экосистем крайнего севера: инвентаризация, мониторинг, охрана: III Всероссийская научная конференция: тезисы докладов. С. 238–240.
  11. Моргунов Н.А., Ломанова Н.В., Масленников А.В., Шеду В.В., 2019. Результаты авиаучета лося в ФГБУ ГООХ “Медведица” и в Рыбинском районе Ярославской области в 2017 г. с применением беспилотных летательных аппаратов // Вестник ТвГУ. Серия “Биология и Экология”. Т. 3. № 55. С. 69–78.
  12. Пригоряну О.М., Абадонова М., Карпачев А.П., 2021. Опыт использования БПЛА с тепловизором в мониторинге вольноживущей популяции зубра на примере национального парка “Орловское полесье” // Труды Мордовского государственного природного заповедника им. П. Г. Смидовича. Т. 28.
  13. Скоробогатов Д.О., Загребельный В., Бурканов, В.Н., 2020. Первый опыт применения квадрокоптера Фантом 4 ПРО для оценки численности тихоокеанского моржа (Odobеnus rosmarus) на лежбище мыс Ванкарем, Чукотка, в 2017 г. // Морские млекопитающие Голарктики: сборник научных трудов по материалам X международной конференции. Т. 2. С. 131–136.
  14. Тюрнева О.Ю., Ван Дер Вольф П., Яковлев Ю.М., 2019. Использование беспилотных летательных аппаратов: дополнительные возможности для лабораторной фотоидентификации серых китов (Еschrichtius robustus) // Морские млекопитающие Голарктики: сборник научных трудов по материалам X международной конференции. Т. 1. С. 343–353.
  15. Федорова Л.Н., 2021. Этические аспекты применения беспилотных летательных аппаратов при фото-, видеофиксации диких животных и птиц // Современные проблемы охотоведенья: материалы международной научно-практической конференции, посвященной 60-летию учебно-опытного охотничьего хозяйства “Голоустное” имени О.В. Жарова в рамках Х международной научно-практической конференции “Климат, экология, сельское хозяйство Евразии”. С. 168–172.
  16. Al-Thani N., Albuainain A., Alnaimi F., Zorba, N., 2020. Drones for Sheep Livestock Monitoring // 20th IEEE Mediterranean Electrotechnical Conference, MELECON 2020 – Proceedings. P. 672–676.
  17. Adams K.R., Gibbs L., Knott N.A., Broad A., Hing M., Taylor M.D., Davis A.R., 2020. Coexisting with sharks: a novel, socially acceptable and non-lethal shark mitigation approach // Scientific Reports. V. 10. № 1. P. 1–12.
  18. Adamе K., Pardo M.A., Salvadeo C., Beier E., Elorriaga-Verplancken F.R., 2017. Detectability and categorization of California sea lions using an unmanned aerial vehicle // Marine Mammal Science. V. 33. № 3. P. 913–925.
  19. Anderson K., Gaston K.J., 2013. Lightweight unmanned aerial vehicles will revolutionize spatial ecology // Frontiers in Ecology and the Environment. V. 11. № 3. P. 138–146.
  20. Aubin J.A., Mikus M.A., Michaud R., Mennill D., Vergara V., 2023. Fly with care: belugas show evasive responses to low altitude drone flights // Marine Mammal Science. V. 39. № 3. P. 718–739.
  21. Azizeh T.R., Sprogis K.R., Soley R., Nielsen M.L.K.K., Uhart M.M., Sironi M., Maron C.F., BejderL., Madsen P.T., Christiansen F., 2021. Acute and chronic behavioral effects of kelp gull micropredation on southern right whale mother-calf pairs off Peninsula Valdes, Argentina // Marine Ecology Progress Series. V. 668. P. 133–148.
  22. Bennitt E., Bartlam-Brooks H.L.A., Hubel T.Y., Wilson A.M., 2019. Terrestrial mammalian wildlife responses to Unmanned Aerial Systems approaches // Scientific Reports. V. 9. № 1. P. 2142.
  23. Bernardes R.C., Lima M.A.P., Guedes R.N.C., da Silva C.B., Martins G.F., 2021. Ethoflow: Computer vision and artificial intelligence-based software for automatic behavior analysis // Sensors. V. 21. № 9. P. 3237.
  24. Brunton E., Bolin J., Leon J., Burnett S., 2019. Fright or Flight? Behavioural responses of kangaroos to drone-based monitoring // Drones. V. 3. № 2. P. 41.
