Unmanned Aircraft Systems complement biologging in spatial ecology studies

High-precision tracking and positioning for monitoring Holstein cattle

Enhanced animal welfare has emerged as a pivotal element in contemporary precision animal husbandry, with bovine monitoring constituting a significant facet of precision agriculture. The evolution of intelligent agriculture in recent years has significantly facilitated the integration of drone flight monitoring tools and innovative systems, leveraging deep learning to interpret bovine behavior. Smart drones, outfitted with monitoring systems, have evolved into viable solutions for wildlife protection and monitoring as well as animal husbandry. Nevertheless, challenges arise under actual and multifaceted ranch conditions, where scale alterations, unpredictable movements, and occlusions invariably influence the accurate tracking of unmanned aerial vehicles (UAVs). To address these challenges, this manuscript proposes a tracking algorithm based on deep learning, adhering to the Joint Detection Tracking (JDT) paradigm established by the CenterTrack algorithm. This algorithm is designed to satisfy the requirements of multi-objective tracking in intricate practical scenarios. In comparison with several preeminent tracking algorithms, the proposed Multi-Object Tracking (MOT) algorithm demonstrates superior performance in Multiple Object Tracking Accuracy (MOTA), Multiple Object Tracking Precision (MOTP), and IDF1. Additionally, it exhibits enhanced efficiency in managing Identity Switches (ID), False Positives (FP), and False Negatives (FN). This algorithm proficiently mitigates the inherent challenges of MOT in complex, livestock-dense scenarios.

The method matters. A comparative study of biologging and camera traps as data sources with which to describe wildlife habitat selection

Habitat use is a virtually universal activity among animals and is highly relevant as regards designing wildlife management and conservation actions. This has led to the development of a great variety of methods to study it, of which resource selection functions combined with biologging-derived data (RSF) is the most widely used for this purpose. However this approach has some constraints, such as its invasiveness and high costs. Analytical approaches taking into consideration imperfect detection coupled with camera trap data (IDM) have, therefore, emerged as a non-invasive cost-effective alternative. However, despite the fact that both approaches (RSF and IDM) have been used in habitat selection studies, they should also be comparatively assessed. The objective of this work is consequently to assess them from two perspectives: explanatory and predictive. This has been done by analyzing data obtained from camera traps (60 sampling sites) and biologging (17 animals monitored: 7 red deer Cervus elaphus, 6 fallow deer Dama dama and 4 wild boar Sus scrofa) in the same periods using IDM and RSF, respectively, in Doñana National Park (southern Spain) in order to explain and predict habitat use patterns for three studied species. Our results showed discrepancies between the two approaches, as they identified different predictors as being the most relevant to determine species intensity of use, and they predicted spatial patterns of habitat use with a contrasted level of concordance, depending on species and scale. Given these results and the characteristics of each approach, we suggested that although partly comparable interpretations can be obtained with both approaches, they are not equivalent but rather complementary. The combination of data from biologging and camera traps would, therefore, appear to be suitable for the development of an analytical framework with which to describe and characterise the habitat use processes of wildlife.

Livestock Management on Grazing Field: A FANET Based Approach

In recent times, designated grazing areas/fields or routes for livestock grazing are usually defined. Hence, their herding activities’ success relies on data extracted from aerial photographs. As such, a direct and cost-effective way of monitoring livestock for perimeter coverage and in other natural situations is required. This paper presents a coverage solution involving multiple interacting unmanned aerial vehicles. The presented approach is built on a graph, with geographic coordinates set such that several Unmanned Aerial Vehicles (UAVs) can successfully cover the area. The maximum flying time determines the number of UAVs employed for coverage. The proposed solution is thought to solve some practical problems encountered during the execution of the task with actual UAVs. It is suitable for long-term monitoring of animal behavior under various weather conditions and observing the relationship between livestock distribution and available resources on a grazing field. The simulation was carried out using MATLAB and aerial images from Google Earth.

