Implications of Pulse Frequency in Terrestrial Laser Scanning on Forest Point Cloud Quality and Individual Tree Structural Metrics
Abstract
:1. Introduction
2. Materials and Methods
2.1. Equipment
2.2. Data Collection
2.3. Impact of PRR on Single Scans
2.4. Impact of PRR on Individual Tree Point Clouds
2.4.1. Point Distribution and Occlusion in Individual Trees
2.4.2. Point Cloud Metrics
2.4.3. Quantitative Structural Models
3. Results
3.1. Impact of PRR on Single Scans
3.2. Impact of PRR on Individual Tree Point Clouds
3.2.1. Visual Occlusion and Point Density
3.2.2. Individual Tree Metrics
4. Discussion
5. Conclusions
Author Contributions
Funding
Data Availability Statement
Acknowledgments
Conflicts of Interest
Abbreviations
TLS | terrestrial laser scanning |
AGB | aboveground biomass |
PRR | pulse repetition rate |
QSM | quantitative structure model |
EO | Earth Observation |
DBH | diameter at breast height |
H | tree height |
CPA | crown projected area |
V | total tree volume |
BL | branch length |
CCC | concordance correlation coefficient |
Appendix A
Appendix A.1
Appendix A.2
Appendix A.3
References
- Herold, M.; Carter, S.; Avitabile, V.; Espejo, A.B.; Jonckheere, I.; Lucas, R.; McRoberts, R.E.; Næsset, E.; Nightingale, J.; Petersen, R.; et al. The role and need for space-based forest biomass-related measurements in environmental management and policy. Surv. Geophys. 2019, 40, 757–778. [Google Scholar] [CrossRef]
- Disney, M.; Burt, A.; Calders, K.; Schaaf, C.; Stovall, A. Innovations in Ground and Airborne Technologies as Reference and for Training and Validation: Terrestrial Laser Scanning (TLS). Surv. Geophys. 2019, 40, 937–958. [Google Scholar] [CrossRef]
- Calders, K.; Verbeeck, H.; Burt, A.; Origo, N.; Nightingale, J.; Malhi, Y.; Wilkes, P.; Raumonen, P.; Bunce, R.G.H.; Disney, M. Laser scanning reveals potential underestimation of biomass carbon in temperate forest. Ecol. Solut. Evid. 2022, 3, e12197. [Google Scholar] [CrossRef]
- Calders, K.; Adams, J.; Armston, J.; Bartholomeus, H.; Bauwens, S.; Bentley, L.P.; Chave, J.; Danson, F.M.; Demol, M.; Disney, M.; et al. Terrestrial laser scanning in forest ecology: Expanding the horizon. Remote Sens. Environ. 2020, 251, 112102. [Google Scholar] [CrossRef]
- Calders, K.; Newnham, G.; Burt, A.; Murphy, S.; Raumonen, P.; Herold, M.; Culvenor, D.; Avitabile, V.; Disney, M.; Armston, J.; et al. Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods Ecol. Evol. 2015, 6, 198–208. [Google Scholar] [CrossRef]
- Demol, M.; Wilkes, P.; Raumonen, P.; Krishna Moorthy Parvathi, S.; Calders, K.; Gielen, B.; Verbeeck, H. Volumetric overestimation of small branches in 3D reconstructions of Fraxinus excelsior. Silva Fenn. 2022, 56, 1. [Google Scholar] [CrossRef]
- Gonzalez de Tanago, J.; Lau, A.; Bartholomeus, H.; Herold, M.; Avitabile, V.; Raumonen, P.; Martius, C.; Goodman, R.C.; Disney, M.; Manuri, S.; et al. Estimation of above-ground biomass of large tropical trees with terrestrial LiDAR. Methods Ecol. Evol. 2018, 9, 223–234. [Google Scholar] [CrossRef]
- Momo Takoudjou, S.; Ploton, P.; Sonké, B.; Hackenberg, J.; Griffon, S.; de Coligny, F.; Kamdem, N.G.; Libalah, M.; Mofack, G.I.; Le Moguédec, G.; et al. Using terrestrial laser scanning data to estimate large tropical trees biomass and calibrate allometric models: A comparison with traditional destructive approach. Methods Ecol. Evol. 2018, 9, 905–916. [Google Scholar] [CrossRef]
- Duncanson, L.; Armston, J.; Disney, M.; Avitabile, V.; Barbier, N.; Calders, K.; Carter, S.; Chave, J.; Herold, M.; MacBean, N.; et al. Aboveground Woody Biomass Product Validation Good Practices Protocol, Version 1.0. 2021. Available online: https://lpvs.gsfc.nasa.gov/PDF/CEOS_WGCV_LPV_Biomass_Protocol_2021_V1.0.pdf (accessed on 2 December 2024).
