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.

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There is a limited understanding of how forest structure affects the performance of methods based on terrestrial laser scanning (TLS) to derive tree and forest structural attributes. We aim to improve the understanding of how different forest management activities that shape tree size distributions affect the TLS-based forest characterization accuracy in managed Scots pine (Pinus sylvestris L.) stands. For that purpose, we investigated 27 sample plots consisting of three different thinning types, two thinning intensities as well as control plots without any treatments. Multi-scan TLS point clouds were collected from the sample plots, and a point cloud processing algorithms were used to segment individual trees and classify the segmented point clouds into stem and crown points that were further used to measure tree attributes. Finally, the forest structural attributes were aggregated from the derived tree attributes. With the TLS-based forest characterization, almost 100% completeness in tree detection, 0.7 cm (3.4%) root-mean-square-error (RMSE) in diameter-at-breast-height measurements, 0.9-1.4 m (4.5-7.3%) RMSE in tree height measurements, and less than 6% relative RMSE in estimates of forest structural attributes (i.e. mean basal area, number of trees per hectare, mean volume, basal-area weighted mean diameter and - height) were obtained depending on the applied thinning type. Thinnings decrease variation in horizontal and vertical forest structure which favoured TLS-based tree detection and tree height measurement, enabling reliable estimates for forest structural attributes. Considerably lower performance was recorded for control plots without any treatments. Thinning intensity was noticed to affect more on the accuracy of TLS-based forest characterization than thinning type. Number of trees per hectare and the proportion of suppressed trees were recognized as the main factors affecting the accuracy of TLS-based forest characterization. The more variation there was in the tree size distribution, the more challenging it was for the TLS-based method to capture all the trees and measure the tree and forest structural attributes. In general, consistent and reliable tree and forest attributes can be expected when using TLS for characterizing managed boreal forests.

Forests are the largest terrestrial ecosystem covering one third of the earth's surface area (Roxburgh and Noble, 2013), and they provide a range of services such as carbon uptake (Hardiman et al., 2011), productivity (Puettmann et al., 2015), biodiversity (Fedrowitz et al., 2014), and resilience (Messier et al., 2013). Processes of growth and regeneration are closely related to these services also linking them with forest structure (von Gadow et al., 2012). The current forest structure is a result of tree and stand dynamics affected

Terrestrial laser scanning (TLS) has been adopted as a feasible technique to digitize trees and forest stands, providing accurate information on tree and forest structural attributes. However, there is limited understanding on how a variety of forest structural changes can be quantified using TLS in boreal forest conditions. In this study, we assessed the accuracy and feasibility of TLS in quantifying changes in the structure of boreal forests. We collected TLS data and field reference from 37 sample plots in 2014 (T1) and 2019 (T2). Tree stems typically have planar, vertical, and cylindrical characteristics in a point cloud, and thus we applied surface normal filtering, point cloud clustering, and RANSAC-cylinder filtering to identify these geometries and to characterize trees and forest stands at both time points. The results strengthened the existing knowledge that TLS has the capacity to characterize trees and forest stands in space and showed that TLS could characterize structural changes in time in boreal forest conditions. Root-mean-square-errors (RMSEs) in the estimates for changes in the tree attributes were 0.99–1.22 cm for diameter at breast height (Δdbh), 44.14–55.49 cm2 for basal area (Δg), and 1.91–4.85 m for tree height (Δh). In general, tree attributes were estimated more accurately for Scots pine trees, followed by Norway spruce and broadleaved trees. At the forest stand level, an RMSE of 0.60–1.13 cm was recorded for changes in basal area-weighted mean diameter (ΔDg), 0.81–2.26 m for changes in basal area-weighted mean height (ΔHg), 1.40–2.34 m2/ha for changes in mean basal area (ΔG), and 74–193 n/ha for changes in the number of trees per hectare (ΔTPH). The plot-level accuracy was higher in Scots pine-dominated sample plots than in Norway spruce-dominated and mixed-species sample plots. TLS-derived tree and forest structural attributes at time points T1 and T2 differed significantly from each other (p < 0.05). If there was an increase or decrease in dbh, g, h, height of the crown base, crown ratio, Dg, Hg, or G recorded in the field, a similar outcome was achieved by using TLS. Our results provided new information on the feasibility of TLS for the purposes of forest ecosystem growth monitoring.

In this review, we summarize the current state-of-the-art in the utilization of close-range sensing in forest monitoring. We include technologies, such as terrestrial and mobile laser scanning as well as unmanned aerial vehicles, which are mainly used for collecting detailed information from single trees, forest patches or small forested landscapes. Based on the current published scientific literature, the capacity to characterize changes in forest ecosystems using close-range sensing has clearly been recognized. Forest growth has been the most investigated cause for changes and terrestrial laser scanner the most applied sensor for capturing forest structural changes. Unmanned aerial vehicles, on the other hand, have been used to acquire aerial imagery for detecting tree height growth and monitoring forest health. Mobile laser scanning has not yet been used in forest change monitoring except for a few early investigations. Considering the length of the forest growth process, investigated time spans have been rather short, less than 10 years. In addition, data from only two time points have been used in many of the studies, which has further been limiting the capability of understanding dynamics related to forest growth. In general, method development and quantification of changes have been the main interests so far regardless of the driver of change. This shows that the close-range remote sensing community has just started to explore the time dimension and its possibilities for forest characterization.