Counting marmots…
Forests play a crucial role in mitigating climate change by sequestering large amounts of carbon dioxide from the atmosphere, so it is critical to evaluate their capacity to store carbon over time. Existing research on forest carbon sequestration relies on field methods that do not allow for sampling in rugged terrain and have limited coverage areas. This study utilizes field measurements of trees to calibrate UAS remote sensing estimations of forest carbon, allowing us to rigorously scale up data collection to large areas. Diameter at breast height (DBH) measurements and tree increment cores were obtained from 34 Picea engelmannii (Engelmann Spruce) trees in the vicinity of the Rocky Mountain Biological Laboratory in Colorado. A generalized linear model successfully relates field measurements of DBH to UAS estimates of canopy size and stand. Further, we find evidence of a “lagged” correlation between canopy and stem growth where canopy growth more strongly correlates to stem growth in the following year rather than the current year. Through a better understanding of canopy growth and stem growth correlations we can estimate forest carbon exclusively through UAV data, reducing the necessity for ground measurements and allowing effective scaling-up of data collection to the landscape level.
Knowledge graph centered on Stem and canopy growth analysis in Picea Engelmann with 25 nodes and 64 connections. Top connected: Spruce, Picea engelmannii, Engelmann spruce, Abies lasiocarpa, Abies.
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