Measuring Tree Sway Frequency Using Video Processing

Abstract

Measurements of tree properties and fluxes are necessary for improving surface models, monitoring the impact of phenomena such as drought, and mitigating tree related damage. However, collecting tree measurements can be challenging, often requiring invasive, expensive, or cumbersome instruments. A growing body of research has demonstrated that measurements of tree sway frequency can be used to quantify processes, such as drought stress and changes in mass resulting from precipitation interception, that are otherwise difficult to measure. While robust, existing methods for measuring 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 camera field of view, thus enabling scalable measurements of tree properties. Comparing sway frequencies extracted from video and accelerometer data at two sites, we show video processing of tree sway can reproduce accelerometer sway frequencies with ±0.03 Hz accuracy. The results suggest the video processing algorithms may be suitable for applications where the approximate sway frequency or large changes in sway frequency are associated with the property of interest, for example the identification of trees vulnerable to wind throw and the measurement of snow in trees, respectively.