Increasing the accuracy and efficiency of wildlife census with unmanned aerial vehicles: a simulation study
Pascal Fust
A * and Jacqueline Loos A B
+ Author Affiliations
- Author Affiliations
A Institute of Ecology, Leuphana University Lüneburg, Universitätsallee 1, 21335 Lüneburg, Germany.
B Social-Ecological Systems Institute, Leuphana University Lüneburg, Universitätsallee 1, 21335 Lüneburg, Germany.
Wildlife Research 50(12) 1008-1020 https://doi.org/10.1071/WR22074
Submitted: 27 April 2022 Accepted: 7 January 2023 Published: 9 February 2023
© 2023 The Author(s) (or their employer(s)). Published by CSIRO Publishing. This is an open access article distributed under the Creative Commons Attribution 4.0 International License (CC BY)
Abstract
Context: Manned aerial surveys are an expensive endeavour, which is one of the core reasons for insufficient data coverage on wildlife monitoring in many regions. Unmanned aerial vehicles (UAVs) can be a valid, cost-efficient alternative, but the application of UAVs also comes with challenges.
Aim: In this explorative simulation study, our aim was to develop an efficient layout of UAV surveys that could potentially overcome challenges related to double counts of individuals and even area coverage, and that would minimise off-effort travel costs.
Methods: Based on different simulated survey layouts we developed hypothetically for the Katavi National Park in Tanzania, we quantified the advantages that UAVs might offer. We then compared these findings with manned aerial surveys.
Key results: The proposed new survey design and layout indicated an increase in survey efficiency of up to 21% when compared with conventional survey designs using parallel transect lines. Despite the complex flight pattern, the accuracy of the flight paths of the UAV outcompeted those of manned aerial surveys. The adapted survey layout enabled a team of two operators with a small battery-powered UAV to cover an area of up to 1000 km2 per day, without specific infrastructural requirements.
Conclusion: Our calculations may serve as a vital spark for innovation for future UAV survey designs that may have to deal with large areas and complex topographies while reducing operational effort.
Implications: UAV applications, if well designed, provide useful complementation, if not replacement, for manned aerial surveys and other remotely sensed data collections. Our suggested survey design is transferable to other study regions, and may be useful for applying UAVs efficiently.
Keywords: accuracy, conservation, drones, protected area, simulation, survey design, wildlife census, zigzag survey.
References
Andrasik, R, and Bil, M (2016). Efficient road geometry identification from digital vector data. Journal of Geographical Systems 18, 249–264.| Efficient road geometry identification from digital vector data.Crossref | GoogleScholarGoogle Scholar |
Barasona, JA, Mulero-Pázmány, M, Acevedo, P, Negro, JJ, Torres, MJ, Gortázar, C, and Vicente, J (2014). Unmanned aircraft systems for studying spatial abundance of ungulates: relevance to spatial epidemiology. PLoS ONE 9, .
| Unmanned aircraft systems for studying spatial abundance of ungulates: relevance to spatial epidemiology.Crossref | GoogleScholarGoogle Scholar |
Barnas, AF, Felege, CJ, Rockwell, RF, and Ellis-Felege, SN (2018). A pilot(less) study on the use of an unmanned aircraft system for studying polar bears (Ursus maritimus). Polar Biology 41, 1055–1062.
| A pilot(less) study on the use of an unmanned aircraft system for studying polar bears (Ursus maritimus).Crossref | GoogleScholarGoogle Scholar |
Blanco, G, Sergio, F, Sanchéz-Zapata, JA, Pérez-García, JM, Botella, F, Martínez, F, Zuberogoitia, I, Frías, O, Roviralta, F, Martínez, JE, and Hiraldo, F (2012). Safety in numbers? Supplanting data quality with fanciful models in wildlife monitoring and conservation. Biodiversity and Conservation 21, 3269–3276.
| Safety in numbers? Supplanting data quality with fanciful models in wildlife monitoring and conservation.Crossref | GoogleScholarGoogle Scholar |
Bonnin, N, Van Andel, AC, Kerby, JT, Piel, AK, Pintea, L, and Wich, SA (2018). Assessment of chimpanzee nest detectability in drone-acquired images. Drones 2, .
