Data driven mission planning: Airborne Marine Megafauna Survey
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Date
2023-06
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Publisher
University of New Brunswick
Abstract
The Government of Canada uses aerial population surveys to inform decisions relating to conservational and economic policy. These surveys aim to monitor species to estimate the distribution and abundance of marine megafauna, given its impact on policy survey accuracy is important. The primary factor hindering the success of surveys is poor sub-surface visibility, caused by glare. To address this adversity survey coordinators, the Department of Fisheries and Oceans (DFO), utilize a detection function to account for areas with sub-optimal visibility by predicting the likelihood of megafauna existing below the surface of interest. The parameters used to tune this function are produced from subjective human input which introduces uncertainty into population estimates. This research presents a means of accurately classifying glare intensity to inform detection function parameters reliably. Additionally, this research presents a glare prediction model, to mitigate glare before it occurs, a phenomenon long viewed as an occupational hazard.