Spatial and Temporal Variability in Shipping Traffic Off San Francisco, California

Jensen, Caitlin M.; Hines, Ellen; Holzman, Barbara A.; Moore, Thomas J.; Jahncke, Jaime; Redfern, Jessica V.

doi:10.1080/08920753.2015.1086947

Cited by 26 publications

(31 citation statements)

References 24 publications

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“…For vessels transmitting AIS data, this identification number was the Maritime Mobile Service Identity (MMSI). Second, vessel tracks provided lines connecting a vessel's detection points joined in chronological order (Jensen et al, 2015;Redfern et al, 2017) and reported static information, including the unique voyage and vessel identification number, vessel type, and start date and time. For vessels transmitting AIS data, vessel type was reported in the static data via an AIS vessel type number.…”

Section: Sailing Vesselmentioning

confidence: 99%

“…Since the accuracy of AIS data is dependent on vessel user input and onboard equipment and transmitter functioning (Schwehr and McGillivary, 2007), we removed tracks from consideration that were likely erroneous or duplicates. Vessels transmitting AIS data with an invalid or missing MMSI number (<201000000 or >775999999) or AIS vessel type number (<10 or >100) were removed (Jensen et al, 2015;Redfern et al, 2017) since they could not be accurately identified (607 of 37,500 total AIS tracks observed, or 1.6%). We calculated the maximum speed statistic for all vessel tracks using the speed over ground attribute provided in the detection points, and removed any track with an unreasonable maximum speed (Rycyk et al, 2018).…”

Section: Sailing Vesselmentioning

confidence: 99%

“…Using the start date and time of each vessel track, we analyzed daily vessel presence and speed across three temporal resolutions: season, day/night using local Pacific Time (PST/PDT), and weekday/weekend. We quantified vessel presence by summing the length of all tracks per day over the study area for each vessel class (Jensen et al, 2015). The speed over ground for all detection points within the study area was averaged for each day and for each vessel class.…”

Section: Temporal Differences In Vessel Trafficmentioning

confidence: 99%

“…The speed over ground for all detection points within the study area was averaged for each day and for each vessel class. Seasons were assigned using commonly defined calendar quarters in the region: winter was assigned to January-March, spring to April-June, summer to July-September, and autumn to October-December (Forney and Barlow, 1998;Becker et al, 2014;Jensen et al, 2015). Tracks were assigned to day/night using the StreamMetabolism package in R (Sefick, 2016), which utilized the NOAA Solar Calculator to provide past sunrise and sunset times for Tiburon, California.…”

Section: Temporal Differences In Vessel Trafficmentioning

confidence: 99%

“…The International Maritime Organization (IMO) requires all transiting ships of greater than 300 gross tonnage, and all commercial passenger vessels, to broadcast static information on a vessel and its voyage and dynamic information on the vessel's geolocation, heading, and speed throughout the voyage (International Maritime Organization [IMO], 2018). AIS data has helped detect illegal fishing (Longépé et al, 2018;Kurekin et al, 2019), model underwater noise pollution (Erbe et al, 2014;Redfern et al, 2017;Cholewiak et al, 2018), and inform risk assessments of collisions between ships and large whales (Williams and O'Hara, 2010;Wiley et al, 2011;van der Hoop et al, 2012;Conn and Silber, 2013;Redfern et al, 2013;Jensen et al, 2015). Since AIS was not originally designed for environmental management, there are limitations to the use of AIS in this capacity (Robards et al, 2016).…”

Section: Introductionmentioning

confidence: 99%

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Application of a New Shore-Based Vessel Traffic Monitoring System Within San Francisco Bay

