Beyond Platform Type: Effects of Vegetation Density, Sensor Modality, and Search Strategy on Aerial Search and Rescue Performance
DOI:
https://doi.org/10.66050/sja2vn07Keywords:
Search and Rescue (SAR), Unmanned Aerial Systems (UAS), helicopter, probability of detection (POD), comparative analysis, airspace management, synergistic aerial operationsAbstract
Timely detection of missing persons is critical for successful Search and Rescue (SAR) operations, especially under challenging environmental conditions. Modern SAR efforts utilize both manned helicopters and unmanned aerial systems (UAS), often equipped with electro-optical (EO) and infrared (IR) sensors, while helicopters may also employ visual observers. Despite their widespread use, limited empirical data exists on how these platforms, sensor types, and search techniques perform across varying terrain and vegetation densities. To our knowledge, no prior field study has jointly examined how platform type, sensor modality, search strategy, and vegetation density affect detection performance in realistic SAR conditions. This study presents results from the SAVIOUR 2024 quasi-experimental field experiment, conducted during a large-scale SAR exercise in Rogaland, Norway. Twelve professional SAR aircrews (six helicopters, six UAS teams) conducted 48 search sorties across sectors with low, medium, and high vegetation density, targeting 251 human subjects. Key metrics were Probability of Detection (POD) and Time to Detection. Both platforms achieved high detection rates (mean POD >83%), with 54% of sorties reaching 100% POD. Vegetation density was the strongest predictor of POD, with reduced performance in high-density forest (helicopters: 71.4%, UAS: 73.3%). Platform type was not a significant predictor of POD when controlling for vegetation density; in contrast, vegetation density and sensor modality seemed to have stronger effects on detection performance. Helicopters detected targets faster, likely due to initial sweep strategies. UAS teams favored systematic detailed searches, resulting in longer detection intervals. Sensor-based searches outperformed visual-only methods, though visual-only data were limited. As an operational implication, we suggest that coordinated, vertically separated operations - helicopters at high altitude and UAS at low altitude - may enhance efficiency through concurrent coverage. However, this coordination model was not directly tested as an intervention and should be validated in future studies. These findings offer guidance for integrated SAR practices and highlight future research needs, including AI-assisted detection and performance evaluation under diverse thermal and geographical conditions.
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References
1. Ajith, V. S., & Jolly, K. G. (2021). Unmanned aerial systems in search and rescue applications with their path planning: A review. Journal of Physics: Conference Series, 2115(2021), 012020, 1-13. https://doi.org/10.1088/1742-6596/2115/1/012020
2. Antonsen, Y., Sivertsen, A. H., Grydeland, T., Johansen, K. S., Storvold, R., Hagen, S., Rognmo-Hodge, A., Sørensen, G.-A., Sydnes, M., Sydnes, A. K., & Hansen, B. I. (2015). SARiNOR WP 3 «SØK» (Norut Rapport 11/2015). Norut. https://www.researchgate.net/publication/326009714_SARINOR_WP_3_SOK
3. Bashyam, A., & Guggenheim, J. (2019). UAVs for wilderness search and rescue: Real-world considerations and technology roadmap for fixed wing UAVs. Journal of Search and Rescue, 3(1), 1–20. https://doi.org/10.61618/GLOX4896
4. Cameron, A. C., Gelbach, J. B., & Miller, D. L. (2008). Bootstrap-based improvements for inference with clustered errors. The Review of Economics and Statistics, 90(3), 414–427. https://www.jstor.org/stable/40043157
5. Campbell, D. T., & Stanley, J. C. (1963). Experimental and quasi-experimental designs for research. Houghton Mifflin Company.
6. Ferrari, J. F. (2020). A study of optimal search and rescue operations planning problems [Doctoral dissertation] Concordia University. https://spectrum.library.concordia.ca/id/eprint/986413/1/Ferrari_PhD_S2020.pdf
7. FLIR Systems. (n.d.). Star Safire 380-HDc datasheet. https://www.flir.eu/support/products/star-safire-380-hdc/?vertical=surveillance+general&segment=surveillance#Documents
8. Frost, J. R. (1996). The theory of search: A simplified explanation. Office of Search and Rescue, U.S. Coast Guard. https://navcen.uscg.gov/sites/default/files/pdf/Theory_of_Search.pdf
9. Gelman, A., & Hill, J. (2007). Data analysis using regression and multilevel/hierarchical models. Cambridge University Press.
10. Goodrich, M. A., Morse, B. S., Gerhardt, D., Cooper, J. L., Quigley, M., Adams, J. A., & Humphrey, C. (2008). Supporting wilderness search and rescue using a camera-equipped mini UAV. Journal of Field Robotics, 25(1), 89–110. https://doi.org/10.1002/rob.20226
11. Holgado-Tello, P., Chacón-Moscoso, S., Sanduvete-Chaves, S. & Pérez-Gil, J.A. (2016). A Simulation Study of Threats to Validity in Quasi-Experimental Designs: Interrelationship between Design, Measurement, and Analysis. Frontiers in Psychology, 7, 897, 1-9. https://doi.org/10.3389/fpsyg.2016.00897
12. International Civil Aviation Organization & International Maritime Organization. (2016). IAMSAR Manual, Volume III: Mobile Facilities (3rd ed., pp. C1–C10). ICAO/IMO.
