Detecting and characterizing downed dead wood using terrestrial laser scanning

Yrttimaa, Tuomas; Saarinen, Ninni; Luoma, Ville; Tanhuanpää, Topi; Kankare, Ville; Liang, Xinlian; Hyyppä, Juha; Holopainen, Markus; Vastaranta, Mikko

doi:10.1016/j.isprsjprs.2019.03.007

Cited by 30 publications

(28 citation statements)

References 50 publications

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“…To ensure the clusters represented the cross-sections of trees, a cylinder filtering procedure was applied. The cylinder filtering procedure was based on RANSAC-(Random Sample Consensus) cylinder fitting and was similar to the one used in [28]: A cylinder was fitted into points in each cluster, and points on the surface of the fitted cylinder were considered as inliers. Points located outside the cylinder were considered as outliers and removed.…”

Section: Stage-one Tree Detectionmentioning

confidence: 99%

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

et al. 2019

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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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Section: Stage-one Tree Detectionmentioning

confidence: 99%

“…These are major advantages compared to conventional forest inventory methods. In addition to standing trees, TLS can provide information also on downed dead wood [27,28], which is an important structural forest component and a biodiversity indicator in boreal forests [29,30].…”

Section: Introductionmentioning

confidence: 99%

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

et al. 2019

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“…Conifer trees are typically evergreen, but leaves of deciduous trees emerge in the spring and fall in the autumn. Forest growth process is also linked to the decomposition process which affects the amount of dead wood (Yrttimaa et al 2019). Natural phenomena, such as insect damage, the spread of pathogens, wind damage, and forest fires, shape forests at different spatial and temporal scales.…”

Section: Contextmentioning

confidence: 99%

Monitoring Forests in Space and Time Using Close-Range Sensing

Vastaranta¹,

Saarinen²,

Yrttimaa³

et al. 2020

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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.

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“…Conifer trees are typically evergreen but leaves of deciduous trees emerge in the spring and fall in the autumn. Forest growth process is also linked to the decomposition process which affects the amount of dead wood [1]. Insect damage, the spread of pathogens, wind damage, and forest fires shape forests at different spatial and temporal scales.…”

Section: Introductionmentioning

confidence: 99%

“…Close-range sensing technologies, such as terrestrial laser scanning (TLS) provide state-of-the-art tools in characterizing forests. TLS is a powerful close-range sensing method for characterizing forests in three dimensions (3D) [1,[7][8][9][10]. Individual trees can be detected from a TLS point cloud by detecting circular shapes (e.g., [11,12]) or clusters of points (e.g., [13,14]), these two representing the most common tree detection methods implemented in forest applications [8].…”

Section: Introductionmentioning

confidence: 99%

Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning

et al. 2020

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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.

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Detecting and characterizing downed dead wood using terrestrial laser scanning

Cited by 30 publications

References 50 publications

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

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

Monitoring Forests in Space and Time Using Close-Range Sensing

Structural Changes in Boreal Forests Can Be Quantified Using Terrestrial Laser Scanning

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