Modeling Swallow Roosts Using Weather Radar



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Every day, large flocks of swallows fly away from nocturnal roosting locations at sunrise. These flocks, comprised of any one of the 4-5 swallow species present in the US, are known as roosts. Along with storm systems and precipitation, roosts are visible on weather radar. It is estimated over 200 million radar scans have been collected containing potentially invaluable information pertaining to bird roosting behavior. Biologists could use this data to gain a better understanding of bird migratory patterns, and the effect of climate change on avian behavior. However, it is difficult for biologists to interpret information from the radar scans due to the lack of established methods for detecting roosts in these scans. This thesis project seeks to simplify the process of interpreting bird roost radar data by developing an automated method of detecting and locating roosts. We consider two approaches to solving this problem; the development of empirical parametric models based on data products returned by the radar, and adaptation of existing computer vision algorithms. For the first approach, we find a hypothesized Gaussian relationship between reflectivity of volumes of atmosphere, and their distance from the center of a labeled roost. We then use this relationship to construct a model for localizing roosts within radar scans, and compare the results to a similar model that uses radial velocity. For the second approach, we begin by adapting an open-source Haar feature-based cascade classifier object detection method. We also consider convolutional neural networks. In each case, we observe the suitability of the model for detection or localization of roosts within radar scans, as well as precision and accuracy compared to human annotation. Our experiments on a subset of a pre-labeled database of roost scans show that a machine learning approach to roost labeling can improve on existing methods of annotation, though false detections due to the difficulty of distinguishing between roosts and precipitation are an ongoing issue. Fine-tuning of the models could improve precision.



Computer Science, Computational Ecology, Swallow, Roost, Machine Learning, Swallow Roost