Growing stock estimation for alpine forests in Austria: a robust lidar-based approach

Abstract

The overall goal of this study was to describe a novel area-based semiempirical model for estimating growing stock from small-footprint light detection and ranging (lidar) data. The model assumes a linear relationship between growing stock and lidar-derived canopy volume that is stratified according to several canopy height classes to account for height dependent differences in canopy structure and nonlinear tree size-shape relationships. It was applied over a 128 km2 alpine area in Austria where operational forest inventory data and lidar data acquired in winter and summer were available. The analysis showed that the semiempirical model was quite robust against changes in laser point density and acquisition time. Further, it was found that the model performed as well as a widely used iterative regression method based on a multiplicative model. Both models reached a high coefficient of determination (R2 = 0.76–0.86) and a standard deviation of the residuals in the order of 20.4%–29.1%. Although it is less flexible than the multiplicative model, the advantages of the semiempirical model are its simplicity and the fact that its coefficients can be physically interpreted. These traits can be expected to enhance the applicability of the model in regions where high-quality inventory data are lacking.