Real-time thermal imagery from an unmanned aerial vehicle can locate ground nests of a grassland songbird at rates similar to traditional methods

Scholten, C.N.; Kamphuis, A.J.; Vredevoogd, K.J.; Lee-Strydhorst, K.G.; Atma, Jenna L.; Shea, Chris; Lamberg, O.N.; Proppe, Darren S.

doi:10.1016/j.biocon.2019.03.001

Cited by 36 publications

(39 citation statements)

References 20 publications

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“…While widely perceived as a promising tool for surveying wildlife, drone technology combined with various associated sensors, such as thermal or RGB cameras, has been applied in a very narrow range of conditions. Such cases include surveying large animals 3 , 4 , 10 or locating birds’ nests 9 , 11 , 12 . However, most such attempts have interpreted the large number of resulting images manually (e.g.…”

Section: Introductionmentioning

confidence: 99%

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Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land

Santangeli

Chen

Kluen

et al. 2020

Sci Rep

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In conservation, the use of unmanned aerial vehicles (drones) carrying various sensors and the use of deep learning are increasing, but they are typically used independently of each other. Untapping their large potential requires integrating these tools. We combine drone-borne thermal imaging with artificial intelligence to locate ground-nests of birds on agricultural land. We show, for the first time, that this semi-automated system can identify nests with a high performance. However, local weather, type of arable field and height of the drone can affect performance. The results’ implications are particularly relevant to conservation practitioners working across sectors, such as biodiversity conservation and food production in farmland. Under a rapidly changing world, studies like this can help uncover the potential of technology for conservation and embrace cross-sectoral transformations from the onset; for example, by integrating nest detection within the precision agriculture system that heavily relies on drone-borne sensors.

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Section: Introductionmentioning

confidence: 99%

“…However, most such attempts have interpreted the large number of resulting images manually (e.g. 12 ).…”

Section: Introductionmentioning

confidence: 99%

Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land

Santangeli

Chen

Kluen

et al. 2020

Sci Rep

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“…It would be worthwhile to investigate the possibilities of evaluating orthophotos based on an automated or semi-automated image analysis algorithm in independent research. It is also worth adding to the procedure with thermal imagers, although due to the high flight altitude, we do not expect such good results as we see in the thermal counting works [26,27].…”

Section: Conclusion and Discussionmentioning

confidence: 79%

Accurate Non-Disturbance Population Survey Method of Nesting Colonies in the Reedbed with Georeferenced Aerial Imagery

Bakó¹,

Molnár²,

Szilágyi³

et al. 2020

Sensors

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High altitude aerial surveys have the potential to improve disturbance-free data collection in wildlife research, but previously, bird species were not recognizable in high-altitude orthophotos. This method of aerial surveying is effective and can be repeated frequently due to its low cost; it also has the additional advantage of being able to monitor the status of protected areas. In the case of waterbirds, due to the low vegetation coverage, aerial remote sensing is an exceptionally effective technique for surveying populations and detecting nests. Aerial surveys made at low altitudes can cause serious stress for birds. The method we developed and employed is unlikely to be detected by either ground-based or nesting birds but is far more reliable compared to the low-resolution imaging methods and to the evaluation of non-georeferenced photo series. The modern sensors and photogrammetric procedures enable the use of the present method worldwide; furthermore, the large-scale ortho image-derived information has become obtainable more frequently. Direct georeferencing makes the field geodetic survey unnecessary. Orthophotos with a 0.7 cm spatial resolution allow us to reliably identify even the individuals of smaller species, and by the use of oblique images, they can be tracked from two or four different directions.

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“…Drone-assisted monitoring is less invasive and faster than traditional methods (i.e., manually traversing the natural habitat) 10 . However, it potentially introduces a new source of disturbance, especially towards birds 43 , which needs to be minimized.…”

Section: Discussionmentioning

confidence: 99%

“…Nests are often hidden under dense tree crowns and are neither visible from the the ground nor the air. Modern observation methods utilize aerial color and thermal imaging using camera drones [8][9][10][11] . But animals and nests occluded by branches, leaves, and shrubbery cannot be detected with conventional imaging techniques 5,6 .…”

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confidence: 99%

Airborne Optical Sectioning for Nesting Observation

Schedl

Kurmi

Bimber

2020

Sci Rep

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We describe how a new and low-cost aerial scanning technique, airborne optical sectioning (AOS), can support ornithologists in nesting observation. After capturing thermal and color images during a seven minutes drone flight over a 40 × 12 m patch of the nesting site of Austria’s largest heron population, a total of 65 herons and 27 nests could be identified, classified, and localized in a sparse 3D reconstruction of the forest. AOS is a synthetic aperture imaging technique that removes occlusion caused by leaves and branches. It registers recorded images to a common 3D coordinate system to support the reconstruction and analysis of the entire forest volume, which is impossible with conventional 2D or 3D imaging techniques. The recorded data is published with open access.

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Real-time thermal imagery from an unmanned aerial vehicle can locate ground nests of a grassland songbird at rates similar to traditional methods

Cited by 36 publications

References 20 publications

Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land

Integrating drone-borne thermal imaging with artificial intelligence to locate bird nests on agricultural land

Accurate Non-Disturbance Population Survey Method of Nesting Colonies in the Reedbed with Georeferenced Aerial Imagery

Airborne Optical Sectioning for Nesting Observation

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