Assessment of understory vegetation in a plantation forest of the southeastern United States using terrestrial laser scanning.

Forests of the southeastern United States are home to large timber industries with substantial contributions to global round wood production and paper products. Despite the success of plantations and the large timber industries in this area, pine growth remains constrained due to the competition between planted pine and the species in the understory. Moreover, effective control of this interspecies competition had shown a significant two- to four-times increase in stand productivity. Thus, this study aims to evaluate the use of laser scanning derived data from Terrestrial Laser Scanner (TLS) to assess understory vegetation biomass, as conventional methods utilizing optical imagery have yet to be effective in quantifying and mapping evergreen understory in the southeastern coastal forests of the United States. For this study, we utilized TLS to scan the entire forest profile within 60 sample plots of an operational loblolly pine plantation in Nassau County, Florida, and collected understory biomass data through destructive sampling. We compared three TLS-based volume estimation methods for predicting understory biomass and applied the Adaptive Least Absolute Shrinkage and Selection Operator (ALASSO) regression method to derive an optimal model by integrating the most efficient volume estimation methods and other TLS-derived standard metrics. Our study identifies the 20th percentile of the echo height and a 3D volume metric based on mean height and understory cover as the most significant explanatory variables for the optimal model. The model exhibits high accuracy, with Adjusted R-squared (Adj. R²) of 0.80 and Root Mean Square Error (RMSE) of 234.8 g per square meter (g/m²). Additionally, the mean height and understory-based volume estimation method outperformed other methods, such as voxel count and three dimensional (3D) alpha hall-based method, with Adj. R² of 0.79, 0.47, and 0.57, and RMSE values of 288, 448.6, and 413 g/m², respectively, when used as a single variable in the model. The resulting model successfully predicted and quantified understory vegetation, showcasing TLS's potential to accurately capture biomass variation, particularly in evergreen-dominated pine plantation forests in the southeastern United States coastal regions. As a tool for monitoring understory, TLS can be used to aid plantation forest managers in identifying areas that require control measures for enhanced management practices. • Terrestrial Laser Scanning (TLS) derived data were used to predict evergreen understory biomass in pine plantation forests. • An efficient Model (R² adj = 0.80) based on TSL-derived variables was proposed to predict understory biomass. • The mean height and understory cover-based volume estimation method outperformed voxel and 3D alpha hall-based 3D methods. • TLS data demonstrates the potential for accurately quantifying biomass variation in the forest understory. [ABSTRACT FROM AUTHOR]