  25. Chabot D., Stapleton S., Francis C.M., 2019. Measuring the spectral signature of polar bears from a drone to improve their detection from space // Biological Conservation. V. 237. P. 125–132.
  26. Chrétien L.-P., Theau J., Menard P., 2015. Wildlife multispecies remote sensing using visible and thermal infrared imagery acquired from an unmanned aerial vehicle (UAV) // The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. V. XL-1/W4. P. 241–248.
  27. Chrétien L.-P., Theau J., Menard P., 2016. Visible and thermal infrared remote sensing for the detection of white-tailed deer using an unmanned aerial system // Wildlife Society Bulletin. V. 40. № 1. P. 181–191.
  28. Christiansen F., Rojano-Doñate L., Madsen P.T., Bejder L., Harcourt R., 2016. Noise levels of multi-rotor unmanned aerial vehicles with implications for potential underwater impacts on marine Mammals // Frontiers in Marine Science. V. 3. P. 277.
  29. Christiansen F., Sironi M., Moore M. J., Di Martino M., Ricciardi M., Warick H.A., Irschick D.J., Gutierrez R., Uhart M.M., 2019. Estimating body mass of free-living whales using aerial photogrammetry and 3D volumetrics // Methods in Ecology and Evolution. V. 10. № 12. P. 2034–2044.
  30. Christie K.S., Gilbert S.L., Brown C.L., Hatfield M., Hanson L., 2016. Unmanned aircraft systems in wildlife research: current and future applications of a transformative technology // Frontiers in Ecology and the Environment. V. 14. № 5. P. 241–251.
  31. Colefax A.P., Butcher P.A., Kelaher B.P., 2018. The potential for unmanned aerial vehicles (UAVs) to conduct marine fauna surveys in place of manned aircraft // ICES Journal of Marine Science. V. 75. № 1. P. 1–8.
  32. Colefax A.P., Butcher P.A., Pagendam D.E., Kelaher B.P., 2019. Reliability of marine faunal detections in drone-based monitoring // Ocean Coastal Management. V. 174. P. 108–115.
  33. Corcoran E., Winsen M., Sudholz A., Hamilton G., 2021. Automated detection of wildlife using drones: Synthesis, opportunities and constraints // Methods in Ecology and Evolution. V. 12. № 6. P. 1103–1114.
  34. Costa H., Rogan A., Zadra C., Larsen O., Rikardsen A.H., Waugh C., 2022. Blowing in the wind: using a consumer drone for the collection of humpback whale (Megaptera novaeangliae) blow samples during the Arctic polar nights // Drones. V. 7. № 1. P. 15.
  35. Ditmer M.A., Vincent J.B., Werden L.K., Tanner J.C., Laske T.G., Iaizzo P.A., Garshelis D.L., Fieberg J.R., 2015. Bears show a physiological but limited behavioral response to unmanned aerial vehicles // Current Biology. V. 25. № 17. P. 2278–2283.
  36. Ditmer M.A., Werden L.K., Tanner J.C., Vincent J.B., Callahan P., Iaizzo P.A., Laske T.G., Garshelis D.L., 2019. Bears habituate to the repeated exposure of a novel stimulus, unmanned aircraft systems // Conservation Physiology. V. 7. № 1. P. coy067.
  37. Ednie G., Bird D.M., Elliott K.H., 2021. Fewer bat passes are detected during small, commercial drone flights // Scientific Reports. V. 11. № 1. P. 11529.
  38. Frouin-Mouy H., Tenorio-Halle L., Thode A., Swartz S., Urban J., 2020. Using two drones to simultaneously monitor visual and acoustic behaviour of gray whales (Eschrichtius robustus) in Baja California, Mexico // Journal of Experimental Marine Biology and Ecology. V. 525. P. 151321.
  39. Fu Y., Kinniry M., Kloepper L.N., 2018. The Chirocopter: A UAV for recording sound and video of bats at altitude // Methods in Ecology and Evolution. V. 9. № 6. P. 1531–1535.
  40. Giles A.B., Butcher P.A., Colefax A.P., Pagendam D.E., Mayjor M., Kelaher B.P., 2021. Responses of bottlenose dolphins (Tursiops spp.) to small drones // Aquatic Conservation: Marine and Freshwater Ecosystems. V. 31. № 3. P. 677–684.
