The Potential of Satellite Imagery for Surveying Whales

The emergence of very high-resolution (VHR) satellite imagery (less than 1 m spatial resolution) is creating new opportunities within the fields of ecology and conservation biology. The advancement of sub-meter resolution imagery has provided greater confidence in the detection and identification of features on the ground, broadening the realm of possible research questions. To date, VHR imagery studies have largely focused on terrestrial environments; however, there has been incremental progress in the last two decades for using this technology to detect cetaceans. With advances in computational power and sensor resolution, the feasibility of broad-scale VHR ocean surveys using VHR satellite imagery with automated detection and classification processes has increased. Initial attempts at automated surveys are showing promising results, but further development is necessary to ensure reliability. Here we discuss the future directions in which VHR satellite imagery might be used to address urgent questions in whale conservation. We highlight the current challenges to automated detection and to extending the use of this technology to all oceans and various whale species. To achieve basin-scale marine surveys, currently not feasible with any traditional surveying methods (including boat-based and aerial surveys), future research requires a collaborative effort between biology, computation science, and engineering to overcome the present challenges to this platform’s use

Multi-modal survey of Adélie penguin mega-colonies reveals the Danger Islands as a seabird hotspot

© The Author(s), 2018. This article is distributed under the terms of the Creative Commons Attribution License. The definitive version was published in Scientific Reports 8 (2018): 3926, doi:10.1038/s41598-018-22313-w.Despite concerted international effort to track and interpret shifts in the abundance and distribution of Adélie penguins, large populations continue to be identified. Here we report on a major hotspot of Adélie penguin abundance identified in the Danger Islands off the northern tip of the Antarctic Peninsula (AP). We present the first complete census of Pygoscelis spp. penguins in the Danger Islands, estimated from a multi-modal survey consisting of direct ground counts and computer-automated counts of unmanned aerial vehicle (UAV) imagery. Our survey reveals that the Danger Islands host 751,527 pairs of Adélie penguins, more than the rest of AP region combined, and include the third and fourth largest Adélie penguin colonies in the world. Our results validate the use of Landsat medium-resolution satellite imagery for the detection of new or unknown penguin colonies and highlight the utility of combining satellite imagery with ground and UAV surveys. The Danger Islands appear to have avoided recent declines documented on the Western AP and, because they are large and likely to remain an important hotspot for avian abundance under projected climate change, deserve special consideration in the negotiation and design of Marine Protected Areas in the region.We gratefully acknowledge the financial support of the Dalio Foundation, Inc. through the Dalio Explore Fund, which provided all the financing for the Danger Island Expedition. We would like to thank additional support for analysis from the National Science Foundation (NSF PLR&GSS 1255058 - H.J.L. and P.M.; NSF PLR 1443585 – M.J.P.) and the National Aeronautical and Space Administration (NNX14AC32G; H.J.L. and M.S.). Geospatial support for the analysis of high resolution satellite imagery provided by the Polar Geospatial Center under NSF PLR awards 1043681 & 1559691

Update on the global abundance and distribution of breeding Gentoo Penguins (Pygoscelis papua)

Though climate change is widely known to negatively affect the distribution and abundance of many species, few studies have focused on species that may benefit. Gentoo Penguin (Pygoscelis papua) populations have grown along the Western Antarctic Peninsula (WAP), a region accounting for ~ 30% of their global population. These trends of population growth in Gentoo Penguins are in stark contrast to those of Adélie and Chinstrap Penguins, which have experienced considerable population declines along the WAP attributed to environmental changes. The recent discovery of previously unknown Gentoo Penguin colonies along the WAP and evidence for southern range expansion since the last global assessment in 2013 motivates this review of the abundance and distribution of this species. We compiled and collated all available recent data for every known Gentoo Penguin colony in the world and report on previously unknown Gentoo Penguin colonies along the Northwestern section of the WAP. We estimate the global population of Gentoo Penguins to be 432,144 (95th CI 338,059 – 534,114) breeding pairs, with approximately 364,359 (95th CI 324,052 – 405,132) breeding pairs (85% of the population) living in the Atlantic sector. Our estimates suggest that the global population has increased by approximately 11% since 2013, with even greater increases (23%) along the WAP. The Falkland Islands population, which comprises 30% of the global population, has remained stable, though only a subset of colonies have been surveyed since the last comprehensive survey in 2010. Our assessment identifies South Georgia and sub-Antarctic islands in the Indian Ocean as being the most critical data gaps for this species

Aerial-trained deep learning networks for surveying cetaceans from satellite imagery.

Most cetacean species are wide-ranging and highly mobile, creating significant challenges for researchers by limiting the scope of data that can be collected and leaving large areas un-surveyed. Aerial surveys have proven an effective way to locate and study cetacean movements but are costly and limited in spatial extent. Here we present a semi-automated pipeline for whale detection from very high-resolution (sub-meter) satellite imagery that makes use of a convolutional neural network (CNN). We trained ResNet, and DenseNet CNNs using down-scaled aerial imagery and tested each model on 31 cm-resolution imagery obtained from the WorldView-3 sensor. Satellite imagery was tiled and the trained algorithms were used to classify whether or not a tile was likely to contain a whale. Our best model correctly classified 100% of tiles with whales, and 94% of tiles containing only water. All model architectures performed well, with learning rate controlling performance more than architecture. While the resolution of commercially-available satellite imagery continues to make whale identification a challenging problem, our approach provides the means to efficiently eliminate areas without whales and, in doing so, greatly accelerates ocean surveys for large cetaceans