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

Abstract

(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%.

Generating Tree-Lists by Fusing Individual Tree Detection and Nearest Neighbor Imputation Using Airborne LiDAR Data

“…It was found that the number of detected trees decreased as the resolution of CHM decreased (Stereńczak et al, 2008), and this was also observed in our work, excluding height cutoffs p10 and p20. Smreček et al (2018) showed very similar results to ours for SPH estimation based on ITD. At the highest resolution (0.5 m), the number of trees identified was hugely overestimated; the number of trees identified decreased as the CHM resolution decreased from 0.5 m to 2.0 m, as was the case in our study.…”

“…The optimal resolutions for tree identification were 1.0 and 1.5 m depending on the sample plot. Smreček et al (2018) claimed that this was because the CHM with 0.5 m resolution was too detailed. We observed many estimated trees from ITD with extreme small areas compared to their estimated heights.…”

A Comparison of Two Tree Detection Methods for Estimation of Forest Stand and Ecological Variables from Airborne LiDAR Data in Central European Forests

“…Vauhkonen et al [42] stated that the accuracy of tree detection in Scandinavia and Central Europe forests should be around 70%. Regarding algorithms used in this study, the accuracy of tree detection is found within the interval of 52%-84% according to the results from this study ( Table 2) and previous research for the same study area [26,27]. However, the commission rate of the raster-based method may exceed 45%, while the commission rate of multisource-based method is usually within 8%.…”

“…With respect to other studies (e.g., [37]), negative biases are achievable with higher probability and they are basically expected for the ITD approach; if some type of correction is not used and the fundamental requirement is also a low rate of commission error. Based on results from this study ( Table 2) as well as previous research [24,27], however, the raster-based method provides relatively many false positive trees. In this context, results provided by the multisource-based method could be considered more relevant and, ultimately, more accurate if some type of correction is not used.…”

“…of Geodesy and Geoinformation at the University of Technology in Vienna, Austria). The process of tree identification was carried out at input spatial resolutions of 1.5 m [27]. At first, the "cell module" was used for the derivation of models through accumulating the laser heights.…”