Health assessment of plantations based on LiDAR canopy spatial structure parameters

The Yellow River Delta (YRD) has China's largest artificial <em>Robinia pseudoacacia</em> forest, which was planted in the late 1970s and suffered extensive dieback in the 1990s. The health grade of the <em>R.pseudoacacia</em> forest (named canopy vigor grade, CVG) could be achieved by using high-resolution images and canopy vigor indicators (CVIs). However, a previous study showed that there was no significant correlation between CVG and the field-estimated aboveground biomass (AGB) of <em>R.pseudoacacia</em> forest. Therefore, this study aims to construct forest health indicators (FHIs) based on canopy spatial structure parameters extracted from LiDAR. The FHIs included Weibull_α (the scale parameter of the Weibull density function that reflects the shape of the tree canopy), VCI (vertical complexity index), sdCC (the standard deviation of canopy cover), H₉₉ (the 99th percentile height) and cvLAD (the coefficient of variation of leaf area density), and could significantly distinguish three forest health grades (FHG) (<em>p</em> &lt; 0.05). The FHG was positively correlated with forest AGB (<em>r_s</em> = 0.51, <em>p</em> = 0.004), and the similarity value with CVG was 63.33%. The results of this study confirmed that the FHIs can reflect both canopy vigor and tree productivity, and distinguish forest health status without prior classification information.