Inventory of close-to-nature forest stands using terrestrial mobile laser scanning

Abstract

• Hand-held mobile LiDAR data of a close-to-nature forest in the leaf-on season. • Fully automatic processing using modified mean-shift and RANSAC algorithms. • 96.5 % detection and 3.5 cm DBH RMSE for trees with DBHs from 4 to 80 cm. • A comparison of different levels of low-intensity filtering is performed. • A walking trajectory consisting of concentric circles is recommended. In this work, we evaluated mobile laser scanning (MLS) technology for the acquisition of individual tree data in a close-to-natural forest structure. The data was collected during the leaf-on period in a single-tree selection forest stand. Individual tree positions and diameters at breast height (DBHs) were acquired in an automatic process. MLS data was collected on 1000 m 2 circular inventory plots. We compared three trajectories consisting of the plot perimeter and 1), two perpendicular lines, 2) an additional concentric circle, and 3) four parallel lines. We compared two algorithms for tree segmentation: 1) a density-based approach and 2) a modified mean-shift algorithm. The diameters were estimated using a modified random sample consensus (RANSAC) algorithm. We tested a series of intensity thresholds for filtering returns from green vegetation. We achieved the best results with an intensity threshold of 0.7 quantile of point intensities, and mean-shift segmentation, resulting in the correct identification of trees representing 96.5 % of basal area and an overestimation of 6.8 % of the total basal area. The algorithm omitted mainly small trees and trees at close distances. False detections mainly comprised unvalidated detections of real trees that were not field-measured as their diameter did not exceed the registration limit, or were caused by point structures representing leaves and understory vegetation. Diameters were estimated with a mean error of 0.03 cm and a root mean square error of 3.5 cm. A joinpoint regression model demonstrated that for small trees (<9 cm) the diameters were generally overestimated. Diameters above 12 cm were underestimated consistently by 1 cm. The trajectory comprising two concentric circles was the most efficient.