Computer-automated bird detection and counts in high-resolution aerial images: a review

Abstract

Bird surveys conducted using aerial images can be more accurate than those using airborne observers, but can also be more time‐consuming if images must be analyzed manually. Recent advances in digital cameras and image‐analysis software offer unprecedented potential for computer‐automated bird detection and counts in high‐resolution aerial images. We review the literature on this subject and provide an overview of the main image‐analysis techniques. Birds that contrast sharply with image backgrounds (e.g., bright birds on dark ground) are generally the most amenable to automated detection, in some cases requiring only basic image‐analysis software. However, the sophisticated analysis capabilities of modern object‐based image analysis software provide ways to detect birds in more challenging situations based on a variety of attributes including color, size, shape, texture, and spatial context. Some techniques developed to detect mammals may also be applicable to birds, although the prevalent use of aerial thermal‐infrared images for detecting large mammals is of limited applicability to birds because of the low pixel resolution of thermal cameras and the smaller size of birds. However, the increasingly high resolution of true‐color cameras and availability of small unmanned aircraft systems (drones) that can fly at very low altitude now make it feasible to detect even small shorebirds in aerial images. Continued advances in camera and drone technology, in combination with increasingly sophisticated image analysis software, now make it possible for investigators involved in monitoring bird populations to save time and resources by increasing their use of automated bird detection and counts in aerial images. We recommend close collaboration between wildlife‐monitoring practitioners and experts in the fields of remote sensing and computer science to help generate relevant, accessible, and readily applicable computer‐automated aerial photographic census techniques.

An approach for using off-the-shelf object-based image analysis software to detect and count birds in large volumes of aerial imagery

“…In some cases, it may be possible to reduce commission error rates to the point where manual review of automated analysis results is not necessary, while in other cases, like ours, varying degrees of manual postanalysis effort may be required. Although animal contrast in thermal-infrared imagery has proven useful for automated detection of mammals (Conn et al 2014, Chrétien et al 2015, 2016, Seymour et al 2017, the very coarse pixel resolution of thermal cameras compared to RGB cameras generally renders them ineffective for aerial detection of comparatively smaller birds (Chabot and Francis 2016). It should be noted that any aerial image-based survey will only allow detection of subjects that are visible from overhead and miss subjects that are, for example, concealed under canopy or diving underwater.…”

“…The use of computer-automated techniques to count birds in aerial imagery dates back three decades (Gilmer et al 1988), but did not gain much traction until the 21st century when a combination of advancements in image analysis software, computer processing performance, and digital camera technology have progressively made the techniques more accessible (Chabot and Francis 2016). When the color of subjects contrasts sharply with image backgrounds, they can in some cases be automatically isolated and counted using simple spectral thresholding in general-purpose image editing software (Chabot and Bird 2012).…”

“…OBIA has notably been used to detect birds at sea (Groom et al 2007(Groom et al , 2013) and on inland waters (Groom et al 2011, Liu et al 2015, as well as terrestrial mammals in a nature park (Chrétien et al 2015(Chrétien et al , 2016. In recent years, commercial offthe-shelf OBIA software has become increasingly accessible, but there is a need to develop standardized and readily adaptable procedures for using the software to detect and count birds (Chabot and Francis 2016).…”

Automated detection and enumeration of marine wildlife using unmanned aircraft systems (UAS) and thermal imagery

“…However, recent advances in automated counting and computer vision/machine learning approaches can help overcome these inefficiencies20. For example, automated detection and counting of birds21 and marsupials and mammals2223 can speed up population assessments and, depending on the sensors used, real-time applications are possible. Thermal sensors are particularly useful for mammalian wildlife, which tend to emit thermal energy at 9–14 μm wavelengths that is detectable by a range of commercially available sensors24.…”

The Drone Sector in Europe

“…3. Hardy et al (2017) Mapping malaria vector habitats Pulver et al (2016) Transporting automated external defibrillators Chabot and Francis (2016) Bird detection Hodgson et al (2017) Surveying marine fauna Sankey et al (2017) Forest monitoring Casella et al (2017) Mapping coral reefs Szantol et al (2017) Mapping orangutan habitat Chowdhury et al (2017) Disaster response and relief Restas (2015) Supporting disaster management (earthquakes, floods, fires) Source Own elaboration from different sources …”