- Wilkes, P.; Lau, A.; Disney, M.; Calders, K.; Burt, A.; Gonzalez de Tanago, J.; Bartholomeus, H.; Brede, B.; Herold, M. Data acquisition considerations for Terrestrial Laser Scanning of forest plots. Remote Sens. Environ. 2017, 196, 140–153. [Google Scholar] [CrossRef]
- RIEGL Laser Measurement Systems GmbH. 3D Terrestrial Laser Scanning System RIEGL VZ-i Series. Data Sheet, RIEGL VZ-400i, 2024/09/02. Available online: http://www.riegl.com/uploads/tx_pxpriegldownloads/RIEGL_VZ-400i_Datasheet_2024-09-02.pdf (accessed on 2 December 2024).
- Calders, K.; Armston, J.; Newnham, G.; Herold, M.; Goodwin, N. Implications of sensor configuration and topography on vertical plant profiles derived from terrestrial LiDAR. Agric. For. Meteorol. 2014, 194, 104–117. [Google Scholar] [CrossRef]
- Calders, K.; Disney, M.I.; Armston, J.; Burt, A.; Brede, B.; Origo, N.; Muir, J.; Nightingale, J. Evaluation of the Range Accuracy and the Radiometric Calibration of Multiple Terrestrial Laser Scanning Instruments for Data Interoperability. IEEE Trans. Geosci. Remote Sens. 2017, 55, 2716–2724. [Google Scholar] [CrossRef]
- Team, R.C. R: A Language and Environment for Statistical Computing; R Foundation for Statistical Computing: Vienna, Austria, 2013; Available online: http://www.R-project.org/ (accessed on 2 December 2024).
- Dowle, M.; Srinivasan, A. data.table: Extension of ‘data.frame’; 2023. Available online: https://rdrr.io/cran/data.table/ (accessed on 2 December 2024).
- CloudCompare. CloudCompare. 2022. Available online: https://www.cloudcompare.org/ (accessed on 2 December 2024).
- Burt, A.; Disney, M.; Calders, K. Extracting individual trees from lidar point clouds using treeseg. Methods Ecol. Evol. 2019, 10, 438–445. [Google Scholar] [CrossRef]
- Calders, K.; Origo, N.; Burt, A.; Disney, M.; Nightingale, J.; Raumonen, P.; Åkerblom, M.; Malhi, Y.; Lewis, P. Realistic Forest Stand Reconstruction from Terrestrial LiDAR for Radiative Transfer Modelling. Remote Sens. 2018, 10, 933. [Google Scholar] [CrossRef]
- Lowe, T.; Pinskier, J. Tree Reconstruction Using Topology Optimisation. Remote Sens. 2023, 15, 172. [Google Scholar] [CrossRef]
- Wilkes, P.; Disney, M.; Armston, J.; Bartholomeus, H.; Bentley, L.; Brede, B.; Burt, A.; Calders, K.; Chavana-Bryant, C.; Clewley, D.; et al. TLS2trees: A scalable tree segmentation pipeline for TLS data. Methods Ecol. Evol. 2023, 14, 3083–3099. [Google Scholar] [CrossRef]
- Roussel, J.R.; Auty, D.; Coops, N.C.; Tompalski, P.; Goodbody, T.R.; Meador, A.S.; Bourdon, J.F.; De Boissieu, F.; Achim, A. lidR: An R package for analysis of Airborne Laser Scanning (ALS) data. Remote Sens. Environ. 2020, 251, 112061. [Google Scholar] [CrossRef]
- Roussel, J.R.; Auty, D. Airborne LiDAR Data Manipulation and Visualization for Forestry Applications, R Package Version 3.1.2. 2021. Available online: https://github.com/r-lidar/lidR (accessed on 2 December 2024).
- Terryn, L.; Calders, K.; Åkerblom, M.; Bartholomeus, H.; Disney, M.; Levick, S.; Origo, N.; Raumonen, P.; Verbeeck, H. Analysing individual 3D tree structure using the R package ITSMe. Methods Ecol. Evol. 2023, 14, 231–241. [Google Scholar] [CrossRef]
- Lin, L.I.K. A Concordance Correlation Coefficient to Evaluate Reproducibility. Biometrics 1989, 45, 255–268. [Google Scholar] [CrossRef]
- Signorell, A. DescTools: Tools for Descriptive Statistics. 2023. Available online: https://cran.r-project.org/web/packages/DescTools/index.html (accessed on 2 December 2024).