A practical approach with drones, smartphone and tracking tags for potential real-time tracking animal

In recent years, drones are increasingly used for fauna monitoring and wildlife tracking; however, the application of drones for tracking wildlife is restricted to those users with the technical capacity to develop such systems. We explore the potential of wildlife tracking with drones by using a system consisting of a multirotor drone, smartphones, and commercial tracking devices via Bluetooth and Ultra-Wide Band (UWB) off-the-shelf that is easy to use by non-specialists. We present the system configuration, explore the operational parameters that can affect detection capabilities, and test the effectiveness of the system in locating targets by simulating target animals in savanna and forest environments. The self-contained tracking system was built without the need for hardware or software customization. From 40 tracking flights carried out in the Cerrado biome, we obtained a detection rate of 90% in savanna and 40% in forest areas. Considering the moving tests (N = 20) the detection rates were 90% in the savanna and 30% in the forest areas. The spatial accuracy obtained by the system was 14.61 m, being significantly more accurate in savanna areas (x̄ = 10.53) than in forest areas (x̄ = 13.06). This approach to wildlife tracking facilitates the use of drones by non-specialists and at an affordable cost for conservation projects with limited resources. The reduced size of the tags, the long battery life and the reduced cost in relation to GPS-tags opens up a range of opportunities for tracking small to large fauna present in this type of vegetation.

Unmanned Aerial Vehicle (UAV) Remote Sensing in Grassland Ecosystem Monitoring: A Systematic Review

In recent years, the application of unmanned aerial vehicle (UAV) remote sensing in grassland ecosystem monitoring has increased, and the application directions have diversified. However, there have been few research reviews specifically for grassland ecosystems at present. Therefore, it is necessary to systematically and comprehensively summarize the application of UAV remote sensing in grassland ecosystem monitoring. In this paper, we first analyzed the application trend of UAV remote sensing in grassland ecosystem monitoring and introduced common UAV platforms and remote sensing sensors. Then, the application scenarios of UAV remote sensing in grassland ecosystem monitoring were reviewed from five aspects: grassland vegetation monitoring, grassland animal surveys, soil physical and chemical monitoring, grassland degradation monitoring and environmental disturbance monitoring. Finally, the current limitations and future development directions were summarized. The results will be helpful to improve the understanding of the application scenarios of UAV remote sensing in grassland ecosystem monitoring and to provide a scientific reference for ecological remote sensing research.

Noninvasive Technologies for Primate Conservation in the 21st Century

Observing and quantifying primate behavior in the wild is challenging. Human presence affects primate behavior and habituation of new, especially terrestrial, individuals is a time-intensive process that carries with it ethical and health concerns, especially during the recent pandemic when primates are at even greater risk than usual. As a result, wildlife researchers, including primatologists, have increasingly turned to new technologies to answer questions and provide important data related to primate conservation. Tools and methods should be chosen carefully to maximize and improve the data that will be used to answer the research questions. We review here the role of four indirect methods—camera traps, acoustic monitoring, drones, and portable field labs—and improvements in machine learning that offer rapid, reliable means of combing through large datasets that these methods generate. We describe key applications and limitations of each tool in primate conservation, and where we anticipate primate conservation technology moving forward in the coming years.

Animal Welfare Implications of Digital Tools for Monitoring and Management of Cattle and Sheep on Pasture

Simple Summary

Monitoring the welfare of cattle and sheep in large pastures can be time-consuming, especially if the animals are scattered over large areas in semi-natural pastures. There are several technologies for monitoring animals with wearable or remote equipment for recording physiological or behavioural parameters and trigger alarms when the acquired information deviates from the normal. Automatic equipment allows continuous monitoring and may give more information than manual monitoring. Ear tags with electronic identification can detect visits to specific points. Collars with positioning (GPS) units can assess the animals’ movements and habitat selection and, to some extent, their health and welfare. Digitally determined virtual fences, instead of the traditional physical ones, have the potential to keep livestock within a predefined area using audio signals in combination with weak electric shocks, although some individuals may have difficulties in responding as intended, potentially resulting in reduced animal welfare. Remote technology such as drones equipped with cameras can be used to count animals, determine their position and study their behaviour. Drones can also herd and move animals. However, the knowledge of the potential effects on animal welfare of digital technology for monitoring and managing grazing livestock is limited, especially regarding drones and virtual fences.

