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

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.

Drones as a tool to study and monitor endangered Grey Crowned Cranes (Balearica regulorum): Behavioural responses and recommended guidelines

Crane populations are declining worldwide, with anthropogenically exacerbated habitat loss emerging as the primary causal threat. The endangered Grey Crowned Crane (Balearica regulorum) is the least studied of the three crane species that reside in southern Africa. This data paucity hinders essential conservation planning and is primarily due to ineffective monitoring methods and this species' use of inaccessible habitats. In this study, we compared the behavioural responses of different Grey Crowned Crane social groupings to traditional on-foot monitoring methods and the pioneering use of drones. Grey Crowned Cranes demonstrated a lower tolerance for on-foot monitoring approaches, allowing closer monitoring proximity with drones (22.72 (95% confidence intervals - 13.75, 37.52) m) than on-foot methods (97.59 (86.13, 110.59) m) before displaying evasive behaviours. The behavioural response of flocks was minimal at flight heights above 50 m, whilst larger flocks were more likely to display evasive behaviours in response to monitoring by either method. Families displayed the least evasive behaviours to lower flights, whereas nesting birds were sensitive to the angles of drone approaches. Altogether, our findings confirm the usefulness of drones for monitoring wetland-nesting species and provide valuable species-specific guidelines for monitoring Grey Crowned Cranes. However, we caution future studies on wetland breeding birds to develop species-specific protocols before implementing drone methodologies.

Drone images afford more detections of marine wildlife than real-time observers during simultaneous large-scale surveys

There are many advantages to transitioning from conducting marine wildlife surveys via human observers onboard light-aircraft, to capturing aerial imagery using drones. However, it is important to maintain the validity of long-term data series whilst transitioning from observer to imagery surveys. We need to understand how the detection rates of target species in images compare to those collected from observers in piloted aircraft, and the factors influencing detection rates from each platform. We conducted trial ScanEagle drone surveys of dugongs in Shark Bay, Western Australia, covering the full extent of the drone's range (∼100 km), concurrently with observer surveys, with the drone flying above or just behind the piloted aircraft. We aimed to test the assumption that drone imagery could provide comparable detection rates of dugongs to human observers when influenced by same environmental conditions. Overall, the dugong sighting rate (i.e., count of individual dugongs) was 1.3 (95% CI [0.98-1.84]) times higher from the drone images than from the observers. The group sighting rate was similar for the two platforms, however the group sizes detected within the drone images were significantly larger than those recorded by the observers, which explained the overall difference in sighting rates. Cloud cover appeared to be the only covariate affecting the two platforms differently; the incidence of cloud cover resulted in smaller group sizes being detected by both platforms, but the observer group sizes dropped much more dramatically (by 71% (95% CI [31-88]) compared to no cloud) than the group sizes detected in the drone images (14% (95% CI [-28-57])). Water visibility and the Beaufort sea state also affected dugong counts and group sizes, but in the same way for both platforms. This is the first direct simultaneous comparison between sightings from observers in piloted aircraft and a drone and demonstrates the potential for drone surveys over a large spatial-scale.

Elephants and algorithms: a review of the current and future role of AI in elephant monitoring

Artificial intelligence (AI) and machine learning (ML) present revolutionary opportunities to enhance our understanding of animal behaviour and conservation strategies. Using elephants, a crucial species in Africa and Asia's protected areas, as our focal point, we delve into the role of AI and ML in their conservation. Given the increasing amounts of data gathered from a variety of sensors like cameras, microphones, geophones, drones and satellites, the challenge lies in managing and interpreting this vast data. New AI and ML techniques offer solutions to streamline this process, helping us extract vital information that might otherwise be overlooked. This paper focuses on the different AI-driven monitoring methods and their potential for improving elephant conservation. Collaborative efforts between AI experts and ecological researchers are essential in leveraging these innovative technologies for enhanced wildlife conservation, setting a precedent for numerous other species.

Analysis of the Drinking Behavior of Beef Cattle Using Computer Vision

Monitoring the drinking behavior of animals can provide important information for livestock farming, including the health and well-being of the animals. Measuring drinking time is labor-demanding and, thus, it is still a challenge in most livestock production systems. Computer vision technology using a low-cost camera system can be useful in overcoming this issue. The aim of this research was to develop a computer vision system for monitoring beef cattle drinking behavior. A data acquisition system, including an RGB camera and an ultrasonic sensor, was developed to record beef cattle drinking actions. We developed an algorithm for tracking the beef cattle's key body parts, such as head-ear-neck position, using a state-of-the-art deep learning architecture DeepLabCut. The extracted key points were analyzed using a long short-term memory (LSTM) model to classify drinking and non-drinking periods. A total of 70 videos were used to train and test the model and 8 videos were used for validation purposes. During the testing, the model achieved 97.35% accuracy. The results of this study will guide us to meet immediate needs and expand farmers' capability in monitoring animal health and well-being by identifying drinking behavior.

Multi-Object Tracking in Heterogeneous environments (MOTHe) for animal video recordings

Aerial imagery and video recordings of animals are used for many areas of research such as animal behaviour, behavioural neuroscience and field biology. Many automated methods are being developed to extract data from such high-resolution videos. Most of the available tools are developed for videos taken under idealised laboratory conditions. Therefore, the task of animal detection and tracking for videos taken in natural settings remains challenging due to heterogeneous environments. Methods that are useful for field conditions are often difficult to implement and thus remain inaccessible to empirical researchers. To address this gap, we present an open-source package called Multi-Object Tracking in Heterogeneous environments (MOTHe), a Python-based application that uses a basic convolutional neural network for object detection. MOTHe offers a graphical interface to automate the various steps related to animal tracking such as training data generation, animal detection in complex backgrounds and visually tracking animals in the videos. Users can also generate training data and train a new model which can be used for object detection tasks for a completely new dataset. MOTHe doesn't require any sophisticated infrastructure and can be run on basic desktop computing units. We demonstrate MOTHe on six video clips in varying background conditions. These videos are from two species in their natural habitat-wasp colonies on their nests (up to 12 individuals per colony) and antelope herds in four different habitats (up to 156 individuals in a herd). Using MOTHe, we are able to detect and track individuals in all these videos. MOTHe is available as an open-source GitHub repository with a detailed user guide and demonstrations at: https://github.com/tee-lab/MOTHe-GUI.

