Predicting Individual Tree Diameter of Larch (<i>Larix olgensis</i>) from UAV-LiDAR Data Using Six Different Algorithms

Abstract

Individual tree detection is an increasing trend in LiDAR-based forest inventories. The locations, heights, and crown areas of the detected trees can be estimated rather directly from the LiDAR data by using the LiDAR-based canopy height model and segmentation methods to delineate the tree crowns. However, the most important tree variable is the diameter of the tree stem at the breast height (DBH) which can seldom be interpreted directly from the LiDAR data. Therefore, the use of individually detected trees in forest planning calculations requires predictions for the DBH. This study tested six methods for predicting the DBH from laser scanning data collected by an unmanned aerial vehicle from Larix olgensis plantations located in northeast China. The tested methods were the linear regression model (LM), a linear model with ridge regularization (LMR), support vector regression (SVR), random forest (RF), artificial neural network (ANN), and the k-nearest neighbors (KNN) method. Both tree-level and stand-level metrics derived from the LiDAR point cloud data (for instance percentiles of the height distribution of the echoes) were used as potential predictors of DBH. Compared to the LM, all other methods improved the accuracy of the predictions. On the other hand, all methods tended to underestimate the DBH of the largest trees, which could be due to the inability of the methods to sufficiently describe nonlinear relationships unless different transformations of the LiDAR metrics are used as predictors. The support vector regression was evaluated to be the best method for predicting individual tree diameters from LiDAR data. The benefits of the methods tested in this study can be expected to be the highest in the case of little prior knowledge on the relationships between the predicted variable and predictors, a high number of potential predictors, and strong mutual correlations among the potential predictors.

Keywords

• random forest
• artificial neural network
• k-nearest neighbors
• support vector regression
• ridge regulation
• machine learning