  41. Goldbogen J.A., Cade D.E., Calambokidis J., Friedlaender A.S., Potvin J., Segre P.S., Werth A.J., 2017. How baleen whales feed: the biomechanics of engulfment and filtration // Annual Review of Marine Science. V. 9. № 1. P. 367–386.
  42. Graving J.M., Chae D., Naik H., Li L., Koger B., Costelloe B.R., Couzin I.D., 2019. Deepposekit, a software toolkit for fast and robust animal pose estimation using deep learning // ELife. V. 8. P. e47994.
  43. Grenzdörffer G.J., 2013. UAS-based automatic bird count of a common gull colony // International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. V. XL-1. № W2. P. 169–174.
  44. Hardin P., Jensen R., 2011. Small-scale unmanned aerial vehicles in environmental remote sensing: Challenges and opportunities // GIScience and Remote Sensing. V. 48. № 1. P. 99–111.
  45. Headland T., Ostendorf B., Taggart D., 2021. The behavioral responses of a nocturnal burrowing marsupial (Lasiorhinus latifrons) to drone flight // Ecology and Evolution. V. 11. № 17. P. 12173–12181.
  46. Hodgson A., Kelly N., Peel D., 2013. Unmanned aerial vehicles (UAVs) for surveying marine fauna: a dugong case study // PLOS ONE. V. 8. № 11. P. e79556.
  47. Hodgson J.C., Baylis S.M., Mott R., Herrod A., Clarke R.H., 2016. Precision wildlife monitoring using unmanned aerial vehicles // Scientific Reports. V. 6. № 1. P. 22574.
  48. Hodgson J.C., Mott R., Baylis S.M., Pham T.T., Wotherspoon S., Kilpatrick A.D., Raja Segaran R., Reid I., Terauds A., Koh L.P., 2018. Drones count wildlife more accurately and precisely than humans // Methods in Ecology and Evolution. V. 9. № 5. P. 1160–1167.
  49. Hughey L.F., Hein A.M., Strandburg-Peshkin A., Jensen F.H., 2018. Challenges and solutions for studying collective animal behaviour in the wild // Philosophical Transactions of the Royal Society B: Biological Sciences. V. 373. № 1746. P. 20170005.
  50. Israel M., 2011. A UAV-based roe deer fawn detection system // International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. V. XXXVIII-1/C22. P. 1–5.
  51. Jagielski P.M., Barnas A.F., Grant Gilchrist H., Richardson E.S., Love O.P., Semeniuk C.A.D., 2022. The utility of drones for studying polar bear behaviour in the Canadian Arctic: opportunities and recommendations // Drone Systems and Applications. V. 10. № 1. P. 97–110.
  52. Jewell Z.C., Alibhai S., Law P.R., Uiseb K., Lee S., 2020. Monitoring rhinoceroses in Namibia’s private custodianship properties // PeerJ. V. 8. P. e9670.
  53. Kelaher B.P., Peddemors V.M., Hoade B., Colefax A.P., Butcher P.A., 2020. Comparison of sampling precision for nearshore marine wildlife using unmanned and manned aerial surveys // Journal of Unmanned Vehicle Systems. V. 8. № 1. P. 30–43.
  54. Kellenberger B., Marcos D., Lobry S., Tuia D., 2019. Half a percent of labels is enough: Efficient animal detection in UAV imagery using deep CNNs and active learning // IEEE Transactions on Geoscience and Remote Sensing. V. 57. № 12. P. 9524–9533.
  55. Kellenberger B., Volpi M., Tuia D., 2017. Fast animal detection in UAV images using convolutional neural networks // 2017 IEEE International Geoscience and Remote Sensing Symposium (IGARSS). P. 866– 869.
  56. King L.E., Lala F., Nzumu H., Mwambingu E., Douglas-Hamilton I., 2017. Beehive fences as a multidimensional conflict-mitigation tool for farmers coexisting with elephants // Conservation Biology. V. 31. № 4. P. 743–752.
  57. King S.L., Connor R.C., Krutzen M., Allen S.J., 2021. Cooperation-based concept formation in male bottlenose dolphins // Nature Communications. V. 12. P. 2373.
  58. Koger B., Deshpande A., Kerby J.T., Graving J.M., Costelloe B.R., Couzin I.D., 2023. Quantifying the movement, behaviour and environmental context of group-living animals using drones and computer vision // Journal of Animal Ecology. V. 92. P. 1357–1371.
