Integration of Terrestrial Laser Scanning and field measurements data for tree stem volume estimation: Exploring parametric and non-parametric modeling approaches

Abstract

Terrestrial laser scanning (TLS) has emerged as a powerful tool for acquiring detailed three-dimensional information about tree species. This study focuses on the development of models for tree volume estimation using TLS data for even aged Fagus sylvatica L. stands located in the western part of the Southern Carpathians, Romania. Both parametric and non-parametric modeling approaches were explored, leveraging variables extracted from TLS point clouds such as diameter at breast height (DBH), height, crown radius, and other relevant crown and height parameters. Reference data were collected through high-precision field measurements across 76 circular Permanent Sample Areas (PSA) spanning 500 m2 each. A multi-scan approach was implemented for TLS data collection, involving four scanning stations within each PSA. Concurrently, parametric (regression equations) and non-parametric (Random Forest-RF) models were applied, leveraging all TLS-derived variables to explore potential enhancements in volume estimation accuracy. Among the parametric models, the most effective performer was the one featuring solely DBH as an input variable. The RF non-parametric model yielded more accurate stem volume estimates (RMSE=1.52 m3*0.1ha-1; RRMSE=3.62%; MAE=1.22 m3*0.1ha-1) compared to the best-performing regression model (RMSE=5.24 m3*0.1ha-1; RRMSE=12.48%; MAE=4.28 m3*0.1ha-1). Both types of models identified DBH as the most important predictive variable, while the RF model also included height and crown related parameters among the variables of importance. Results demonstrate the effectiveness of the non-parametric RF model in providing accurate and robust estimates of tree stem volume within even aged European beech stands. The integration of these models in operational forestry can enhance precision in biomass estimation and forest resource management. Future studies should aim to validate these models across diverse forest ecosystems to further refine and enhance their applicability.