Abstract

The opportunities for natural animal behaviours in pastures imply animal welfare benefits. Nevertheless, monitoring the animals can be challenging. The use of sensors, cameras, positioning equipment and unmanned aerial vehicles in large pastures has the potential to improve animal welfare surveillance. Directly or indirectly, sensors measure environmental factors together with the behaviour and physiological state of the animal, and deviations can trigger alarms for, e.g., disease, heat stress and imminent calving. Electronic positioning includes Radio Frequency Identification (RFID) for the recording of animals at fixed points. Positioning units (GPS) mounted on collars can determine animal movements over large areas, determine their habitat and, somewhat, health and welfare. In combination with other sensors, such units can give information that helps to evaluate the welfare of free-ranging animals. Drones equipped with cameras can also locate and count the animals, as well as herd them. Digitally defined virtual fences can keep animals within a predefined area without the use of physical barriers, relying on acoustic signals and weak electric shocks. Due to individual variations in learning ability, some individuals may be exposed to numerous electric shocks, which might compromise their welfare. More research and development are required, especially regarding the use of drones and virtual fences.

Remote Management Architecture of UAV Fleets for Maintenance, Surveillance, and Security Tasks in Solar Power Plants

This article presents a remote management architecture of an unmanned aerial vehicles (UAVs) fleet to aid in the management of solar power plants and object tracking. The proposed system is a competitive advantage for sola r energy production plants, due to the reduction in costs for maintenance, surveillance, and security tasks, especially in large solar farms. This new approach consists of creating a hardware and software architecture that allows for performing different tasks automatically, as well as remotely using fleets of UAVs. The entire system, composed of the aircraft, the servers, communication networks, and the processing center, as well as the interfaces for accessing the services via the web, has been designed for this specific purpose. Image processing and automated remote control of the UAV allow generating autonomous missions for the inspection of defects in solar panels, saving costs compared to traditional manual inspection. Another application of this architecture related to security is the detection and tracking of pedestrians and vehicles, both for road safety and for surveillance and security issues of solar plants. The novelty of this system with respect to current systems is summarized in that all the software and hardware elements that allow the inspection of solar panels, surveillance, and people counting, as well as traffic management tasks, have been defined and detailed. The modular system presented allows the exchange of different specific vision modules for each task to be carried out. Finally, unlike other systems, calibrated fixed cameras are used in addition to the cameras embedded in the drones of the fleet, which complement the system with vision algorithms based on deep learning for identification, surveillance, and inspection.

Measuring disturbance at swift breeding colonies due to the visual aspects of a drone: a quasi-experiment study

There is a growing body of research indicating that drones can disturb animals. However, it is usually unclear whether the disturbance is due to visual or auditory cues. Here, we examined the effect of drone flights on the behavior of great dusky swifts Cypseloides senex and white-collared swifts Streptoprocne zonaris in 2 breeding sites where drone noise was obscured by environmental noise from waterfalls and any disturbance must be largely visual. We performed 12 experimental flights with a multirotor drone at different vertical, horizontal, and diagonal distances from the colonies. From all flights, 17% caused <1% of birds to temporarily abandon the breeding site, 50% caused half to abandon, and 33% caused more than half to abandon. We found that the diagonal distance explained 98.9% of the variability of the disturbance percentage and while at distances >50 m the disturbance percentage does not exceed 20%, at <40 m the disturbance percentage increase to > 60%. We recommend that flights with a multirotor drone during the breeding period should be conducted at a distance of >50 m and that recreational flights should be discouraged or conducted at larger distances (e.g. 100 m) in nesting birds areas such as waterfalls, canyons, and caves.

Long-Term Determinants of Tuberculosis in the Ungulate Host Community of Doñana National Park

Animal tuberculosis (TB) is endemic in wild boar (Sus scrofa), red deer (Cervus elaphus), fallow deer (Dama dama) and cattle in south and central Spain. In order to clarify the processes that operate in the medium and long-term, we studied TB at the wildlife–livestock interface in Doñana National Park for 14 years (2006–2018) in relation to host density, stochastic factors (rainfall) and environmental features (e.g., aggregation points such as waterholes). Wild boar showed the highest prevalence of TB (76.7%), followed by red deer (42.5%), fallow deer (14.4%) and cattle (10.7%). We found evidence of relevant epidemiological processes which operate over the long-term and interact with host and community ecology. Interestingly, the effect of high wild boar population density on increased TB rates was mediated by sows, which could determine high incidence in young individuals already in maternal groups. Rainfall significantly determined a higher risk of TB in male red deer, probably mediated by sex-related differences in life history traits that determined more susceptibility and/or exposure in comparison to females. The positive association between the prevalence of TB in fallow deer and cattle may indicate significant interspecies transmission (in either direction) and/or similar exposure to risk factors mediated by ecological overlapping of grazing species. The identification of long-term drivers of TB provided evidence that its control in extensive pastoral systems can only be achieved by targeting all relevant hosts and integrating measures related to all the factors involved, such as: population abundance and the aggregation of wild and domestic ungulates, environmental exposure to mycobacteria, cattle testing and culling campaigns and adjustments of appropriate densities.

