AIS Data and fishery activities
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- The AIS (Automatic Identification System) Data stream consist of a series of sentences registered every 2 or 5 minutes. The frequency of collected sentences is very high and analyzing them it is reasonably possible to identify, with a great accuracy, the different activities carried out by the vessels during their fishing cruises. Moving through them according to the time sequence, vessel by vessel, it is possible to aggregate group of consecutive points identifying the different vessel activities. A series of points with their latitude and longitude together with Speed Over Ground (SOG) and Coarse Over Ground (COG) can be transformed in a series of vessel’s paths describing its activities (see pictures below).
This approach can be useful for monitoring fishing activities through the following results:
- Identifying fishing tracks and determine their length;
- Estimate fishing effort as value of nautical miles traveled during fishing activities in a specified area;
- Identifying the paths and the distance traveled by fishing vessels to reach their fishing grounds
- Evaluate fuel consumption.
All these information together (or taken individually) are fundamental to support fishing activites.
AIS Data are collected in real time (with a delay of about 1 hour) and are immediately available removing the usual lag time in availability of such kind of public data.
We do not want to use AIS as a tool to identify illicit fishing activities but as a tool to support fishermen to improve and optimize their own job and to help pubblic institutions defining management rules.
Analyzing AIS Data
A specific algorithm has been developed for the analysis and interpretation of AIS data collected from fishing vessels in order to identify the various phases of work that these vessels carry out when they are at sea. In particular, the algorithm can identify the fishing tracks – red lines – (Fishing effort) and the steaming tracks – blue lines – used to reach the fishing grounds. The algorithm was developed to analyze large amounts of data in a very short time and obtain a series of statistics on fishing activity, including the identification of fishing grounds most exploited by the various fleets, the duration of the fishing tracks and their length.
The approach used by the algorithm is a machine learning approach. For each vessel a specific set of parameters are estimated and then the algortihm continue to validate and, if necessary, modify them. Finally it uses this set of parameters to analyze the behaviour of the vessels adapting the algorith itself to the specific fishing habits of the fishing vessel.
The followings are some of the results obtained by the analysis of fishing vessels AIS Data:
Map with live fshing vessels tracks are available for free.
Fishing gear is authomatically identifyed. The algorithm compares some indicators obtained from vessels behaviour with a series of known profiles and find the most liklyhood fishing gear.
The algorithm analyze the sequence of AIS sentences and identify the vessel activities using vessel specific parameters.
Main fishing grounds are indetified as a consequence of the analysis of each fishing vessel, the mapping all the fishing paths together highlights the areas exploited by the fishing fleet.
Spatil distribution is obtained estimating the fishing effort as the number of total nautical miles traveled by all the vessels during their fishing activities in a specified area and producing a map with those values.
Analyzing the different activities of vessels during their fishing trips it is possible to estimate some statistics values (e.g. vessel consumption) that can be used by fishermen in programming their future fishing strategy.