Estimating Reception Coverage Characteristics of AIS

Lapinski, Anna-Liesa S.; Isenor, Anthony W.

doi:10.1017/s0373463311000282

Cited by 15 publications

(11 citation statements)

References 4 publications

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“…Limits to AIS Coverage.-AIS transmissions to ground-based receivers are limited by "line of sight" in the few tens of miles, but topography can lead to areas with poor or no coverage at all (Shelmerdine 2015). Reception distance varies depending on the height of the receiving antenna and topography, but also on other dynamic factors such as meteorological conditions, atmospheric bounce, and interference from other radio signals Bettridge 2010, Lapinski andIsenor 2011). While satellite reception offers larger coverage, reception is highly dependent on antenna placement and characteristics (e.g., omni vs directional, dipole, etc.…”

Section: Relevance Of Aismentioning

confidence: 99%

Conservation science and policy applications of the marine vessel Automatic Identification System (AIS)—a review

Robards¹,

Silber²,

Adams³

et al. 2016

BMS

149

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The continued development of maritime transportation around the world, and increased recognition of the direct and indirect impacts of vessel activities to marine resources, has prompted interest in better understanding vessel operations and their effects on the environment. Such an understanding has been facilitated by Automatic Identification Systems (AIS), a mandatory vessel communication and navigational safety system that was adopted by the International Maritime Organization in 2000 for use in collision avoidance, coastal surveillance, and traffic management. AIS is an effective tool for accomplishing navigational safety goals, and by doing so, can provide critical pre-emptive maritime safety benefits, but also provides a data opportunity with which to understand and help mitigate the impacts of maritime traffic on the marine environment and wildlife. However, AIS was not designed with research or conservation planning in mind, leading to significant challenges in fully benefiting from use of the data for these purposes. We review present experiences using AIS data for strategic conservation applications, and then focus on efforts to ensure archived and real-time AIS data for key variables reflect the best available science (of known limitations and biases). We finish with a suite of recommendations for users of the data and for policy makers. Maritime vessel activities around the globe have frequently resulted in conservation impacts to wildlife; directly impacting individuals or groups of animals through disturbance, fatal strikes, and introduction of pathogens; or impacting habitats through anchoring (especially on corals), introduction of invasive species, air emissions, noise, and fuel spills (e.g.,

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Section: Relevance Of Aismentioning

confidence: 99%

Conservation science and policy applications of the marine vessel Automatic Identification System (AIS)—a review

Robards¹,

Silber²,

Adams³

et al. 2016

BMS

149

View full text Add to dashboard Cite

show abstract

“…Thus, an examination of the time interval between successive reports from the same vessel should be useful in coverage estimation. The Lapinski-Isenor (LIC) estimation algorithm considered in this section (Lapinski and Isenor, 2011) is based on examining such intervals. See the previous section for a discussion of the different sources of variability in these intervals.…”

Section: The Lapinski-isenor Coverage (Lic) Estimatormentioning

confidence: 99%

“…There are two types of approaches to modelling coverage: considering the physics of VHF radio propagation and the characteristics of the relevant antennas (Green, et al, 2011), or using the AIS receptions themselves to estimate the coverage empirically (Lane et al, 2010; Lapinski and Isenor, 2011). Little consideration will be made here of the former because, in many practical situations, the necessary information is not available.…”

Section: Introductionmentioning

confidence: 99%

Estimating AIS Coverage from Received Transmissions

Hammond¹,

Peters²

2012

J. Navigation

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This paper proposes a new method for estimating Automatic Identification System (AIS) coverage empirically from received transmissions. The method is appropriate for stationary coverage assets, as distinct from aircraft and satellites. The key idea behind the method is to interpolate probabilistically between AIS reports in order to reconstruct where the missed transmissions might have occurred. These hypothetical missed transmissions then supplement the received ones in a coverage estimate based on a Bayesian treatment of a binomial model of reception. The final estimate of the coverage is implemented over a spatial grid. The method is demonstrated on simulated AIS data and was found to have lower mean squared error than a previously published method. Assumptions and potential weaknesses of the new method are discussed.

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“…at regular intervals to all AIS receivers in the area. Due to an International Maritime Organization (IMO) mandate, many vessels are now directed to carry AIS systems and broadcast AIS messages (Lapinski and Isenor, 2011). Originally designed for radar augmentation and vessel traffic services (VTS), the system can be also used to collect information about traffic in the area with little effort.…”

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confidence: 99%

Using big data to estimate whale watching effort

Almunia

Macchione

Rosa

2020

Preprint

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The growing concerns about the negative effects caused by whale watching on wild cetacean populations are evincing the need to measure whale watching effort more precisely. The current alternatives do not provide sufficient information or imply time-consuming and staff intensive tasks that limit their effectiveness to establish the maximum carrying capacity for this touristic activity. A methodology based on big data analysis, using Automatic Information System (AIS) messages, can provide valuable vessel activity information, which is necessary to estimate whale watching effort on cetacean populations. We used AIS data to automatically detect whale watching operations and quantify whale watching effort with high spatial and temporal resolution in the Canary Islands. The results obtained in this study are very encouraging, proving that the methodology is able to estimate seasonal and annual trends in the whale watching effort. The methodology has also proved to be effective in providing detailed spatial information about the whale watching effort, which makes an interesting tool to manage spatial regulations and enforce exclusion zones. The widespread use of AIS devices in maritime navigation provides an enormous potential to easily extend this methodology to other regions worldwide. Any public strategy aimed at the sustainable use of the marine resources should enhance the use of this kind of information technologies, collecting and archiving detailed information on the activity of all the vessels, especially in marine protected areas.

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Estimating Reception Coverage Characteristics of AIS

Cited by 15 publications

References 4 publications

Conservation science and policy applications of the marine vessel Automatic Identification System (AIS)—a review

Conservation science and policy applications of the marine vessel Automatic Identification System (AIS)—a review

Estimating AIS Coverage from Received Transmissions

Using big data to estimate whale watching effort

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