Estimating animal populations is critical for wildlife management. Aerial surveys are used for generating population estimates, but can be hampered by cost, logistical complexity, and human risk. Additionally, human counts of organisms in aerial imagery can be tedious and subjective. Automated approaches show promise, but can be constrained by long setup times and difficulty discriminating animals in aggregations. We combine unmanned aircraft systems (UAS), thermal imagery and computer vision to improve traditional wildlife survey methods. During spring 2015, we flew fixed-wing UAS equipped with thermal sensors, imaging two grey seal (Halichoerus grypus) breeding colonies in eastern Canada. Human analysts counted and classified individual seals in imagery manually. Concurrently, an automated classification and detection algorithm discriminated seals based upon temperature, size, and shape of thermal signatures. Automated counts were within 95–98% of human estimates; at Saddle Island, the model estimated 894 seals compared to analyst counts of 913, and at Hay Island estimated 2188 seals compared to analysts’ 2311. The algorithm improves upon shortcomings of computer vision by effectively recognizing seals in aggregations while keeping model setup time minimal. Our study illustrates how UAS, thermal imagery, and automated detection can be combined to efficiently collect population data critical to wildlife management.

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Very high-resolution satellite imagery (≤5 m resolution) has become available on a spatial and temporal scale appropriate for dynamic wetland management and conservation across large areas. Estuarine wetlands have the potential to be mapped at a detailed habitat scale with a frequency that allows immediate monitoring after storms, in response to human disturbances, and in the face of sea-level rise. Yet mapping requires significant fieldwork to run modern classification algorithms and estuarine environments can be difficult to access and are environmentally sensitive. Recent advances in unoccupied aircraft systems (UAS, or drones), coupled with their increased availability, present a solution. UAS can cover a study site with ultra-high resolution (<5 cm) imagery allowing visual validation. In this study we used UAS imagery to assist training a Support Vector Machine to classify WorldView-3 and RapidEye satellite imagery of the Rachel Carson Reserve in North Carolina, USA. UAS and field-based accuracy assessments were employed for comparison across validation methods. We created and examined an array of indices and layers including texture, NDVI, and a LiDAR DEM. Our results demonstrate classification accuracy on par with previous extensive fieldwork campaigns (93% UAS and 93% field for WorldView-3; 92% UAS and 87% field for RapidEye). Examining change between 2004 and 2017, we found drastic shoreline change but general stability of emergent wetlands. Both WorldView-3 and RapidEye were found to be valuable sources of imagery for habitat classification with the main tradeoff being WorldView’s fine spatial resolution versus RapidEye’s temporal frequency. We conclude that UAS can be highly effective in training and validating satellite imagery.

BioOne Complete (complete.BioOne.org) is a full-text database of 200 subscribed and open-access titles in the biological, ecological, and environmental sciences published by nonprofit societies, associations, museums, institutions, and presses.

1. We present a novel application using unoccupied aircraft systems (UAS; drones) for structure-from-motion three-dimensional (3-D) photogrammetry of multiple, free-living animals simultaneously. Pinnipeds reliably haul out on shore for pupping and breeding each year, accompanied by dramatic female-to-pup mass transfer over a short lactation period and males lose mass while defending mating territories. This provides a tractable study system for validating the use of UAS as a non-invasive tool for tracking energy dynamics in wild populations. UAS imagery of grey sealsHalichoerus grypus was collected at Saddle Island, Nova Scotia. A multirotor UAS was piloted in 360-degree orbits around relatively dense animal aggregations and georeferenced images were used for construction of a 3-D point cloud, orthomosaic and Digital Surface Model for animal volumetric measurements. Directly following UAS survey, a subset of adult females were hand-measured (morphometrics, blubber depth, n = 21 handlings [15 were unique animals]) and female-pup pairs were weighed (adult females: n = 32 [24]; pups: n = 33 [23]) to validate that UAS 3-D photogrammetric models provided accurate animal volume and mass estimates.3. UAS two-dimensional body length measurements were sensitive to animal recumbency and posture. The new UAS 3-D photogrammetric method overcame these constraints, and aerial-derived body volume measurements were equivalent to those collected from the ground. UAS body volume measurements precisely predicted 'true' body mass (mean absolute error, adult female: 3.8 kg, 2.1% body mass; pup: 4.1 kg, 9.8%), and exhibited a stronger relationship with total body mass than with blubber volume.4. The method was applied to 673 free-living animals to characterize volume and mass dynamics across lactation and breeding for a much larger sample size than would be possible using traditional ground methods. Indeed, 1-46 animals (M ± SE: 9.2 ± 1.2) were modelled concurrently within the focal area of a UAS flight. Application of the method also captured significant inter-annual variation in body volume/mass dynamics, and female-to-pup energy transfer efficiencies were lower when there was low sea ice extent. The UAS 3-D photogrammetric method presented in this study is likely to be broadly applicable to other species, | 2459Methods in Ecology and Evoluঞon SHERO Et al.

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The Arctic and its adjacent ecosystems are undergoing rapid ecological reorganization in response to the effects of global climate change, and sentinel species provide critical updates as these changes unfold. This study leverages emerging remote sensing techniques to reveal fine-scale drivers of distribution and terrestrial habitat use of two sympatric sentinel species of the central Bering Sea, the Pacific harbor seal (<i>Phoca vitulina richardsi</i>) and the northern fur seal (<i>Callorhinus ursinus</i>), at non-breeding haul-outs in the Pribilof Islands. We surveyed these species using unoccupied aircraft systems with thermal and visible-light photography, and we applied distributional modeling techniques to quantify the relative influence of habitat characteristics and social dynamics on the local distributions of these species. Drone imagery yielded locations and population counts of each species, and spatial data products allowed quantitative characterization of occupied sites, revealing that conspecific attraction is a driver of local site selection for both species, and Pacific harbor seals and northern fur seals are differentially limited by terrain characteristics. These findings represent new applications of species distribution modeling at local scales, made possible by ultra-high resolution drone surveillance and photogrammetric techniques, which add new spatial context to past observations and future scenarios in this changing ecosystem.

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