scholarly journals AIS and VMS Ensemble Can Address Data Gaps on Fisheries for Marine Spatial Planning

2021 ◽  
Vol 13 (7) ◽  
pp. 3769
Author(s):  
Pascal Thoya ◽  
Joseph Maina ◽  
Christian Möllmann ◽  
Kerstin S. Schiele
Keyword(s):  
Case Studies ◽  
Spatial Planning ◽  
Fishing Effort ◽  
Rapid Expansion ◽  
Automatic Identification ◽  
Identification System ◽  
Marine Spatial Planning ◽  
The North ◽  
Address Data ◽  
The Baltic

Spatially explicit records of fishing activities’ distribution are fundamental for effective marine spatial planning (MSP) because they can help to identify principal fishing areas. However, in numerous case studies, MSP has ignored fishing activities due to data scarcity. The vessel monitoring system (VMS) and the automatic identification system (AIS) are two commonly known technologies used to observe fishing activities. However, both technologies generate data that have several limitations, making them ineffective when used in isolation. Here, we evaluate both datasets’ limitations and strengths, measure the drawbacks of using any single dataset and propose a method for combining both technologies for a more precise estimation of the distribution of fishing activities. Using the Baltic Sea and the North Sea–Celtic Sea regions as case studies, we compare the spatial distribution of fishing effort from International Council for the Exploration of the Seas (ICES) VMS data and global fishing watch AIS data. We show that using either dataset in isolation can lead to a significant underestimation of fishing effort. We also demonstrate that integrating both datasets in an ensemble approach can provide more accurate fisheries information for MSP. Given the rapid expansion of MSP activities globally, our approach can be utilised in data-limited regions to improve cross border spatial planning.

A Feature Encoding Approach and a Cloud Computing Architecture to Map Fishing Activities

2021 ◽  
Author(s):  
A. Galdelli ◽  
A. Mancini ◽  
E. Frontoni ◽  
A. N. Tassetti
Keyword(s):  
Spatial Planning ◽  
Low Cost ◽  
Small Scale ◽  
Automatic Identification ◽  
Identification System ◽  
Marine Spatial Planning ◽  
Fishing Activity ◽  
Feature Encoding ◽  
Encoding Method ◽  
New Policies

Abstract Monitoring fish stocks and fleets’ activities is key for Marine Spatial Planning. In recent years Vessel Monitoring System and Automatic Identification System have been developed for vessels longer than 12 and 15m in length, respectively, while small scale vessels (< 12m in length) remain untracked and largely unregulated, even though they account for 83% of all fishing activity in the Mediterranean Sea. In this paper we present an architecture that makes use of a low-cost LoRa/cellular network to acquire and process positioning data from small scale vessels, and a feature encoding approach that can be easily extended to process and map small scale fisheries. The feature encoding method uses a Markov chain to model transitions between successive behavioural states (e.g., fishing, steaming) of each vessel and classify its activity. The approach is evaluated using k-fold and Leave One Boat Out cross-validations and, in both cases, it results in significant improvements in the classification of fishing activities. The use of a such low-cost and open source technology coupled to artificial intelligence could open up potential for more integrated and transparent platforms to inform coastal resource and fisheries management, and cross-border marine spatial planning. It enables a new monitoring strategy that could effectively include small-scale fleets and support the design of new policies oriented to the optimal use of marine resources.

scholarly journals A comprehensive inventory of ship traffic exhaust emissions in the European sea areas in 2011

2016 ◽  
Vol 16 (1) ◽  
pp. 71-84 ◽  
Author(s):  
J.-P. Jalkanen ◽  
L. Johansson ◽  
J. Kukkonen
Keyword(s):  
Assessment Model ◽  
Automatic Identification ◽  
Identification System ◽  
Traffic Emission ◽  
Ship Traffic ◽  
Eu Member States ◽  
The North ◽  
System Data ◽  
The Mediterranean ◽  
The Baltic

