Seeing Seals with SeaBee – exploring drone use in counting seals

How many coastal seals are there?

Where are they along the coast?
Which drone and sensor combinations are suitable for counting them?

These are questions that part of the SeaBee team, led by Martin Biuw at Institute of Marine Research (IMR), are working to answer. Together with scientists and drone pilots from NTNU, they are mapping and monitoring seal populations in different places along coastal Norway.
 
Seals and Drones

Harbour and grey seals (collectively referred to as ‘coastal seals’) play an important role as top predators in the coastal marine ecosystem and interact with coastal fisheries. Such interactions can sometimes lead to bycatch (i.e., seals being caught and drowned in fishing gear), and reliable estimates of population size are critical for assessing whether the level of bycatch is within sustainable limits for seal populations or not. IMR monitors seal population numbers regularly to provide sound management advice to the authorities (such as NFD).

While small drones can be excellent for counting seals in relatively small and distinct colonies, it can be very challenging in large archipelagos where seals can be found spread out any of a large number of skerries. In such areas, a better approach may be the use of larger drones that can operate autonomously for longer periods, covering larger areas.

The field campaign

Tarva is a group of islands off the coast of Trondelag which is home to a variety of diverse marine species, including seals. It is one of the areas IMR surveys regularly and is near Seabee colleagues at NTNU. Therefore, Tarva is a good location to explore the use of new drone technology for counting seals.

A recent field campaign used a Vertical TakeOff and Landing (VTOL) Mugin-2 Pro 2930 CF drone, with a variety of sensors (SeaBee Tech). The team used this kind of drone here as it is the most practical solution where there are no flat areas for take-off/landing. 

“We managed to successfully carry out relatively complex operations involving innovative drone technology, thereby showing it is feasible to collect data over much larger areas than we could with traditional small drones”

– Martin Biuw, IMR 

The MUGIN drone at the coast, ready for the next mission (Photo by Martin Biuw).

During this field mission, the flying height was 200m, a compromise between achieving sufficient spatial coverage within the time restrictions, and the ability to detect seals. The results from the flights gave some useful insight into how the sensors functioned.

While the flight operations worked very well, we found that the image resolution was insufficient for reliably detecting seals.  Many seals could be seen relatively clearly, but it was sometimes difficult to distinguish a seal from the background in the collected images. Higher image resolution and using an infrared (IR) sensor synchronised with the RGB sensor would improve this. It would be easier to see the seals in the images, and seals give off more heat radiation than the background.  

One result of using autonomous drones covering large areas is that we get lots of images, and many of these may not have any seals at all. Manually examining all images is extremely time-consuming. SeaBee is developing a machine-learning system to automatically detect seals in images, which will save a lot of time (led by our partners, Norwegian Computing Center) – Martin Biuw, IMR 

Example picture showing seals laying on rocks at Tarva, jpg picture taken at 150m altitude. Shutter: 1/2000s, 2839x2747 pixels. (Photo from SeaBee).
What comes next

Overall, this field campaign showed promising outcomes for using these drones to detect seals in areas of the coast that are difficult to access. The SeaBee infrastructure gave us access to the latest in drone and sensor technology, as well as expert knowledge on seal behaviour to collect the best population estimates at challenging locations. 

Next, we plan to repeat the tests at Tarva, using the same drone but with a better sensor system (higher image resolution and infra-red sensor), further develop and refine the automated seal detection algorithm, and develop a strategy for implementing the wider-ranging drone operations across a range of important seal localities along the coast for regular monitoring. 

 

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