The role of explainable Artificial Intelligence in high-stakes decision-making systems: a systematic review

A high-stakes event is an extreme risk with a low probability of occurring, but severe consequences (e.g., life-threatening conditions or economic collapse). The accompanying lack of information is a source of high-stress pressure and anxiety for emergency medical services authorities. Deciding on the best proactive plan and action in this environment is a complicated process, which calls for intelligent agents to automatically produce knowledge in the manner of human-like intelligence. Research in high-stakes decision-making systems has increasingly focused on eXplainable Artificial Intelligence (XAI), but recent developments in prediction systems give little prominence to explanations based on human-like intelligence. This work investigates XAI based on cause-and-effect interpretations for supporting high-stakes decisions. We review recent applications in the first aid and medical emergency fields based on three perspectives: available data, desirable knowledge, and the use of intelligence. We identify the limitations of recent AI, and discuss the potential of XAI for dealing with such limitations. We propose an architecture for high-stakes decision-making driven by XAI, and highlight likely future trends and directions.

Current wildlife crime (Indian scenario): major challenges and prevention approaches

The constant depletion of wild flora and fauna in India due to uncontrolled human activities, natural habitat destruction and covert poaching activities is threatening the ecological balance. The poaching and trafficking of wild species in the lure of money as well as fashion has wiped out a range of wildlife species that call for critical attention to tackle this menace. There are many transit routes through the states of Uttar Pradesh, Karnataka, West Bengal, Rajasthan, Madhya Pradesh, and Assam, which are major hubs for wildlife trafficking in India, in both domestic and international markets. The poaching of wild animals and plants slowly erases biodiversity, which in turn affects the survival of humans and other living species. Therefore, there is an urgent need to check ongoing wildlife crimes, raise the number of endangered species, rehabilitate exotic/extinct species and restore natural ecosystems. In this article, we collected wildlife crime data from web portals of various stakeholders, government agencies and authentic news sources, and discuss the current crime trends, challenges, and prevention approaches required to control and restore wildlife biodiversity in India.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10531-023-02577-z.

A Broad View on Robot Self-Defense: Rapid Scoping Review and Cultural Comparison

With power comes responsibility: as robots become more advanced and prevalent, the role they will play in human society becomes increasingly important. Given that violence is an important problem, the question emerges if robots could defend people, even if doing so might cause harm to someone. The current study explores the broad context of how people perceive the acceptability of such robot self-defense (RSD) in terms of (1) theory, via a rapid scoping review, and (2) public opinion in two countries. As a result, we summarize and discuss: increasing usage of robots capable of wielding force by law enforcement and military, negativity toward robots, ethics and legal questions (including differences to the well-known trolley problem), control in the presence of potential failures, and practical capabilities that such robots might require. Furthermore, a survey was conducted, indicating that participants accepted the idea of RSD, with some cultural differences. We believe that, while substantial obstacles will need to be overcome to realize RSD, society stands to gain from exploring its possibilities over the longer term, toward supporting human well-being in difficult times.

A pilot study to estimate the population size of endangered Galápagos marine iguanas using drones

BACKGROUND

Large-scale species monitoring remains a significant conservation challenge. Given the ongoing biodiversity crisis, the need for reliable and efficient methods has never been greater. Drone-based techniques have much to offer in this regard: they allow access to otherwise unreachable areas and enable the rapid collection of non-invasive field data. Herein, we describe the development of a drone-based method for the estimation of population size in Galápagos marine iguanas, Amblyrhynchus cristatus. As a large-bodied lizard that occurs in open coastal terrain, this endemic species is an ideal candidate for drone surveys. Almost all Amblyrhynchus subspecies are Endangered or Critically Endangered according to the IUCN yet since several colonies are inaccessible by foot, ground- based methods are unable to address the critical need for better census data. In order to establish a drone-based approach to estimate population size of marine iguanas, we surveyed in January 2021 four colonies on three focal islands (San Cristobal, Santa Fe and Espanola) using three techniques: simple counts (the standard method currently used by conservation managers), capture mark-resight (CMR), and drone-based counts. The surveys were performed within a 4-day window under similar ambient conditions. We then compared the approaches in terms of feasibility, outcome and effort.

RESULTS

The highest population-size estimates were obtained using CMR, and drone-based counts were on average 14% closer to CMR estimates-and 17-35% higher-than those obtained by simple counts. In terms of field-time, drone-surveys can be faster than simple counts, but image analyses were highly time consuming.

CONCLUSION

Though CMR likely produces superior estimates, it cannot be performed in most cases due to lack of access and knowledge regarding colonies. Drone-based surveys outperformed ground-based simple counts in terms of outcome and this approach is therefore suitable for use across the range of the species. Moreover, the aerial approach is currently the only credible solution for accessing and surveying marine iguanas at highly remote colonies. The application of citizen science and other aids such as machine learning will alleviate the issue regarding time needed to analyze the images.

Drone-assisted collection of environmental DNA from tree branches for biodiversity monitoring

The protection and restoration of the biosphere is crucial for human resilience and well-being, but the scarcity of data on the status and distribution of biodiversity puts these efforts at risk. DNA released into the environment by organisms, i.e., environmental DNA (eDNA), can be used to monitor biodiversity in a scalable manner if equipped with the appropriate tool. However, the collection of eDNA in terrestrial environments remains a challenge because of the many potential surfaces and sources that need to be surveyed and their limited accessibility. Here, we propose to survey biodiversity by sampling eDNA on the outer branches of tree canopies with an aerial robot. The drone combines a force-sensing cage with a haptic-based control strategy to establish and maintain contact with the upper surface of the branches. Surface eDNA is then collected using an adhesive surface integrated in the cage of the drone. We show that the drone can autonomously land on a variety of branches with stiffnesses between 1 and 10³ newton/meter without prior knowledge of their structural stiffness and with robustness to linear and angular misalignments. Validation in the natural environment demonstrates that our method is successful in detecting animal species, including arthropods and vertebrates. Combining robotics with eDNA sampling from a variety of unreachable aboveground substrates can offer a solution for broad-scale monitoring of biodiversity.

