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  • 1
    UID:
    b3kat_BV047908897
    Format: 1 Online-Ressource (31 Files)
    Content: Virtual laser scanning (VLS), the simulation of laser scanning in a computer environment, is as a useful tool for field campaign planning, acquisition optimisation, and development and sensitivity analyses of algorithms in various disciplines including forestry research. One key to meaningful VLS is a suitable 3D representation of the object of interest. For VLS of forests, the way trees are constructed influences both the performance and the realism of the simulations. In this contribution, we analyse how well VLS can reproduce scans of individual trees in a forest. Specifically, we examine how different voxel sizes used to create the virtual forest affect point cloud metrics (e.g. height percentiles) and tree metrics (e.g. tree height and crown base height) derived from simulated point clouds. The level of detail in the voxelisation is dependent on the voxel size, which usually influences the number of voxel cells of the model. A smaller voxel size (i.e., more voxels) increases the computational cost of laser scanning simulations but allows for more detail. We present a method that decouples voxel grid resolution from final voxel cube size by scaling voxels to smaller cubes, whose surface is proportional to estimated normalised local plant area density. Voxel models are created from terrestrial laser scanning point clouds and then virtually scanned in one airborne and one UAV-borne simulation scenario.
    Note: Gesehen am 22.02.2022 , This dataset includes - HELIOS++ data files to reproduce the simulations (forest (voxel) models, scene files and survey files) - Reference point clouds of the forest plots and of individual target trees - Simulated point clouds of the forest plots and of individual target trees - TLS point clouds of the target trees, based on which the trees were reconstructed for the simulations - Metrics computed from the reference and the simulated tree point clouds - Python scripts used for voxel processing, simulation output processing and metric computation - Examples of configuration files for AMAPVox (https://amap-dev.cirad.fr/projects/amapvox), the software for voxel-based plant area density estimation (2021-06-25)
    Additional Edition: Erscheint auch als Druck-Ausgabe
    Language: English
    Keywords: Waldbestand ; Fernerkundung ; Lidarsignal ; Forschungsdaten ; Datenbank
    URL: Volltext  (kostenfrei)
    URL: Volltext  (kostenfrei)
    Author information: Faßnacht, Fabian Ewald
    Author information: Höfle, Bernhard
    Library Location Call Number Volume/Issue/Year Availability
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  • 2
    UID:
    edochu_18452_25740
    Format: 1 Online-Ressource (12 Seiten)
    Content: This paper investigates different methods for quantifying thaw subsidence using terrestrial laser scanning (TLS) point clouds. Thaw subsidence is a slow (millimetre to centimetre per year) vertical displacement of the ground surface common in ice-rich permafrost-underlain landscapes. It is difficult to quantify thaw subsidence in tundra areas as they often lack stable reference frames. Also, there is no solid ground surface to serve as a basis for elevation measurements, due to a continuous moss–lichen cover. We investigate how an expert-driven method improves the accuracy of benchmark measurements at discrete locations within two sites using multitemporal TLS data of a 1-year period. Our method aggregates multiple experts’ determination of the ground surface in 3D point clouds, collected in a web-based tool. We then compare this to the performance of a fully automated ground surface determination method. Lastly, we quantify ground surface displacement by directly computing multitemporal point cloud distances, thereby extending thaw subsidence observation to an area-based assessment. Using the expert-driven quantification as reference, we validate the other methods, including in-situ benchmark measurements from a conventional field survey. This study demonstrates that quantifying the ground surface using 3D point clouds is more accurate than the field survey method. The expert-driven method achieves an accuracy of 0.1 ± 0.1 cm. Compared to this, in-situ benchmark measurements by single surveyors yield an accuracy of 0.4 ± 1.5 cm. This difference between the two methods is important, considering an observed displacement of 1.4 cm at the sites. Thaw subsidence quantification with the fully automatic benchmark-based method achieves an accuracy of 0.2 ± 0.5 cm and direct point cloud distance computation an accuracy of 0.2 ± 0.9 cm. The range in accuracy is largely influenced by properties of vegetation structure at locations within the sites. The developed methods enable a link of automated quantification and expert judgement for transparent long-term monitoring of permafrost subsidence.
    Content: Peer Reviewed
    In: New York, NY [u.a.] : Wiley, 45,7, Seiten 1589-1600
    Language: English
    URL: Volltext  (kostenfrei)
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