Radar Aeroecology with LunAero

SICB Paper: Radar Aeroecology with LunAero

Since I was an invited speaker for SICB 2022, I published a paper on radar aeroecology with LunAero in conjunction with the seminar.  In this paper, my co-authors an I discuss a clever method that we extract the estimated flight ceiling of birds from radar.  Since the LunAero open-source hardware platform is naive, we must tell it what it is looking at.  The assumptions required for lunar bird counts include flight ceiling.  In the classic text on the subject, Lowery makes reasoned assumptions about the average flight ceiling of birds.  With this, we can go beyond that old method. Here, we demonstrate how to use radar aeroecology with LunAero hardware to measure flight ceiling.  We “simply” look at the height bins of radar echoes likely to be birds.  With that info, we can increase the precision of our math.

This is a graphical display of data from procedures discussed in the paper. On the left is a visualization of the radar output from a night during LunAero operation. The processed output on the right shows the radar signatures likely to be associated with birds.You can read in detail about it at the paper published in Integrative and Comparative Biology.  Titled Use of the LunAero Open-Source Hardware Platform to Enhance the Accuracy and Precision of Traditional Nocturnal Migration Bird Counts, it was published in June of 2022.  The print version was released in October, if anyone still gets those.  Remember you can always ask me for pre-prints if you need a copy for research!


Quantification of nocturnal migration of birds through moon watching is a technique ripe for modernization with superior computational power. In this paper, collected by a motorized telescope mount was data analyzed using both video observations by trained observers and modernized approaches using computer vision. The more advanced data extraction used the OpenCV library of computer vision tools to identify bird silhouettes by means of image stabilization and background subtraction. The silhouettes were sanitized and analyzed in sequence to produce stacked relationships between temporally close contours, discriminating birds from noise based on the assumption that birds migrate in stable paths. The flight ceiling of the birds was determined by extracting relevant correlation coefficient data from doppler radar co-located with the LunAero instrument in Norman, OK, USA using a method with low-computational overhead. The bird paths and flight ceiling were combined with lunar ephemera to provide input for the original method used for nocturnal migration quantification as well as an enhanced version of the same method with more advanced computational tools. We found that the manual quantification of migration activity detected 16,300 birds/km•h heading northwest from 110°, whereas the automated analysis reported a density of 43,794 birds/km•h heading northwest from 106.67°. Hence, there was agreement with regard to flight direction, but the automated method overestimated migration density by approximately three times. The reasons for the discrepancy between flight path detection appeared to be due to a substantial amount of noise in the video data as well as a tendency for the computer vision analysis to split single flight paths into two or more segments. The authors discuss ongoing innovations aimed at addressing these methodological challenges.