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.

Maritime nations around the world proactively engage in preparedness, response, and recovery activities related to marine oil spills. In addition to an individual nation’s capabilities, there are a number of response organizations that are actively engaged in the surveillance, monitoring, and remote sensing of spilled oil. A global survey was conducted of these organizations to better understand surveillance/remote sensing capabilities operationally employed today from four aerial platforms: satellites, fixed-wing aircraft, helicopters, and remotely piloted aircraft systems (RPASs). Satellite remote sensing continues to be used for both routine surveillance of coastal environments and in support of response to oil spills. Additionally, there is a strong continued use of fixed-wing aircraft, and in some cases helicopters, particularly to support operational response to oil spills. Many of these fixed-wing aircraft are outfitted with sensor suites optimized for oil spill detection and documentation. Of particular interest is the recent introduction and widespread use of RPASs for the response of marine oil spills and oiled shorelines. Respondents identified operational gaps in remote sensing capabilities to support oil spill response, including the accurate measurement of oil spill thickness and volume, differentiation between petroleum oil and biogenic materials, and the detection of water-in-oil emulsions. Survey respondents also shared remote sensing capabilities used for oiled shorelines, as well as identifying research and operational gaps in the surveillance of oil spills.

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.