13. Kerrin, M. T. (2018). Mission effectiveness in hybrid systems collaboration of small unmanned aerial vehicles in search and rescue land operations: Application of multi-attribute utility theory in a quasi-experimental study [Doctoral dissertation, Capella University]. https://www.researchgate.net/publication/340006143
14. Lyu, M., Zhao, Y., Huang, C., & Huang, H. (2023). Unmanned aerial vehicles for search and rescue: A survey. Remote Sensing, 15(13), 3266, 1-35. https://doi.org/10.3390/rs15133266
15. L3 Harris Technologies. (2020). Wescam MX-15 brochure. https://www.l3harris.com/sites/default/files/2020-07/ims_eo_brochure_wescam_M-MX-15-0501AB-Brochure.pdf
16. Mayo, J. W. (2015). Terrain based probability models for search and rescue (MRA Report, 10(5), 1–45.) Mountain Rescue Association. https://mra.org/wp-content/uploads/2016/05/TerrainProbabilityModelsReport.pdf
17. National Search and Rescue Committee. (2016). Unmanned aircraft system (UAS) search and rescue addendum to the national search and rescue supplement to the international aeronautical and maritime search and rescue manual (Version 1.0). U.S. Coast Guard. http://www.uscg.mil/nsarc
18. Nguyen, T. X. B., Rosser, K., & Chahl, J. (2021). A review of modern thermal imaging sensor technology and applications for autonomous aerial navigation. Journal of Imaging, 7(10), 217, 1-24. https://doi.org/10.3390/jimaging7100217
19. Niedzielski, T., Jurecka, M., Miziński, B., Pawul, W., & Motyl, T. (2021). First successful rescue of a lost person using the human detection system: A case study from Beskid Niski (SE Poland). Remote Sensing, 13, 4903, 1-18. https://doi.org/10.3390/rs13234903
20. Norwegian Civil Aviation Authority. (2015). Regulation relating to unmanned aircraft (FOR-2015-11-30-1404). Lovdata. https://lovdata.no/dokument/SF/forskrift/2015-11-30-1404
21. Norwegian Joint Rescue Coordination Center. (2024). National guidelines for the coordination of aerial resources in the rescue service. https://www.hovedredningssentralen.no/wp-content/uploads/2025/03/NY-Nasjonale-retningslinjer-for-koordinering-av-luftressurser-i-redningstjenesten.pdf
22. Norwegian Ministry of Transport. (1993). Norwegian Aviation Act (LOV-1993-06-11-101). Lovdata. https://lovdata.no/dokument/NL/lov/1993-06-11-101
23. Norwegian Police Unmanned Air Support Unit. (2023). Norwegian Police UAS Search Techniques in SAR Operations (White Paper). https://www.politiet.no/globalassets/tall-og-fakta/droner-i-politiet/white-paper---norwegian-police-drone-search-techniques-in-sar-operations.pdf
24. Olsen, R. E. (2016). Mission effectiveness in hybrid systems collaboration of small unmanned aerial vehicles in search and rescue land operations [Master’s thesis]. Nord Universitet. https://nordopen.nord.no/nord-xmlui/bitstream/handle/11250/2408525/Olsen.pdf?sequence=1
25. Quero, C.O. & Carranza, J.M. (2025). Unmanned aerial systems in search and rescue: A global perspective on current challenges and future applications. International Journal of Disaster Risk Reduction, 118(2025), 105199, 1-23. https://doi.org/10.1016/j.ijdrr.2025.105199
26. Silvagni, M., Tonoli, A., Zenerino, E., & Chiaberge, M. (2017). Multipurpose UAV for search and rescue operations in mountain avalanche events. Geomatics, Natural Hazards and Risk, 8(1), 18–33. https://doi.org/10.1080/19475705.2016.1238852
27. Xing, L., Fan, X., Dong, Y., Xiong, Z., Xing, L., Yang, Y., Bai, H. & Zhou, C. (2022). Multi-UAV cooperative system for search and rescue based on YOLOv5. International Journal of Disaster Risk Reduction, 76(2022), 102972, 1-17. https://doi.org/10.1016/j.ijdrr.2022.102972
28. Zhang, X., Feng, Y., Wang, N., Lu, G., & Mei, S. (2025). Aerial person detection for search and rescue: Survey and benchmarks. Journal of Remote Sensing, 5, 0474, 1-32. https://doi.org/10.34133/remotesensing.0474
29. Zhang, Z., & Zhu, L. (2023). A review on unmanned aerial vehicle remote sensing: Platforms, sensors, data processing methods, and applications. Drones, 7(6), 398, 1-42. https://doi.org/10.3390/drones7060398
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Copyright (c) 2026 Andreas A. Nilsen, Tale R. Størdal, Vegard Johansen, Jørgen L.H. Ronge, Eyvind Grytting (Author)

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