MSU Affiliations
Forest and Wildlife Research Center
Item Type
Research Data
Abstract
Poplars (Populus spp.) and their hybrids are increasingly being grown in coppice production to generate bioenergy feedstocks at frequent intervals. Allometric equations are required to predict aboveground biomass (AGB) of coppiced individuals with minimal field measurements. Likewise, remote sensing tools like LiDAR (light detection and ranging) can be used if models are available to predict AGB from point cloud data. Therefore, this study sought to develop equations to predict dry woody AGB from field measurements and LiDAR data from coppiced poplar field trials containing eastern cottonwood (P. deltoides) and hybrid poplar taxa. We found that taxa-specific allometric models containing the summed basal area of the three largest stems in the coppice provided the best predictive model with stem height and stem count failing to provide additional explanatory power. The best predictive LiDAR-based model was independent of taxa, but had slightly lower adjusted R2 and higher RMSE than the allometric model. It contained four parameters including crown volume, leaf area index, variance of height returns, and density metric 9 (the proportion of points in the highest point interval). In total, these models can be used to quickly and efficiently estimate dry woody AGB of Populus coppice systems for bioenergy feedstock production.
Publication Date
10-27-2025
Recommended Citation
Renninger, Heidi and Poudel, Krishna P., "Allometric and mobile terrestrial LiDAR modeling of above-ground woody biomass of Populus in coppice production" (2025). Research Data. 4.
https://scholarsjunction.msstate.edu/research-data/4
