Whales from space: Assessing the feasibility of using satellite imagery to monitor whales

• Main Author: Cubaynes, Hannah Charlotte
• Format: Text
• Language: English
• Published: 2020
• Subjects: Polar Research
• Online Access: http://nora.nerc.ac.uk/id/eprint/527262/; https://nora.nerc.ac.uk/id/eprint/527262/1/Cubaynes_eThesis.pdf; https://nora.nerc.ac.uk/id/eprint/527262/2/Cubaynes%20Thesis%20AccessConfirmationForm.pdf; https://doi.org/10.17863/CAM.50428

By the mid-twentieth century, the majority of great whale species were threatened with extinction, following centuries of commercial whaling. Since the implementation of a moratorium on commercial whaling in 1985 by the International Whaling Commission, the recovery of whale population is being regularly assessed. Various methods are used to survey whale populations, though most are spatially limited and prevent remote areas from being studied. Satellites orbiting Earth can access most regions of the planet, offering a potential solution to surveying remote locations. With recent improvements in the spatial resolution of satellite imagery, it is now possible to detect wildlife from space, including whales. In this thesis, I aimed to further investigate the feasibility of very high resolution (VHR) satellite imagery as a tool to reliably monitor whales. The first objective was to describe, both visually and spectrally, how four morphologically distinct species appear in VHR satellite imagery. The second objective was to explore different ways to automatically detect whales in such imagery, as the current alternative is manual detection, which is time-consuming and impractical when monitoring large areas. With the third objective, I attempted to give some insights on how to estimate the maximum depth at which a whale can be detected in VHR satellite imagery, as this will be crucial to estimate whale abundance from space. This thesis shows that the four species targeted could be detected with varying degrees of accuracy, some contrasting better with their surroundings. Compared to manual detection, the automated systems trialled here took longer, were not as accurate, and were not transferable to other images, suggesting to focus future automation research on machine learning and the creation of a well-labelled database required to train and validate. The maximum depth of detection could be assessed only approximately using nautical charts. Other methods such as the installation of panels at various depths should ...