| Assessment of chimpanzee nest detectability in drone-acquired images.Crossref | GoogleScholarGoogle Scholar |
Bortolotto, GA, Danilewicz, D, Andriolo, A, Secchi, ER, and Zerbini, AN (2016). Whale, whale, everywhere: increasing abundance of western South Atlantic humpback whales (Megaptera novaeangliae) in their wintering grounds. PLoS ONE 11, .
| Whale, whale, everywhere: increasing abundance of western South Atlantic humpback whales (Megaptera novaeangliae) in their wintering grounds.Crossref | GoogleScholarGoogle Scholar |
Bouché, P, Lejeune, P, and Vermeulen, C (2012). How to count elephants in West African savannahs? Synthesis and comparison of main gamecount methods. Biotechnologie, Agronomie, Société et Environnement 16, 77–91.
Boulinier, T, Nichols, JD, Sauer, JR, Hines, JE, and Pollock, KH (1998). Estimating species richness: the importance of heterogeneity in species detectability. Ecology 79, 1018–1028.
| Estimating species richness: the importance of heterogeneity in species detectability.Crossref | GoogleScholarGoogle Scholar |
Brack, IV, Kindel, A, and Oliveira, LFB (2018). Detection errors in wildlife abundance estimates from Unmanned Aerial Systems (UAS) surveys: synthesis, solutions, and challenges. Methods in Ecology and Evolution 9, 1864–1873.
| Detection errors in wildlife abundance estimates from Unmanned Aerial Systems (UAS) surveys: synthesis, solutions, and challenges.Crossref | GoogleScholarGoogle Scholar |
Bushaw, JD, Ringelman, KM, and Rohwer, FC (2019). Applications of unmanned aerial vehicles to survey mesocarnivores. Drones 3, .
| Applications of unmanned aerial vehicles to survey mesocarnivores.Crossref | GoogleScholarGoogle Scholar |
Cabreira, TM, Brisolara, LB, and Ferreira, PR (2019). Survey on coverage path planning with unmanned aerial vehicles. Drones 3, .
| Survey on coverage path planning with unmanned aerial vehicles.Crossref | GoogleScholarGoogle Scholar |
Chrétien, L-P, Théau, J, and Ménard, P (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.
| Visible and thermal infrared remote sensing for the detection of white-tailed deer using an unmanned aerial system.Crossref | GoogleScholarGoogle Scholar |
Cleguer, C, Kelly, N, Tyne, J, Wieser, M, Peel, D, and Hodgson, A (2021). A novel method for using small unoccupied aerial vehicles to survey wildlife species and model their density distribution. Frontiers in Marine Science 8, .
| A novel method for using small unoccupied aerial vehicles to survey wildlife species and model their density distribution.Crossref | GoogleScholarGoogle Scholar |
Craig GC (2012) Monitoring the illegal killing of elephants: serial survey standards for the MIKE Programme. Version 2.0. (CITES MIKE programme, Nairobi, Kenya)
Dick, DM, and Hines, EM (2011). Development and implementation of distance sampling techniques to determine bottlenose dolphin (Tursiops truncatus) abundance at Turneffe Atoll, Belize. Marine Mammal Science 27, 606–621.
| Development and implementation of distance sampling techniques to determine bottlenose dolphin (Tursiops truncatus) abundance at Turneffe Atoll, Belize.Crossref | GoogleScholarGoogle Scholar |
DNPW (2016) Report on the 2015 aerial survey of elephants in Zambia. (Department of National Parks and Wildlife (DNPW): Zambia)
Duporge, I, Isupova, O, Reece, S, Macdonald, DW, and Wang, T (2021). Using very-high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes. Remote Sensing in Ecology and Conservation 7, 369–381.
| Using very-high-resolution satellite imagery and deep learning to detect and count African elephants in heterogeneous landscapes.Crossref | GoogleScholarGoogle Scholar |
ESRI (2011) ‘ArcGIS Desktop: Release 10.’ (Environmental Systems Research Institute: Redlands, CA, USA)
Field, SA, Tyre, AJ, Thorn, KH, O’Connor, PJ, and Possingham, HP (2005). Improving the efficiency of wildlife monitoring by estimating detectability: a case study of foxes (Vulpes vulpes) on the Eyre Peninsula, South Australia. Wildlife Research 32, 253–258.
