Automated identification system for ships data as a proxy for marine vessel related stressors

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

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Abstract

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 < 0.0001). Non-AIS traffic was much higher during the summer (Apr–Sep) and during the weekend (Sat-Sun), reflecting patterns in recreational vessel traffic not obligated to broadcast AIS. Negative binomial General Additive Models based on the NASP data revealed strong spatial associations with distance from shore (up to 10 km) and non-AIS vessel traffic for both summer and winter seasons. There were also associations between non-AIS vessels and marina and anchorage densities, particularly during the winter, which again reflect seasonal recreational vessel traffic patterns. Overall, our GAMs explained 20–37 % of all vessel traffic during the summer and winter, and highlighted subregions where vessel traffic is under represented by AIS.
Original languageEnglish
Article number160987
JournalScience of the Total Environment
Volume865
Early online date20 Dec 2022
DOIs
Publication statusE-pub ahead of print - 20 Dec 2022

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