Abstract. Emissions originating from ship traffic in European sea areas were modelled using the Ship Traffic Emission Assessment Model (STEAM), which uses Automatic Identification System data to describe ship traffic activity. We have estimated the emissions from ship traffic in the whole of Europe in 2011. We report the emission totals, the seasonal variation, the geographical distribution of emissions, and their disaggregation between various ship types and flag states. The total ship emissions of CO2, NOx, SOx, CO, and PM2.5 in Europe for year 2011 were estimated to be 121, 3.0, 1.2, 0.2, and 0.2 million tons, respectively. The emissions of CO2 from the Baltic Sea were evaluated to be more than a half (55 %) of the emissions of the North Sea shipping; the combined contribution of these two sea regions was almost as high (88 %) as the total emissions from ships in the Mediterranean. As expected, the shipping emissions of SOx were significantly lower in the SOx Emission Control Areas, compared with the corresponding values in the Mediterranean. Shipping in the Mediterranean Sea is responsible for 40 and 49 % of the European ship emitted CO2 and SOx emissions, respectively. In particular, this study reported significantly smaller emissions of NOx, SOx, and CO for shipping in the Mediterranean than the EMEP inventory; however, the reported PM2.5 emissions were in a fairly good agreement with the corresponding values reported by EMEP. The vessels registered to all EU member states are responsible for 55 % of the total CO2 emitted by ships in the study area. The vessels under the flags of convenience were responsible for 25 % of the total CO2 emissions.

scholarly journals The global network of ports supporting high seas fishing

2021 ◽  
Vol 7 (9) ◽  
pp. eabe3470
Author(s):  
Jorge P. Rodríguez ◽  
Juan Fernández-Gracia ◽  
Carlos M. Duarte ◽  
Xabier Irigoien ◽  
Víctor M. Eguíluz
Keyword(s):  
Fishing Effort ◽  
Global Network ◽  
Automatic Identification ◽  
Identification System ◽  
Middle Income ◽  
Middle Income Countries ◽  
Fishing Vessels ◽  
National Jurisdiction ◽  
The Commons ◽  
High Seas

Fisheries in waters beyond national jurisdiction (“high seas”) are difficult to monitor and manage. Their regulation for sustainability requires critical information on how fishing effort is distributed across fishing and landing areas, including possible border effects at the exclusive economic zone (EEZ) limits. We infer the global network linking harbors supporting fishing vessels to fishing areas in high seas from automatic identification system tracking data in 2014, observing a modular structure, with vessels departing from a given harbor fishing mostly in a single province. The top 16% of these harbors support 84% of fishing effort in high seas, with harbors in low- and middle-income countries ranked among the top supporters. Fishing effort concentrates along narrow strips attached to the boundaries of EEZs with productive fisheries, identifying a free-riding behavior that jeopardizes efforts by nations to sustainably manage their fisheries, perpetuating the tragedy of the commons affecting global fishery resources.

scholarly journals Positioning Error Analysis of Ranging-Mode Using AIS Signals in China

2016 ◽  
Vol 2016 ◽  
pp. 1-11 ◽  
Author(s):  
Kai Zheng ◽  
Qing Hu ◽  
Jingbo Zhang
Keyword(s):  
Measurement Errors ◽  
Phase Locking ◽  
Automatic Identification ◽  
Identification System ◽  
Time Of Arrival ◽  
Positioning Error ◽  
North East ◽  
The North ◽  
Error Distributions ◽  
Shift Keying

In order to provide resilient position, navigation, and time (PNT) information forE-Navigation, the ranging-mode (R-Mode) positioning using automatic identification system (AIS) signals is encouraged. As the accuracy is the key for the positioning system, this paper investigates the position error of the R-Mode positioning based on AIS shore-based station in China. The measurement errors of Gaussian filtered minimum shift keying (GMSK) demodulation based on carrier phase locking loop are investigated in theory. The dilution of precision (DOP) for time of arrival (TOA) and time difference of arrival (TDOA) used in R-Mode positioning of AIS is discussed in two measurement mechanisms. The positioning error distributions in the North, East, and South Sea regions of China based on the existing AIS shore-based stations are evaluated. The positioning accuracy is at the meter level in the most traffic dense areas to meet the requirements for vessel navigation.

scholarly journals Defining high-resolution dredge fishing grounds with Automatic Identification System (AIS) data

2017 ◽  
Vol 30 ◽  
pp. 39 ◽  
Author(s):  
Damien Le Guyader ◽  
Cyril Ray ◽  
Françoise Gourmelon ◽  
David Brosset
Keyword(s):  
High Resolution ◽  
Data Access ◽  
Relevant Information ◽  
Fishing Effort ◽  
Automatic Identification ◽  
Identification System ◽  
Automatic Identification System ◽  
Local Decision ◽  
Bay Of Brest ◽  
Maritime Spatial Planning

