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

摘要

This study investigated the influence of sampling design parameters on biomass predictionaccuracy obtained from airborne lidar data. A one-factor-at-a-time and a global sensitivityanalyses were applied to identify the parameters most impacting model accuracy. Wefocused 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/m~2) led to asmall decrease in prediction accuracy (-3%). However, variability in the number of fieldplots, positioning accuracy, and plot size, significantly impacted model performance. Toobtain a robust model, a minimum of 40 field plots, along with field plot position accuracyof 5m or lower, and field plot radius exceeding 13m are recommended. The minimumdiameter at breast height (DBH) threshold and the choice of the allometric biomass equationwere found to have lesser impacts on model accuracy. In addition, accuracies of DBHand tree height measurements were respectively shown to have a minor and negligiblecontribution to the prediction error. Significant field measurement costs will still be neededto ensure good-quality models for biomass mapping. However, by reducing pulse density,cost savings can be made on lidar acquisition.