1. Thermal imaging technology is a developing field in wildlife management.  Most thermal imaging work in wildlife science has been limited to larger ungulates and surface-dwelling mammals.  Little work has been undertaken on the use of thermal imagers to detect fossorial animals and/or their burrows.  Survey methods such as white-light spotlighting can fail to detect the presence of burrows (and therefore the animals within), particularly in areas where vegetation obscures burrows.  Thermal imagers offer opportunity to detect the radiant heat from these burrows, and therefore the presence of the animal, particularly in vegetated areas.  Thermal imaging technology has become increasingly available through the provision of smaller, more cost-effective units. Their integration with drone technology provides opportunities for researchers and land managers to utilise this technology in their research/management practices.  
2. We investigated the ability of both consumer (<AUD$20,000) and professional imagers (>AUD$65,000) mounted on drones to detect rabbit burrows (warrens) and entrances in the landscape as compared to visual assessment. 
3. Thermal imagery and visual inspection detected active rabbit warrens when vegetation was scarce. The presence of vegetation was a significant factor in detecting entrances (P<0.001, α=0.05).  The consumer imager did not detect as many warren entrances as either the professional imager or visual inspection (P=0.009, α=0.05).   Active warren entrances obscured by vegetation could not be accurately identified on exported imagery from the consumer imager and several false-positive detections occurred when reviewing this footage. 
4. We suggest that the exportable frame rate (Hz)was the key factor in image quality and subsequent false positive detections.  This feature should be considered when selecting imagers and suggest that a minimum export rate of 30Hz is required. Thermal imagers are a useful additional tool to aid in identification of entrances for active warrens and professional imagers detected more warrens and entrances than either consumer imagers or visual inspection.

We used three uncooled microbolometer arrays (Table 1) of varying sensor size and cost.  The Jenoptik VarioCAMⓇ HD (hereafter referred to as the “Jenoptik”) professional thermal imager was used to evaluate part 1, with the FLIR Zenmuse XT640 and Sierra-Olympic VayuHD used in part 2 (hereafter referred to as the “Zenmuse” and “Vayu” respectively).  The Zenmuse came as an integrated system with the DJI Inspire 1 drone; however, both the Jenoptik and the Vayu were heavier non-integrated imagers.  Both of these imagers required mounting to a Ronin MX gimbal (https://www.dji.com/au/ronin-mx) for image stabilisation.  The Jenoptik was mounted to a DJI S1000+ drone (https://www.dji.com/au/spreading-wings-s1000/spec ) and the Vayu mounted to a DJI Matrice 600 drone (https://www.dji.com/au/matrice600/info#specs).  All video was collected and processed as “white-hot” grayscale imagery.  

Determining which warren entrances belong to which warrens can be challenging in high density rabbit populations.  For the purposes of this research, an entrance was part of the same warren if it was within 5m of another entrance.  When an entrance was detected that was more than 5m away from another entrance, this was deemed to be part of a new warren.  Single entrances that were >5m away from other entrances were considered a single-entrance warren.  Warrens were regarded as active when one or more entrance had signs of use.  This includes a lack of vegetation growing in the entrance, the presence of freshly excavated soil, fresh scat and/or the presence of rabbit footprints.  Warrens where all entrances were covered in either debris (leaves and sticks), with cobwebs and with hard crusted soil were considered inactive.  No further validation (e.g. excavation or trapping) was undertaken to confirm warren activity status.

All thermal imager surveys were conducted in the morning before first light to maximise the temperature differential between warren entrances and the surrounding terrain.  All sites were visually inspected for rabbit warrens (active and inactive) on foot during the day (prior to the thermal survey) and all identified warrens were mapped with their GPS locations recorded.  The ground and aerial surveys were independent, i.e. the thermal imager transects were designed prior to visual inspection.  In Part 1 we determined whether active rabbit warrens could be detected with a thermal imager. We flew the drone with the Jenoptik imager directly to the warren locations. In Part 2 we compared a professional imager (Vayu) to a consumer imager (Zenmuse).  We established parallel flight transects to allow complete coverage of the area being investigated and to mimic the actual survey method that should be employed to search for warrens.  We undertook visual counts of warrens and warren entrances in Part 2.  Visual counts were undertaken upon arrival and before the drone flights.  Parallel line transects approximately 10m apart were walked and all warrens and associated entrances were recorded.  Once imagery from the drone flights were processed (see below), we undertook an additional visual inspection on foot to confirm entrances identified from the thermal imagery and to identify any false-positives or -negatives.

Prior to undertaking the surveys, we flew each imager at various flight heights and speeds to determine optimum picture quality. For the survey, the Zenmuse was flown at 3 m/s at 10m above ground level (AGL).  This resulted in a swath width of 15.6m and a resolution of 1.4 pixels/cm.  The Vayu was flown at 5 m/s at 40m AGL resulting in a swath width of 39.8m and a resolution of 2 pixels/cm.  Transect spacing for each imager for the survey was determined by the swath width.  Transects spacing for the Zenmuse was 11m resulting in a transect overlap of 2.3m either side of the image.  Sixteen transects were required to cover the area taking two flights to complete.  Transect spacing for the Vayu was 22m resulting in a transect overlap of 8.5m.  Eight transects were required to cover the area which took one flight to complete.  For both the Zenmuse and the Vayu the imager was pointed 90 degrees to the horizontal during the surveys.  During the surveys proper the drone was not stopped over entrances or warrens for confirmation of detection.

We downloaded the footage from the thermal imagers to an external hard drive and reviewed the footage from this drive using VLC media player 3.0.8. We recorded observations in a custom-built Microsoft Excel (Microsoft Corporation 2018) workbook which utilised the drone’s tracklog to georeference observation locations.  This file was then exported as a KML file and viewed in Google Earth Pro (Google Earth Pro 2019) to aid in comparison between thermal imager and visual inspection detections.

Where transect imagery overlapped, double observations of warren entrances were removed from the worksheet before analysis.  If a warren complex was identified on one transect, and additional warren entrances were identified on the immediate next transect in the same location, then a determination was made on whether these entrances belonged to the same warren or constituted a new warren.  This ensured warren counts were not over-estimated. 

Warrens were classified by the amount of vegetation present that was likely to obscure entrances.  Warrens with no vegetation present were classified as “open”, warrens obscured by vegetation (e.g. entrances were beneath shrubs) were classified as “vegetated” and warrens that had entrances in the open and obscured by vegetation were classified as “mixed”.  These classifications also applied to the entrances associated with that warren for analysis (i.e. individual entrances in “mixed” warrens were not further classified into “open” or “vegetated” categories for analysis).

Statistical analysis

We used the lme4 (Bates, Maechler et al. 2015) and lmerTest (Kuznetsova, Brockhoff et al. 2017) packages in R (R Core Team 2019) to test for any difference in entrance count associated with imager. We used a mixed model with Poisson likelihood to account for the nested structure of imagers within warrens and the contrast of vegetation class between distinct warren sets.  Package emmeans (Lenth 2019) was used to inspect the mean entrance count under each vegetation and imager class.  Additionally, we plotted difference between estimates vs average of the estimates to check for any patterning in case agreement depended on magnitude of observation as suggested by Altman and Bland (1983).  To address any disagreement in terms of presence or absence of entrances detected, the three pairings of methods (visual vs Vayu, visual vs Zenmuse and Vayu vs Zenmuse) were examined by classifying entrance counts as equal to or greater than zero and forming two-way tables.

Please see ReadMe file