Bears habituate to the repeated exposure of a novel stimulus, unmanned aircraft systems

Unmanned aircraft systems (UAS; i.e. 'drones') provide new opportunities for data collection in ecology, wildlife biology and conservation. Yet, several studies have documented behavioral or physiological responses to close-proximity UAS flights. We experimentally tested whether American black bears (Ursus americanus) habituate to repeated UAS exposure and whether tolerance levels persist during an extended period without UAS flights. Using implanted cardiac biologgers, we measured heart rate (HR) of five captive bears before and after the first of five flights each day. Spikes in HR, a measure of stress, diminished across the five flights within each day and over the course of 4 weeks of twice-weekly exposure. We halted flights for 118 days, and when we resumed, HR responses were similar to that at the end of the previous trials. Our findings highlight the capacity of a large mammal to become and remain habituated to a novel anthropogenic stimulus in a relatively short time (3-4 weeks). However, such habituation to mechanical noises may reduce their wariness of other human threats. Also, whereas cardiac effects diminished, frequent UAS disturbances may have other chronic physiological effects that were not measured. We caution that the rate of habituation may differ between wild and captive animals: while the captive bears displayed large initial spikes in HR change (albeit not as large as wild bears), these animals were accustomed to regular exposure to humans and mechanical noises that may have hastened habituation to the UAS.

Drones for Conservation in Protected Areas: Present and Future

Park managers call for cost-effective and innovative solutions to handle a wide variety of environmental problems that threaten biodiversity in protected areas. Recently, drones have been called upon to revolutionize conservation and hold great potential to evolve and raise better-informed decisions to assist management. Despite great expectations, the benefits that drones could bring to foster effectiveness remain fundamentally unexplored. To address this gap, we performed a literature review about the use of drones in conservation. We selected a total of 256 studies, of which 99 were carried out in protected areas. We classified the studies in five distinct areas of applications: “wildlife monitoring and management”; “ecosystem monitoring”; “law enforcement”; “ecotourism”; and “environmental management and disaster response”. We also identified specific gaps and challenges that would allow for the expansion of critical research or monitoring. Our results support the evidence that drones hold merits to serve conservation actions and reinforce effective management, but multidisciplinary research must resolve the operational and analytical shortcomings that undermine the prospects for drones integration in protected areas.

Positioning Methods and the Use of Location and Activity Data in Forests

In this paper, we provide an overview of positioning systems for moving resources in forest and fire management and review the related literature. Emphasis is placed on the accuracy and range of different localization and location-sharing methods, particularly in forested environments and in the absence of conventional cellular or internet connectivity. We then conduct a second review of literature and concepts related to several emerging, broad themes in data science, including the terms location-based services (LBS), geofences, wearable technology, activity recognition, mesh networking, the Internet of Things (IoT), and big data. Our objective in this second review is to inform how these broader concepts, with implications for networking and analytics, may help to advance natural resource management and science in the future. Based on methods, themes, and concepts that arose in our systematic reviews, we then augmented the paper with additional literature from wildlife and fisheries management, as well as concepts from video object detection, relative positioning, and inventory-tracking that are also used as forms of localization. Based on our reviews of positioning technologies and emerging data science themes, we present a hierarchical model for collecting and sharing data in forest and fire management, and more broadly in the field of natural resources. The model reflects tradeoffs in range and bandwidth when recording, processing, and communicating large quantities of data in time and space to support resource management, science, and public safety in remote areas. In the hierarchical approach, wearable devices and other sensors typically transmit data at short distances using Bluetooth, Bluetooth Low Energy (BLE), or ANT wireless, and smartphones and tablets serve as intermediate data collection and processing hubs for information that can be subsequently transmitted using radio networking systems or satellite communication. Data with greater spatial and temporal complexity is typically processed incrementally at lower tiers, then fused and summarized at higher levels of incident command or resource management. Lastly, we outline several priority areas for future research to advance big data analytics in natural resources.

