Concept: Unmanned vehicles
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.
Regulations have allowed for increased unmanned aircraft systems (UAS) operations over the last decade, yet operations over people are still not permitted. The objective of this study was to estimate the range of injury risks to humans due to UAS impact. Three commercially-available UAS models that varied in mass (1.2-11 kg) were evaluated to estimate the range of risk associated with UAS-human interaction. Live flight and falling impact tests were conducted using an instrumented Hybrid III test dummy. On average, live flight tests were observed to be less severe than falling impact tests. The maximum risk of AIS 3+ injury associated with live flight tests was 11.6%, while several falling impact tests estimated risks exceeding 50%. Risk of injury was observed to increase with increasing UAS mass, and the larger models tested are not safe for operations over people in their current form. However, there is likely a subset of smaller UAS models that are safe to operate over people. Further, designs which redirect the UAS away from the head or deform upon impact transfer less energy and generate lower risk. These data represent a necessary impact testing foundation for future UAS regulations on operations over people.
Remote physiological measurement might be very useful for biomedical diagnostics and monitoring. This study presents an efficient method for remotely measuring heart rate and respiratory rate from video captured by a hovering unmanned aerial vehicle (UVA). The proposed method estimates heart rate and respiratory rate based on the acquired signals obtained from video-photoplethysmography that are synchronous with cardiorespiratory activity.
Unmanned aerial vehicles (UAVs) have the potential to revolutionize the way research is conducted in many scientific fields [1, 2]. UAVs can access remote or difficult terrain , collect large amounts of data for lower cost than traditional aerial methods, and facilitate observations of species that are wary of human presence . Currently, despite large regulatory hurdles , UAVs are being deployed by researchers and conservationists to monitor threats to biodiversity , collect frequent aerial imagery [7-9], estimate population abundance [4, 10], and deter poaching . Studies have examined the behavioral responses of wildlife to aircraft [12-20] (including UAVs ), but with the widespread increase in UAV flights, it is critical to understand whether UAVs act as stressors to wildlife and to quantify that impact. Biologger technology allows for the remote monitoring of stress responses in free-roaming individuals , and when linked to locational information, it can be used to determine events [19, 23, 24] or components of an animal’s environment  that elicit a physiological response not apparent based on behavior alone. We assessed effects of UAV flights on movements and heart rate responses of free-roaming American black bears. We observed consistently strong physiological responses but infrequent behavioral changes. All bears, including an individual denned for hibernation, responded to UAV flights with elevated heart rates, rising as much as 123 beats per minute above the pre-flight baseline. It is important to consider the additional stress on wildlife from UAV flights when developing regulations and best scientific practices.
Remote-controlled aerial drones (or unmanned aerial vehicles; UAVs) are employed for surveillance by the military and police, which suggests that drone-captured footage might provide sufficient information for person identification. This study demonstrates that person identification from drone-captured images is poor when targets are unfamiliar (Experiment 1), when targets are familiar and the number of possible identities is restricted by context (Experiment 2), and when moving footage is employed (Experiment 3). Person information such as sex, race and age is also difficult to access from drone-captured footage (Experiment 4). These findings suggest that such footage provides a particularly poor medium for person identification. This is likely to reflect the sub-optimal quality of such footage, which is subject to factors such as the height and velocity at which drones fly, viewing distance, unfavourable vantage points, and ambient conditions.
Low-elevation surveys with small aerial drones (micro-unmanned aerial vehicles [UAVs]) may be used for a wide variety of applications in plant ecology, including mapping vegetation over small- to medium-sized regions. We provide an overview of methods and procedures for conducting surveys and illustrate some of these applications.
- Scandinavian journal of trauma, resuscitation and emergency medicine
- Published over 1 year ago
The use of an automated external defibrillator (AED) prior to EMS arrival can increase 30-day survival in out-of-hospital cardiac arrest (OHCA) significantly. Drones or unmanned aerial vehicles (UAV) can fly with high velocity and potentially transport devices such as AEDs to the site of OHCAs. The aim of this explorative study was to investigate the feasibility of a drone system in decreasing response time and delivering an AED.
The use of remote imagery captured by unmanned aerial vehicles (UAV) has tremendous potential for designing detailed site-specific weed control treatments in early post-emergence, which have not possible previously with conventional airborne or satellite images. A robust and entirely automatic object-based image analysis (OBIA) procedure was developed on a series of UAV images using a six-band multispectral camera (visible and near-infrared range) with the ultimate objective of generating a weed map in an experimental maize field in Spain. The OBIA procedure combines several contextual, hierarchical and object-based features and consists of three consecutive phases: 1) classification of crop rows by application of a dynamic and auto-adaptive classification approach, 2) discrimination of crops and weeds on the basis of their relative positions with reference to the crop rows, and 3) generation of a weed infestation map in a grid structure. The estimation of weed coverage from the image analysis yielded satisfactory results. The relationship of estimated versus observed weed densities had a coefficient of determination of r(2)=0.89 and a root mean square error of 0.02. A map of three categories of weed coverage was produced with 86% of overall accuracy. In the experimental field, the area free of weeds was 23%, and the area with low weed coverage (<5% weeds) was 47%, which indicated a high potential for reducing herbicide application or other weed operations. The OBIA procedure computes multiple data and statistics derived from the classification outputs, which permits calculation of herbicide requirements and estimation of the overall cost of weed management operations in advance.
Monitoring of intertidal reefs is traditionally undertaken by on-ground survey methods which have assisted in understanding these complex habitats; however, often only a small spatial footprint of the reef is observed. Recent developments in unmanned aerial vehicles (UAVs) provide new opportunities for monitoring broad scale coastal ecosystems through the ability to capture centimetre resolution imagery and topographic data not possible with conventional approaches. This study compares UAV remote sensing of intertidal reefs to traditional on-ground monitoring surveys, and investigates the role of UAV derived geomorphological variables in explaining observed intertidal algal and invertebrate assemblages. A multirotor UAV was used to capture <1 cm resolution data from intertidal reefs, with on-ground quadrat surveys of intertidal biotic data for comparison. UAV surveys provided reliable estimates of dominant canopy-forming algae, however, understorey species were obscured and often underestimated. UAV derived geomorphic variables showed elevation and distance to seaward reef edge explained 19.7% and 15.9% of the variation in algal and invertebrate assemblage structure respectively. The findings of this study demonstrate benefits of low-cost UAVs for intertidal monitoring through rapid data collection, full coverage census, identification of dominant canopy habitat and generation of geomorphic derivatives for explaining biological variation.
This Australian benthic data set (BENTHOZ-2015) consists of an expert-annotated set of georeferenced benthic images and associated sensor data, captured by an autonomous underwater vehicle (AUV) around Australia. This type of data is of interest to marine scientists studying benthic habitats and organisms. AUVs collect georeferenced images over an area with consistent illumination and altitude, and make it possible to generate broad scale, photo-realistic 3D maps. Marine scientists then typically spend several minutes on each of thousands of images, labeling substratum type and biota at a subset of points. Labels from four Australian research groups were combined using the CATAMI classification scheme, a hierarchical classification scheme based on taxonomy and morphology for scoring marine imagery. This data set consists of 407,968 expert labeled points from around the Australian coast, with associated images, geolocation and other sensor data. The robotic surveys that collected this data form part of Australia’s Integrated Marine Observing System (IMOS) ongoing benthic monitoring program. There is reuse potential in marine science, robotics, and computer vision research.