A Critical Review on Artificial Intelligence-Based Microplastics Imaging Technology: Recent Advances, Hot-Spots and Challenges

Due to the rapid artificial intelligence technology progress and innovation in various fields, this research aims to use science mapping tools to comprehensively and objectively analyze recent advances, hot-spots, and challenges in artificial intelligence-based microplastic-imaging field from the Web of Science (2019-2022). By text mining and visualization in the scientific literature we emphasized some opportunities to bring forward further explication and analysis by (i) exploring efficient and low-cost automatic quantification methods in the appearance properties of microplastics, such as shape, size, volume, and topology, (ii) investigating microplastics water-soluble synthetic polymers and interaction with other soil and water ecology environments via artificial intelligence technologies, (iii) advancing efficient artificial intelligence algorithms and models, even including intelligent robot technology, (iv) seeking to create and share robust data sets, such as spectral libraries and toxicity database and co-operation mechanism, (v) optimizing the existing deep learning models based on the readily available data set to balance the related algorithm performance and interpretability, (vi) facilitating Unmanned Aerial Vehicle technology coupled with artificial intelligence technologies and data sets in the mass quantities of microplastics. Our major findings were that the research of artificial intelligence methods to revolutionize environmental science was progressing toward multiple cross-cutting areas, dramatically increasing aspects of the ecology of plastisphere, microplastics toxicity, rapid identification, and volume assessment of microplastics. The above findings can not only determine the characteristics and track of scientific development, but also help to find suitable research opportunities to carry out more in-depth research with many problems remaining.

Do you get what you see? Insights of using mAP to select architectures of pretrained neural networks for automated aerial animal detection

The vast amount of images generated by aerial imagery in the context of regular wildlife surveys nowadays require automatic processing tools. At the top of the mountain of different methods to automatically detect objects in images reigns deep learning's object detection. The recent focus given to this task has led to an influx of many different architectures of neural networks that are benchmarked against standard datasets like Microsoft's Common Objects in COntext (COCO). Performance on COCO, a large dataset of computer vision images, is given in terms of mean Average Precision (mAP). In this study, we use six pretrained networks to detect red deer from aerial images, three of which have never been used, to our knowledge, in a context of aerial wildlife surveys. We compare their performance along COCO's mAP and a common test metric in animal surveys, the F1-score. We also evaluate how dataset imbalance and background uniformity, two common difficulties in wildlife surveys, impact the performance of our models. Our results show that the mAP is not a reliable metric to select the best model to count animals in aerial images and that a counting-focused metric like the F1-score should be favored instead. Our best overall performance was achieved with Generalized Focal Loss (GFL). It scored the highest along both metrics, combining most accurate counting and localization (with average F1-score of 0.96 and 0.97 and average mAP scores of 0.77 and 0.89 on both datasets respectively) and is therefore very promising for future applications. While both imbalance and background uniformity improved the performance of our models, their combined effect had twice as much impact as the choice of architecture. This finding seems to confirm that the recent data-centric shift in the deep learning field could also lead to performance gains in wildlife surveys.

Real-time monitoring of work-at-height safety hazards in construction sites using drones and deep learning

INTRODUCTION

The construction field is considered one of the most dangerous industries. Accidents and fatalities take place on a daily basis in construction projects. Globally, different levels of government have implemented strict rules and regulations to protect workers on job sites. However, despite the efforts to implement the rules and regulations, accidents occur frequently. Falling from heights is considered the most common cause of death in construction. This study developed a novel system integrating deep learning and drones to monitor workers in real-time when performing at-height activities.

METHOD

Specifically, a pre-trained deep learning model was used to detect Personal Fall Arrest System components (e.g., safety harness, lifeline, and helmet). The drone was utilized to take images and videos from the construction site, and the data were relayed to the model to detect safety violations. The system was tested and validated in real construction sites and in a controlled lab environment to verify the model's effectiveness under different light and weather conditions.

RESULTS

The overall accuracy of the system was 90%. The model's precision and recall were 97.2 % and 90.2%, respectively. The average time taken to detect a violation was around 12 seconds.

CONCLUSIONS

Moreover, the Area Under Curve - Receiver Operating Characteristics chart showed that the trained model was very good and precise in detecting and differentiating the desired objects.

PRACTICAL APPLICATIONS

This fast, reliable, and economical system can aid in saving many lives if implemented and utilized properly in real construction sites.

Towards the fully automated monitoring of ecological communities

High-resolution monitoring is fundamental to understand ecosystems dynamics in an era of global change and biodiversity declines. While real-time and automated monitoring of abiotic components has been possible for some time, monitoring biotic components-for example, individual behaviours and traits, and species abundance and distribution-is far more challenging. Recent technological advancements offer potential solutions to achieve this through: (i) increasingly affordable high-throughput recording hardware, which can collect rich multidimensional data, and (ii) increasingly accessible artificial intelligence approaches, which can extract ecological knowledge from large datasets. However, automating the monitoring of facets of ecological communities via such technologies has primarily been achieved at low spatiotemporal resolutions within limited steps of the monitoring workflow. Here, we review existing technologies for data recording and processing that enable automated monitoring of ecological communities. We then present novel frameworks that combine such technologies, forming fully automated pipelines to detect, track, classify and count multiple species, and record behavioural and morphological traits, at resolutions which have previously been impossible to achieve. Based on these rapidly developing technologies, we illustrate a solution to one of the greatest challenges in ecology: the ability to rapidly generate high-resolution, multidimensional and standardised data across complex ecologies.

