Remote sensing techniques for automated marine mammals detection: a review of methods and current challenges

• Published in: PeerJ (San Francisco, CA) CA), 2022-06, Vol.10, p.e13540, Article e13540
• Main Authors: Rodofili, Esteban N, Lecours, Vincent, LaRue, Michelle
• Format: Article
• Language: English
• Subjects: Accuracy metrics; Aircraft; Analysis; Animals; Automation; Bycatch; Cameras; Caniformia; Caretta caretta; Cetacea; Classification; Climate change; Climatic changes; Conservation; Conservation Biology; Conservation surveys; Deep learning; Ecology; Equipment and supplies; Image processing; Marine accidents; Marine Biology; Marine mammals; Mechanization; Morphology; Neural networks; Neural Networks, Computer; Object-based image analysis; Protection and preservation; Remote sensing; Remote Sensing Technology - methods; Satellites; Spatial and Geographic Information Science; Surveys; Thermal infrared; Whales; Zoology
• ISSN: 2167-8359; 2167-8359
• DOI: 10.7717/peerj.13540

Marine mammals are under pressure from multiple threats, such as global climate change, bycatch, and vessel collisions. In this context, more frequent and spatially extensive surveys for abundance and distribution studies are necessary to inform conservation efforts. Marine mammal surveys have been performed visually from land, ships, and aircraft. These methods can be costly, logistically challenging in remote locations, dangerous to researchers, and disturbing to the animals. The growing use of imagery from satellite and unoccupied aerial systems (UAS) can help address some of these challenges, complementing crewed surveys and allowing for more frequent and evenly distributed surveys, especially for remote locations. However, manual counts in satellite and UAS imagery remain time and labor intensive, but the automation of image analyses offers promising solutions. Here, we reviewed the literature for automated methods applied to detect marine mammals in satellite and UAS imagery. The performance of studies is quantitatively compared with metrics that evaluate false positives and false negatives from automated detection against manual counts of animals, which allows for a better assessment of the impact of miscounts in conservation contexts. In general, methods that relied solely on statistical differences in the spectral responses of animals and their surroundings performed worse than studies that used convolutional neural networks (CNN). Despite mixed results, CNN showed promise, and its use and evaluation should continue. Overall, while automation can reduce time and labor, more research is needed to improve the accuracy of automated counts. With the current state of knowledge, it is best to use semi-automated approaches that involve user revision of the output. These approaches currently enable the best tradeoff between time effort and detection accuracy. Based on our analysis, we identified thermal infrared UAS imagery as a future research avenue for marine mammal detection and also recommend the further exploration of object-based image analysis (OBIA). Our analysis also showed that past studies have focused on the automated detection of baleen whales and pinnipeds and that there is a gap in studies looking at toothed whales, polar bears, sirenians, and mustelids.