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YOLO: A FUSION APPROACH TO GEOLOCATING NATURAL RESOURCES 

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Advancing near real-time AI and drone technology to improve wildlife monitoring and conservation in hard-to-reach landscapes.

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Resources

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Media

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User Guide

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Protocols

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Golden Eagle Monitoring

  1. Katzner, T. E., M. N. Kochert, K. Steenhof, C. L. McIntyre, E. H. Craig, and T. A. Miller (2020). Golden Eagle (Aquila chrysaetos), version 2.0. In Birds of the World (P. G. Rodewald and B. K. Keeney, Editors). Cornell Lab of Ornithology, Ithaca, NY, USA. https://doi.org/10.2173/bow.goleag.02

  2. Kochert, Michael N. and Steenhof, Karen (2024) "Golden Eagles in the Us and Canada: Status, Trends, and Conservation Challenges," Journal of Raptor Research: Vol. 36 : Iss. 5 , Article 9.

  3. Millsap, B. A., Zimmerman, G. S., Sauer, J. R., Nielson, R. M., Otto, M., Bjerre, E., & Murphy, R. (2013). Golden eagle population trends in the western United States: 1968–2010. The Journal of Wildlife Management, 77(7), 1436–1448. https://doi.org/10.1002/jwmg.588

  4. Santiago, A. L., Cruz, J., Delparte, D. M., Kochert, M. N., Steenhof, K., Weldon, J. M., & Heath, J. A. (2025). Landscape changes and declines in Aquila chrysaetos (Golden Eagle) territory occupancy in southwestern Idaho. Ornithological Applications, duaf054. https://doi.org/10.1093/ornithapp/duaf054

  5. Pagel, J. E., Whittington, D. M., & Allen, G. T. (2010). Interim Golden Eagle Inventory and Monitoring Protocols. U.S. Fish and Wildlife Service.

  6. Spaul, R. J., & Heath, J. A. (2016). Nonmotorized recreation and motorized recreation in shrub‐steppe habitats affects behavior and reproduction of golden eagles (Aquila chrysaetos). Ecology and Evolution, 6(22), 8037–8049.

  7. Sasse, D. B. (2003). Job-Related Mortality of Wildlife Workers in the United States, 1937-2000. Wildlife Society Bulletin (1973-2006), 31(4), 1015–1020. JSTOR.

The Use of Drones in Wildlife Research

  1. Elmore, J. A., Schultz, E. A., Jones, L. R., Evans, K. O., Samiappan, S., Pfeiffer, M. B., Blackwell, B. F., & Iglay, R. B. (2023). Evidence on the efficacy of small unoccupied aircraft systems (UAS) as a survey tool for North American terrestrial, vertebrate animals: A systematic map. Environmental Evidence, 12(1), 3. https://doi.org/10.1186/s13750-022-00294-8

  2. Hodgson, A., Kelly, N., and Peel, D. 2013. Unmanned aerial vehicles (UAVs) for surveying marine fauna: A dugong case study. PLoS ONE, 8(11): e79556. doi: 10.1371/journal.pone.0079556. PMID: 24223967.

  3. Johnston DW. Unoccupied Aircraft Systems in Marine Science and Conservation. Ann Rev Mar Sci. 2019 Jan 3;11:439-463. doi: 10.1146/annurev-marine-010318-095323. Epub 2018 Jul 18. PMID: 30020850.

  4. Koski, W.R., Gamage, G., Davis, A.R., Mathews, T., LeBlanc, B., and Ferguson, S.H. 2015. Evaluation of UAS for photographic re-identification of bowhead whales, Balaena mysticetus. J. Unmanned Veh. Syst. 3(1): 22–29. doi: 10.1139/ juvs-2014-0014.

  5. Pirotta, V., Smith, A., Ostrowski, M., Russell, D., Jonsen, I.D., Grech, A., and Harcourt, R. 2017. An economical custombuilt drone for assessing whale health. Front. Mar. Sci. 4: 425. doi: 10.3389/fmars.2017.00425.

