Quantification of Migration Birds Based on Polarimetric Weather Radar

Author:

Wang Rui12,Mao Huafeng1,Cui Kai23ORCID,Sun Zhuoran1,Hu Cheng12ORCID,Wu Dongli4

Affiliation:

1. The Key Laboratory of Electronic and Information Technology in Satellite Navigation, Ministry of Education, Beijing Institute of Technology, Beijing 100081, China

2. The Advanced Technology Research Institute, Beijing Institute of Technology, Jinan 250300, China

3. The School of Computer Sciences, Beijing Institute of Technology, Beijing 100081, China

4. The Meteorological Observation Center of China Meteorological Administration, Beijing 100081, China

Abstract

Weather radar plays an important role in monitoring aerial animal migration, providing a stable data source for biological studies with large-scale coverage and consecutive-time samples. The accurate estimation of bird density from weather radar echoes is fundamental for quantitative biological studies. We analyzed the bird observation model in weather radar, and proposed a method to build the bird quantification model by jointly utilizing dual-polarization Doppler weather radar and scanning bird radar. We designed a detailed process to remove tracks or echoes from non-bird targets, ensuring the effectiveness of bird observations. The field experiments validated the quantification method, showing that the average radar cross section of birds in Jinan was 19.09 dBscm (i.e., 81.19 cm2; 95% confidence interval, CI: 18.92–19.27 dBscm) for the S-band weather radar, with an R2 of 0.79. In addition, through the correlation analysis, we found that the ground terrain may affect the distribution pattern of aerial bird density.

Funder

National Natural Science Foundation of China

Publisher

MDPI AG

Subject

General Earth and Planetary Sciences

Cited by 2 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Extracting Bird and Insect Migration Echoes From Single-Polarization Weather Radar Data Using Semi-Supervised Learning;IEEE Transactions on Geoscience and Remote Sensing;2024

2. Classification of Biological Scatters Using Polarimetric Weather Radar;IEEE Journal of Selected Topics in Applied Earth Observations and Remote Sensing;2024

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