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

doi:10.1016/j.scitotenv.2022.160987

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

Sci Total Environ. 2023 Mar 20:865:160987. doi: 10.1016/j.scitotenv.2022.160987. Epub 2022 Dec 20.

Authors

Patrick D O'Hara¹, Norma Serra-Sogas², Lauren McWhinnie³, Kim Pearce⁴, Nicole Le Baron⁵, Gregory O'Hagan⁵, Andrea Nesdoly⁵, Tunai Marques⁶, Rosaline Canessa⁵

Affiliations

¹ Canadian Wildlife Service, Environment and Climate Change Canada, Institute of Ocean Sciences, Sidney, BC, Canada; CORAL Group, Department of Geography, University of Victoria, Victoria, BC, Canada. Electronic address: Patrick.OHara@ec.gc.ca.
² CORAL Group, Department of Geography, University of Victoria, Victoria, BC, Canada; Fisheries and Oceans Canada, Institute of Ocean Sciences, Sidney, BC, Canada.
³ Institute of Life and Earth Sciences, Heriot-Watt University, United Kingdom; CORAL Group, Department of Geography, University of Victoria, Victoria, BC, Canada.
⁴ National Aerial Surveillance Program, Transport Canada, Vancouver, BC, Canada.
⁵ CORAL Group, Department of Geography, University of Victoria, Victoria, BC, Canada.
⁶ Department of Electrical and Computer Engineering, University of Victoria, Victoria, BC, Canada.

PMID: 36563755
DOI: 10.1016/j.scitotenv.2022.160987

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.

Keywords: AIS; Aerial survey; Autonomous optical data collection; Non-AIS vessels; Risk assessments; Salish Sea.