Investigating the Feasibility of Multi-Scan Terrestrial Laser Scanning to Characterize Tree Communities in Southern Boreal Forests

Abstract

Terrestrial laser scanning (TLS) has proven to accurately represent individual trees, while the use of TLS for plot-level forest characterization has been studied less. We used 91 sample plots to assess the feasibility of TLS in estimating plot-level forest inventory attributes, namely the stem number (N), basal area (G), and volume (V) as well as the basal area weighed mean diameter (Dg) and height (Hg). The effect of the sample plot size was investigated by using different-sized sample plots with a fixed scan set-up to also observe possible differences in the quality of point clouds. The Gini coefficient was used to measure the variation in tree size distribution at the plot-level to investigate the relationship between stand heterogeneity and the performance of the TLS-based method. Higher performances in tree detection and forest attribute estimation were recorded for sample plots with a low degree of tree size variation. The TLS-based approach captured 95% of the variation in Hg and V, 85% of the variation in Dg and G, and 67% of the variation in N. By increasing the sample plot size, the tree detection rate was decreased, and the accuracy of the estimates, especially G and N, decreased. This study emphasizes the feasibility of TLS-based approaches in plot-level forest inventories in varying southern boreal forest conditions.

Multisensorial Close-Range Sensing Generates Benefits for Characterization of Managed Scots Pine (<em>Pinus sylvestris </em>L.) Stands

“…This finding was somewhat unexpected as different measurement geometries were assumed to also lead to improved tree detection as different trees were anticipated to be occluded in TLS and UAV data. Though, it should be noted that the sample plots used located in managed Scots pine stands and already the TLS-based tree detection rate was better than has been reported in most of the studies in boreal forests [21,49]. In more complex forests, use of multisensorial data should theoretically improve the tree detection rate and therefore estimation of TPH, G, Dg as well as Vmean.…”

“…Point cloud normalization was conducted separately for TLS and UAV point clouds using LAStools software [48]. TLS point clouds were normalized following a similar procedure presented in [49] whereas publicly available 2 m x 2 m digital terrain model (DTM) with an expected vertical accuracy of 30 cm (National Land Survey of Finland) was utilized when normalizing the UAV point clouds. The normalized datasets were then registered and merged using 3D rigid transformation where the transformation matrix was computed based on the coordinates of tie points manually extracted for each sample plot.…”

“…2b). The classification was based on a general assumption that stem points have more planar, vertical and cylindrical characteristics than points representing branches and foliage [30,49]. These characteristics were distinguished by applying surface normal filtering, point cloud clustering, and Random Sample Consensus (RANSAC)-cylinder filtering on horizontal point cloud slices.…”

Assessing the effects of thinning on stem growth allocation of individual Scots pine trees

“…Point clouds from TLS and UAV were combined for characterizing tree height and crown as comprehensively as possible. Individual tree detection and characterization from the combined point clouds followed the methodology presented by Yrttimaa et al (2020) and Yrttimaa et al (2019), respectively. The process included four steps: 1) point cloud normalization, 2) tree segmentation 3) point cloud classification, and 4) generation of tree-level attributes.…”

Detailed point cloud data on stem size and shape of Scots pine trees

“…Point clouds from TLS and UAV were combined for characterizing Scots pine trees as comprehensively as possible. Individual tree detection and characterization from the combined point clouds followed the methodology presented by Yrttimaa et al (2020) and Yrttimaa et al (2019), respectively. The process included three steps: 1) point cloud normalization, 2) tree segmentation, and 3) point cloud classification.…”