Measurements of biophysical tree properties and hydrologic fluxes are necessary for improving models and monitoring the impact of disturbances. Prior research has demonstrated that measurements of tree sway frequency can be used to quantify important ecohydrologic processes, such as drought stress and snow 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 and enable scalable measurements of ecohydrologic processes. Comparing sway frequencies extracted from video and accelerometer data at two sites, we show that for 30-60 s videos, the video processing algorithms can reproduce accelerometer sway frequencies with ±0.03 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 in trees. Further work is needed to clarify the accuracy of the algorithms when applied to longer videos, which may be required to monitor processes with more subtle changes in sway frequency, such as diurnal changes in tree water content.