Emergence of new technologies in remote sensing give scientists a new way to detect and monitor wildlife populations. In this study we assess the ability to detect and classify two emblematic Arctic cetaceans, the narwhal (Monodon monoceros) and beluga whale (Delphinapterus leucas), using very high-resolution (VHR) satellite imagery. We analyzed 12 VHR images acquired in August 2017 and 2019, collected by the WorldView-3 satellite, which has a maximum resolution of 0.31 m per pixel. The images covered Clearwater Fiord (138.8 km2), an area on eastern Baffin Island, Canada where belugas spend a large part of the summer, and Tremblay Sound (127.0 km2), a narrow water body located on the north shore of Baffin Island that is used by narwhals during the open water season. A total of 292 beluga whales and 109 narwhals were detected in the images. This study contributes to our understanding of Arctic cetacean distribution and highlights the capabilities of using satellite imagery to detect marine mammals.

Population and species management of long-lived species such as narwhal (Monodon monoceros) require long-term ecological monitoring programs to provide baseline information on population structure and dynamics. The success of such programs is dependent on the repeatability of the methods. Here, we propose a dichotomous key to identify narwhal newborns from aerial photography based on cetaceans’ mother–newborn dyad behavioral and narwhal newborn physical description. The key was tested by three inexperienced observers and one expert observer with interobserver agreement classified as fair according to the Cohen Kappa algorithm and criteria thresholds. This study gives some insight into narwhal-newborn spatial position, showing a predominant number of newborns located in the infant and echelon position.

To ensure effective cetacean management and conservation policies, it is necessary to collect and rigorously analyze data about these populations. Remote sensing allows the acquisition of images over large observation areas, but due to the lack of reliable automatic analysis techniques, biologists usually analyze all images by hand. In this paper, we propose a human-in-the-loop approach to couple the power of deep learning-based automation with the expertise of biologists to develop a reliable artificial intelligence assisted annotation tool for cetacean monitoring. We tested this approach to analyze a dataset of 5334 aerial images acquired in 2017 by Fisheries and Oceans Canada to monitor belugas (Delphinapterus leucas) from the threatened Cumberland Sound population in Clearwater Fjord, Canada. First, we used a test subset of photographs to compare predictions obtained by the fine-tuned model to manual annotations made by three Observers, expert marine mammal biologists. With only 100 annotated images for training, the model obtained between 90% and 91.4% mutual agreement with the three Observers, exceeding the minimum inter-observer agreement of 88.6% obtained between the experts themselves. Second, this model was applied to the full dataset. The predictions were then verified by an Observer and compared to annotations made completely manually and independently by another Observer. The annotating Observer and the human-in-the-loop pipeline detected 4051 belugas in common, out of a total of 4572 detections for the Observer and 4298 for our pipeline. This experiment shows that the proposed human-in-the-loop approach is suitable for processing novel aerial datasets for beluga counting and can be used to scale cetacean monitoring. It also highlights that human observers, even experienced ones, have varied detection bias, underlining the need to discuss standardization of annotation protocols.

Effective wildlife management and conservation require knowledge of distribution, sex composition, and age structure of a population. We explored the distribution of the Baffin Bay narwhal (Monodon monoceros) population in August 2013 by documenting sex and age distribution across the Canadian Arctic Archipelago covering 2,317,152 km2. For 6,314 narwhals identified in 3,393 aerial images taken across the Eastern Canadian Arctic, we calculated a matrix of swimming distances between all individuals. We then used a quantitative clustering approach to partition our dataset (partitioning around the medoids). The clusters obtained from the analysis supported the delimitation of the 5 narwhal management stocks currently used by the Department of Fisheries and Oceans but did not support the hypothesized division of Jones Sound and Smith Sound stocks. Across the 5 clusters, male:female ratios varied between 0.72 and 1.44 and the proportion of newborns relative to the number of females varied between 0.07 and 0.18. As a highly detailed snapshot of narwhal distribution across a very large region, our study is a step toward better documentation of the basic population information required for stock assessment, sustainable harvest, and habitat protection of narwhals in an era of rapid Arctic change. © 2019 The Wildlife Society.