Forest stand height determination from low point density airborne laser scanning data in Roznava Forest enterprise zone (Slovakia)

The presented paper discusses the potential of low point density airborne laser scanning (ALS) data for use in forestry management. Scanning was carried out in the Rožnava Forest enterprise zone, Slovakia, with a mean laser point density of 1 point per 3 m2. Data were processed in SCOP++ using the hierarchic robust filtering technique. Two DTMs were created from airborne laser scanning (ALS) and contour data and one DSM was created using ALS data. For forest stand height, two normalised DSMs (nDSMs) were created by subtraction of the DSM and DTM. The forest stand heights derived from these nDSMs and the application of maximum and mean zonal functions were compared with those contained in the current Forest Management Plan (FMP). The forest stand heights derived from these data and the application of maxima and mean zonal functions were compared with those contained in the current Forest management plan. The use of the mean function and the contour-derived DTM resulted in forest stand height being underestimated by approximately 3% for stands of densities 0.9 and 1.0, and overestimated by 6% for a stand density of 0.8. Overestimation was significantly greater for lower forest stand densities: 81% for a stand density of 0.0 and 37% for a density of 0.4, with other discrepancies ranging between 15 and 30%. Although low point density ALS should be used carefully in the determination of other forest stand parameters, this low-cost method makes it useful as a control tool for felling, measurement of disaster areas and the detection of gross errors in the FMP data. Through determination of forest stand height, tree felling in three forest stands was identified. Because of big differences between the determined forest stand height and the heights obtained from the FMP, tree felling was verified on orthoimages

Determining basic forest stand characteristics using airborne laser scanning in mixed forest stands of Central Europe

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%