Impact of Beam Diameter and Scanning Approach on Point Cloud Quality of Terrestrial Laser Scanning in Forests

Abegg, Meinrad; Boesch, Ruedi; Schaepman, Michael E.; Morsdorf, Felix

doi:10.1109/tgrs.2020.3037763

Cited by 18 publications

(27 citation statements)

References 19 publications

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“…Previous in situ, ex situ and in silico tests had identified problems in retrieving accurate estimates of volume and diameters of small-sized branches (Hackenberg et al 2015b;Lau et al 2019;Abegg et al 2020). This was largely confirmed in the current study, which featured 265 manually measured branch diameters.…”

Section: Tls-derived Tree Volume Is Overestimated In Smaller Branchessupporting

confidence: 81%

“…Scattering errors are an important source of error around small branches that happen synchronously with alignment errors (Abegg et al 2020). At the top of the canopy of a 20 m tall tree, the average beam spot size of the RIEGL VZ-400i is about 14 mm.…”

Section: Branch Diameter Overestimation Due To Scattering Errorsmentioning

confidence: 99%

“…A return can be registered even when the centroid of the cross-section of the laser beam is not on the surface of the intercepting object. The main drivers are limitations to the 3D precision and accuracy of the sensor and laser beams that intersect either only partially or with multiple objects at once (Abegg et al 2020). Scattering is attributed to non-zero beam size which increases with ranging distance as a result of beam divergence.…”

Section: Introductionmentioning

confidence: 99%

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Volumetric overestimation of small branches in 3D reconstructions of <i>Fraxinus excelsior</i>

Demol¹,

Wilkes²,

Raumonen³

et al. 2022

Silva Fenn.

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Terrestrial laser scanning (TLS) has been applied to estimate forest wood volume based on detailed 3D tree reconstructions from point cloud data. However, sources of uncertainties in the point cloud data (alignment and scattering errors, occlusion, foliage...) and the reconstruction algorithm type and parameterisation are known to affect the reconstruction, especially around finer branches. To better understand the impacts of these uncertainties on the accuracy of TLS-derived woody volume, high-quality TLS scans were collected in leaf-off conditions prior to destructive harvesting of two forest-grown common ash trees ( L.; diameter at breast height ~28 cm, woody volume of 732 and 868 L). We manually measured branch diameters at 265 locations in these trees. Estimates of branch diameters and tree volume from Quantitative Structure Models (QSM) were compared with these manual measurements. The accuracy of QSM branch diameter estimates decreased with smaller branch diameters. Tree woody volume was overestimated (+336 L and +392 L) in both trees. Branches measuring < 5 cm in diameter accounted for 80% and 83% of this overestimation respectively. Filtering for scattering errors or improved coregistration approximately halved the overestimation. Range filtering and modified scanning layouts had mixed effects. The small branch overestimations originated primarily in limitations in scanner characteristics and coregistration errors rather than suboptimal QSM parameterisation. For TLS-derived estimates of tree volume, a higher quality point cloud allows smaller branches to be accurately reconstructed. Additional experiments need to elucidate if these results can be generalised beyond the setup of this study.Fraxinus excelsior

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Section: Tls-derived Tree Volume Is Overestimated In Smaller Branchessupporting

confidence: 81%

Section: Branch Diameter Overestimation Due To Scattering Errorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Volumetric overestimation of small branches in 3D reconstructions of <i>Fraxinus excelsior</i>

Demol¹,

Wilkes²,

Raumonen³

et al. 2022

Silva Fenn.

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“…1. the quality of the TLS point cloud that depends on scanning conditions and/or scanner technical characteristics (variations in point density, occlusion, noise caused by wind or "mixed pixel effect", point deviation from the object surface, and low detection rate of small objects [28,68]); 2.…”

Section: Real-life Examplementioning

confidence: 99%

“…In addition, using noise filtering will help to improve scan quality by reducing the influence of the "mixed pixel effect". However, one should acknowledge that TLS have inherent limitations [68] and that, even when applying all the previous recommendations, TLS point clouds still does not represent a perfect image of a tree. QSM and skeletonization methods are a very active field of research, and one can expect that the development of new methods will help to reduce limitation 2.…”

Section: Real-life Examplementioning

confidence: 99%

Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth

Lecigne

Delagrange

Taugourdeau³

2021

Forests

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The development of terrestrial laser scanning (TLS) has opened new avenues in the study of trees. Although TLS provides valuable information on structural elements, fine-scale analysis, e.g., at the annual shoots (AS) scale, is currently not possible. We present a new model to segment and classify AS from tree skeletons into a finite set of “physiological ages” (i.e., state of specialization and physiological age (PA)). When testing the model against perfect data, 90% of AS year and 99% of AS physiological ages were correctly extracted. AS length-estimated errors varied between 0.39 cm and 2.57 cm depending on the PA. When applying the model to tree reconstructions using real-life simulated TLS data, 50% of the AS and 77% of the total tree length are reconstructed. Using an architectural automaton to deal with non-reconstructed short axes, errors associated with AS number and length were reduced to 5% and 12%, respectively. Finally, the model was applied to real trees and was consistent with previous findings obtained from manual measurements in a similar context. This new method could be used for determining tree phenotype or for analyzing tree architecture.

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Remote Sensing Data and Methods in NFI

Sims

2022

Managing Forest Ecosystems

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Impact of Beam Diameter and Scanning Approach on Point Cloud Quality of Terrestrial Laser Scanning in Forests

Cited by 18 publications

References 19 publications

Volumetric overestimation of small branches in 3D reconstructions of <i>Fraxinus excelsior</i>

Volumetric overestimation of small branches in 3D reconstructions of <i>Fraxinus excelsior</i>

Annual Shoot Segmentation and Physiological Age Classification from TLS Data in Trees with Acrotonic Growth

Remote Sensing Data and Methods in NFI

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