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

• Published in: Avian Conservation and Ecology
• Main Authors: Dominique Chabot, Christopher Dillon, Charles M. Francis
• Format: Article in Journal/Newspaper
• Language: English
• Published: Resilience Alliance 2018
• Subjects: abundance estimation; aerial surveys; automation; census techniques; data analysis; image processing; population monitoring; remote sensing; Plant culture; SB1-1110; Environmental sciences; GE1-350; Plant ecology; QK900-989; Arctic
• Online Access: https://doi.org/10.5751/ACE-01205-130115; https://doaj.org/article/ea924fb6d7314bbcbccc5a6e4e60add5

Computer-automated image analysis techniques can save time and resources for detecting and counting birds in aerial imagery. Sophisticated object-based image analysis (OBIA) software is now widely available and has proven effective for various challenging detection tasks, but there is a need to develop accessible and readily adaptable procedures that can be implemented in an operational context. We developed a systematic, repeatable approach using commercial off-the-shelf OBIA software, and tested its effectiveness and efficiency to detect and count Lesser Snow Geese (Chen caerulescens caerulescens) in large numbers of images of breeding colonies across the Canadian Arctic that present a variety of landscapes, numerous confounding features, and varying illumination conditions and exposure levels. Coarse-scale review of analysis results was necessary to remove conspicuous clusters of commission errors, thus rendering the technique semiautomated. It was effective for imagery with spatial resolutions of 4-5 cm, producing overall accurate estimates of goose numbers compared to manual counts (R2 = 0.998, regression coefficient = 0.974) in 41 test images drawn from several breeding colonies. The total automated count (19,920) across all test images exceeded the manual count (19,836) by just 0.4%. We estimate the typical time required to review images for errors to be only 5-10% of that required to count birds manually. This could reduce the person-time required to analyze aerial photos of the major Arctic colonies of Snow Geese from several months to several days. Our approach could be adapted to many other bird detection tasks in aerial imagery by anyone possessing at least basic skills in image analysis and geographic information systems.