  59. Koski W.R., Allen T., Ireland D., Buck G., Smith P.R., Macrender A.M., Halick M.A., Rushing C., Sliwa D.J., McDonald T.L., 2009. Evaluation of an unmanned airborne system for monitoring marine mammals // Aquatic Mammals. V. 35. № 3. P. 347–357.
  60. Landeo-Yauri S.S., Castelblanco-Martinez D.N., Henaut Y., Arreola M.R., Ramos E.A., 2021. Behavioural and physiological responses of captive Antillean manatees to small aerial drones // Wildlife Research. V. 49. № 1. P. 24–33.
  61. Larsen H.L., Møller-Lassesen K., Enevoldsen E.M.E., Madsen S.B., Obsen M.T., Povlsen P. et al., 2023. Drone with mounted thermal infrared cameras for monitoring terrestrial mammals // Drones. V. 7. № 11. P. 680.
  62. Lee S., Song Y., Kil S.H., 2021. Feasibility analyses of real-time detection of wildlife using UAV-derived thermal and RGB images // Remote Sensing. V. 13. № 11. P. 2169.
  63. Lenzi J., Felege C.J., Newman R., McCann B., Ellis-Felege S.N., 2022. Feral horses and bison at Theodore Roosevelt National Park (North Dakota, United States) exhibit shifts in behaviors during drone flights // Drones. V. 6. № 6. P. 136.
  64. Lethbridge M., Stead M., Wells C., 2019. Estimating kangaroo density by aerial survey: a comparison of thermal cameras with human observers // Wildlife Research. V. 46. № 8. P. 639–648.
  65. Lhoest S., Linchant J., Quevauvillers S., Vermeulen C., Lejeune P., 2015. How many hippos (HOMHIP): algorithm for automatic counts of animals with infra-red thermal imagery from UAV // The International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences. V. XL-3. № W3. P. 355–362.
  66. Linchant J., Lisein J., Semeki J., Lejeune P., Vermeulen C., 2015. Are unmanned aircraft systems (UASs) the future of wildlife monitoring? A review of accomplishments and challenges // Mammal Review. P. 45. № 4. P. 239–252.
  67. López J. J., Mulero-Pázmány M., 2019. Drones for conservation in protected areas: present and future // Drones. V. 3. № 1. P. 10.
  68. Lu V., Xu F., Turghan M.A., 2021. Przewalski’s Horses (Equus ferus przewalskii) responses to unmanned aerial vehicles flights under semireserve conditions: conservation implication // International Journal of Zoology. V. 2021. P. 6687505.
  69. Maeda T., Ochi S., Ringhofer M., Sosa S., Sueur C., Hirata S., Yamamoto S., 2021. Aerial drone observations identified a multilevel society in feral horses // Scientific Reports. V. 11. № 1. P. 71.
  70. McCarthy E.D., Martin J.M., Boer M.M., Welbergen J.A., 2021. Drone‐based thermal remote sensing provides an effective new tool for monitoring the abundance of roosting fruit bats // Remote Sensing in Ecology and Conservation. V. 7. № 3. P. 461–474.
  71. McEvoy J.F., Hall G.P., McDonald P.G., 2016. Evaluation of unmanned aerial vehicle shape, flight path and camera type for waterfowl surveys: disturbance effects and species recognition // PeerJ. V. 4. P. e1831.
  72. McIntosh R.R., Holmberg R., Dann P., 2018. Looking without landing — using remote piloted aircraft to monitor fur seal populations without disturbance // Frontiers in Marine Science. V. 5. P. 202.
  73. Mesquita G.P., Rodriguez-Teijeiro J.D., De Oliveira R.R., Mulero-Pazmany M., 2021. Steps to build a DIY low-cost fixed-wing drone for biodiversity conservation // PLOS ONE. V. 16. №8. P. e0255559.
  74. Mo M., Bonatakis K., 2022. An examination of trends in the growing scientific literature on approaching wildlife with drones // Drone Systems and Applications. V. 10. № 1. P. 111–139.
  75. Mufford J.T., Hill D.J., Flood N.J., Church J.S., 2019. Use of unmanned aerial vehicles (UAVs) and photogrammetric image analysis to quantify spatial proximity in beef cattle // Journal of Unmanned Vehicle Systems. V. 7. № 3. P. 194–206.
  76. Mulero-Pázmány M., Barasona J.Á., Acevedo P., Vicente J., Negro J. J., 2015. Unmanned Aircraft Systems complement biologging in spatial ecology studies // Ecology and Evolution. V. 5. № 21. P. 4808–4818.
