Tree stem diameter estimation using inexpensive UAV photogrammetric data and Monte Carlo methods

Abstract

Accurate diameter estimation from point cloud data allows for characterizing stem volume and shape without resorting to destructive methods. Typically, circles are fitted at various stem heights using statistical techniques. However, these techniques are susceptible to noise and occlusion in the point cloud, often caused by obstacles or weather phenomena. This susceptibility reduces the feasibility of applying such methods to point clouds captured by low-cost sensors, which tend to be less precise and noisier. Photogrammetry, however, can be used together with consumer-grade cameras and inexpensive UAVs to generate high-quality point clouds from under-canopy data. This study presents MACiF (Morphology-Aware Circle Fit), a novel method to accurately estimate diameters at various heights from noisy point clouds. Our approach uses robust statistical methods and Monte Carlo simulation to filter the point cloud. We also leverage how stems vary gradually to iteratively correct erroneous estimates. This iterative correction enables estimating diameters with an error lower than -3.34 cm, even when data quality limits the use of other methods. These results support the use of undercanopy low-cost photogrammetry as a viable source of data for automatic stem characterization.