Large-scale simulation of bird localization systems in forests with distributed microphone arrays

Acoustic wildlife monitoring systems are important tools for capturing information about animal habitation in ecosystems. Previous work has demonstrated the effectiveness of audio-based bird localization techniques. However, few studies have investigated the performance and robustness of distributed systems in large forests. Here, the performance of distributed microphone arrays for localizing birds is examined by simulating forest scenes with added reverberation, ambient noise, and measurement errors. The simulation revealed the importance of the signal-to-noise ratio and the spectral weighting in the localization algorithm. These results may guide the design of large-scale wildlife monitoring systems and suggest promising directions for further improvements.

UAV remote sensing applications in marine monitoring: Knowledge visualization and review

With the booming development of information technology and the growing demand for remote sensing data, unmanned aerial vehicle (UAV) remote sensing technology has emerged. In recent years, UAV remote sensing technology has developed rapidly and has been widely used in the fields of military defense, agricultural monitoring, surveying and mapping management, and disaster and emergency response and management. Currently, increasingly serious marine biological and environmental problems are raising the need for effective and timely monitoring. Compared with traditional marine monitoring technologies, UAV remote sensing is becoming an important means for marine monitoring thanks to its flexibility, efficiency and low cost, while still producing systematic data with high spatial and temporal resolutions. This study visualizes the knowledge domain of the application and research advances of UAV remote sensing in marine monitoring by analyzing 1130 articles (from 1993 to early 2022) using a bibliometric approach and provides a review of the application of UAVs in marine management mapping, marine disaster and environmental monitoring, and marine wildlife monitoring. It aims to promote the extensive application of UAV remote sensing in the field of marine research.

Detection of passageways in natural foliage using biomimetic sonar

The ability of certain bat species to navigate in dense vegetation based on trains of short biosonar echoes could provide for an alternative parsimonious approach to obtaining the sensory information that is needed to achieve autonomy in complex natural environments. Although bat biosonar has much lower data rates and spatial (angular) resolution than commonly used human-made sensing systems such as LiDAR or stereo cameras, bat species that live in dense habitats have the ability to reliably detect narrow passageways in foliage. To study the sensory information that the animals may have available to accomplish this, we have used a biomimetic sonar system that was combined with a camera to record echoes and synchronized images from 10 different field sites that featured narrow passageways in foliage. The synchronized camera and sonar data allowed us to create a large data set (130 000 samples) of labeled echoes using a teacher-student approach that used class labels derived from the images to provide training data for echo-based classifiers. The performance achieved in detecting passageways based on the field data closely matched previous results obtained for gaps in an artificial foliage setup in the laboratory. With a deep feature extraction neural network (VGG16) a foliage-versus-passageway classification accuracy of 96.64% was obtained. A transparent artificial intelligence approach (class-activation mapping) indicated that the classifier network relied heavily on the initial rising flank of the echoes. This finding could be exploited with a neuromorphic echo representation that consisted of times where the echo envelope crossed a certain amplitude threshold in a given frequency channel. Whereas a single amplitude threshold was sufficient for this in the previous laboratory study, multiple thresholds were needed to achieve an accuracy of 92.23%. These findings indicate that despite many sources of variability that shape clutter echoes from natural environments, these signals contain sufficient sensory information to enable the detection of passageways in foliage.

Warm beach, warmer turtles: Using drone-mounted thermal infrared sensors to monitor sea turtle nesting activity

For decades sea turtle projects around the world have monitored nesting females using labor-intensive human patrolling techniques. Here we describe the first empirical testing of a drone-mounted thermal infrared sensor for nocturnal sea turtle monitoring; on the Osa peninsula in Costa Rica. Preliminary flights verified that the drone could detect similar sea turtle activities as identified by on-the-ground human patrollers – such as turtles, nests and tracks. Drone observers could even differentiate tracks of different sea turtle species, detect sea turtle hatchlings, other wildlife, and potential poachers. We carried out pilot flights to determine optimal parameters for detection by testing different thermal visualization modes, drone heights, and gimbal angles. Then, over seven nights, we set up a trial to compare the thermal drone and operators’ detections with those observed by traditional patrollers. Our trials showed that thermal drones can record more information than traditional sea turtle monitoring methods. The drone and observer detected 20% more sea turtles or tracks than traditional ground-based patrolling (flights and patrols carried out across the same nights at the same time and beach). In addition, the drone operator detected 39 other animals/predators and three potential poachers that patrollers failed to detect. Although the technology holds great promise in being able to enhance detection rates of nesting turtles and other beach activity, and in helping to keep observers safer, we detail challenges and limiting factors; in drone imagery, current cost barriers, and technological advances that need to be assessed and developed before standardized methodologies can be adopted. We suggest potential ways to overcome these challenges and recommend how further studies can help to optimize thermal drones to enhance sea turtle monitoring efforts worldwide.

Evaluation of Mesoporous TiO2 Layers as Glucose Optical Sensors

Porous materials are currently the basis of many optical sensors because of their ability to provide a higher interaction between the light and the analyte, directly within the optical structure. In this study, mesoporous TiO₂ layers were fabricated using a bottom-up synthesis approach in order to develop optical sensing structures. In comparison with more typical top-down fabrication strategies where the bulk constitutive material is etched in order to obtain the required porous medium, the use of a bottom-up fabrication approach potentially allows increasing the interconnectivity of the pore network, hence improving the surface and depth homogeneity of the fabricated layer and reducing production costs by synthesizing the layers on a larger scale. The sensing performance of the fabricated mesoporous TiO₂ layers was assessed by means of the measurement of several glucose dilutions in water, estimating a limit of detection even below 0.15 mg/mL (15 mg/dL). All of these advantages make this platform a very promising candidate for the development of low-cost and high-performance optical sensors.