- Burt, A.; Boni Vicari, M.; da Costa, A.C.L.; Coughlin, I.; Meir, P.; Rowland, L.; Disney, M. New insights into large tropical tree mass and structure from direct harvest and terrestrial lidar. R. Soc. Open Sci. 2021, 8, 201458. [Google Scholar] [CrossRef]
- Tian, Z.; Li, S. Graph-Based Leaf–Wood Separation Method for Individual Trees Using Terrestrial Lidar Point Clouds. IEEE Trans. Geosci. Remote Sens. 2022, 60, 1–11. [Google Scholar] [CrossRef]
- Raumonen, P.; Kaasalainen, M.; Åkerblom, M.; Kaasalainen, S.; Kaartinen, H.; Vastaranta, M.; Holopainen, M.; Disney, M.; Lewis, P. Fast Automatic Precision Tree Models from Terrestrial Laser Scanner Data. Remote Sens. 2013, 5, 491–520. [Google Scholar] [CrossRef]
- Abegg, M.; Boesch, R.; Schaepman, M.E.; Morsdorf, F. Impact of Beam Diameter and Scanning Approach on Point Cloud Quality of Terrestrial Laser Scanning in Forests. IEEE Trans. Geosci. Remote Sens. 2021, 59, 8153–8167. [Google Scholar] [CrossRef]
- Schraik, D.; Wang, D.; Hovi, A.; Rautiainen, M. Quantifying stand-level clumping of boreal, hemiboreal and temperate European forest stands using terrestrial laser scanning. Agric. For. Meteorol. 2023, 339, 109564. [Google Scholar] [CrossRef]
- Liu, C.; Calders, K.; Meunier, F.; Gastellu-Etchegorry, J.P.; Nightingale, J.; Disney, M.; Origo, N.; Woodgate, W.; Verbeeck, H. Implications of 3D Forest Stand Reconstruction Methods for Radiative Transfer Modeling: A Case Study in the Temperate Deciduous Forest. J. Geophys. Res. Atmos. 2022, 127, e2021JD036175. [Google Scholar] [CrossRef]
300 kHz | 600 kHz | 1200 kHz | |
---|---|---|---|
Effective measurement rate (Hz) | 125,000 | 252,000 | 500,000 |
Maximum measurement range (m) for natural targets | 480 | 350 | 250 |
Maximum measurement range (m) for natural targets | 230 | 160 | 120 |
Unambiguous range (m) | 500 | 240 | 120 |
Minimum range (m) | 1.2 | 0.5 | 0.5 |
Maximum number of returns | 15 | 8 | 4 |
Scan duration (s) at angular resolution = 0.04° | 180 | 90 | 45 |
Plot Code | Forest Type | Country | Lat, Lon | Acquisition Date | Grid | Plot Size |
---|---|---|---|---|---|---|
BEoff | Deciduous temperate forest (leaf-off) | Belgium | 50°58’N, 3°48’E | 12/01/2022 | 15 × 15 m | 45 × 45 m |
BEon | Deciduous temperate forest (leaf-on) | Belgium | 50°58’N, 3°48’E | 29/06/2022 | 15 × 15 m | 45 × 45 m |
AUS | Remnant Eucalyptus species woodland | Australia | 33°36’S, 150°43’E | 08/05/2022 | 15 × 15 m | 45 × 45 m |
GAB | Old-growth tropical rainforest | Gabon | 0°30’N, 12°47’E | 24/01/2022 | 10 × 10 m | 30 × 30 m |
UK | Dense conifer plantation | UK | 54°29’N, 1°53’W | 15/05/2022 | 10 × 10 m | 30 × 30 m |
Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content. |
© 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https://creativecommons.org/licenses/by/4.0/).
Share and Cite
Verhelst, T.E.; Calders, K.; Burt, A.; Demol, M.; D’hont, B.; Nightingale, J.; Terryn, L.; Verbeeck, H. Implications of Pulse Frequency in Terrestrial Laser Scanning on Forest Point Cloud Quality and Individual Tree Structural Metrics. Remote Sens. 2024, 16, 4560. https://doi.org/10.3390/rs16234560
Verhelst TE, Calders K, Burt A, Demol M, D’hont B, Nightingale J, Terryn L, Verbeeck H. Implications of Pulse Frequency in Terrestrial Laser Scanning on Forest Point Cloud Quality and Individual Tree Structural Metrics. Remote Sensing. 2024; 16(23):4560. https://doi.org/10.3390/rs16234560
Chicago/Turabian StyleVerhelst, Tom E., Kim Calders, Andrew Burt, Miro Demol, Barbara D’hont, Joanne Nightingale, Louise Terryn, and Hans Verbeeck. 2024. "Implications of Pulse Frequency in Terrestrial Laser Scanning on Forest Point Cloud Quality and Individual Tree Structural Metrics" Remote Sensing 16, no. 23: 4560. https://doi.org/10.3390/rs16234560
APA StyleVerhelst, T. E., Calders, K., Burt, A., Demol, M., D’hont, B., Nightingale, J., Terryn, L., & Verbeeck, H. (2024). Implications of Pulse Frequency in Terrestrial Laser Scanning on Forest Point Cloud Quality and Individual Tree Structural Metrics. Remote Sensing, 16(23), 4560. https://doi.org/10.3390/rs16234560