Migratory Bird Identification and Density Prediction Based on Weather Radar Products and Weather Forecast Information

Bird activity levels present an increasing trend in China. Bird strike risks for the aviation industry are positively correlated with bird activity. Migratory birds pose greater threats to airplanes due to their broader distribution and larger mass. A reliable broad-area surveillance solution for migratory birds is critical for bird strike warning and prevention. This paper introduces a broad-area migratory bird identification model based on weather radar systems. A customized feature space and a sample-separation optimization strategy are proposed for more accurate identification of bird targets in weather radar resolution volumes. A new bird density calculation model is proposed to accommodate nonuniform bird distributions and provide a more reasonable bird quantity interpretation. The relevance between bird density and weather parameters is modeled by specific data sampling and a numerical regression model. Weather radar data files from two stations in China are used to verify the effectiveness of the proposed method. Results indicate that the bird identification model could provide reasonable bird surveillance information, and the density prediction models present good consistency with existing datasets. Differences among radar stations and seasons prove the necessity of building individual bird density prediction models. Identification and prediction models are expected to be further optimized with more data support and cross-validation information. Quantitative and qualitative prediction products provide valuable references for bird strike prediction and prevention.