Evaluation of thermal infrared imaging from uninhabited aerial vehicles for arboreal wildlife surveillance

An important component of wildlife management and conservation is monitoring the health and population size of wildlife species. Monitoring the population size of an animal group can inform researchers of habitat use, potential changes in habitat and resulting behavioral adaptations, individual health, and the effectiveness of conservation efforts. Arboreal monkeys are difficult to monitor as their habitat is often poorly accessible and most monkey species have some degree of camouflage, making them hard to observe in and below the tree canopy. Surveys conducted using uninhabited aerial vehicles (UAVs) equipped with thermal infrared (TIR) cameras can help overcome these limitations by flying above the canopy and using the contrast between the warm body temperature of the monkeys and the cooler background vegetation, reducing issues with impassable terrain and animal camouflage. We evaluated the technical and procedural elements associated with conducting UAV-TIR surveys for arboreal and terrestrial macaque species. Primary imaging missions and analyses were conducted over a monkey park housing approximately 160 semi-free-ranging Japanese macaques (Macaca fuscata). We demonstrate Repeat Station Imaging (RSI) procedures using co-registered TIR image pairs facilitate the use of image differencing to detect targets that were moving during rapid sequence imaging passes. We also show that 3D point clouds may be generated from highly overlapping UAV-TIR image sets in a forested setting using structure from motion (SfM) image processing techniques. A point cloud showing area-wide elevation values was generated from TIR imagery, but it lacked sufficient point density to reliably determine the 3D locations of monkeys.

Robust Algorithms for Drone-Assisted Monitoring of Big Animals in Harsh Conditions of Siberian Winter Forests: Recovery of European elk (Alces alces) in Salair Mountains

Simple Summary

Forest animals can be used as a sensitive indicator of the real state of biodiversity. The research objective was to study the potential of drone planes equipped with thermal infrared imaging cameras for large animal monitoring in the conditions of Siberian winter forests with snow background at temperatures of −5 °C to −30 °C. The surveyed territory included the Salair State Nature Reserve in the Kemerovo Region, Russia. Drone planes were effective in covering large areas, while thermal infrared cameras provided accurate information in the harsh winter conditions of Siberia. The research featured the population of the European elk (Alces alces), which is gradually deteriorating due to poaching and deforestation. The designed technical methods and analytic algorithms are cost-efficient and they can be applied for monitoring large areas of Siberian, Canadian and Alaskan winter forests.

Abstract

There are two main reasons for monitoring the population of forest animals. First, regular surveys reveal the real state of biodiversity. Second, they guarantee a prompt response to any negative environmental factor that affects the animal population and make it possible to eliminate the threat before any permanent damage is done. The research objective was to study the potential of drone planes equipped with thermal infrared imaging cameras for large animal monitoring in the conditions of Siberian winter forests with snow background at temperatures −5 °C to −30 °C. The surveyed territory included the Salair State Nature Reserve in the Kemerovo Region, Russia. Drone planes were effective in covering large areas, while thermal infrared cameras provided accurate statistics in the harsh winter conditions of Siberia. The research featured the population of the European elk (Alces alces), which is gradually deteriorating due to poaching and deforestation. The authors developed an effective methodology for processing the data obtained from drone-mounted thermal infrared cameras. The research provided reliable results concerning the changes in the elk population on the territory in question. The use of drone planes proved an effective means of ungulate animal surveying in snow-covered winter forests. The designed technical methods and analytic algorithms are cost-efficient and they can be applied for monitoring large areas of Siberian and Canadian winter forests.

Uncovering interrelationships between barriers to unmanned aerial vehicles in humanitarian logistics

Recent disasters, such as the ongoing COVID-19 pandemic, have sparked an interest in new applications for unmanned aerial vehicles (UAVs) in humanitarian aid. Nevertheless, there are still many divisive changes that need to be made in order to implement UAVs into a country’s humanitarian sector successfully. Hence, this paper aims to analyze the various barriers hindering the implementation of UAVs in humanitarian logistics for both developed and developing nations. To accomplish this, the study is presented in three steps. First, previous literature and opinions from experts are analyzed to illuminate particular factors that hinder UAV implementation. Next, we propose an interval-valued intuitionistic fuzzy set (IVIFS) based graph theory and matrix approach (GTMA) to calculate a drone implementation hindrance index (DIHI). The GTMA method used in this paper utilizes the PERMAN algorithm to calculate the permanent function. Finally, the DIHI values are plotted and analyzed to compare the readiness of drone implementation between developed and developing economies. A sensitivity analysis is then performed to provide validity to the results obtained. The study has revealed that both types of countries must first improve their inadequate government regulations regarding humanitarian UAVs. Developing countries must also focus on enhancing the technological awareness of their population. The results of this study can be used by policymakers and practitioners to smoothly implement UAVs in their country's humanitarian sector. The general index defined in this paper can also be calculated for specific countries using the steps mentioned in the manuscript.

Flying Chameleons: A New Concept for Minimum-Deployment, Multiple-Target Tracking Drones

In this paper, we aim to open up new perspectives in the field of autonomous aerial surveillance and target tracking systems, by exploring an alternative that, surprisingly, and to the best of the authors’ knowledge, has not been addressed in that context by the research community thus far. It can be summarized by the following two questions. Under the scope of such applications, what are the implications and possibilities offered by mounting several steerable cameras onboard of each aerial agent? Second, how can optimization algorithms benefit from this new framework, in their attempt to provide more efficient and cost-effective solutions on these areas? The paper presents the idea as an additional degree of freedom to be exploited, which can enable more efficient alternatives in the deployment of such applications. As an initial approach, the problem of the optimal positioning with respect to a set of targets of one single agent, equipped with several onboard tracking cameras with different or variable focal lengths, is addressed. As a consequence of this allowed heterogeneity in focal lengths, the notion of distance needs to be adapted into a notion of optical range, as the agent can trade longer Euclidean distances for correspondingly longer focal lengths. Moreover, the proposed optimization indices try to balance, in an optimal way, the verticality of the viewpoints along with the optical range to the targets. Under these premises, several positioning strategies are proposed and comparatively evaluated.