| Improving the efficiency of wildlife monitoring by estimating detectability: a case study of foxes (Vulpes vulpes) on the Eyre Peninsula, South Australia.Crossref | GoogleScholarGoogle Scholar |
Franchomme J (2020) Détection et suivi des populations animales par télédétection : une revue des technologies disponibles et grille d’aide à la decision. MSc thesis, Sherbrooke University, Faculty of Science, CA, USA.
Frederick H, Moyer D, Plumptre AJ (2010) Aerial procedures manual, version 1.0. (Wildlife Conservation Society (WCS): New York, USA)
Friess, DA, and Webb, EL (2011). Bad data equals bad policy: how to trust estimates of ecosystem loss when there is so much uncertainty? Environmental Conservation 38, 1–5.
| Bad data equals bad policy: how to trust estimates of ecosystem loss when there is so much uncertainty?Crossref | GoogleScholarGoogle Scholar |
Fust, P, and Loos, J (2020). Development perspectives for the application of autonomous, unmanned aerial systems (UASs) in wildlife conservation. Biological Conservation 241, .
| Development perspectives for the application of autonomous, unmanned aerial systems (UASs) in wildlife conservation.Crossref | GoogleScholarGoogle Scholar |
Georgiadis NJ, Olwero N, Ojwang G, Aike G (2011) Reassessing aerial sample surveys for wildlife monitoring, conservation, and management. In ‘Conserving Wildlife in African Landscapes: Kenya’s Ewaso Ecosystem’. Smithsonian Contributions to Zoology; no. 632 (Ed. NJ Georgiadis) pp. 31–42. (Smithsonian Institution Scholarly Press: Washington, DC, USA)
Grossmann F, Lopes Pereira C, Chambal D, Maluleque G, Bendzane E, Parker N, Foloma M, Ntumi C, Polana E, Nelson A (2014) Aerial survey of elephant, other wildlife and human activity in Limpopo National Park and the southern extension. (Wildlife Conservation Society (WCS): New York, USA)
Guo, X, Shao, Q, Li, Y, Wang, Y, Wang, D, Liu, J, Fan, J, and Yang, F (2018). Application of UAV remote sensing for a population census of large wild herbivores – taking the Headwater Region of the Yellow River as an example. Remote Sensing 10, .
| Application of UAV remote sensing for a population census of large wild herbivores – taking the Headwater Region of the Yellow River as an example.Crossref | GoogleScholarGoogle Scholar |
Hambrecht, L, Brown, RP, Piel, AK, and Wich, SA (2019). Detecting ‘poachers’ with drones: Factors influencing the probability of detection with TIR and RGB imaging in miombo woodlands, Tanzania. Biological Conservation 233, 109–117.
| Detecting ‘poachers’ with drones: Factors influencing the probability of detection with TIR and RGB imaging in miombo woodlands, Tanzania.Crossref | GoogleScholarGoogle Scholar |
Hammond, PS, Macleod, K, Berggren, P, Borchers, DL, Burt, L, Cañadas, A, Desportes, G, Donovan, GP, Gilles, A, Gillespie, D, Gordon, J, Hiby, L, Kuklik, I, Leaper, R, Lehnert, K, Leopold, M, Lovell, P, Øien, N, Paxton, CGM, Ridoux, V, Rogan, E, Samarra, F, Scheidat, M, Sequeira, M, Siebert, U, Skov, H, Swift, R, Tasker, ML, Teilmann, J, Van Canneyt, O, and Vázquez, JA (2013). Cetacean abundance and distribution in European Atlantic shelf waters to inform conservation and management. Biological Conservation 164, 107–122.
| Cetacean abundance and distribution in European Atlantic shelf waters to inform conservation and management.Crossref | GoogleScholarGoogle Scholar |
Harbitz, A (2019). A zigzag survey design for continuous transect sampling with guaranteed equal coverage probability. Fisheries Research 213, 151–159.
