Evidence on the efficacy of small unoccupied aircraft systems (UAS) as a survey tool for North American terrestrial, vertebrate animals: a systematic map

Abstract

Background Small unoccupied aircraft systems (UAS) are replacing or supplementing occupied aircraft and ground-based surveys in animal monitoring due to improved sensors, efficiency, costs, and logistical benefits. Numerous UAS and sensors are available and have been used in various methods. However, justification for selection or methods used are not typically offered in published literature. Furthermore, existing reviews do not adequately cover past and current UAS applications for animal monitoring, nor their associated UAS/sensor characteristics and environmental considerations. We present a systematic map that collects and consolidates evidence pertaining to UAS monitoring of animals. Methods We investigated the current state of knowledge on UAS applications in terrestrial animal monitoring by using an accurate, comprehensive, and repeatable systematic map approach. We searched relevant peer-reviewed and grey literature, as well as dissertations and theses, using online publication databases, Google Scholar, and by request through a professional network of collaborators and publicly available websites. We used a tiered approach to article exclusion with eligible studies being those that monitor (i.e., identify, count, estimate, etc.) terrestrial vertebrate animals. Extracted metadata concerning UAS, sensors, animals, methodology, and results were recorded in Microsoft Access. We queried and catalogued evidence in the final database to produce tables, figures, and geographic maps to accompany this full narrative review, answering our primary and secondary questions. Review findings We found 5539 articles from our literature searches of which 216 were included with extracted metadata categories in our database and narrative review. Studies exhibited exponential growth over time but have levelled off between 2019 and 2021 and were primarily conducted in North America, Australia, and Antarctica. Each metadata category had major clusters and gaps, which are described in the narrative review. Conclusions Our systematic map provides a useful synthesis of current applications of UAS-animal related studies and identifies major knowledge clusters (well-represented subtopics that are amenable to full synthesis by a systematic review) and gaps (unreported or underrepresented topics that warrant additional primary research) that guide future research directions and UAS applications. The literature for the use of UAS to conduct animal surveys has expanded intensely since its inception in 2006 but is still in its infancy. Since 2015, technological improvements and subsequent cost reductions facilitated widespread research, often to validate UAS technology to survey single species with application of descriptive statistics over limited spatial and temporal scales. Studies since the 2015 expansion have still generally focused on large birds or mammals in open landscapes of 4 countries, but regulations, such as maximum altitude and line-of-sight limitations, remain barriers to improved animal surveys with UAS. Critical knowledge gaps include the lack of (1) best practices for using UAS to conduct standardized surveys in general, (2) best practices to survey whole wildlife communities in delineated areas, and (3) data on factors affecting bias in counting animals from UAS images. Promising advances include the use of thermal sensors in forested environments or nocturnal surveys and the development of automated or semi-automated machine-learning algorithms to accurately detect, identify, and count animals from UAS images.

Controllable factors affecting accuracy and precision of human identification of animals from drone imagery

“…High versus low flight altitude surveys also produce fewer single images for human observers to review or fewer images to stitch together for composite images, which may save time and resources. A final consideration for altitude is that regulations in many countries where drone studies are regularly conducted restrict drone flights to a maximum altitude of approximately 120 m without a waiver (Elmore et al, 2023).…”

“…Animal surveys are critical to quantify populations for conservation and management purposes worldwide (Silvy, 2020). In the last decade, drones (unoccupied aircraft systems [UAS]), have become popular tools to survey myriad animals among disparate environments (Elmore et al, 2023), from penguin colonies in Antarctica (Borowicz et al, 2018; Hayes et al, 2021) to primates in the tropics (Bonnin et al, 2018; He et al, 2020). Surveying animal populations with drones offers several advantages, such as greater accuracy compared with ground counts in open areas (Brunton et al, 2020; Hodgson et al, 2016; Jones et al, 2021) and increased safety and less cost than surveys using airplanes or helicopters (Anderson & Gaston, 2013).…”

“…The literature on surveying animals with drones with visual sensors (red, green, blue [RGB] cameras) has grown exponentially in the last decade (Elmore et al, 2023). However, based on a comprehensive review of the 216 published articles on animal surveys using drones up to early 2022, 66% of studies on 295 animal species or genera focused on single species, and the authors concluded that few best practices exist to date for surveying a community of animals (Elmore et al, 2023). Moreover, multiple‐species management is a common conservation objective, including animal monitoring for informing decision‐making (Dickens et al, 2021; Elmore et al, 2023; Mustafa et al, 2019).…”

“…However, based on a comprehensive review of the 216 published articles on animal surveys using drones up to early 2022, 66% of studies on 295 animal species or genera focused on single species, and the authors concluded that few best practices exist to date for surveying a community of animals (Elmore et al, 2023). Moreover, multiple‐species management is a common conservation objective, including animal monitoring for informing decision‐making (Dickens et al, 2021; Elmore et al, 2023; Mustafa et al, 2019). Determining best practices for drone surveys requires identifying factors affecting animal detection and classification in drone images by humans that apply broadly to animal communities and ecosystems.…”

“…Drone survey factors involving flight parameters and logistics, such as flight altitude, camera resolution, camera angle, or time of day, can also affect human accuracy and precision in animal classification (Augustine & Burchfield, 2022; Brunton et al, 2020; Mustafa et al, 2019). Various drone studies have quantified the effects of one or two factors affecting animal classification by humans (Elmore et al, 2023); however, advancing best practices of drone surveys among animal communities requires testing multiple factors and their potential interactions in a quantitatively rigorous framework to determine which factors and combinations thereof are less prone to human observer bias or therefore applicable to diverse survey situations (i.e., multiple species, diverse ecosystems) for future research and monitoring objectives. One effective way to begin to tease apart the myriad factors and their potential interactions is to focus on factors under direct control of the study design or drone pilot.…”