I hated everything about this. It took five months. I had to code the tool to make this and don’t even know basic HTML. There was calculus for plants.
Trees oscillate at a particular frequency that fluctuates throughout the year. That’s linked to unknown physiological processes and environmental cues. If we understand why by using accelerometers to measure their displacement, potentially you could hook a bunch of those up to a forest and passively monitor its health. Wildland firefighters could have Cybersen for trees.
This graph measures noise density, which fucks with data collection, on the Y axis and the frequency in hertz on the X. The blue line on top is the accelerometer the team had been using. The bottom red line is the only one I found after dozens of hours of searching non-standardised datasheets which came close to matching it on paper. Since trees oscillate in the sub-1Hz range which is well below what most cheap accelerometers measure, that dip from 0-1 is about a two order of magnitude improvement for the exact frequency range that the PI wanted to investigate.
I thought tree research would involve climbing trees and thinking about them real good. It isn’t. It’s all mathematics and coding and making motherboards. Science isn’t as fun as cartoons said it would be even when you go into the interdisciplinary ones because you know you can’t do mathematics or code.
This is really cool. Where can I read more about this? What does this graph say about tree health during the year?
https://royalsocietypublishing.org/rsif/article/16/155/20190116/87087/An-architectural-understanding-of-natural-sway
It’s a pretty new field of forestry research. I’m a horticulturist so it’s way outside of my expertise and I don’t know much about it.
This graph was just comparing the two accelerometers on two similar 10m-tall trees. It only measured for a week and I used an arbitrary hour’s data to generate the graph, which just shows the sensitivity of the accelerometers as they undergo similar windloads with similar canopies. When they do install this new chip, they’ll get a cleaner reading of the really tiny vibrational shifts that correlate with something.
edit: Potentially though the factors involved could be the biomass of the leaves, the moisture levels in the stem/roots/soil, damage from a pathogen/weather event, temperature stress, and/or changes in the biochemistry. The PI thinks it’s some combination but that those have distinct enough changes to be isolated and flagged by some algorithm. Eventually that could become like a heatmap of drought, pest presence, microclimates, or seasonal changes as clusters of trees vibrate in the same way.