A comment on the limitations of UAVS in wildlife research – the example of colonial nesting waterbirds

Callaghan, Corey T.; Brandis, Kate J.; Lyons, Mitchell; Ryall, Sharon

doi:10.1111/jav.01825

Cited by 20 publications

(11 citation statements)

References 35 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…(), almost two decades ago, demonstrated improved aerial counting from a kite‐mounted camera, drones are now becoming part of the toolkit. Furthermore, researchers and managers can be excited about access to fast and accurate counting, without adequately considering the potential uncertainty, labour and skills required for effective use of drones for monitoring large and complex wildlife aggregations, and that drones still cannot produce all the required biodiversity metrics for monitoring (Callaghan, Brandis, Lyons, Ryall, & Kingsford, ).…”

Section: Discussionmentioning

confidence: 99%

Monitoring large and complex wildlife aggregations with drones

et al. 2019

Self Cite

View full text Add to dashboard Cite

Recent advances in drone technology have rapidly led to their use for monitoring and managing wildlife populations but a broad and generalised framework for their application to complex wildlife aggregations is still lacking. We present a generalised semi‐automated approach where machine learning can map targets of interest in drone imagery, supported by predictive modelling for estimating wildlife aggregation populations. We demonstrated this application on four large spatially complex breeding waterbird colonies on floodplains, ranging from c. 20,000 to c. 250,000 birds, providing estimates of bird nests. Our mapping and modelling approach was applicable to all four colonies, without any modification, effectively dealing with variation in nest size, shape, colour and density and considerable background variation (vegetation, water, sand, soil, etc.). Our semi‐automated approach was between three and eight times faster than manually counting nests from imagery at the same level of accuracy. This approach is a significant improvement for monitoring large and complex aggregations of wildlife, offering an innovative solution where ground counts are costly, difficult or not possible. Our framework requires minimal technical ability, is open‐source (Google Earth Engine and R), and easy to apply to other surveys.

show abstract

Section: Discussionmentioning

confidence: 99%

Monitoring large and complex wildlife aggregations with drones

et al. 2019

Self Cite

View full text Add to dashboard Cite

show abstract

“…2020, 12, 1185 2 of 17 semi-automated methods. The former can be extremely labour-intensive and consequently expensive, particularly for large aggregations of wildlife [26], further complicated when more than one species is counted. Semi-automated methods, including the counting of animals from photographs (e.g., camera traps) and drone imagery, are increasingly being developed around the world [27].…”

mentioning

confidence: 99%

Counting Mixed Breeding Aggregations of Animal Species Using Drones: Lessons from Waterbirds on Semi-Automation

et al. 2020

Self Cite

View full text Add to dashboard Cite

Using drones to count wildlife saves time and resources and allows access to difficult or dangerous areas. We collected drone imagery of breeding waterbirds at colonies in the Okavango Delta (Botswana) and Lowbidgee floodplain (Australia). We developed a semi-automated counting method, using machine learning, and compared effectiveness of freeware and payware in identifying and counting waterbird species (targets) in the Okavango Delta. We tested transferability to the Australian breeding colony. Our detection accuracy (targets), between the training and test data, was 91% for the Okavango Delta colony and 98% for the Lowbidgee floodplain colony. These estimates were within 1-5%, whether using freeware or payware for the different colonies. Our semi-automated method was 26% quicker, including development, and 500% quicker without development, than manual counting. Drone data of waterbird colonies can be collected quickly, allowing later counting with minimal disturbance. Our semi-automated methods efficiently provided accurate estimates of nesting species of waterbirds, even with complex backgrounds. This could be used to track breeding waterbird populations around the world, indicators of river and wetland health, with general applicability for monitoring other taxa.Remote Sens. 2020, 12, 1185 2 of 17 semi-automated methods. The former can be extremely labour-intensive and consequently expensive, particularly for large aggregations of wildlife [26], further complicated when more than one species is counted. Semi-automated methods, including the counting of animals from photographs (e.g., camera traps) and drone imagery, are increasingly being developed around the world [27]. These methods reduce the time required to count and process drone images [28], accelerating the data entry stage and encouraging the use of drones as scientific tools for management. Such benefits allow for real-time monitoring and management decisions and could, for example, assist in the targeted delivery of environmental flows for waterbird breeding events [29].Generally, semi-automated counting methods are most effective for species where there are strong contrasts against the backgrounds, particularly when background colours and shapes are consistent [28]. They can distinguish large single species aggregations on relatively simple backgrounds [30][31][32], up to sixteen avian species (numbering in the hundreds) on simple single colour backgrounds, such as oceans [33,34], or single species aggregations of hundreds of thousands on complex backgrounds [3].Development of flexible, repeatable and efficient methods, using open source software, is important in ensuring methods are applicable across a range of datasets [35,36]. Further, there are potential cost implications of processing data, given that some processing software can be expensive (i.e., compulsory licence fees, called 'payware' in this paper) and so are often only accessible to large organisations in high-income countries [37]. Open source software, or software with optional lic...

