Forest aboveground biomass (AGB) is an important variable in assessing carbon stock or ecosystem functioning, as well as for forest management. Among methods of forest AGB estimation laser scanning attracts attention because it allows for detailed measurements of forest structure. Here we evaluated variables that influence forest AGB estimation from airborne laser scanning (ALS), specifically characteristics of ALS inputs and of a derived canopy height model (CHM), and role of allometric equations (local vs. global models) relating tree height, stem diameter (DBH), and crown radius. We used individual tree detection approach and analyzed forest inventory together with ALS data acquired for 11 stream catchments with dominant Norway spruce forest cover in the Czech Republic. Results showed that the ALS input point densities (4–18 pt/m2) did not influence individual tree detection rates. Spatial resolution of the input CHM rasters had a greater impact, resulting in higher detection rates for CHMs with pixel size 0.5 m than 1.0 m for all tree height categories. In total 12 scenarios with different allometric equations for estimating stem DBH from ALS-derived tree height were used in empirical models for AGB estimation. Global DBH models tend to underestimate AGB in young stands and overestimate AGB in mature stands. Using different allometric equations can yield uncertainty in AGB estimates of between 16 and 84 tons per hectare, which in relative values corresponds to between 6% and 37% of the mean AGB per catchment. Therefore, allometric equations (mainly for DBH estimation) should be applied with care and we recommend, if possible, to establish one’s own site-specific models. If that is not feasible, the global allometric models developed here, from a broad variety of spruce forest sites, can be potentially applicable for the Central European region.

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This study presents a method for three-dimensional (3D) reconstruction of forest tree species that are, for instance, required for simulations of 3D canopies in radiative transfer modelling. We selected three forest species of different architecture: Norway spruce (Picea abies) and European beech (Fagus sylvatica), representatives of European production forests, and white peppermint (Eucalyptus pulchella), a common forest species of Tasmania. Each species has a specific crown structure and foliage distribution. Our algorithm for 3D model construction of a single tree is based on terrestrial laser scanning (TLS) and ancillary field measurements of leaf angle distribution, percentage of current-year and older leaves, and other parameters that could not be derived from TLS data. The algorithm comprises four main steps: i) segmentation of a TLS tree point cloud separating wooden parts from foliage, ii) reconstruction of wooden parts (trunks and branches) from TLS data, iii) biologically genuine distribution of foliage within the tree crown, and iv) separation of foliage into two age categories (for spruce trees only). The reconstructed 3D models of the tree species were used to build virtual forest scenes in the DART model and to simulate canopy optical signals, specifically: angularly anisotropic top-of-canopy reflectance (for retrieval of leaf biochemical compounds from nadir canopy reflectance signatures captured in airborne imaging spectroscopy data) and solar-induced chlorophyll fluorescence signal (for experimentally unfeasible sensitivity analyses).

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