  77. Mulero-Pázmány M., Jenni-Eiermann S., Strebel N., Sattler T., Negro J.J., Tablado Z., 2017. Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review // PLOS ONE. V. 12. № 6. P. e0178448.
  78. Mulero-Pázmány M., Stolper R., Van Essen L.D., Negro J.J., Sassen T., 2014. Remotely piloted aircraft systems as a rhinoceros anti-poaching tool in Africa // PLOS ONE. V. 9. № 1. P. e83873.
  79. Niethammer U., James M.R., Rothmund S., Travelletti J., Joswig M., 2012. UAV-based remote sensing of the Super-Sauze landslide: Evaluation and results // Engineering Geology. V. 128. P. 2–11.
  80. Nyamuryekung’e S., Cibils A.F., Estell R.E., Gonzalez A.L., 2016. Use of an Unmanned Aerial Vehicle − Mounted Video Camera to Assess Feeding Behavior of Raramuri Criollo Cow // Rangeland Ecology & Management. V. 69. № 5. P. 386–389.
  81. Oishi Y., Oguma H., Tamura A., Nakamura R., Matsunaga T., 2018. Animal detection using thermal images and its required observation conditions // Remote Sensing. V. 10. № 7. P. 1050.
  82. Pirotta V., Smith A., Ostrowski M., Russell D., Jonsen I.D., Grech A., Harcourt R., 2017. An economical custom-built drone for assessing whale health // Frontiers in Marine Science. V. 4. P. 425.
  83. Pollock T.I., Hunter D.O., Hocking D.P., Evans A.R., Pollock T.I., Hunter D.O., Hocking D.P., Evans A.R., 2022. Eye in the sky: observing wild dingo hunting behaviour using drones // Wildlife Research. V. 50. № 3. P. 212–223.
  84. Pomeroy P., O’ Connor L., Davies P., 2015. Assessing use of and reaction to unmanned aerial systems in gray and harbor seals during breeding and molt in the UK // Journal of Unmanned Vehicle Systems. V. 3. № 3. P. 102–113.
  85. Prosekov A., Vesnina A., Atuchin V., Kuznetsov A., 2022. Robust algorithms for drone-assisted monitoring of big animals in harsh conditions of Siberian winter forests: Recovery of European elk (Alces alces) in Salair Mountain // Animals. V. 12. № 12. P. 1483.
  86. Rathore A., Isvaran K., Guttal V., 2023. Lekking as collective behavior // Philosophical Transactions of the Royal Society B: Biological Sciences. V. 378. P. 20220066
  87. Saitoh T., Kobayashi M., 2021. Appropriate drone flight altitude for horse behavioral observation // Drones. V. 5. № 3. P. 71.
  88. Sasse D.B., 2003. Job-related mortality of wildlife workers in the United States, 1937-2000 // Wildlife Society Bulletin. V. 31. № 4. P. 1015–1020.
  89. Schad L., Fischer J., 2022. Opportunities and risks in the use of drones for studying animal behavior // Methods in Ecology and Evolution. V. 14. № 8. P. 1864–1872.
  90. Schofield G., Esteban N., Katselidis K.A., Hays G.C., 2019. Drones for research on sea turtles and other marine vertebrates – A review // Biological Conservation. V. 238. P. 108214.
  91. Schroeder N.M., Panebianco A., 2021. Sociability strongly affects the behavioural responses of wild guanacos to drones // Scientific Reports. V. 11. P. 20901.
  92. Schroeder N.M., Panebianco A., Gonzalez Musso R., Carmanchahi P., 2020. An experimental approach to evaluate the potential of drones in terrestrial mammal research: A gregarious ungulate as a study model // Royal Society Open Science. V. 7. № 1. P. 191482.
  93. Serin S., Chur J.S., 2022. Choosing the right drone for animal research // Proceedings of the Joint 12th International Conference on Methods and Techniques in Behavioral Research. V. 2. P. 219.
  94. Smith C. E., Sykora-Bodie S.T., Bloodworth B., Pack S.M., Spradlin T.R., LeBoeuf N.R., 2016. Assessment of known impacts of unmanned aerial systems (UAS) on marine mammals: data gaps and recommendations for researchers in the United States // Journal of Unmanned Vehicle Systems. V. 4. № 1. P. 31.