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.

Applying Digital Twins to Research the Relationship Between Urban Expansion and Vegetation Coverage: A Case Study of Natural Preserve

With the growth of the world population, cities expand and encroach on forests and plants, causing many environmental problems. Digital Twin, as the rapidly developing technique in recent years, provides the opportunity to implement the specific situation of forests and plants at present or in the future, which has great performance on predictive analysis and optimization. From the consideration of plants and forests, this study provides a comprehensive case study to research the relationship between urban development boundary and natural environment in a natural preserve in a coastal city. Multispectral data of the study area is collected by Unmanned Aerial Vehicle (UAV), combining satellite remote sensing (RS) historical data and geographic data to establish the digital twin model for plant identification. In conjunction with local Master planning of land use, the results of modeling are used to analyze the influences of urban construction on the natural environment, and the inappropriate aspects of the planning are discovered and summarized. In addition, 6 suggestions for effective management and planning strategies are presented. As plants and forests are effective factors of natural conditions, this study offered an objective assessment for the sustainability and rationality of urban planning with some guidance and bases.

Quantitative photogrammetric methodology for measuring mammalian belly score in the painted dog

The use of "belly scoring" can offer a novel, non-invasive objective management tool to gauge food intake between individuals, groups, and populations, and thus, population fitness. As food availability is increasingly affected by predation, ecological competition, climate change, habitat modification, and other human activities, an accurate belly scoring tool can facilitate comparisons among wildlife populations, serving as an early warning indicator of threats to wildlife population health and potential population collapse. In social species, belly scores can also be a tool to understand social behavior and ranking. We developed and applied the first rigorous quantitative photogrammetric methodology to measure belly scores of wild painted dogs (Lycaon pictus). Our methodology involves: (1) Rigorous selection of photographs of the dorso/lateral profile of individuals at a right angle to the camera, (2) photogrammetrically measuring belly chord length and "belly drop" in pixels, (3) adjusting belly chord length as a departure from a standardized leg angle, and (4) converting pixel measurements to ratios to eliminate the need to introduce distance from the camera. To highlight a practical application, this belly score method was applied to 631 suitable photographs of 15 painted dog packs that included 186 individuals, all collected between 2004-2015 from allopatric painted dog populations in and around Hwange (n = 462) and Mana Pools National Parks (n = 169) in Zimbabwe. Variation in mean belly scores exhibited a cyclical pattern throughout the year, corresponding to biologically significant patterns to include denning demand and prey availability. Our results show significant differences between belly scores of the two different populations we assessed, thus highlighting food stress in the Hwange population. In the face of growing direct and indirect anthropogenic disturbances, this standardised methodology can provide a rapid, species-specific non-invasive management tool that can be applied across studies to rapidly detect emergent threats.

Bottom-Up Synthesis of Mesoporous TiO2 Films for the Development of Optical Sensing Layers

Many optical sensors exploit the interesting properties of porous materials, as they ensure a stronger interaction between the light and the analyte directly within the optical structure. Most porous optical sensors are mainly based on porous silicon and anodized aluminum oxide, showing high sensitivities. However, the top-down strategies usually employed to produce those materials might offer a limited control over the properties of the porous layer, which could affect the homogeneity, reducing the sensor reproducibility. In this work, we present the bottom-up synthesis of mesoporous TiO2 Fabry-Pérot optical sensors displaying high sensitivity, high homogeneity, and low production cost, making this platform a very promising candidate for the development of high-performance optical sensors.

A Review of Modern Thermal Imaging Sensor Technology and Applications for Autonomous Aerial Navigation

Limited navigation capabilities of many current robots and UAVs restricts their applications in GPS denied areas. Large aircraft with complex navigation systems rely on a variety of sensors including radio frequency aids and high performance inertial systems rendering them somewhat resistant to GPS denial. The rapid development of computer vision has seen cameras incorporated into small drones. Vision-based systems, consisting of one or more cameras, could arguably satisfy both size and weight constraints faced by UAVs. A new generation of thermal sensors is available that are lighter, smaller and widely available. Thermal sensors are a solution to enable navigation in difficult environments, including in low-light, dust or smoke. The purpose of this paper is to present a comprehensive literature review of thermal sensors integrated into navigation systems. Furthermore, the physics and characteristics of thermal sensors will also be presented to provide insight into challenges when integrating thermal sensors in place of conventional visual spectrum sensors.

A Comprehensive Overview of Technologies for Species and Habitat Monitoring and Conservation

The range of technologies currently used in biodiversity conservation is staggering, with innovative uses often adopted from other disciplines and being trialed in the field. We provide the first comprehensive overview of the current (2020) landscape of conservation technology, encompassing technologies for monitoring wildlife and habitats, as well as for on-the-ground conservation management (e.g., fighting illegal activities). We cover both established technologies (routinely deployed in conservation, backed by substantial field experience and scientific literature) and novel technologies or technology applications (typically at trial stage, only recently used in conservation), providing examples of conservation applications for both types. We describe technologies that deploy sensors that are fixed or portable, attached to vehicles (terrestrial, aquatic, or airborne) or to animals (biologging), complemented with a section on wildlife tracking. The last two sections cover actuators and computing (including web platforms, algorithms, and artificial intelligence).

