Growth-Competition-Based Stem Diameter and Volume Modeling for Tree-Level Forest Inventory Using Airborne LiDAR Data

An individual tree within a forest stand will have its height and diameter growth restricted by the influence of neighboring trees. This is because trees in close proximity compete for resources and space to enable growth. In this paper, the position of trees, tree height (LH), tree crown radius (LCR), and growth competition index (LCI) were extracted from a light-detection-and-ranging (LiDAR)-based rasterized canopy height model using the multilevel morphological active-contour algorithm. The diameter and volume of individual trees are tested and validated to be an exponential function of those LiDAR-derived tree parameters. The best LiDAR-based diameter estimation model and volume estimation model were tested as significant with an $R^{2}$ value of 0.84 and 0.9 and evaluated with an estimation bias of 8.7 cm and 0.91 $\hbox {m}^{3}$ , respectively. Results also showed that LH and LCR are positively related to the LiDAR-derived diameter at breast height (DBH) and the LiDAR-derived volume of individual trees in a forest stand, whereas LCI is negatively related. The proposed algorithm of individual tree volume estimation was further applied to predict the volume of three sample plots in mountainous forest stands. It was found that the LVM could be used to predict an acceptable volume estimate of old-aged forest stands. The estimation bias, i.e., percentage RMSE (RMSE%), is averaged at around 4% using the LiDAR metrics $\ln\hbox {LH}$ , LCI, and LCR, whereas the RMSE% increases to 50% if only $\ln\hbox {LH}$ is applied. Results suggest that LCI is an important regulation factor in the estimation of forest volume stocks using LiDAR remote sensing.