  95. Torres L.G., Nieukirk S.L., Lemos L., Chandler T.E., 2018. Drone up! Quantifying whale behavior from a new perspective improves observational capacity // Frontiers in Marine Science. V. 5. P. 319.
  96. Tuia D., Kellenberger B., Beery S., Costelloe B.R., Zuffi S., Risse B. et al., 2022. Perspectives in machine learning for wildlife conservation // Nature Communications. V. 13. № 1. P. 792.
  97. Vermeulen C., Lejeune P., Lisein J., Sawadogo P., Bouche P., 2013. Unmanned aerial survey of elephants // PLoS ONE. V. 8. № 2. P. e54700.
  98. Wang D., Shao Q., Yue H., 2019. Surveying wild animals from satellites, manned aircraft and unmanned aerial systems (UASs): A review // Remote Sensing. V. 11. № 11. P. 1308.
  99. Weimerskirch H., Prudor A., Schull Q., 2018. Flights of drones over sub-Antarctic seabirds show species- and status-specific behavioural and physiological responses // Polar Biology. V. 41. № 2. P. 259–266.
  100. Weissensteiner M.H., Poelstra J.W., Wolf J.B.W., 2015. Low-budget ready-to-fly unmanned aerial vehicles: an effective tool for evaluating the nesting status of canopy-breeding bird species // Journal of Avian Biology. V. 46. № 4. P. 425–430.
  101. Whitehead K., Hugenholtz C.H., 2014. Remote sensing of the environment with small unmanned aircraft systems (UASs), part 1: a review of progress and challenges // Journal of Unmanned Vehicle Systems. V. 2. № 3. P. 69–85.
  102. Witczuk J., Pagacz S., Zmarz A., Cypel M., 2017. Exploring the feasibility of unmanned aerial vehicles and thermal imaging for ungulate surveys in forests – preliminary results // International Journal of Remote Sensing. V. 39. № 15-16. P. 5504–5521.
  103. Xiang H., Tian L., 2011. Development of a low-cost agricultural remote sensing system based on an autonomous unmanned aerial vehicle (UAV) // Biosystems Engineering. V. 108. № 2. P. 174–190.

Arquivos suplementares

Arquivos suplementares
Ação
1. JATS XML
2. Fig. 1. Models of UAVs used for animal research: A - with fixed wing Avian-P, B - with fixed wing Skylark II, C - with fixed wing and push propeller Topodrone-100, D - multi-rotor (octocopter) type Kraken-130, E - multi-rotor (quadcopter) type Phantom, E - airship type Happy Win HWEA5202. Illustrations are under an open licence for reuse: A - D - by: McEvoy et al., 2016; E - by: Adams et al., 2020

Baixar (248KB)
3. Fig. 2. UAV with additional capabilities beyond the standard survey. Study of microflora from humpback whale exhalation: A - modified model of DJI Mavic Pro 2 drone with foam floats and six fixed Petri dishes for (B) sampling (drone in flight is marked with a red circle). C - modification of the DJI Spreading Wings S900 hexacopter to record ultrasound of man-eating bats with parallel thermal video recording: 1 - ULTRAMIC250K ultrasonic microphone, 2 - foam ball to absorb sound from screws, 3 - thermal imaging camera, 4 - recorder for recording thermal video and 5 - recorder for recording sound from bats. D - use of a thermal imager to study terrestrial mammals of different species during darkness, where species identity was successfully determined from body proportions in thermal footprint images. From left to right, top row: hare, red deer, marten, badger; bottom row: roe deer and cow. Illustrations are under open licence for re-use: A, B - Costa et al., 2022; C - Fu et al., 2018; D - Larsen et al., 2023

Baixar (872KB)
4. Fig. 3. Use of computer vision algorithms to analyse UAV footage. A - simultaneous automatic detection and tracking of two ungulate species with visualised bounding box objects (bbox) for two video frames using trained models (zebras - blue, impala - white). Example of automatic detection of animals on a complex background (B) using the Sobel operator function to detect boundaries and (C) rendering them onto the original image. D - A summary infographic demonstrating the current capabilities of using computer vision to: recognise the species identity of mammals by their position in space (purple), track their movements (pink), individually identify individuals (green), categorise body poses (orange), and reconstruct habitats, including damaged ones (blue). Illustrations are under an open licence for reuse: A - Koger et al., 2023; B, C - Lee et al., 2021; D - Tuia et al., 2022

Baixar (1MB)

Declaração de direitos autorais © Russian Academy of Sciences, 2024