Browsing by Subject "Drone"

Pinniped species undergo a life history, unique among marine mammals, that includes discrete periods of occupancy on land or ice within a predominantly marine existence. This makes many pinniped species valuable sentinels of marine ecosystem health and models of marine mammal physiology and behavior. Pinniped research has often progressed hand-in-hand with advances at the technological frontiers of wildlife biology, and drones represent a leap forward in the long-established field of aerial photography, heralding opportunities for data collection and integration at new scales of biological importance. The following chapters employ and evaluate recent and emerging methods of wildlife surveillance that are uniquely enabled and facilitated by drone methods, in applied research and management campaigns with near-polar pinniped species. These methods represent advancements in abundance estimation and distribution modeling of pinniped populations that are dynamically shifting amid climate change, fishing pressure, and recovery from historical depletion.Conventional methods of counting animals from aerial imagery—typically visual interpretation by human analysts—can be time-consuming and limits the practical use of this data type. Deep learning methods of computer vision can ease this burden when applied to drone imagery, but are not yet characterized for practical and generalized use. To this end, I used a common implementation of deep learning for object detection in imagery to train and test models on a variety of datasets describing breeding populations of gray seals (Halichoerus grypus) in the northwest Atlantic Ocean (Chapter 2). I compare standardized performance metrics of models trained and tested on different combinations of datasets, demonstrating that model performance varies depending on both training and testing data choices. We find that models require careful validation to estimate error rates, and that they can be effectively deployed to aid, but not replace, conventional human visual interpretation of novel datasets for gray seal detection, location, age-classification and abundance estimation. Spatial analysis and species distribution modeling can use fine-scale drone-derived data to describe local species–habitat relationships at the scale of individual animals. I applied structure-from-motion methods to a survey of three pinniped species, pacific harbor seals (Phoca vitulina richardii), northern fur seals (Callorhinus ursinus), and Steller sea lions (Eumetopias jubatus), in adjacent non-breeding haul-outs to compare occupancy and habitat selection (Chapter 3). I describe and compare fitted occupancy models of pacific harbor seals and northern fur seals, finding that conspecific attraction is a key driver of habitat selection for each species, and that each species exhibits distinct topographic preferences. These findings illustrate both opportunities and limitations of spatial analysis at the scale of individual pinnipeds. Ease of deployment and rapid data collection make drones a powerful tool for monitoring populations of interest over time, while animal locations, revealed in high-resolution imagery, and contextual habitat products can reveal spatial relationships that persist beyond local contexts. I designed and carried out a campaign of drone surveillance over coastal habitats near Palmer Station, Antarctica, in the austral summer of 2020 to assess the seasonal abundance and habitat use of Antarctic fur seals (Arctocephalus gazella) in the Palmer Archipelago and adjacent regions (Chapter 4). I modeled abundance as a function of date, with and without additional terms to capture variance by site, and used these models to estimate peak abundance near Palmer Station in the 2020 summer season. These findings leverage the spatial and temporal advantages of drone methods to estimate species phenology, distribution and abundance. Together, these chapters describe emerging applications of drone technology that can advance pinniped research and management into new scales of analytical efficiency and ecological interpretation. These studies describe methods that have been proven in concept, but not yet standardized for practical deployment, and their findings reveal new ecological insights, opportunities for methodological advancement, and current limitations of drone methods for the study of pinnipeds in high-latitude environments.

Given their low operating costs and flight capabilities, Unmanned Aircraft Vehicles(UAVs), especially small size UAVs, have a wide range of applications, from civilian rescue missions to military surveillance. Easy control from a highly automated system has made these compact UAVs particularly efficient and effective devices by alleviating human operator workload. However, whether or not automation can lead to increased performance is not just a matter of system design but requires operators’ thorough understanding of the behavior of the system. Then, a question arises: which type of training and level of automation can help UAV operators perform the best?

To address this problem, an experiment was designed and conducted to compare the differences in performance between 3 groups of UAV operators. For this experiment, 2 different interfaces were first developed - Manual Control, which represents low LOA interface, and Supervisory Control, which represents high LOA interface - and people were recruited and randomly divided into 3 groups. Group 1 was trained using Manual Control, and Group 3 was trained using Supervisory Control while Group 2 was trained using both Manual and Supervisory Control. Participants then flew a drone in the Test Mission stage to compare performance.

The results of the experiment were rather surprising. Although group 3 outperformed group 1, as expected, the poor performance of group 2 was unexpected and gave us new perspectives on additional training. That is, additional training could lead not just to a mere surplus of extra skills but also a degradation of existing skills. An extended work using a more mathematical approach should allow for a more precise, quantitative description on the relation between extra training and performance.