Surveys of Large Waterfowl and Their Habitats Using an Unmanned Aerial Vehicle: A Case Study on the Siberian Crane

Waterfowl surveys, especially for endangered waterfowl living in wetlands, are essential to protect endangered waterfowl and to create a management scenario of their habitats. Unmanned aerial vehicles (UAVs) are powerful new tools for waterfowl surveys. In this paper, we propose one method for a habitat survey and another for a waterfowl species distribution survey. The habitat survey method obtained the waterfowl’s habitat and spatial distribution with a UAV automatic flight plan in the aggregation area. The waterfowl species distribution survey was used to detect and identify waterfowl species with high-spatial-resolution images from a free UAV flight plan in the aggregation area or areas where individuals were suspected to be present. The UAV-based data showed not only the area where waterfowl were found, but also additional ground surveys. The results showed that the species and locations of the waterfowl were recorded more accurately and efficiently using the distribution method based on the images from the UAV. The waterfowl habitat type and the number of waterfowl were obtained in detail using the habitat survey method. UAV-derived counts of waterfowl were greater (+37%) than ground counts. The results indicated the feasibility and advantages of using a low-cost UAV survey of large waterfowl in wetland regions with complex vegetation. This study provides one case study of large waterfowl numbers and habitat surveys. The UAV-based methods also provide a feasible and scientific way to obtain basic data for the protection and management of waterfowl.

Towards Fully Autonomous UAVs: A Survey

Unmanned Aerial Vehicles have undergone rapid developments in recent decades. This has made them very popular for various military and civilian applications allowing us to reach places that were previously hard to reach in addition to saving time and lives. A highly desirable direction when developing unmanned aerial vehicles is towards achieving fully autonomous missions and performing their dedicated tasks with minimum human interaction. Thus, this paper provides a survey of some of the recent developments in the field of unmanned aerial vehicles related to safe autonomous navigation, which is a very critical component in the whole system. A great part of this paper focus on advanced methods capable of producing three-dimensional avoidance maneuvers and safe trajectories. Research challenges related to unmanned aerial vehicle development are also highlighted.

Mapping Arctic cetaceans from space: A case study for beluga and narwhal

Emergence of new technologies in remote sensing give scientists a new way to detect and monitor wildlife populations. In this study we assess the ability to detect and classify two emblematic Arctic cetaceans, the narwhal (Monodon monoceros) and beluga whale (Delphinapterus leucas), using very high-resolution (VHR) satellite imagery. We analyzed 12 VHR images acquired in August 2017 and 2019, collected by the WorldView-3 satellite, which has a maximum resolution of 0.31 m per pixel. The images covered Clearwater Fiord (138.8 km²), an area on eastern Baffin Island, Canada where belugas spend a large part of the summer, and Tremblay Sound (127.0 km²), a narrow water body located on the north shore of Baffin Island that is used by narwhals during the open water season. A total of 292 beluga whales and 109 narwhals were detected in the images. This study contributes to our understanding of Arctic cetacean distribution and highlights the capabilities of using satellite imagery to detect marine mammals.

Moving People Tracking and False Track Removing with Infrared Thermal Imaging by a Multirotor

Infrared (IR) thermal imaging can detect the warm temperature of the human body regardless of the light conditions, thus small drones equipped with the IR thermal camera can be utilized to recognize human activity for smart surveillance, road safety, and search and rescue missions. However, the unpredictable motion of the drone poses more challenges than a fixed camera. This paper addresses the detection and tracking of people through IR thermal video captured by a multirotor. For object detection, each frame is first registered with a reference frame to compensate for its coordinates. Then, the objects in each frame are segmented through k-means clustering and morphological operations. Falsely detected objects are removed considering the actual size and the shape of the object. The centroid of the segmented area is considered the measured position for target tracking. The track is initialized with two-point differencing initialization, and the target states are continuously estimated by the interacting multiple model (IMM) filter. The nearest neighbor association rule assigns the measurement to the track. Tracks that move slower than the minimum speed are terminated at the proposed criteria. In the experiments, three videos were captured with a long-wave IR band thermal imaging camera mounted on a multirotor. In the first and second videos, eight pedestrians on a pavement and three hikers on a mountain on winter nights were captured, respectively. In the third video, two walking people with complex backgrounds were captured on a windy summer day. The image characteristics vary between videos depending on the climate and surrounding objects, but the proposed scheme shows the robust performance in all cases; the average root mean squared errors in position and velocity are obtained as 0.08 m and 0.53 m/s, respectively for the first video, 0.06 m and 0.58 m/s, respectively for the second video, and 0.18 m and 1.84 m/s, respectively for the third video. The proposed method reduces false tracks from 10 to 1 in the third video.

Feasibility Analyses of Real-Time Detection of Wildlife Using UAV-Derived Thermal and RGB Images

Wildlife monitoring is carried out for diverse reasons, and monitoring methods have gradually advanced through technological development. Direct field investigations have been replaced by remote monitoring methods, and unmanned aerial vehicles (UAVs) have recently become the most important tool for wildlife monitoring. Many previous studies on detecting wild animals have used RGB images acquired from UAVs, with most of the analyses depending on machine learning–deep learning (ML–DL) methods. These methods provide relatively accurate results, and when thermal sensors are used as a supplement, even more accurate detection results can be obtained through complementation with RGB images. However, because most previous analyses were based on ML–DL methods, a lot of time was required to generate training data and train detection models. This drawback makes ML–DL methods unsuitable for real-time detection in the field. To compensate for the disadvantages of the previous methods, this paper proposes a real-time animal detection method that generates a total of six applicable input images depending on the context and uses them for detection. The proposed method is based on the Sobel edge algorithm, which is simple but can detect edges quickly based on change values. The method can detect animals in a single image without training data. The fastest detection time per image was 0.033 s, and all frames of a thermal video could be analyzed. Furthermore, because of the synchronization of the properties of the thermal and RGB images, the performance of the method was above average in comparison with previous studies. With target images acquired at heights below 100 m, the maximum detection precision and detection recall of the most accurate input image were 0.804 and 0.699, respectively. However, the low resolution of the thermal sensor and its shooting height limitation were hindrances to wildlife detection. The aim of future research will be to develop a detection method that can improve these shortcomings.

Evaluating new technology for biodiversity monitoring: Are drone surveys biased?

