Influence of Sampling Design Parameters on Biomass Predictions Derived from Airborne LiDAR Data

Abstract

This study investigated the influence of sampling design parameters on biomass prediction accuracy obtained from airborne lidar data. A one-factor-at-a-time and a global sensitivity analyses were applied to identify the parameters most impacting model accuracy. We focused on several lidar and field survey parameters that can be easily controlled by users. In this pine plantations study site, a decrease in pulse density (4 to 0.5 pulse/m2) led to a small decrease in prediction accuracy (−3%). However, variability in the number of field plots, positioning accuracy, and plot size, significantly impacted model performance. To obtain a robust model, a minimum of 40 field plots, along with field plot position accuracy of 5 m or lower, and field plot radius exceeding 13 m are recommended. The minimum diameter at breast height (DBH) threshold and the choice of the allometric biomass equation were found to have lesser impacts on model accuracy. In addition, accuracies of DBH and tree height measurements were respectively shown to have a minor and negligible contribution to the prediction error. Significant field measurement costs will still be needed to ensure good-quality models for biomass mapping. However, by reducing pulse density, cost savings can be made on lidar acquisition.