Browsing by Subject "AIS"

Now showing 1 - 3 of 3

Open Access
Assessing fishing vessel compliance with area closures on the high seas
(2021-04-30) Mullaney, Claire
High seas marine ecosystems are facing a variety of threats, including fish stock overexploitation; the destruction of deep-sea habitats by harmful fishing gears; and illegal, unreported, and unregulated fishing. To help combat these threats, regional fisheries management organizations (RFMOs) can implement area-based management tools (ABMTs)—methods of spatial regulation, such as closed areas, that address human impacts on marine spaces by restricting certain activities. While ABMTs offer benefits that can help reduce stress on high seas ecosystems, research suggests that they should be coupled with strong monitoring to best provide these benefits. In the past, insufficient technology posed an obstacle to monitoring high seas fishing effort. However, increasingly common automatic identification system (AIS) data, which are broadcast from fishing vessels and communicate vessel identity and location information, provide an ideal mechanism to assess fishing activity in international waters. To understand vessel compliance with ABMTs implemented by RFMOs, I used AIS data from Global Fishing Watch to evaluate fishing effort inside closures on the high seas from 2017 to 2019. Results revealed that 11 of the 14 ABMTs examined likely experienced some level of illegal fishing across all three years, with a total of 13,259.7 hours of fishing effort classified as illegal based on my analysis occurring in 2017, 12,664.3 hours in 2018, and 14,541.1 hours in 2019. These analyses give insight into the success of current RFMO closures and suggest future considerations in the use of ABMTs by regional fishery bodies.
Open Access
Automating Offshore Infrastructure & Vessel Identifications Using Synthetic Aperture Radar & Distributive Geoprocessing
(2018-04-27) Wong, Brian
Global Fishing Watch (GFW) recently published the first worldwide industrial fishing effort data set learned from processing 22 billion Automatic Identification System (AIS) observations. Despite quantifying 40 million hours of fishing activity that extended to over 55% of the ocean’s surface area in 2016, GFW now aims to quantify fishing effort not captured by current analyses through multimodal remotely-sensed imagery. Such imagery-based vessel identifications are commonly confounded with offshore infrastructure, though, so a global offshore infrastructure data set is first required to disentangle the two. This study first establishes robust and scalable methods for automating offshore infrastructure identifications using synthetic aperture radar in the Gulf of Mexico, and then evaluates the feasibility to adopt these methods for vessel identifications. Results indicate our model identifies offshore infrastructure with a probability of detection of 96.3%, an overall accuracy of 91.9%, a commission error rate of 4.7%, and an omission error rate 3.7%. Additionally, a cloud-native geoprocessing framework using the Google Earth Engine Python API was implemented to automate vessel identifications globally. Over 45,000 SAR images or approximately 100TB of data were processed to build a new database overlaying both SAR-derived and AIS-derived vessel locations.
Open Access
How weather shocks impact the flow of energy-related goods on the Lower Mississippi River
(2019-04-23) Murnan, Gabrielle; Vanchosovych, Yuliya; Wu, Fan
The Lower Mississippi River (LMR) is a pivotal transport route for American imports and exports. Disruptions on the LMR could impact the timely movement of goods up and down the river, particularly energy-related products. This study evaluates how droughts and floods impact energy barge traffic along the LMR. We examine the effect water level has on barge travel rate along various legs of the LMR and on the count of vessels at selected study regions. The results indicate a negative relationship between water level and vessel count: as water level increases, the number of vessels at specific study regions decreases; as water level decreases, the number of vessels at specific study regions increases. Additionally, water level has a greater impact on downstream travel rate for vessels in comparison to upstream travel rate. We find that these results will likely have minimal impact on the energy resiliency of counties along the LMR but could increase energy barge operating costs and thus lead to a modal switch toward less energy efficient and more costly freight alternatives.