Advancing vegetation monitoring with virtual laser scanning of dynamic scenes (VLS-4D): Opportunities, implementations and future perspectives
Virtual laser scanning (VLS) is an established and valuable research tool in forestry and ecology, widely used to simulate labelled light detection and ranging (LiDAR) point cloud data for sensitivity analysis, model training for machine learning (ML) and method testing. In VLS, vegetation has tradi...
Gespeichert in:
| Hauptverfasser: | , |
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| Dokumenttyp: | Article (Journal) |
| Sprache: | Englisch |
| Veröffentlicht: |
2026
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| In: |
Methods in ecology and evolution
Year: 2026, Jahrgang: 17, Heft: 1, Pages: 33-51 |
| ISSN: | 2041-210X |
| DOI: | 10.1111/2041-210x.70189 |
| Online-Zugang: | Verlag, kostenfrei, Volltext: https://doi.org/10.1111/2041-210x.70189 Verlag, kostenfrei, Volltext: https://onlinelibrary.wiley.com/doi/abs/10.1111/2041-210x.70189 
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| Verfasserangaben: | Hannah Weiser, Bernhard Höfle |
MARC
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| 520 | |a Virtual laser scanning (VLS) is an established and valuable research tool in forestry and ecology, widely used to simulate labelled light detection and ranging (LiDAR) point cloud data for sensitivity analysis, model training for machine learning (ML) and method testing. In VLS, vegetation has traditionally been modelled as static, neglecting the influence of vegetation dynamics on LiDAR point cloud representations and limiting applications to mono-temporal analyses. In this review, we propose VLS-4D, a novel framework that extends traditional VLS by using dynamic (i.e. 4D: 3D + time) input scenes. These scenes may describe momentary motion, such as wind movement, or long-term processes, such as plant growth, and may be scanned in a single virtual survey or in multiple consecutive surveys. The advancement to 4D scenes opens up new avenues for vegetation research, including studying the impact of wind sway on point cloud quality, optimising data acquisition by considering vegetation dynamics and monitoring forest growth and vitality. To facilitate wider adoption of the framework, we outline key concepts for representing dynamic scenes in LiDAR simulations, review technical implementations and present innovative VLS-4D applications. The main applications can be grouped into three key methodological areas: (i) investigating LiDAR data acquisition and vegetation movement effects, (ii) testing and validating new methods for change detection and analysis and (iii) generating labelled training data for ML and deep learning (DL). We find that current simulation frameworks suitable for vegetation applications do not yet fully support dynamic scenes. While LiDAR time series of, for example, vegetation growth can be generated from multiple static scene snapshots, simulating the effects of vegetation movement during a scan remains a challenge. We recommend that future efforts focus on extending the functionality of current LiDAR simulators to efficiently handle animated 3D scenes which deform during simulation and on increasing the availability of open-source tools for modelling dynamic vegetation to enable more realistic simulations. Used as a complement, not a replacement, to real data, VLS-4D has the potential to significantly advance LiDAR-based vegetation monitoring by improving our understanding of point cloud representations, enabling reliable algorithm validation and providing high-quality training data for DL. | ||
| 650 | 4 | |a 3D animation | |
| 650 | 4 | |a LiDAR simulation | |
| 650 | 4 | |a machine learning | |
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