Reliability of LiDAR derived predictors of forest inventory attributes: A case study with Norway spruce

To increase the application domain (re-use) of LiDAR-based models the random replication effects in the predictor(s) must be considered. We quantify these effects in a linear predictor (X) of four forest inventory attributes (Lorey's height HT, basal area BA, volume VOL, and stem density TPH) with LiDAR data acquired over 40 spruce-dominated large plots in southeastern Norway. A grid-based random thinning of the raw multi-echo LiDAR data, to five target densities between 0.25 m^− 2 and 2.0 m^− 2, generated 100 replications with each density. A DTM was estimated for each replicate and target pulse density. The four linear predictors were constructed from two indicators of canopy density and a posited average effect of a power-transform of echoes classified as canopy returns. Replication variance varied significantly among plots but the reliability ratio of X was high (≥ 0.92) for HT, BA and VOL but lower for TPH, especially at low pulse densities. Reliability ratios increased with pulse density. Replication variance attenuated the linear regression coefficients by about 10% and inflated the residual variance by 3-6%. A proposed calibration was effective in reducing the impact of replication effects. A proposed bootstrap procedure can be used in practice to obtain good approximations of the replication variance. With echo-densities of approximately 1 m^− 2 or higher the replication effects do not warrant the effort of a calibration.