Abstract
We describe how a new and low-cost aerial scanning technique, airborne optical sectioning (AOS), can support ornithologists in nesting observation. After capturing thermal and color images during a seven minutes drone flight over a 40 × 12 m patch of the nesting site of Austria’s largest heron population, a total of 65 herons and 27 nests could be identified, classified, and localized in a sparse 3D reconstruction of the forest. AOS is a synthetic aperture imaging technique that removes occlusion caused by leaves and branches. It registers recorded images to a common 3D coordinate system to support the reconstruction and analysis of the entire forest volume, which is impossible with conventional 2D or 3D imaging techniques. The recorded data is published with open access.
Highlights
Civil drone applications increase radically world wide to support many areas, such search and rescue, remote sensing, delivery of goods, and wildlife observation[1]
We present the results of a field experiment at the wild life resort Lower Inn, Reichersberg (Fig. 1b) that utilized a new synthetic aperture imaging technique, called airborne optical sectioning (AOS)[12,13,14], to count herons at Austria’s largest colony during nesting season
We show that AOS is an adequate aerial imaging technology for capturing occluded birds and nests that remain invisible to normal cameras or binoculars
Summary
Civil drone applications increase radically world wide to support many areas, such search and rescue, remote sensing, delivery of goods, and wildlife observation[1]. Modern observation methods utilize aerial color and thermal imaging using camera drones[8,9,10,11]. Our drone was equipped with synchronized color (RGB) and thermal cameras and autonomously recorded a sequence of images at an altitude of 10 m to 15 m above the forest (Fig. 1). These images were computationally combined to form the signal of a wide synthetic aperture which allowed slicing the forest optically from the ground to the tree crowns – similar to optical sectioning commonly used in high-NA microscopy. They could be located in the thermal signal, classified in the RGB signal, and visualized in a sparse 3D reconstruction of the forest
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