  6. Corcoran, E., Winsen, M., Sudholz, A., and Hamilton, G., Automated detection of wildlife using drones: Synthesis, opportunities and constraints, Methods Ecol. Evol., 2021, vol. 12, no. 6, pp. 1103–1114. https://doi.org/10.1111/2041-210x.13581

  7. Hu, J., Wu, X., and Dai, M. 2020. Estimating the population size of migrating Tibetan antelopes Pantholops hodgsonii with unmanned aerial vehicles. Oryx, 54(1): 101–109. doi: 10.1017/S0030605317001673.

  8. Larsen, H. L., Møller-Lassesen, K., Enevoldsen, E. M. E., Madsen, S. B., Obsen, M. T., Povlsen, P., Bruhn, D., Pertoldi, C., & Pagh, S. (2023). Drone with Mounted Thermal Infrared Cameras for Monitoring Terrestrial Mammals. Drones, 7(11), 680. https://doi.org/10.3390/drones7110680

  9. Elsey, R.M., and Trosclair, P.L., III. 2016. The use of an unmanned aerial vehicle to locate alligator nests. Southeast. Nat. 15(1): 76–82. doi: 10.1656/058.015.0106.

  10. Varela-Jaramillo, A., Rivas-Torres, G., Guayasamin, J.M. et al. A pilot study to estimate the population size of endangered Galápagos marine iguanas using drones. Front Zool 20, 4 (2023). https://doi.org/10.1186/s12983-022-00478-5

  11. Albores-Barajas, Y. V., Soldatini, C., & Ramos-Rodr, A. (2018). A new use of technology to solve an old problem: Estimating the population size of a burrow nesting seabird. PLoS One, 13(9). https://doi.org/10.1371/journal.%20pone.0202094

  12. Bird, D. M., Petalas, C., Pace, P., & Elliott, K. H. (2024). Using drones to measure the status of cavity-nesting raptors. Drone Systems and Applications, 12, 1–7. https://doi.org/10.1139/dsa-2023-0121

  13. Hodgson JC, Mott R, Baylis SM, et al. Drones count wildlife more accurately and precisely than humans. Methods Ecol Evol. 2018; 9: 1160–1167. https://doi.org/10.1111/2041-210X.12974

  14. Murphy, N. K., Elmore, J. A., Boudreau, M. R., Dorr, B. S., & Rush, S. A. (2024). Monitoring active osprey nests with drones is more time efficient and less disturbing than conventional methods. Wildlife Biology, e01341. https://doi.org/10.1002/wlb3.01341

  15. Barnas, A. F., Chabot, D., Hodgson, A. J., Johnston, D. W., Bird, D. M., & Ellis-Felege, S. N. (2020). A standardized protocol for reporting methods when using drones for wildlife research. Journal of Unmanned Vehicle Systems, 8, 89–98. https://doi.org/juvs-2019-0011

  16. Johnston, D. W. (2019). Unoccupied Aircraft Systems in Marine Science and Conservation. In Annual Review of Marine Science (Vol. 11, Issue Volume 11, 2019, pp. 439–463). Annual Reviews. https://doi.org/10.1146/annurev-marine-010318-095323

  17. Linchant, J., Lisein, J., Semeki, J., Lejeune, P., & Vermeulen, C. (2015). Are unmanned aircraft systems ( UAS s) the future of wildlife monitoring? A review of accomplishments and challenges. Mammal Review, 45(4), 239–252. https://doi.org/10.1111/mam.12046

  18. Rush, G. P., Clarke, L. E., Stone, M., & Wood, M. J. (2018). Can drones count gulls? Minimal disturbance and semiautomated image processing with an unmanned aerial vehicle for colony‐nesting seabirds. Ecology and Evolution, 8, 12322–12334. https://doi.org/10.1002/ece3.4495

  19. Mazumdar, S. (2022). Drone Applications in Wildlife Research—A Synoptic Review. In P. K. Paul, A. Choudhury, A. Biswas, & B. K. Singh (Eds.), Environmental Informatics (pp. 237–257). Springer Nature Singapore. https://doi.org/10.1007/978-981-19-2083-7_14

  20. Bird, D. M., Petalas, C., Pace, P., & Elliott, K. H. (2024). Using drones to measure the status of cavity-nesting raptors. Drone Systems and Applications, 12, 1–7. https://doi.org/10.1139/dsa-2023-0121

