Visible and thermal images acquired from drones (unoccupied aircraft systems) have substantially improved animal monitoring. Combining complementary information from both image types provides a powerful approach for automating detection and classification of multiple animal species to augment drone surveys. We compared eight image fusion methods using thermal and visible drone images combined with two supervised deep learning models, to evaluate the detection and classification of white-tailed deer (Odocoileus virginianus), domestic cow (Bos taurus), and domestic horse (Equus caballus). We classified visible and thermal images separately and compared them with the results of image fusion. Fused images provided minimal improvement for cows and horses compared to visible images alone, likely because the size, shape, and color of these species made them conspicuous against the background. For white-tailed deer, which were typically cryptic against their backgrounds and often in shadows in visible images, the added information from thermal images improved detection and classification in fusion methods from 15 to 85%. Our results suggest that image fusion is ideal for surveying animals inconspicuous from their backgrounds, and our approach uses few image pairs to train compared to typical machine-learning methods. We discuss computational and field considerations to improve drone surveys using our fusion approach.

In recent years, small unmanned aircraft systems (sUAS) have been used widely to monitor animals because of their customizability, ease of operating, ability to access difficult to navigate places, and potential to minimize disturbance to animals. Automatic identification and classification of animals through images acquired using a sUAS may solve critical problems such as monitoring large areas with high vehicle traffic for animals to prevent collisions, such as animal-aircraft collisions on airports. In this research we demonstrate automated identification of four animal species using deep learning animal classification models trained on sUAS collected images. We used a sUAS mounted with visible spectrum cameras to capture 1288 images of four different animal species: cattle (Bos taurus), horses (Equus caballus), Canada Geese (Branta canadensis), and white-tailed deer (Odocoileus virginianus). We chose these animals because they were readily accessible and white-tailed deer and Canada Geese are considered aviation hazards, as well as being easily identifiable within aerial imagery. A four-class classification problem involving these species was developed from the acquired data using deep learning neural networks. We studied the performance of two deep neural network models, convolutional neural networks (CNN) and deep residual networks (ResNet). Results indicate that the ResNet model with 18 layers, ResNet 18, may be an effective algorithm at classifying between animals while using a relatively small number of training samples. The best ResNet architecture produced a 99.18% overall accuracy (OA) in animal identification and a Kappa statistic of 0.98. The highest OA and Kappa produced by CNN were 84.55% and 0.79 respectively. These findings suggest that ResNet is effective at distinguishing among the four species tested and shows promise for classifying larger datasets of more diverse animals.

Rapid advancements in technology often yield research inquiry into novel applications and drone (i.e., unoccupied aircraft systems or UAS) applications in wildlife management are no exception. We questioned the time lag between drone‐related research and end‐user assessments. We implemented an online, cross‐sectional survey of wildlife professionals to better understand current drone use and benefits or concerns, complemented by a review of contemporary peer‐reviewed and gray literature. We found little disparity between scientific inquiry and end‐user experiences (i.e., similar trends among concerns in published literature and survey results). Exploring new (i.e., advancements in computer vision) and refining original drone applications (i.e., evaluating animal behavior responses during monitoring) were strong among pilots of relatively minimal experience (1–5 years). Advancements in drone technology and changes in drone‐related legislation will continue to offer benefits and challenges.

Background: Small unmanned aircraft systems (sUAS) are replacing or supplementing manned aircraft and ground-based surveys in many animal monitoring situations due to better coverage at finer spatial and temporal resolutions, access, cost, bias, impacts, safety, efficiency, and logistical benefits. Various sUAS models and sensors are available with varying features and usefulness depending on survey goals. However, justification for selection of sUAS and sensors are not typically offered in published literature and existing reviews do not adequately cover past and current sUAS applications for animal monitoring nor their associated sUAS model and sensor technologies, taxonomic and geographic scope, flight conditions and considerations, spatial distributions of sUAS applications, and reported technical difficulties. We outline a systematic map protocol to collect and consolidate evidence pertaining to sUAS monitoring of animals. Our systematic map will provide a useful synthesis of current applications of sUAS-animal related studies and identify major knowledge clusters (well-represented subtopics that are amenable to full synthesis by a systematic review) and gaps (unreported or underrepresented topics that warrant additional primary research) that may influence future research directions and sUAS applications. Methods: Our systematic map will investigate the current state of knowledge using an accurate, comprehensive, and repeatable search. We will find relevant peer-reviewed and grey literature as well as dissertations and theses using online publication databases, Google Scholar, and by request through a professional network of collaborators and publicly available websites. We will use a tiered approach to article exclusion with eligible studies being those that monitor (i.e., identify, count, estimate, etc.) terrestrial vertebrate animals. Extracted data concerning sUAS, sensors, animals, methodology, and results will be recorded in Microsoft Access. We will query and catalogue evidence in the final database to produce tables, figures, and geographic maps to accompany a full narrative review that answers our primary and secondary questions.