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Detecting ‘poachers’ with drones: Factors influencing the probability of detection with TIR and RGB imaging in miombo woodlands, Tanzania

Hambrecht, L, Brown, RP, Piel, AK and Wich, SA (2019) Detecting ‘poachers’ with drones: Factors influencing the probability of detection with TIR and RGB imaging in miombo woodlands, Tanzania. Biological Conservation, 233. pp. 109-117. ISSN 0006-3207

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Abstract

Conservation biologists increasingly employ drones to reduce poaching of animals. However, there are no published studies on the probability of detecting poachers and the factors influencing detection. In an experimental setting with voluntary subjects, we evaluated the influence of various factors on poacher detection probability: camera (visual spectrum: RGB and thermal infrared: TIR), density of canopy cover, subject distance from the image centreline, subject contrast against the background, altitude of the drone and image analyst. We manually analysed the footage and marked all recorded subject detections. A multilevel model was used to analyse the TIR image data and a general linear model approach was used for the RGB image data. We found that the TIR camera had a higher detection probability than the RGB camera. Detection probability in TIR images was significantly influenced by canopy density, subject distance from the centreline and the analyst. Detection probability in RGB images was significantly influenced by canopy density, subject contrast against the background, altitude and the analyst. Overall, our findings indicate that TIR cameras improve human detection, particularly at cooler times of the day, but this is significantly hampered by thick vegetation cover. The effects of diminished detection with increased distance from the image centreline can be improved by increasing the overlap between images although this requires more flights over a specific area. Analyst experience also contributed to increased detection probability, but this might cease being a problem following the development of automated detection using machine learning.

Item Type: Article
Uncontrolled Keywords: 05 Environmental Sciences, 06 Biological Sciences, 07 Agricultural and Veterinary Sciences
Subjects: G Geography. Anthropology. Recreation > GE Environmental Sciences
Q Science > QH Natural history
T Technology > TL Motor vehicles. Aeronautics. Astronautics
Divisions: Natural Sciences and Psychology
Publisher: Elsevier
Date Deposited: 15 Mar 2019 10:59
Last Modified: 16 Mar 2019 11:00
DOI or Identification number: 10.1016/j.biocon.2019.02.017
URI: http://researchonline.ljmu.ac.uk/id/eprint/10327

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