  21. Charbonneau, P., Lemaître, J., & Tremblay, G. (2024). Contribution du drone aux suivis de la productivité de l’aigle royal et du faucon pèlerin. Le Naturaliste canadien, 148(1), 25. https://doi.org/10.7202/1110196ar

Reducing Risk

  1. Barnas, A. F., Chabot, D., Hodgson, A. J., Johnston, D. W., Bird, D. M., & Ellis-Felege, S. N. (2020). A standardized protocol for reporting methods when using drones for wildlife research. Journal of Unmanned Vehicle Systems, 8, 89–98. https://doi.org/juvs-2019-0011

  2. Spaulding, R., Gallego-García, D., & Bird, D. M. (2024). Conservation Letter: The Use of Drones in Raptor Research. Journal of Raptor Research, 58(4), 535–546. https://doi.org/10.3356/jrr2427

  3. Cantu De Leija, A., Mirzadi, R., Randall, J., Portmann, M., Mueller, E., & Gawlik, D. (2023). A meta-analysis of disturbance caused by drones on nesting birds. Journal of Field Ornithology, 94(2), art3. https://doi.org/10.5751/JFO-00259-940203

  4. Bishop, A. M., Brown, C. L., Christie, K. S., Kettle, A. B., Larsen, G. D., Renner, H. M., & Younkins, L. (2022). Surveying cliff-nesting seabirds with unoccupied aircraft systems in the Gulf of Alaska. Polar Biology, 45(12), 1703–1714. https://doi.org/10.1007/s00300-022-03101-9

  5. Charbonneau, P., Lemaître, J., & Tremblay, G. (2024). Contribution du drone aux suivis de la productivité de l’aigle royal et du faucon pèlerin. Le Naturaliste canadien, 148(1), 25. https://doi.org/10.7202/1110196ar

Processing Drone Data

  1. Farley, S. S., Dawson, A., Goring, S. J., & Williams, J. W. (2018). Situating Ecology as a Big-Data Science: Current Advances, Challenges, and Solutions. BioScience, 68(8), 563–576. https://doi.org/10.1093/biosci/biy068

  2. Anderson, K., & Gaston, K. J. (2013). Lightweight unmanned aerial vehicles will revolutionize spatial ecology. Frontiers in Ecology and the Environment, 11(3), 138–146. https://doi.org/10.1890/120150

  3. Redmon, J., Divvala, S., Girshick, R., & Farhadi, A. (2016). You Only Look Once: Unified, Real-Time Object Detection. 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), 779–788. https://doi.org/10.1109/CVPR.2016.91

  4. Roy, A. M., Bhaduri, J., Kumar, T., & Raj, K. (2023). WilDect-YOLO: An efficient and robust computer vision-based accurate object localization model for automated endangered wildlife detection. Ecological Informatics, 75, 101919. https://doi.org/10.1016/j.ecoinf.2022.101919

  5. Chalmers, C., Fergus, P., Wich, S., Longmore, S. N., Walsh, N. D., Oliver, L., Warrington, J., Quinlan, J., & Appleby, K. (2025). AI-Driven Real-Time Monitoring of Ground-Nesting Birds: A Case Study on Curlew Detection Using YOLOv10. Remote Sensing, 17(5), 769. https://doi.org/10.3390/rs17050769

  6. Lawrence, B., de Lemmus, E., & Cho, H. (2023). UAS-Based Real-Time Detection of Red-Cockaded Woodpecker Cavities in Heterogeneous Landscapes Using YOLO Object Detection Algorithms. Remote Sensing, 15(4), 883. https://doi.org/10.3390/rs15040883

  7. Wang, J., Wang, N., Li, L., & Ren, Z. (2020). Real-time behavior detection and judgment of egg breeders based on YOLO v3. Neural Computing and Applications, 32(10), 5471–5481. https://doi.org/10.1007/s00521-019-04645-4

  8. Kim, J., Kim, J., & Cho, J. (2019). An advanced object classification strategy using YOLO through camera and LiDAR sensor fusion. 2019 13th International Conference on Signal Processing and Communication Systems (ICSPCS), 1–5. https://doi.org/10.1109/ICSPCS47537.2019.9008742

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