Unmanned aircraft systems as a new source of disturbance for wildlife: A systematic review

The use of small Unmanned Aircraft Systems (UAS; also known as “drones”) for professional and personal-leisure use is increasing enormously. UAS operate at low altitudes (<500 m) and in any terrain, thus they are susceptible to interact with local fauna, generating a new type of anthropogenic disturbance that has not been systematically evaluated. To address this gap, we performed a review of the existent literature about animals’ responses to UAS flights and conducted a pooled analysis of the data to determine the probability and intensity of the disturbance, and to identify the factors influencing animals’ reactions towards the small aircraft. We found that wildlife reactions depended on both the UAS attributes (flight pattern, engine type and size of aircraft) and the characteristics of animals themselves (type of animal, life-history stage and level of aggregation). Target-oriented flight patterns, larger UAS sizes, and fuel-powered (noisier) engines evoked the strongest reactions in wildlife. Animals during the non-breeding period and in large groups were more likely to show behavioral reactions to UAS, and birds are more prone to react than other taxa. We discuss the implications of these results in the context of wildlife disturbance and suggest guidelines for conservationists, users and manufacturers to minimize the impact of UAS. In addition, we propose that the legal framework needs to be adapted so that appropriate actions can be undertaken when wildlife is negatively affected by these emergent practices.

Testing unmanned aircraft systems for salmon spawning surveys

Unmanned aircraft systems (UASs) were tested for counting Chinook salmon ( Oncorhynchus tshawytscha) redds as a more accurate, safer alternative to manned helicopter flights. Counting redds from the helicopter was less expensive and time consuming, but of the total redds counted at selected sites with a UAS, an average (± SD) of only 77% ± 14% was counted from the helicopter. A river-wide census of redds was not possible with a UAS because the study area was too large for the single field crew to survey. Simulation analyses were used to compare stratified random sampling (STRS) and sampling proportional to size (PPS) for estimating annual total redd counts from data collected with a UAS. The STRS estimates were more accurate and precise, whereas the PPS estimates, though biased, had 95% CIs that included the observed redd count more frequently. We strongly recommend that researchers conduct simulation analyses to evaluate alternative survey sampling methods if they are considering replacing census counts made from manned aircraft with counts estimated from data collected with a UAS. We conclude that UAS application reduces the risk inherent to manned aircraft flights, but the reduction in risk can come at the cost of estimates of population parameters that can sometimes be inaccurate and lack 95% CI coverage.

Unmanned Aerial Vehicles (UAVs) and Artificial Intelligence Revolutionizing Wildlife Monitoring and Conservation

Surveying threatened and invasive species to obtain accurate population estimates is an important but challenging task that requires a considerable investment in time and resources. Estimates using existing ground-based monitoring techniques, such as camera traps and surveys performed on foot, are known to be resource intensive, potentially inaccurate and imprecise, and difficult to validate. Recent developments in unmanned aerial vehicles (UAV), artificial intelligence and miniaturized thermal imaging systems represent a new opportunity for wildlife experts to inexpensively survey relatively large areas. The system presented in this paper includes thermal image acquisition as well as a video processing pipeline to perform object detection, classification and tracking of wildlife in forest or open areas. The system is tested on thermal video data from ground based and test flight footage, and is found to be able to detect all the target wildlife located in the surveyed area. The system is flexible in that the user can readily define the types of objects to classify and the object characteristics that should be considered during classification.

Unmanned Aircraft Systems complement biologging in spatial ecology studies

The knowledge about the spatial ecology and distribution of organisms is important for both basic and applied science. Biologging is one of the most popular methods for obtaining information about spatial distribution of animals, but requires capturing the animals and is often limited by costs and data retrieval. Unmanned Aircraft Systems (UAS) have proven their efficacy for wildlife surveillance and habitat monitoring, but their potential contribution to the prediction of animal distribution patterns and abundance has not been thoroughly evaluated. In this study, we assess the usefulness of UAS overflights to (1) get data to model the distribution of free‐ranging cattle for a comparison with results obtained from biologged (GPS‐GSM collared) cattle and (2) predict species densities for a comparison with actual density in a protected area. UAS and biologging derived data models provided similar distribution patterns. Predictions from the UAS model overestimated cattle densities, which may be associated with higher aggregated distributions of this species. Overall, while the particular researcher interests and species characteristics will influence the method of choice for each study, we demonstrate here that UAS constitute a noninvasive methodology able to provide accurate spatial data useful for ecological research, wildlife management and rangeland planning.