(1) This study focused on the derivation of basic stand characteristics from airborne laser scanning (ALS) data, aiming to elucidate which characteristics (mean height and diameter, dominant height and diameter) are best approximated by the variables obtained using ALS data. The height of trees of different species in four permanent plots located in the Slovak Republic was derived from the normalised digital surface model (nDSM) representing the canopy surface, using an automatic approach to identify local maxima (individual treetops). Tree identification was carried out using four different spatial resolutions of the nDSM (0.5 m, 1.0 m, 1.5 m, and 2.0 m) and the number of trees identified was compared with reference data obtained from field measurements. The highest percentage of tree detection (69-75%) was observed at the spatial resolutions of 1.0 and 1.5 m. Absolute differences of tree height between reference and ALS datasets ranged from 0 to 36% at all spatial resolutions. The smallest difference in mean height was obtained using the higher spatial resolution (0.5 m), while the smallest difference in the dominant height of the relative number of thickest trees (h10% and h20%) was observed using the lower spatial resolution (2 m). The same trends also apply to diameters. The average errors at resolution of 1.0 and 1.5 m was 8.7%, 5.9% and 9.7% for mean height, h20% and h10%, respectively. ALS-derived diameters (obtained using regression models from reference data and ALS-derived individual height as predictor) showed absolute errors in the range 0-48% at all spatial resolutions. The deviation in mean diameter at a resolution of 0.5 m ranged from -12.1% to 15.3%.

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In the work, a fully automatic approach for vegetation delineation using ALS data is presented. Nowadays, in Slovakia, aerial images and satellite scenes are used for this purpose. For vegetation identification, the measurement of local transparency and roughness directly in 3D point cloud was used. The aim was the identification of groups of trees with area bigger than 0.1 ha and individual trees. On the experimental area, 33 polygons representing groups of trees and 120 individual trees were identified. For groups of trees the accuracy of identification was 100%. For comparison, an area with reference polygons, which were manually vectorised by the operator on the orthophotos with spatial resolution 30 cm, was used. The average difference in the area was –0.26%, with standard deviation ±8.17%. The distance of borders of reference polygons and polygons derived from ALS data was also compared, average distance for border parts that fall inside the reference polygons was 2.24 m with standard deviation of ±2.8 m. The average distance for borders parts that fall outside of the reference polygons was 1.84 m with standard deviation ±2.04 m. The accuracy of individual trees identification was 98%.