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

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.     

Evaluating two new methods for capturing large wetland birds

ABSTRACT.   Waterbird research increasingly involves capturing birds to measure attributes of individuals and for attachment of telemetry devices, but there are few established techniques for capturing wading birds away from their nests. We describe the construction and testing of a new trapping technique for wading birds based on a small flip trap. Our trap was developed to capture wading birds in the Florida Everglades in water less than 30 cm deep, but could also be used to capture other waterbirds and upland species. The primary advantages of this new trap design are its ability to be easily concealed, ability to selectively capture birds, low injury rate, ease of resetting the trap after captures, minimal training requirements for operator, capacity to be fired remotely, and moderate cost. We also evaluated the effectiveness of using a net gun to capture wading birds in wetlands. Our results demonstrated that the net gun was the most effective method for capturing large numbers of wading birds, however, the modified flip trap provides a safer alternative that may be appropriate for endangered species.     

普达措国家公园越冬期鸟类群落功能空间及驱动机制

解析群落中生物多样性维持的决定因素是生态学研究的一个重要目标。为了获得普达措国家公园碧塔海-属都湖片区鸟类物种多样性，2021年8月至2022年7月，设置8条总长17km的鸟类调查样线，并于2021年11月至2022年10月在片区24个位点布设红外相机进行辅助调查。为了重点研究越冬期鸟类群落功能多样性格局及其驱动机制，2021年11月至2022年3月，对碧塔海-属都湖片区内高原湖泊、高山草甸、针阔混交林和针叶林4种生境共30个样点进行了越冬期鸟类群落的调查。利用15个鸟类功能性状计算群落功能丰富度（FRic）和平均最近邻接功能距离（MNND），并构建零模型，探讨环境过滤和极限相似性作用对越冬期鸟类群落构建的相对重要性。结果表明，本研究在普达措国家公园碧塔海-属都湖片区共记录13目42科114种鸟类，与该区域前期野外记录相比新增了74个野外记录鸟种。该片区不同生境类型的鸟类物种组成差异明显，针阔混交林和针叶林物种组成相似，但未完全重合。在该区域高原湖泊和高山草甸越冬的16种鸟类显著扩大了越冬期鸟类群落的功能空间，但同时显著减小了功能空间中物种的平均最近邻接功能距离。4种生境鸟类群落功能空...