Discussion
We believe this is the first study of its kind to show that thermal
imagers can be used in systematic surveys to detect previously unknown
burrows of fossorial animals. All three thermal imagers tested could
detect active rabbit warrens. Both professional imagers could detect
active rabbit warrens that were either obscured by, or under,
vegetation, including blackberry bushes. The Vayu detected five more
warrens in vegetated areas, three more warrens and 14 more warren
entrances in mixed vegetation and 16 more entrances in open areas than
visual inspection. The Zenmuse initially appeared to detect more warrens
and warren entrances than any other method, particularly in vegetated
areas, however, inspection of these detections revealed a high number of
false positives.
The Vayu, Zenmuse and visual inspection detected the same number of
entrances in vegetated habitat (Table 2), but the Vayu detected twice as
many warrens, particularly single-entrance warrens. Single entrance
warrens may indicate a breeding stop or can be the start of a new
warren. Either way, these single entrance warrens are important features
to manage during a control program.
The professional imagers had far superior exported imagery resulting in
no false-positive detections of rabbit warren entrances during
post-survey processing. We suspect that it is the exportable frame rate
(Hz) that contributed to the poor performance of the Zenmuse. This
lower-quality imagery made it difficult to distinguish between single
warren entrances and other hot material such as rocks, which effectively
looked like “hot blobs”. These “hot blobs” had little definition
which made identification difficult. The viewing rate of the live stream
from the imager (30Hz) was enough to see rabbit warrens as the drone was
flying, yet the exported video file at <9Hz resulted in poor
quality blurred imagery that was unsuitable for post-survey analysis.
Given that this technology is likely to be used to survey an area and
have the imagery post-processed and geo-tagged so that warrens can be
mapped and subsequently removed, the lower export frame rate of
<9Hz of these imagers is insufficient for the task. These
issues did not exist for the professional imagers which both had an
export frame rate of 30Hz. Consumers can overcome the low export frame
rate through the addition of an external high-speed recorder to record
the feed from the imager at the viewing frame rate. This will add
additional cost to the setup, but this cost is insignificant compared to
that of professional imagers.
Detecting active warrens and entrances gave no indication of the number
of animals’ present. The use of thermal imagers to estimate rabbit
abundance in these scenarios is unreliable and not recommended. This
technique provides presence data only. It is unknown how many rabbits
are required to generate a heat signature at an entrance. Boonstra et al
(1994) and Hubbs et al (2000) used thermal imaging to estimate the
average number of hot entrances per arctic ground squirrel and then
estimated abundance. Theoretically, the same should be possible for
rabbits. However, factors such as warren size (number of entrances),
warren depth and even soil type are likely to influence the thermal
signatures from entrances. Additionally, how many rabbits are required
to generate a heat signature in a variety of these conditions needs to
be understood. Further research should include the removal of all
rabbits from warrens of varying depths in varying soil types to
determine the minimum number of rabbits required to emit a detectable
heat signature.
Thermal imaging technology is becoming more widely available but is
still a costly technique. However, the cost of missing warren entrances
in a ripping program may be greater. The opportunity for rabbits to
re-open warrens through missed entrances has the potential to negate
tens-of-thousands of dollars of work on a local scale and millions of
dollars on the national scale. Australians spend approximately AUD$6
million per year on rabbit control programs (Gong, Sinden et al. 2009),
and many programs include warren ripping. Warren ripping can cost
anywhere from AUD$50-$150 per hectare, depending on the size of the
equipment used, the level of infestation and the soil type. For our
survey area in western NSW, we estimated that ripping would cost
AUD$1200-1800 for the 6Ha given the very high rabbit population.
Ripping programs tend not to happen in isolation and they are often part
of a multi-tool approach with associated poisoning programs (approx.
AUD$50/Ha) at a minimum. This brings the cost of initial control for
these 6Ha to AUD$1500-$2100. Rabbits from surrounding areas can
quickly reinvade and repopulate these 6Ha if all the warrens and
entrances are not detected (McPhee and Butler 2010). The professional
imager detected 30 more entrances and eight more warrens overall than
visual inspection or the corrected consumer imager. If warren ripping
was undertaken at this site using the visual or corrected consumer
imager data alone, then up to eight warrens could have been missed,
rendering the control program ineffective. The next active warren was
only 150m outside of these 6Ha. If we assume this is the same in all
directions, then approximately 6 out of every 15Ha’s would require
treatment. Repeated across warrens on average small holdings (30-100Ha),
the cost of missed warrens/entrances and having to repeat control
programs soon becomes considerable.
While this research focuses on the detection of active rabbit warrens
and their entrances, the inadequacies of the exported imagery from the
consumer imager will be important in other areas of thermal research. We
expect that professional grade thermal imagers will not be widely used
in many wildlife research projects simply due to their cost. However, as
consumer grade thermal imaging equipment becomes increasingly available,
there is an opportunity to incorporate thermal imagery more
cost-effectively into ecology research projects. More information needs
to be gathered on how these consumer grade thermal imagers perform in
detecting a range of wildlife species. In particular, how the low
exportable Frame rate affects post-flight image processing and the
occurrence of “hot blobs” and species identification.