Hakimipanah, Mona2025-12-092025-12-092025-12-06https://hdl.handle.net/10222/85544Unmanned Aerial Vehicles (UAVs) are increasingly used to support Search and Rescue (SAR) operations in Canada’s Arctic and Northern regions, where long distances, severe weather, and limited infrastructure create major response challenges. Yet, year-round UAV operability and detection performance in these environments remain under-examined. This study addresses that gap through two objectives: RQ1 evaluates UAV operability across Northern Canada using environmental thresholds and hourly ECCC weather data (2018-2024) for 46 communities and 2 staffed High Arctic outposts. RQ2 develops a fuzzy-logic model of the Probability of Detection (PoD) using a Type-1 Mamdani FIS built from expert-elicited factors. Operability findings show strong geographic and seasonal variability, with high operability during summer and sharply reduced opportunities in winter months. The PoD model highlights Crew Experience, Contrast, and Flight Speed as major contributors to detectability. Together, the results provide a decision-support framework to guide UAV-SAR planning and operational readiness across northern Canada.enSearch and Rescue (SAR)Unmanned Aerial Vehicles (UAVs)Probability of Detection (PoD)Northern CanadaUAV-Assisted SearchFuzzy Inference System (FIS)Mamdani ModelOperational Decision SupportEmergency Response