et al. 2020

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Vessel traffic management systems can be employed for environmental management where vessel activity may be of concern. One such location is in San Francisco Bay where a variety of vessel types transit a highly developed urban estuary. We analyzed vessel presence and speed across space and time using vessel data from the Marine Monitor, a vessel tracking system that integrates data from the Automatic Identification System and a marine-radar sensor linked to a high-definition camera. In doing so, we provide data that can inform collision risk to cetaceans who show an increased presence in the Bay and evaluation of the value in incorporating data from multiple sources when observing vessel traffic. We found that ferries traveled the greatest distance of any vessel type. Ferries and other commercial vessels (e.g., cargo and tanker ships and tug boats) traveled consistently in distinct paths while recreational traffic (e.g., motorized recreational craft and sailing vessels) was more dispersed. Large shipping vessels often traveled at speeds greater than 10 kn when transiting the study area, and ferries traveled at speeds greater than 30 kn. We found that distance traveled and speed varied by season for tugs, motorized recreational and sailing vessels. Distance traveled varied across day and night for cargo ships, tugs, and ferries while speed varied between day and night only for ferries. Between weekdays and weekends, distance traveled varied for cargo ships, ferries, and sailing vessels, while speed varied for ferries, motorized recreational craft, and sailing vessels. Radar-detected vessel traffic accounted for 33.9% of the total track distance observed, highlighting the need to include data from multiple vessel tracking systems to fully assess and manage vessel traffic in a densely populated urban estuary.

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Section: Sailing Vesselmentioning

confidence: 99%

Section: Sailing Vesselmentioning

confidence: 99%

Section: Temporal Differences In Vessel Trafficmentioning

confidence: 99%

Section: Temporal Differences In Vessel Trafficmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

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Application of a New Shore-Based Vessel Traffic Monitoring System Within San Francisco Bay

et al. 2020

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Predicting summer fin whale distribution in the Pelagos Sanctuary (north‐western Mediterranean Sea) to identify dynamic whale–vessel collision risk areas

Ham

Lahaye

Rosso

et al. 2021

Aquatic Conservation

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1. Mediterranean fin whales aggregating in the Pelagos Sanctuary in summer to feed are exposed to vessel collision risk, particularly from high-speed ferries.2. This study developed models to predict summer fin whale distribution using a generalized additive model (GAM) and MaxEnt, with the aim of providing a tool to identify potential high whale-ferry collision risk areas along ferry routes within the Pelagos Sanctuary during summertime.3. Models were trained using sightings data collected in the summer months of 2009-2018 on board ferries crossing the central area of the Pelagos Sanctuary.Environmental predictors were bathymetry and mean sea surface chlorophyll concentration of the annual spring bloom period.

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Past, present, and future of the satellite-based automatic identification system: areas of applications (2004–2016)

Fournier

Hilliard²,

Rezaee

et al. 2018

WMU J Marit Affairs

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In 2016, the world shipping fleet grew by 3.5%. Even if the annual growth rate remains at its lowest since 2013, the global situation is still in overcapacity (UNCTAD 2016). Ninety percent of global trade, by volume, is done by sea. Monitoring this fleet helps with vessel navigation, informing to help avoid critical situations such as collisions, accidents leading to oil pollution, grounding, or ships in distress, but also because traffic management in congested areas is essential. For system wide management, in regions such as MPAs (marine protected areas), conservation is the key factor, and movements can be monitored and analyzed in order to determine illegal or suspicious activities, or in order to limit and/or divert traffic, to mitigate the risks to species subject to protection. It is among these efforts that the automatic identification system (AIS) can play a key role. Since 2004, this VHF transceiver-based reporting system, imposed by the International Maritime Organization (IMO), has shifted from a traditional vessel identification device to a tool used in a wide variety of applications. The most common uses are safety and security; these issues are quite visible in the media and may touch more people on a global scale (e.g., piracy, oil spills). Over the years, AIS has become, especially with the emergence of the satellite-based capture of the signal in 2011, a widely used tool for developing applications such as fisheries monitoring, marine conservation, air pollution forecasting and modeling, ballast water monitoring, invasive species transport, and many more. In this paper, we propose to review the peerreviewed publications related to the uses and applications of the AIS.

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Spatial and Temporal Variability in Shipping Traffic Off San Francisco, California

Cited by 26 publications

References 24 publications

Application of a New Shore-Based Vessel Traffic Monitoring System Within San Francisco Bay

Application of a New Shore-Based Vessel Traffic Monitoring System Within San Francisco Bay

Predicting summer fin whale distribution in the Pelagos Sanctuary (north‐western Mediterranean Sea) to identify dynamic whale–vessel collision risk areas

Past, present, and future of the satellite-based automatic identification system: areas of applications (2004–2016)

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