High resolution estimates of bottom towed fishing gears are needed to provide relevant information for natural resource management, impact assessment and maritime spatial planning. The use of satellite-based vessel monitoring system (VMS) data is constrained by data access restrictions as well as rather coarse data resolution. This study focuses on mapping dredge gear fishing grounds using fishing effort estimates at the métier level based on automatic identification system (AIS) data. The performance of the approach was evaluated in terms of correct discrimination between fishing and non-fishing activities for known fishing positions as well as appropriate error propagation. The test was conducted in the Bay of Brest (France) in partnership with a committee of local fishers. The results identified dredge fishing grounds for great scallop (Pecten maximus) in the western part of the Bay of Brest and provided high-resolution information for scientists and local decision makers on the spatial and temporal seasonal variability of fishing effort. The proposed method is semi-automatic and generic making it suitable for other applications.

The Automated Risk Estimation for the Navigation of Autonomous Ships by Learning with Navigation Feature

2020 ◽  
pp. 2041003
Author(s):  
Wei Chian Tan ◽  
Kie Hian Chua ◽  
Yanling Wu
Keyword(s):  
Nearest Neighbor ◽  
Dimensional Space ◽  
Risk Estimation ◽  
Nearest Neighbor Search ◽  
Mathematical Representation ◽  
Automatic Identification ◽  
Identification System ◽  
North West ◽  
The North ◽  
Neighbor Search

This work presents a data-driven approach for the automated risk estimation of the voyage of a vessel or ship. While the industry is moving from a compliance-based framework with existing rules to a risk-based one, there is also a need to monitor the risk of a vessel from the perspective of the navigation. This is of even higher importance for the case of autonomous ships. Built based on the state-of-the-art mathematical representation, the navigation feature, each existing voyage is transformed into a corresponding series of points in [Formula: see text]-dimensional space. During the stage of pre-processing, given a set of historical Automatic Identification System (AIS) data, those records that belong to the same vessel within a certain period of time are taken as a voyage and mapped to the corresponding space of the navigation feature. After the pre-processing and during the online monitoring, the current trajectory of the vessel is transformed into the corresponding representation in the same way. Based on a nearest-neighbor search scheme, the distance from the nearest neighbor is taken as the risk of the current voyage. In other words, the deviation from the closest route in the historical data is taken as the risk. The developed method has demonstrated encouraging performance on a set of challenging historical AIS data from the Australian Maritime Safety Authority, covering three regions in the Australian territory, namely, the Bass Strait, the Great Australian Bight and the North West.

scholarly journals Sensing the Same Fishing Fleet with AIS and VIIRS: A Seven-Year Assessment of Squid Jiggers in FAO Major Fishing Area 41

2019 ◽  
Vol 12 (1) ◽  
pp. 32
Author(s):  
Javier Ruiz ◽  
Isabel Caballero ◽  
Gabriel Navarro
Keyword(s):  
Infrared Imaging ◽  
Spatial Scales ◽  
Fishing Effort ◽  
Automatic Identification ◽  
Identification System ◽  
Sources Of Information ◽  
Food And Agriculture ◽  
Food And Agriculture Organization ◽  
High Degree ◽  
Fishing Area

Global Fishing Watch and VIIRS-DNB (visible infrared imaging radiometer suite day/night band) signals are compared for the jigger fleet in FAO (Food and Agriculture Organization of the United Nations) Major Fishing Area 41 during the maximum feasible time span (2012–2018). Both signals have shown a high degree of consistency at all temporal and spatial scales analyzed, including seasonal cycles, lack of signal for some years and interannual tendencies. This indicates that both signals are a fair representation of the fishing effort exerted by the jigger fleet in this zone. The high degree of consistency does not support views questioning satellite AIS (automatic identification system) as a reliable tool to survey fishing activities. Instead, our results add evidence supporting the value of remote sensing, in particular, when independent sources of information (such as VIIRS-DNB and AIS) are combined, as a relevant tool to add transparency and support compliance of fishing activities in vast and distant regions of the ocean.

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