Automatic Identification System for Ships Data as a Proxy for Marine Vessel Related Stressors

作     者：O’Hara, Patrick D. Serra-Sogas, Norma McWhinnie, Lauren Pearce, Kim Le Baron, Nicole O’Hagan, Gregory Nesdoly, Andrea Marques, Tunai Canessa, Rosaline 

作者机构：Canadian Wildlife Service Environment and Climate Change Canada Institute of Ocean Sciences SidneyBC Canada CORAL Group Department of Geography University of Victoria VictoriaBC Canada Fisheries and Oceans Canada Institute of Ocean Sciences SidneyBC Canada Institute of Life and Earth Sciences Heriot-Watt University United Kingdom National Aerial Surveillance Program Transport Canada VancouverBC Canada Department of Electrical and Computer Engineering University of Victoria VictoriaBC Canada 

出 版 物：《SSRN》 

年 卷 期：2022年

核心收录：

主　　题：Risk assessment 

摘      要：An increasing number of marine conservation initiatives rely on data from Automatic Identification System (AIS) to inform marine vessel traffic associated impact assessments and mitigation policy. However, a considerable proportion of vessel traffic is not captured by AIS in many regions of the world. Here we introduce two complementary techniques for collecting traffic data in the Canadian Salish Sea that rely on optical imagery. Vessel data pulled from imagery captured using a shore-based autonomous camera system (Photobot) were used for temporal analyses, and data from imagery collected by the National Aerial Surveillance Program (NASP) were used for spatial analyses. The photobot imagery captured vessel passages through Boundary Pass every minute (Jan - Dec 2017), and NASP data collection occurred opportunistically across most of the Canadian Salish Sea (2017-2018). Based on photobot imagery data, we found that up to 72% of total vessel passages through Boundary Pass were not broadcasting AIS, and in some vessel categories this proportion was much higher (i.e., 96%). We fit negative binomial General Linearized Models to our photobot data and found a strong seasonal variation in non-AIS, and a weekend/weekday component that also varied by season (interaction term p © 2022, The Authors. All rights reserved.