| A zigzag survey design for continuous transect sampling with guaranteed equal coverage probability.Crossref | GoogleScholarGoogle Scholar |
Harrity, EJ, Stevens, BS, and Conway, CJ (2020). Keeping up with the times: mapping range-wide habitat suitability for endangered species in a changing environment. Biological Conservation 250, .
| Keeping up with the times: mapping range-wide habitat suitability for endangered species in a changing environment.Crossref | GoogleScholarGoogle Scholar |
Hatch, SK, Connelly, EE, Divoll, TJ, Stenhouse, IJ, and Williams, KA (2013). Offshore observations of eastern red bats (Lasiurus borealis) in the Mid-Atlantic United States using multiple survey methods. PLoS ONE 8, .
| Offshore observations of eastern red bats (Lasiurus borealis) in the Mid-Atlantic United States using multiple survey methods.Crossref | GoogleScholarGoogle Scholar |
Hodgson AJ, Noad M, Marsh H, Lanyon J, Kniest E (2010) Using unmanned aerial vehicles for surveys of marine mammals in Australia: test of concept. Final Report to the Australian Marine Mammal Centre. University of Queensland, Brisbane, Qld, Australia
Hodgson, JC, Mott, R, Baylis, SM, Pham, TT, Wotherspoon, S, Kilpatrick, AD, Segaran, RR, Reid, I, Terauds, A, and Koh, LP (2018). Drones count wildlife more accurately and precisely than humans. Methods in Ecology and Evolution 9, 1160–1167.
| Drones count wildlife more accurately and precisely than humans.Crossref | GoogleScholarGoogle Scholar |
Hu, J, Wu, X, and Dai, M (2020). Estimating the population size of migrating Tibetan antelopes Pantholops hodgsonii with unmanned aerial vehicles. Oryx 54, 101–109.
| Estimating the population size of migrating Tibetan antelopes Pantholops hodgsonii with unmanned aerial vehicles.Crossref | GoogleScholarGoogle Scholar |
Hyun, C-U, Park, M, and Lee, WY (2020). Remotely Piloted Aircraft System (RPAS)-based wildlife detection: a review and case studies in maritime Antarctica. Animals 10, .
| Remotely Piloted Aircraft System (RPAS)-based wildlife detection: a review and case studies in maritime Antarctica.Crossref | GoogleScholarGoogle Scholar |
Jachmann, H (2002). Comparison of aerial counts with ground counts for large African herbivores. Journal of Applied Ecology 39, 841–852.
| Comparison of aerial counts with ground counts for large African herbivores.Crossref | GoogleScholarGoogle Scholar |
Koh, LP, and Wich, SA (2012). Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation. Tropical Conservation Science 5, 121–132.
| Dawn of drone ecology: low-cost autonomous aerial vehicles for conservation.Crossref | GoogleScholarGoogle Scholar |
Lamprey, R, Pope, F, Ngene, S, Norton-Griffiths, M, Frederick, H, Okita-Ouma, B, and Douglas-Hamilton, I (2020). Comparing an automated high-definition oblique camera system to rear-seat-observers in a wildlife survey in Tsavo, Kenya: taking multi-species aerial counts to the next level. Biological Conservation 241, .
| Comparing an automated high-definition oblique camera system to rear-seat-observers in a wildlife survey in Tsavo, Kenya: taking multi-species aerial counts to the next level.Crossref | GoogleScholarGoogle Scholar |
Linchant J, Lhoest S, Quevauvillers S, Semeki J, Lejeune P, Vermeulen C (2015) WIMUAS: developing a tool to review wildlife data from various UAS flight plans. International Archives of the Photogrammetry, Remote Sensing and Spatial Information Sciences, Vol. 40. pp. 379–384. (International Society for Photogrammetry and Remote Sensing: Hannover, Germany)
Osborne M (2020) Mission Planner (Version 1.3.74) [computer software]. Available at https://ardupilot.org/planner
Ott MC (2020) Using unmanned aerial systems (drones) with a thermal sensor to map and count deer population. Honors Research Projects 1068, Williams Honors College, University of Akron, Akron, OH, USA.