show abstract

“…We surveyed a relatively small pod (maximum 14 individuals), whereas hippo pods can sometimes number in their hundreds [46,61]. A larger pod size would still be relatively easy to survey using a drone, though it would take longer to count and differentiate demographic groups; time-consuming data processing is a drone cost [24,62]. Increasingly, such data processing could lend itself to automation through machine learning, which has already proven successful at identifying hippos on thermal infrared images [35], although it may be more difficult using RGB images, given the low colour contrast.…”

Section: Resultsmentioning

confidence: 99%

Drone-based effective counting and ageing of hippopotamus (Hippopotamus amphibius) in the Okavango Delta in Botswana

Inman

Leggett²,

Chase

2019

Preprint

Self Cite

View full text Add to dashboard Cite

Accurately estimating hippopotamus (Hippopotamus amphibius) numbers is difficult due to their aggressive nature, amphibious lifestyle, and habit of diving and surfacing. Traditionally, hippos are counted using aerial surveys and land/boat surveys. We compared estimates of numbers of hippos in a lagoon in the Okavango Delta, counted from land and video taken from a DJI Phantom 4 TM drone, testing for effectiveness at three heights (40 m, 80 m, and 120 m) and four times of day (early morning, late morning, early afternoon, and late afternoon). In addition, we determined effectiveness for differentiating age classes (juvenile, subadult, and adult), based on visual assessment and measurements from drone images, at different times and heights.Estimates in the pool averaged 9.18 (± 0.25SE, range 1 -14, n = 112 counts). Drone counts at 40 m produced the highest counts of hippos, 10.6% higher than land counts and drone counts at 80 m, and 17.6% higher than drone counts at 120 m. Fewer hippos were counted in the early morning, when the hippos were active and most likely submerged, compared to all other times of day, when they tended to rest in shallow water with their bodies exposed. We were able to assign age classes to similar numbers of hippos from land counts and counts at 40 m, although land counts were better at identifying juveniles and subadults. Early morning was the least effective time to age hippos given their active behaviour, increasingly problematic with increasing height. Use of a relatively low-cost drone provided a rigorous and repeatable method for estimating numbers and ages of hippos, but not in the early morning. Introduction1 Hippopotamus or hippo (Hippopotamus amphibius) are Vulnerable on the IUCN Red List 2 of Threatened Species, numbering 115,000-130,000 [1]. Habitat loss and hunting for meat and 3 ivory are driving declines [1], but much of the population data originates from aerial surveys, 4 which can be inaccurate [2-6]. Reliable and accurate spatial and temporal data on abundances 5 and demographics of hippo populations are essential for effective conservation management 6 [1,7,8] but hippos are inherently difficult to count because individuals regularly submerge and 7 surface throughout the day and have uniform appearance. They are also among the more 8 dangerous animals in Africa [9-11], limiting effectiveness of on-land and water methods of 9 counting [12]. 10 Hippos are usually surveyed from the air [9-12], but also from boats and land [9,13,14]; 11 each method has advantages and disadvantages. Aerial surveys cover large areas [4] but with 12 limited time to scan waterbodies and count hippos, given their speed. Also, aircraft noise may 13 cause hippos to submerge [19], contributing to underestimation [20]. Aerial surveys are costly 14 and logistically difficult, resulting in long intervals between surveys [21-24]. Slow, low-flying 15 microlight aircraft or helicopters capturing images may overcome some of these challenges [25] 16 but remain costly and potentially logistically difficult, ...

show abstract

A comment on the limitations of UAVS in wildlife research – the example of colonial nesting waterbirds

Cited by 20 publications

References 35 publications

Monitoring large and complex wildlife aggregations with drones

Monitoring large and complex wildlife aggregations with drones

Counting Mixed Breeding Aggregations of Animal Species Using Drones: Lessons from Waterbirds on Semi-Automation

Drone-based effective counting and ageing of hippopotamus (Hippopotamus amphibius) in the Okavango Delta in Botswana

Contact Info

Product

Resources

About