Estimation of Forest Stand Parameters from Airborne Laser Scanning Using Calibrated Plot Databases

摘要

Airborne laser scanning (also known as LiDAR) is rapidly turning into a popular method for operational forest assessment. In the course of this development, classic regression methods have been replaced by nonparametric or Bayesian methods. Accurate estimates with such methods require a large collection of several hundreds of sample plots, which is costly. We propose replacing most of these sample plots by ones collected during earlier missions in different, but similar, forests. However, using such replacement plots requires resolution of two problems. The first one is overcoming differences between different LiDAR scanners, scanning parameters, and scanning conditions, and the second one is avoiding bias due to use of alien plots. We propose a method that resolves both problems and uses only approximately 50 new plots. The method is tested for accuracy in total forest parameters and shown to provide satisfactory estimates for total forest parameters and to be robust against random variation in the selection of the few new plots. [PUBLICATION ABSTRACT]