Pollard JH, Buckland ST (1997) A strategy for adaptive sampling in shipboard line transect surveys. Report of the International Whaling Commission. vol. 47, pp. 921–931. (International Whaling Commission: Impington, UK)
Pollock, KH, Marsh, HD, Lawler, IR, and Alldredge, MW (2006). Estimating animal abundance in heterogeneous environments: an application to aerial surveys for dugongs. The Journal of Wildlife Management 70, 255–262.
| Estimating animal abundance in heterogeneous environments: an application to aerial surveys for dugongs.Crossref | GoogleScholarGoogle Scholar |
Preston, TM, Wildhaber, ML, Green, NS, Albers, JL, and Debenedetto, GP (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 |
QGIS Development Team (2021) QGIS Geographic Information System (Open Source Geospatial Foundation Project) Available at http://qgis.osgeo.org
Quang PX, Becker EF (1999) Aerial survey sampling of contour transects using double-count and covariate data. In: ‘Marine Mammal Survey and Assessment Methods’. (Eds GW Garner, SC Amstrup, JL Laake, BFJ Manly, LL McDonald, DG Robertson) pp. 87–97. (Balkema Press: Rotterdam, Netherlands)
R Core Team (2020) ‘R: a language and environment for statistical computing.’ (R Foundation for Statistical Computing: Vienna, Austria)
Rey, N, Volpi, M, Joost, S, and Tuia, D (2017). Detecting animals in African Savanna with UAVs and the crowds. Remote Sensing of Environment 200, 341–351.
| Detecting animals in African Savanna with UAVs and the crowds.Crossref | GoogleScholarGoogle Scholar |
Schlossberg, S, Chase, MJ, and Griffin, CR (2016). Testing the accuracy of aerial surveys for large mammals: an experiment with African savanna elephants (Loxodonta africana). PLoS ONE 11, .
| Testing the accuracy of aerial surveys for large mammals: an experiment with African savanna elephants (Loxodonta africana).Crossref | GoogleScholarGoogle Scholar |
Shelden KEW, Sims CL, Vate Brattström L, Goetz KT, Hobbs RC (2015) Aerial surveys of beluga whales (Delphinapterus leucas) in Cook Inlet, Alaska, June 2014. AFSC Processed Rep. 2015-03. (Alaska Fisheries Science Center, NOAA: Seattle WA, USA)
Strindberg, S, and Buckland, ST (2004). Zigzag survey designs in line transect sampling. Journal of Agricultural, Biological, and Environmental Statistics 9, 443–461.
| Zigzag survey designs in line transect sampling.Crossref | GoogleScholarGoogle Scholar |
Sykora-Bodie, ST, Bezy, V, Johnston, DW, Newton, E, and Lohmann, KJ (2017). Quantifying nearshore sea turtle densities: applications of unmanned aerial systems for population assessments. Scientific Reports 7, .
| Quantifying nearshore sea turtle densities: applications of unmanned aerial systems for population assessments.Crossref | GoogleScholarGoogle Scholar |
TAWIRI (2016) Aerial census in the Tarangire–Manyara Ecosystem, Dry Season, 2016, Tanzania Wildlife Research Institute Aerial Survey Report. (TAWIRI: Arusha, Tanzania)
TAWIRI (2019) Aerial wildlife survey of large snimals and human activities in the Selous–Mikumi Ecosystem, dry season 2018. Tanzania Wildlife Research Institute Aerial Survey Report. (TAWIRI: Arusha, Tanzania)
Vermeulen, C, Lejeune, P, Lisein, J, Sawadogo, P, and Bouché, P (2013). Unmanned aerial survey of elephants. PLoS ONE 8, .
| Unmanned aerial survey of elephants.Crossref | GoogleScholarGoogle Scholar |
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, .
| Surveying wild animals from satellites, manned aircraft and unmanned aerial systems (UASs): a review.Crossref | GoogleScholarGoogle Scholar |
World Bank (2018) Tanzania: DTIS update 2017 – boosting growth and prosperity through agribusiness, extractives, and tourism. (World Bank: Washington, DC, USA)
