1 / 27

Using VMS data to predict fishing effort Dr Janette Lee 7 January 2009

Using VMS data to predict fishing effort Dr Janette Lee 7 January 2009. Vessel Monitoring System (VMS). 2 hourly satellite location of vessels EU legislation for vessels > 15m Cefas has access to UK vessels & foreign vessels in UK waters Collected for operational enforcement

jack
Télécharger la présentation

Using VMS data to predict fishing effort Dr Janette Lee 7 January 2009

An Image/Link below is provided (as is) to download presentation Download Policy: Content on the Website is provided to you AS IS for your information and personal use and may not be sold / licensed / shared on other websites without getting consent from its author. Content is provided to you AS IS for your information and personal use only. Download presentation by click this link. While downloading, if for some reason you are not able to download a presentation, the publisher may have deleted the file from their server. During download, if you can't get a presentation, the file might be deleted by the publisher.

E N D

Presentation Transcript

  1. Using VMS data to predict fishing effortDr Janette Lee7 January 2009

  2. Vessel Monitoring System (VMS) • 2 hourly satellite location of vessels • EU legislation for vessels > 15m • Cefas has access to UK vessels & foreign vessels in UK waters • Collected for operational enforcement • Secondary use for science: estimating fishing effort • Of limited use to science unless it can be linked to fishing gear used

  3. Catch data Vessel data VMS data Discard data Logbook data DATABASE Algorithms GIS

  4. Extracting the data … • Using a combined database of vessel information, log book details, commercial landings and positional information, data can be selected by: • specified dates • geographical region • fishing vessels nationality • gear type • fish species

  5. Identifying fishing or steaming … Speed alone allowed successful estimation (~96% correct) of fishing/ steaming activity from VMS data in 3 fleets around England and Wales Mills, C. M., Townsend, S. E., Jennings, S., Eastwood, P. D., and Houghton, C. A. 2007. Estimating high resolution trawl fishing effort from satellite-based vessel monitoring system data. – ICES Journal of Marine Science, 64: 248–255.

  6. Observer Data within Study Area:Number of Observations

  7. Observer Data within Study Area:Observations by Gear Type

  8. Observer Data within Study Area:Number of Vessels Observed

  9. Overflight Data within Study Area:Number of Observations

  10. Overflight Data within Study Area:Observations by Gear Type

  11. From points to surfaces …

  12. From points to surfaces …removal of ‘bad’ data • Duplicate VMS points: same lat/long, date, time, vessel id • VMS points within 1km of a port and with a speed of 0 knots • VMS points falling ‘inside’ the boundary of the coastline

  13. Speed Bands for Fishing Activity(from Mills et al. 2007) • Beam Trawls (TBB): 2-8 knots • Boat Dredges (DRB): 2-8 knots • Mechanised Dredges (HMD): 2-8 knots • Bottom Otter Trawls (OTB): 1-6 knots • Midwater Otter Trawls (OTM): 1-6 knots • Twin Otter Trawls (OTT):1-6 knots

  14. Speed Bands for Fishing Activity: Pots (FPO) • 1,005 overflight sightings identified as fishing using FPO gear between 2005 and 2007   • 865 of these (86%) fall within the 12-mile limit • The average speed is 1.02 knots (std.dev = 1.35) • 20 out of 1,005 sightings (2%) record speeds in excess of 3 knots • 593 out of 1,005 sightings (59%) record a speed of zero • Speed <= 3 knots was used to filter VMS records as fishing • There are NO records in the observer database for potting

  15. Speed for all FPO VMS data

  16. Speed Bands for Fishing Activity: Set Gillnets, anchored (GNS) • 391 overflight sightings identified as fishing using GNS gear between 2005 and 2007   • 205 of these (52%) fall within the 12-mile limit • The average speed is 1.7 knots (std.dev = 1.6) • 4 out of 391 sightings (1%) record speeds in excess of 4 knots • 166 out of 391 sightings (42%) record a speed of zero • Speed <= 4 knots was used to filter VMS records as fishing • Data for 200 hauls observed in 2006 and 2007 • The time between shooting and hauling of nets ranged from 8hr 30mins to 96hrs (average=22 hrs) • Distance between shoot location and haul location ranged from 0 to 7 nautical miles (average=2 Nm)

  17. Speed for all GNS VMS data

  18. Speed Bands for Fishing Activity: Driftnets (GND) • 327 overflight sightings identified as fishing using GND gear between 2005 and 2007   • 7 of these (2%) fall within the 12-mile limit • The average speed is 3 knots (std.dev = 1.7) • 7 out of 327 sightings (2%) record speeds in excess of 3 knots • 17 out of 327 sightings (5%) record a speed of zero • Speed <= 3 knots was used to filter VMS records as fishing • There are NO records in the observer database for driftnets

  19. Speed for all GND VMS data

  20. VMS Time Interval • The actual ‘time interval’ is determined by computing the difference in the VMS time stamp for consecutive records for the same vessel ID • If the calculated time interval for a given VMS record is > 4 hours (i.e. two VMS records have been missed) then the VMS record is flagged as being the ‘start’ of a new trip and a default ‘time interval’ of 2 hours is assigned

  21. Fishing Activity Grid • A sum of the ‘time interval’ for all VMS points falling within each cell of a 0.05 degree grid is computed • This summation can be carried out for any specified time period (monthly, quarterly, annually) and for any specified gear code • Grids can easily be added to e.g. produce annual values from the summation of four quarterly grids; or to produce details for gear grouping by summing the grids for specified gears

  22. The thin white line … … inshore waters !

  23. Filling the gap … Project has just started in partnership with SSFDC: • Eastern English Channel area • To identify methods to integrate inshore observations and offshore VMS data • To try to develop methods that produce the same reference scale for both areas • To compensate for the different sampling intervals of the datasets

More Related