Influence of volume and height prediction from LiDAR on prediction of basal area, diameter, and density in a system of equations approach
University of New Brunswick
Light detection and ranging (LiDAR) is used to estimate tree, stand, and forest characteristics across large geographic areas. If used properly, LiDAR can simplify forest inventory processes. Understanding how well LiDAR can predict different expressions of stand averages and how prediction errors impact accuracies of inventory estimates is key to refining LiDAR-derived inventory processes. In this study, we used 37 different expressions of stand height and explored how each height measure impacted volume, basal area, quadratic mean diameter, and density estimation in a system of equations approach. We examined performance using field data only, using stand height predicted from LIDAR, and using stand height and volume predicted from LIDAR. For volume and density estimates, basal area weighted (size-biased) moments of the height distribution were better estimators. For basal area and quadratic mean diameter, largest tree averages of height were better measures. Performance of the systems of equations was still acceptable when only the height measure was predicted from LiDAR; however, when both height and volume were predicted, errors were quite large. All three data sources (field data, predicted height only, and predicted height and volume) produced very poor estimates of quadratic mean diameter and density. In the original system of equations formulation, stand type (species composition) was incorporated as a random effect. This was not done here and may have affected the results. Understanding how stand structure influences both the system of equations and the LiDAR point cloud data is necessary to design better systems equations and better stand structural measures. This study is an important first step in that process.