Drones and machine learning-based automated detection methods are being used by ecologists to conduct wildlife surveys with increasing frequency. When traditional survey methods have been evaluated, a range of factors have been found to influence detection probabilities, including individual differences among conspecific animals, which can thus introduce biases into survey counts. There has been no such evaluation of drone-based surveys using automated detection in a natural setting. This is important to establish since any biases in counts made using these methods will need to be accounted for, to provide accurate data and improve decision-making for threatened species. In this study, a rare opportunity to survey a ground-truthed, individually marked population of 48 koalas in their natural habitat allowed for direct comparison of the factors impacting detection probability in both ground observation and drone surveys with manual and automated detection. We found that sex and host tree preferences impacted detection in ground surveys and in manual analysis of drone imagery with female koalas likely to be under-represented, and koalas higher in taller trees detected less frequently when present. Tree species composition of a forest stand also impacted on detections. In contrast, none of these factors impacted on automated detection. This suggests that the combination of drone-captured imagery and machine learning does not suffer from the same biases that affect conventional ground surveys. This provides further evidence that drones and machine learning are promising tools for gathering reliable detection data to better inform the management of threatened populations.

Passive acoustic monitoring for detecting the Yellow-bellied Glider, a highly vocal arboreal marsupial

Passive acoustic monitoring (PAM) is increasingly being used for the survey of vocalising wildlife species that are otherwise cryptic and difficult to survey. Our study aimed to develop PAM guidelines for detecting the Yellow-bellied Glider, a highly vocal arboreal marsupial that occurs in native Eucalyptus forests in eastern and south-eastern Australia. To achieve this, we considered the influence of background noise, weather conditions, lunar illumination, time since sunset and season on the probability of detecting vocalisations. We deployed Autonomous Recording Units (ARUs) at 43 sites in the Central Highlands of Victoria during two periods: spring/summer (October 2018 to January 2019), and autumn/winter (May to August 2019). ARUs were programmed to record for 11 hours from sunset for 14 consecutive days during each period. Background noise resulted from inclement weather (wind and rain) and masked vocalisations in spectrograms of the recordings, thus having the greatest influence on detection probability. Vocalisations were most common in the four hours after sunset. Rainfall negatively influenced detection probability, especially during the autumn/winter sampling period. Detection of Yellow-bellied Gliders with PAM requires deploying ARUs programmed to record for four hours after sunset, for a minimum of six nights with minimal inclement weather (light or no wind or rain). The survey period should be extended to 12 nights when rain or wind are forecast. Because PAM is less labour intensive than active surveys (i.e., spotlighting and call playbacks with multiple observers and several nights’ survey per site), its use will facilitate broad-scale surveys for Yellow-bellied Gliders.

Machine Learning Approach to Real-Time 3D Path Planning for Autonomous Navigation of Unmanned Aerial Vehicle

The need for civilian use of Unmanned Aerial Vehicles (UAVs) has drastically increased in recent years. Their potential applications for civilian use include door-to-door package delivery, law enforcement, first aid, and emergency services in urban areas, which put the UAVs into obstacle collision risk. Therefore, UAVs are required to be equipped with sensors so as to acquire Artificial Intelligence (AI) to avoid potential risks during mission execution. The AI comes with intensive training of an on-board machine that is responsible to autonomously navigate the UAV. The training enables the UAV to develop humanoid perception of the environment it is to be navigating in. During the mission, this perception detects and localizes objects in the environment. It is based on this AI that this work proposes a real-time three-dimensional (3D) path planner that maneuvers the UAV towards destination through obstacle-free path. The proposed path planner has a heuristic sense of A⋆ algorithm, but requires no frontier nodes to be stored in a memory unlike A⋆. The planner relies on relative locations of detected objects (obstacles) and determines collision-free paths. This path planner is light-weight and hence a fast guidance method for real-time purposes. Its performance efficiency is proved through rigorous Software-In-The-Loop (SITL) simulations in constrained-environment and preliminary real flight tests.

A Programming Approach to Collective Autonomy

Research and technology developments on autonomous agents and autonomic computing promote a vision of artificial systems that are able to resiliently manage themselves and autonomously deal with issues at runtime in dynamic environments. Indeed, autonomy can be leveraged to unburden humans from mundane tasks (cf. driving and autonomous vehicles), from the risk of operating in unknown or perilous environments (cf. rescue scenarios), or to support timely decision-making in complex settings (cf. data-centre operations). Beyond the results that individual autonomous agents can carry out, a further opportunity lies in the collaboration of multiple agents or robots. Emerging macro-paradigms provide an approach to programming whole collectives towards global goals. Aggregate computing is one such paradigm, formally grounded in a calculus of computational fields enabling functional composition of collective behaviours that could be proved, under certain technical conditions, to be self-stabilising. In this work, we address the concept of collective autonomy, i.e., the form of autonomy that applies at the level of a group of individuals. As a contribution, we define an agent control architecture for aggregate multi-agent systems, discuss how the aggregate computing framework relates to both individual and collective autonomy, and show how it can be used to program collective autonomous behaviour. We exemplify the concepts through a simulated case study, and outline a research roadmap towards reliable aggregate autonomy.

Advances in Forest Robotics: A State-of-the-Art Survey

The development of robotic systems to operate in forest environments is of great relevance for the public and private sectors. In this sense, this article reviews several scientific papers, research projects and commercial products related to robotic applications for environmental preservation, monitoring, wildfire firefighting, inventory operations, planting, pruning and harvesting. After conducting critical analysis, the main characteristics observed were: (a) the locomotion system is directly affected by the type of environmental monitoring to be performed; (b) different reasons for pruning result in different locomotion and cutting systems; (c) each type of forest, in each season and each type of soil can directly interfere with the navigation technique used; and (d) the integration of the concept of swarm of robots with robots of different types of locomotion systems (land, air or sea) can compensate for the time of executing tasks in unstructured environments. Two major areas are proposed for future research works: Internet of Things (IoT)-based smart forest and navigation systems. It is expected that, with the various characteristics exposed in this paper, the current robotic forest systems will be improved, so that forest exploitation becomes more efficient and sustainable.