North Atlantic right whale during an aerial survey. Credit: NOAA Fisheries, Northeast Fisheries Science Center.
An interesting report from Canada caught our attention this week, describing how Earth Observation (EO) satellite data was being used to monitor and protect the North Atlantic right whale within Canadian waters. The Canadian Space Agency (CSA) has released details of progress & achievements of the smartWhales initiative, which they launched in 2021 together with Fisheries and Oceans Canada, and Transport Canada.
North Atlantic right whale (NARW)
The North Atlantic right whale (NARW) is a baleen whale, meaning it has no teeth and filters tiny crustaceans, known as copepods, through their baleen plates, which operate like a sieve. A NARW consumes around 2,200 pounds of copepod each every day, and produces a gassy waste product that fertilizes the ocean and helps feed phytoplankton. They also accumulate tons of carbon in their bodies over their lifetime to help mitigate climate change.
Four hundred years ago, it was estimated that there 20,000 NARW in the North Atlantic Ocean. However, they were hunted for years until the League of Nations ended right whale hunting in 1935. It is estimated that currently they are only approximately 360 NARW remain, including fewer than 70 reproductively active females. Actions to help develop a better future for the NARW are critical to ensure their populations don’t further diminish.
smartWhales initiative
The smartWhales initiative began in 2021, and CSA gave $5.3 million in funding to five companies to develop innovative space-based solutions to help NARWs. These companies, together with the work they have done over the last three years are:
- ARCTUS: Satellite imagery to better understand NARW habitat – Uses satellite data about ocean conditions such as ocean colour, combined with habitat prediction models and maps of the zooplankton they feed on provide a better understanding of NARW behaviour, movement and where they are most likely to gather.
- Global Spatial Technology Solutions: Operational object-detection models for detecting and monitoring North Atlantic right whales with OCIANATM – Satellite data from intelligent tasking is processed into an object detection model on an artificial intelligence platform in novel approach providing information to maritime vessel operators and other stakeholders to mitigate risks of hitting the NARWs.
- Fluvial Systems Research: Satellite observation of North Atlantic right whales and RADARSAT Constellation Mission (RCM) detection of their feeding areas – The Satellite Acquisition and Right Whale Detection Algorithm (SARDA) uses optical and RCM’s synthetic aperture radar (SAR) data to detect and identify the NARW and its potential feeding areas by focusing on the concentration of zooplankton. This solution can detect and confirm the NARW at a species level and has demonstrated its ability to identify a specific individual NARW.
- Hatfield: Finding whales in satellite imagery automatically and quickly using artificial intelligence – This solution uses artificial intelligence to identify the areas most likely to find NARW on satellite images, reducing the area to check by 98%. In 2023, they tested the solution on area of 2,000 square kilometres, and found 75% of the whales in the imagery.
- WSP Canada et DHI Water & Environment Inc.: A predictive modelling system for the enhanced protection of the NARW in the Gulf of St. Lawrence – This solution is able to forecast suitable whales’ habitats and predict their movements in the Gulf of St. Lawrence up to 3 days into the future. Combining this with commercial vessel traffic and snow crab fishing areas enables an ability to highlight, and prevent potential collision and fishing gear entanglements. The model includes hydrodynamic and ecological characteristics such as ocean currents, temperature and nutrients.
Summary
It is fantastic to see a project using satellite data and making a tangible difference to the future of the NARW. The work here on the detection and monitoring of the NARW, combined with modelling their behaviour and movement in their habitats, is a great start to this work, and it will be exciting to see how these solutions develop in the coming years, both for the rest of the project period and beyond.
