Visible and thermal infrared remote sensing for the detection of white‐tailed deer using an unmanned aerial system

Abstract

Wildlife management is based on various measurements representative of the health of populations and their habitats. Some agencies are focusing on animal surveys to manage species such as white‐tailed deer ( Odocoileus virginianus ). Current survey methods are faced with the challenge of reduced operating costs as well as estimating and correcting detection biases. Our pilot study (data collected on 6 Nov 2012 at Saint‐David‐de‐Falardeau, QC, Canada) assessed the potential of a new approach detect and count deer based on visible and thermal infrared image processing at very‐high spatial resolutions using an unmanned aerial system (UAS). Supervised and unsupervised pixel‐based image classification approaches as well as object‐based image analysis (OBIA) were assessed for different spatial resolutions and with different combinations of spectral bands. None of the pixel‐based approaches were effective for detecting deer. The OBIA approach detected deer with a rate of up to 100% under the best conditions by using a combination of visible and thermal infrared imagery at a spatial resolution of 0.8 cm/pixel. Overall, this approach had an average detection rate of 0.5, which is comparable to conventional aerial surveys. Visual obstructions by coniferous canopy and the spectral confusion associated with certain elements (e.g., bare soil, rocks) are problems that remain unresolved. Using UASs with image processing for surveys of deer and other species of large mammals is promising, but currently limited by the flight range of unmanned aerial vehicles and the associated regulations. © 2016 The Wildlife Society.