Infrared sensor for the detection and protection of wildlife

Haschberger, Peter; Bundschuh, Michael; Tank, Volker

doi:10.1117/1.601005

Cited by 13 publications

(12 citation statements)

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“…behavior [26] to surveying population sizes of large mammals such as white-tailed deer (Odocoileus virginianus) [27]. Miniaturization of thermal imaging cameras has allowed them to be attached to an UAV and used for everything from monitoring wildfires [28], flying search and rescue missions [29], searching farm land for animals before mowing operations [30], and conducting wildlife surveys [31][32][33]. Witczuk, et al [34] utilized UAVs and thermal cameras to survey for ungulates in forested habitats and were able to accurately and reliably detect large ungulate heat signatures, although they had difficulty identifying them to species.…”

Section: Introductionmentioning

confidence: 99%

Applications of Unmanned Aerial Vehicles to Survey Mesocarnivores

Bushaw

Ringelman

Rohwer

2019

Drones

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With the widespread extirpation of top predators over the past two centuries, mesocarnivores play an increasingly important role in structuring terrestrial trophic webs. However, mesocarnivores are difficult to survey at a population level because their widely spaced territories and nocturnal behavior result in low detection probability. Existing field survey techniques such as track plates and motion-sensitive camera traps are time-consuming and expensive, and yet still yield data prone to systematic errors. Unmanned Aerial Vehicles (UAVs) have recently emerged as a new tool for conducting population surveys on a wide variety of wildlife, eclipsing the efficiency and even accuracy of traditional methods. We used a UAV equipped with a thermal imaging camera to conduct nighttime mesocarnivore surveys in the prairie pothole region of southern Manitoba, Canada. This was part of a much larger ecological study evaluating how lethal removal of mesocarnivores affects duck nest success. Here, our objective was to describe methods and equipment that were successful in detecting mesocarnivores. We used a modified point-count survey from six waypoints that surveyed a spatial extent of 29.5 ha. We conducted a total of 200 flights over 53 survey nights during which we detected 32 mesocarnivores of eight different species. Given the large home ranges of mesocarnivores relative to the spatial and temporal scale of our spot sampling approach, results of these types of point-count surveys should be considered estimates of minimum abundance and not a population census. However, more frequent sampling and advanced statistics could be used to formally estimate population occupancy and abundance. UAV-mounted thermal imaging cameras appear to be an effective tool for conducting nocturnal population surveys on mesocarnivores at a moderate spatial scale.

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Section: Introductionmentioning

confidence: 99%

Applications of Unmanned Aerial Vehicles to Survey Mesocarnivores

Bushaw

Ringelman

Rohwer

2019

Drones

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“…Therefore, attempts have been made to develop automatic systems capable of detecting wild animals in the crop without unnecessary cessation of the farming operation. For example, a detection system based on infrared sensors has been reported to reduce wildlife mortality in Germany [58]. In [64] a UAVbased 3 system for roe deer fawn detection is presented.…”

Section: Mowing Operations and Wildlifementioning

confidence: 99%

“…The idea of automatic detection of wildlife in grasslands is not novel, and various attempts have been made to develop such a system. In [58] a manual operated portable system is presented. The system is based on infrared technology and works very well under defined weather conditions (early morning and cloudy days), and it has been applied the patented Infrared Wild Savior system since 1999.…”

Section: Automatic Detection Of Wildlifementioning

confidence: 99%

Pattern Recognition Methods for Reduction of Human-Wildlife Conflicts

Steen

2014

Pattern Recognition Methods for Reduction of Human-Wildlife Conflicts

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Conflicts between human activities and wildlife are an increasing problem, and in many parts of the world damage caused by wildlife creates significant economic challenges to human communities. Methods for reducing humanwildlife conflicts are either ineffective, time consuming or costly. Both lethal and non-lethal techniques are used for reduction of human-wildlife conflicts. However, the use of lethal methods is often controversial, as there is a public desire to co-exist with wildlife. This thesis is focused on human-wildlife conflicts in agriculture. The research has been driven by two very different conflicts between wildlife and agricultural activities, namely the problems with large flocks of birds in agricultural fields and wildlife mortality during mowing operations. The scientific contributions of this Ph.D. thesis focus on how sensor technologies and pattern recognition methods can be applied in the design of solutions for the reduction of human-wildlife conflicts in agriculture and thereby contribute to more ethical and efficient wildlife damage management. The result is a collection of contributions to the design of pattern recognition and signal processing algorithms to enable the use of smart sensing in the solution for human-wildlife conflicts. The achieved results are a significant step towards a more efficient sensor based solutions for wildlife-friendly farming and reduction of human-wildlife conflicts within agriculture. I would like to thank my supervisor Henrik Karstoft from the Department of Engineering, University of Aarhus, for providing valuable feedback and motivation during my Ph.D. project. It has been a great pleasure to work with Henrik, who has always left the door open for theoretical discussions and personal guidance. I would also like to thank my co-supervisor Ole Green from the Department of Engineering, University of Aarhus, for giving me the opportunity and challenge of pursuing a Ph.D. degree. Both Henrik and Ole have been an inspiration to constantly perform well during the Ph.D. project. For this, I thank you. Ole Roland Therkildsen has also been an important part of this Ph.D. project. His dedication to research within human-wildlife conflicts and his skills within scientific writing have been very rewarding. I would also like to thank Rasmus Nyholm Jørgensen who has helped me during my work regarding wildlife-friendly farming. His constant flow of new ideas and critical questions have been an eye-opener for my approach to this research. I would like to thank my colleagues Morten Stigaard Laursen, Thomas Jensen, Martin Christiansen and Peter Ahrendt for their contributions to technical discussion, feedback and motivational working environment. I have enjoyed discussing my work with you, and I am thankful for your patience during this. I would like to thank James H. McClellan for giving me the opportunity to visit Georgia Institute of Technology during my Ph.D. project. This experience was rewarding both personally and professionally. I would also like to thank my parents J...

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“…Throughout the last 20 years, traditional ways of saving the animals [2] have been extended by technological wildlife detection systems that were mostly based on thermal infrared sensors [3,4]. Ar ecent approach facilitates amultistatic radar array in order to detect the animals [5].…”

Section: Introductionmentioning

confidence: 99%

A Synthetic Aperture Acoustic Short-Range Imaging System in the Audible Domain

Hammer¹,

Pichler²

2015

Acta Acustica united with Acustica

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We present as onic imaging system based on as ingle audible sound source, al arge aperture microphone array and existing aperture synthesis signal processing. The aim of our study wasthe computation of an interferometric image of the measurement surface from the phases of twoa perture synthesis images resulting from vertically shifted sensor arrays. The results of our measurements demonstrate that objects can be detected and localized in the intensity image within as hort range. Topographic mapping based on interferometric phase calculations appears not to be possible due to the influence of the room acoustics on the phases of the reflected received signals. PACS no. 43.60.Fg, 43.60.Rw,43.60.Lq

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Infrared sensor for the detection and protection of wildlife

Cited by 13 publications

References 0 publications

Applications of Unmanned Aerial Vehicles to Survey Mesocarnivores

Applications of Unmanned Aerial Vehicles to Survey Mesocarnivores

Pattern Recognition Methods for Reduction of Human-Wildlife Conflicts

A Synthetic Aperture Acoustic Short-Range Imaging System in the Audible Domain

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