Comparing regression methods in estimation of biophysical properties of forest stands from two different inventories using laser scanner data

Mean tree height, dominant height, mean diameter, stem number, basal area, and timber volume of 233 field sample plots were estimated from various canopy height and canopy density metrics— derived by means of a small-footprint laser scanner over young and mature forest stands— using ordinary least-squares (OLS) regression analysis, seemingly unrelated regression (SUR), and partial least-squares (PLS) regression. The sample plots were distributed systematically throughout two separate inventory areas with size 1000 and 6500 ha, respectively. The plots were divided into three predefined strata. Separate regression models were estimated for each inventory as well as common models utilizing the plots of both inventories simultaneously. In the models estimated by combining data from the two areas, the statistical effect of inventory was found to be significant (p<0.05) in the mean height models only. A total of 115 test stands and plots with size 0.3–11.7 ha were used to validate the estimated regression models. The bias and standard deviations (parenthesized) of the differences between predicted and ground reference values of mean height, dominant height, mean diameter, stem number, basal area, and volume were −5.5% to 4.7% (3.1–7.3%), −6.0% to 0.4% (2.9–8.2%), −0.2% to 7.9% (5.5–15.8%), −21.3% to 12.5% (13.4–29.3%), −7.3% to 8.4% (7.1–13.6%), and −3.9% to 10.1% (8.3–14.9%), respectively. It was revealed that only minor discrepancies occurred between the three investigated estimation techniques. None of the techniques provided predicted values that were superior to the other techniques over all combinations of strata and variables.