Measuring tree sway frequency with videos for ecohydrologic applications: Assessing the efficacy of Eulerian processing algorithms

Measurements of tree sway frequency can be used to quantify important ecohydrologic processes, such as drought stress and canopy interception, that otherwise require expensive measurement techniques. However, existing instruments used to measure tree sway lack spatial scalability. We investigate whether the virtual vision sensor and multilevel binary thresholding video processing algorithms can be used to accurately extract tree sway frequency at multiple points in a video camera field of view to enable scalable measurements of ecohydrologic processes. Comparing sway frequencies extracted from video and accelerometer data at three sites, we show that for 30–60 second videos, the video processing algorithms can reproduce 30-minute accelerometer sway frequencies with ±0.02 Hz accuracy. The results suggest that video processing algorithms may be suitable for applications where changes in sway frequency are on the order of tenths of hertz or larger, for example the measurement of snow intercepted in tree canopies. Further work is needed to clarify the accuracy of the algorithms when applied to longer videos, which may be required to monitor processes that result in more subtle changes in sway frequency, such as diurnal changes in tree water content and impacts from drought stress.