AIS data to inform small scale fisheries management and marine spatial planning

Mark James; Tania Mendo; Esther L. Jones; Kyla Orr; Ali Mcknight; John Thompson

doi:10.1016/j.marpol.2018.02.012

AIS data to inform small scale fisheries management and marine spatial planning

Mark James, Tania Mendo, Esther L. Jones, Kyla Orr, Ali Mcknight, John Thompson

Research output: Contribution to journal › Article › peer-review

Abstract

Automatic Identification Systems (AIS) are collision avoidance devices used on-board both commercial and leisure craft. These systems report the position, track and speed of the vessel through Very High Frequency radio transmissions which are accessible to any suitable receiver. This paper explores the potential to use AIS data to inform small scale fisheries management and marine spatial planning. First, the propagation and reception of the line of sight AIS transmissions was modelled around the coast of Scotland to identify areas where the use of AIS may be compromised. Using open source Geographic Information System and relational database software, computationally efficient methods of processing and analysing AIS data were explored. Three months of AIS data derived from 274 Scottish small scale fishing vessels were used to provide spatio-temporal analyses of trip duration and distance travelled, location of fishing activities, and vessel dependency on fishing grounds. The coverage, opportunities and challenges of using AIS are discussed together with broader applications and future developments.

Original language	English
Pages (from-to)	113-121
Journal	Marine Policy
Volume	91
Early online date	24 Feb 2018
DOIs	https://doi.org/10.1016/j.marpol.2018.02.012
Publication status	Published - May 2018

Keywords

Monitoring
Inshore fisheries
Trips
PostgreSQL
Trajectories

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

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10.1016/j.marpol.2018.02.012

JMPO_2017_493_Revision 3_V0 - Final
© 2018, Elsevier Ltd. This work has been made available online in accordance with the publisher’s policies. This is the author created, accepted version manuscript following peer review and may differ slightly from the final published version. The final published version of this work is available at https://doi.org/10.1016/j.marpol.2018.02.012
Accepted author manuscript, 3.39 MBLicence: CC BY-NC-ND

Cite this

@article{01d6cb3ca82842f987e9de3e5d074491,

title = "AIS data to inform small scale fisheries management and marine spatial planning",

abstract = "Automatic Identification Systems (AIS) are collision avoidance devices used on-board both commercial and leisure craft. These systems report the position, track and speed of the vessel through Very High Frequency radio transmissions which are accessible to any suitable receiver. This paper explores the potential to use AIS data to inform small scale fisheries management and marine spatial planning. First, the propagation and reception of the line of sight AIS transmissions was modelled around the coast of Scotland to identify areas where the use of AIS may be compromised. Using open source Geographic Information System and relational database software, computationally efficient methods of processing and analysing AIS data were explored. Three months of AIS data derived from 274 Scottish small scale fishing vessels were used to provide spatio-temporal analyses of trip duration and distance travelled, location of fishing activities, and vessel dependency on fishing grounds. The coverage, opportunities and challenges of using AIS are discussed together with broader applications and future developments.",

keywords = "Monitoring, Inshore fisheries, Trips, PostgreSQL, Trajectories",

author = "Mark James and Tania Mendo and Jones, {Esther L.} and Kyla Orr and Ali Mcknight and John Thompson",

note = "This study was funded by the 2014/15 European Fisheries Fund {\textquoteleft}Evidence Gathering in Support of Sustainable Scottish Inshore Fisheries{\textquoteright} (Grant Number: MI-NC-3-0093). Mark James, Tania Mendo, and John Thompson were supported by the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland). Their support is gratefully acknowledged. MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions. ",

year = "2018",

month = may,

doi = "10.1016/j.marpol.2018.02.012",

language = "English",

volume = "91",

pages = "113--121",

journal = "Marine Policy",

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T1 - AIS data to inform small scale fisheries management and marine spatial planning

AU - James, Mark

AU - Mendo, Tania

AU - Jones, Esther L.

AU - Orr, Kyla

AU - Mcknight, Ali

AU - Thompson, John

N1 - This study was funded by the 2014/15 European Fisheries Fund ‘Evidence Gathering in Support of Sustainable Scottish Inshore Fisheries’ (Grant Number: MI-NC-3-0093). Mark James, Tania Mendo, and John Thompson were supported by the MASTS pooling initiative (The Marine Alliance for Science and Technology for Scotland). Their support is gratefully acknowledged. MASTS is funded by the Scottish Funding Council (grant reference HR09011) and contributing institutions.

PY - 2018/5

Y1 - 2018/5

N2 - Automatic Identification Systems (AIS) are collision avoidance devices used on-board both commercial and leisure craft. These systems report the position, track and speed of the vessel through Very High Frequency radio transmissions which are accessible to any suitable receiver. This paper explores the potential to use AIS data to inform small scale fisheries management and marine spatial planning. First, the propagation and reception of the line of sight AIS transmissions was modelled around the coast of Scotland to identify areas where the use of AIS may be compromised. Using open source Geographic Information System and relational database software, computationally efficient methods of processing and analysing AIS data were explored. Three months of AIS data derived from 274 Scottish small scale fishing vessels were used to provide spatio-temporal analyses of trip duration and distance travelled, location of fishing activities, and vessel dependency on fishing grounds. The coverage, opportunities and challenges of using AIS are discussed together with broader applications and future developments.

AB - Automatic Identification Systems (AIS) are collision avoidance devices used on-board both commercial and leisure craft. These systems report the position, track and speed of the vessel through Very High Frequency radio transmissions which are accessible to any suitable receiver. This paper explores the potential to use AIS data to inform small scale fisheries management and marine spatial planning. First, the propagation and reception of the line of sight AIS transmissions was modelled around the coast of Scotland to identify areas where the use of AIS may be compromised. Using open source Geographic Information System and relational database software, computationally efficient methods of processing and analysing AIS data were explored. Three months of AIS data derived from 274 Scottish small scale fishing vessels were used to provide spatio-temporal analyses of trip duration and distance travelled, location of fishing activities, and vessel dependency on fishing grounds. The coverage, opportunities and challenges of using AIS are discussed together with broader applications and future developments.

KW - Monitoring

KW - Inshore fisheries

KW - Trips

KW - PostgreSQL

KW - Trajectories

U2 - 10.1016/j.marpol.2018.02.012

DO - 10.1016/j.marpol.2018.02.012

M3 - Article

SN - 0308-597X

VL - 91

SP - 113

EP - 121

JO - Marine Policy

JF - Marine Policy

ER -

AIS data to inform small scale fisheries management and marine spatial planning

Abstract

Keywords

UN SDGs

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