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Deep-learning-based extraction of the animal migration patterns from weather radar images

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Abstract

Continental coverage and year-round operation of the weather radar networks provide an unprecedented opportunity for studying large-scale airborne migration. The broad and local-scale airborne information collected by these infrastructures can answer many ecological questions. However, extracting and interpreting the biological information from such massive weather radar data remains an intractable problem. Recently, many big-data problems have been solved using the deep learning technology. In this study, the biological information in the weather radar data is identified using the advanced deep learning method. The proposed method consists of two main parts, i.e., a rendering and casting procedure and an image segmentation procedure based on a convolutional neural network. The biological data are automatically extracted by rendering and mapping, image segmentation, and result masking. By analyzing the typical radar data from single and multiple stations, we partly reveal the intensity and speed of the migration pattern. We present the first feasibility study of the extraction of local and large-scale biological phenomena from the Chinese weather radar network data.

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References

  1. van Doren B M, Horton K G. A continental system for forecasting bird migration. Science, 2018, 361: 1115–1118

    Article  Google Scholar 

  2. Kelly J F, Horton K G. Toward a predictive macrosystems framework for migration ecology. Glob Ecol Biogeogr, 2016, 25: 1159–1165

    Article  Google Scholar 

  3. Chilson P B, Frick W F, Stepanian P M, et al. Estimating animal densities in the aerosphere using weather radar: to Z or not to Z? Ecosphere, 2012, 3: 1–19

    Article  Google Scholar 

  4. Rosenberg K V, Dokter A M, Blancher P J, et al. Decline of the North American avifauna. Science, 2019, 366: 120–124

    Article  Google Scholar 

  5. Hu C, Cui K, Wang R, et al. A retrieval method of vertical profiles of reflectivity for migratory animals using weather radar. IEEE Trans Geosci Remote Sens, 2020, 58: 1030–1040

    Article  Google Scholar 

  6. Stepanian P M, Horton K G. Extracting migrant flight orientation profiles using polarimetric radar. IEEE Trans Geosci Remote Sens, 2015, 53: 6518–6528

    Article  Google Scholar 

  7. Hu C, Kong S, Wang R, et al. Identification of migratory insects from their physical features using a decision-tree support vector machine and its application to radar entomology. Sci Rep, 2018, 8: 5449

    Article  Google Scholar 

  8. Hu C, Li W, Wang R, et al. Accurate insect orientation extraction based on polarization scattering matrix estimation. IEEE Geosci Remote Sens Lett, 2017, 14: 1755–1759

    Article  Google Scholar 

  9. Hu C, Li W Q, Wang R, et al. Insect flight speed estimation analysis based on a full-polarization radar. Sci China Inf Sci, 2018, 61: 109306

    Article  Google Scholar 

  10. Hu C, Wang Y X, Wang R, et al. An improved radar detection and tracking method for small UAV under clutter environment. Sci China Inf Sci, 2019, 62: 029306

    Article  Google Scholar 

  11. Krizhevsky A, Sutskever I, Hinton G E. Imagenet classification with deep convolutional neural networks. In: Proceedings of the 25th International Conference on Neural Information Processing Systems, 2012. 1097–1105

  12. Lin T Y, Winner K, Bernstein G, et al. MistNet: measuring historical bird migration in the us using archived weather radar data and convolutional neural networks. Methods Ecol Evol, 2019, 10: 1908–1922

    Article  Google Scholar 

  13. Hu C, Li S, Wang R, et al. Extracting animal migration pattern from weather radar observation based on deep convolutional neural networks. J Eng, 2019, 93: 6541–6545

    Article  Google Scholar 

  14. Chilson C, Avery K, McGovern A, et al. Automated detection of bird roosts using NEXRAD radar data and convolutional neural networks. Remote Sens Ecol Conserv, 2019, 5: 20–32

    Article  Google Scholar 

  15. Xu X F. Construction, techniques and application of new generation doppler weather radar network in China. Eng Sci, 2004, 1: 15–25

    Google Scholar 

  16. Zhu X, Zhu J. New generation weather radar network in China (in Chinese). Meteorolog Sci Tech, 2004, 32

  17. Lakshmanan V, Hondl K, Potvin C K, et al. An improved method for estimating radar echo-top height. Wea Forecast, 2013, 28: 481–488

    Article  Google Scholar 

  18. Bruderer B, Liechti F. Variation in density and height distribution of nocturnal migration in the south of Israel. Israel J Zoology, 2013, 41: 477–487

    Google Scholar 

  19. Rennie S J, Curtis M, Peter J, et al. Bayesian echo classification for Australian single-polarization weather radar with application to assimilation of radial velocity observations. J Atmos Ocean Technol, 2015, 32: 1341–1355

    Article  Google Scholar 

  20. Zhang P, Liu S, Xu Q. Identifying Doppler velocity contamination caused by migrating birds. part i: feature extraction and quantification. J Atmos Ocean Technol, 2005, 22: 1105–1113

    Article  Google Scholar 

  21. Lakshmanan V, Fritz A, Smith T, et al. An automated technique to quality control radar reflectivity data. J Appl Meteor Climatol, 2007, 46: 288–305

    Article  Google Scholar 

  22. Hu G, Lim K S, Horvitz N, et al. Mass seasonal bioflows of high-flying insect migrants. Science, 2016, 354: 1584–1587

    Article  Google Scholar 

  23. Chen L C, Zhu Y, Papandreou G, et al. Encoder-decoder with atrous separable convolution for semantic image segmentation. In: Proceedings of the European Conference on Computer Vision (ECCV), 2018. 801–818

  24. Everingham M, Eslami S M A, van Gool L, et al. The pascal visual object classes challenge: a retrospective. Int J Comput Vis, 2015, 111: 98–136

    Article  Google Scholar 

  25. Abadi M, Agarwal A, Barham P, et al. Tensorflow: large-scale machine learning on heterogeneous distributed systems. 2016. ArXiv: 1603.04467

  26. Browning K A, Wexler R. The determination of kinematic properties of a wind field using doppler radar. J Appl Meteor, 1968, 7: 105–113

    Article  Google Scholar 

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Acknowledgements

This work was supported by National Natural Science Foundation of China (Grant No. 31727901). The authors thank Prof. Kongming WU, Dr. Qiulin WU and Haowen ZHANG, Institute of Plant Protection, Chinese Academy of Agricultural Sciences, for their kindly discussion and useful suggestions. The authors thank Dongli WU and Dasheng YANG, Meteorological Observation Center, China Meteorological Administration, for providing Chinese weather radar data.

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Authors and Affiliations

  1. Radar Research Laboratory, School of Information and Electronics, Beijing Institute of Technology, Beijing, 100081, China

    Kai Cui, Cheng Hu, Rui Wang, Yi Sui, Huafeng Mao & Huayu Li

  2. Key Laboratory of Electronic and Information Technology in Satellite Navigation (Beijing Institute of Technology), Ministry of Education, Beijing, 100081, China

    Kai Cui, Cheng Hu, Rui Wang, Yi Sui, Huafeng Mao & Huayu Li

Authors
  1. Kai Cui
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  2. Cheng Hu
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  3. Rui Wang
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  4. Yi Sui
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  5. Huafeng Mao
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  6. Huayu Li
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Correspondence to Rui Wang.

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Cui, K., Hu, C., Wang, R. et al. Deep-learning-based extraction of the animal migration patterns from weather radar images. Sci. China Inf. Sci. 63, 140304 (2020). https://doi.org/10.1007/s11432-019-2800-0

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  • DOI: https://doi.org/10.1007/s11432-019-2800-0

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