Computer‐automated bird detection and counts in high‐resolution aerial images: a review. Issue 4 (3rd October 2016)

• Record Type: Journal Article
• Authors: Chabot, Dominique; Francis, Charles M.
• Is Part Of: Journal of field ornithology. Volume 87:Issue 4(2016)
• Page Start: 343
• Page End: 359
• Publication Date: 2016-10-03
• Subjects: aerial surveys -- OBIA -- population monitoring -- survey methods -- UAV -- waterbirds -- wildlife management; Birds -- Periodicals; Ornithology -- Periodicals; Ornithologie -- Périodiques; Oiseaux -- Baguage -- Périodiques; Oiseaux -- Périodiques; 598
• DOI: 10.1111/jofo.12171
• Languages: English
• ISSNs: 0273-8570

Abstract

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.