Abstract
For many applications, in both forestry and urban environments, knowledge of the biometric parameters of trees is essential. In this sense the use of the Quantitative Structure Models (QSMs), generated from Terrestrial Laser Scanners (TLSs), to recreate the structure of trees has increased in recent years. However, the utilisation of TLS has two main limitations which can be summarised as follows: (i) very long acquisition time and (ii) limited portability. The use of Mobile Laser Scanners (MLSs) with Simultaneous Localization and Mapping (SLAM) technology can overcome these kinds of limitations, allowing both complex environments to be detected in a short time and surveys to be made possible in impervious areas. Therefore, in this research we illustrate a workflow based on an open-source software to model and analyse a tree using the data produced by a MLS. In addition, the model of the same tree is also generated on the basis of the TLS-derived point cloud, in order to evaluate the differences. The process of modelling the tree, starting from the MLS data, leads to the creation of a model that fits the original point cloud very well, with an average distance from it of 0.30 cm. However, it was not always possible, especially in the summit parts, to reconstruct the tree structure. In addition, it was noted that, due to the noisiness of the source cloud, this model also tends to overestimate the diameter and, consequently, the biomass of the tree. In order to reduce this overestimation, a corrective factor will be identified in the future. Our study confirms that open-source software-based reconstruction of a tree model from an MLS-derived point cloud is possible. With the help of this method, it is possible to extract numerous biometric and structural information about several trees in a short time. Such information can help to carry out fast broad-scale analyses, such as assessing plant growth, or provide basic information for the development of automatic pruning technologies.
This is a preview of subscription content, log in via an institution to check access.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
References
Shvidenko, A., et al.: Chapter 21 forest and woodland systems coordinating lead, pp. 585–621 (2006)
Livesley, S.J., McPherson, E.G., Calfapietra, C.: The urban forest and ecosystem services: impact on urban water, heat, and pollution cycles at the tree, street, and city scale. J. Environ. Qual. 45, 119–124 (2016). https://doi.org/10.2134/jeq2015.11.0567
Ulrich, R.S., Parsons, R.: Influences of passive experiences with plants on individual well-being and health. In: Relf, D. (ed.) The Role of Horticulture in Human Wellbeing and Social Development: A National Symposium, pp. 93–105. TimberPress, Portland (1992)
Calders, K., et al.: Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Methods Ecol. Evol. 6, 198–208 (2015). https://doi.org/10.1111/2041-210X.12301
Simonse, M., Aschoff, T., Spiecker, H., Thies, M.: Automatic determination of forest inventory parameters using terrestrial laser scanning, pp. 252–258 (2003)
Aschoff, T., Spiecker, H.: Algorithms for the automatic detection of trees in laser scanner data. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 36, 71–75 (2004)
Pfeifer, N., Winterhalder, D.: Modelling of tree cross sections from terrestrial laser scanning data with free-form curves. Int. Arch. Photogramm. Remote Sens. Spat. Inf. Sci. 36, 76–81 (2004)
Thies, M., Pfeifer, N., Winterhalder, D., Gorte, B.G.: Three-dimensional reconstruction of stems for assessment of taper, sweep and lean based on laser scanning of standing trees. Scand. J. For. Res. 19(6), 571–581 (2004). https://doi.org/10.1080/02827580410019562
Binney, J., Sukhatme, G.S.: 3D tree reconstruction from laser range data. In: Proceedings - IEEE International Conference on Robotics and Automation, pp. 1321–1326 (2009). https://doi.org/10.1109/ROBOT.2009.5152684
Côté, J.F., Widlowski, J.L., Fournier, R.A., Verstraete, M.M.: The structural and radiative consistency of three-dimensional tree reconstructions from terrestrial lidar. Remote Sens. Environ. 113(5), 1067–1081 (2009). https://doi.org/10.1016/j.rse.2009.01.017
Rutzinger, M., Pratihast, A.K., Oude Elberink, S.J., Vosselman, G.: Tree modelling from mobile laser scanning data-sets. Photogram. Rec. 26(135), 361–372 (2011). https://doi.org/10.1111/j.1477-9730.2011.00635.x
Raumonen, P., et al.: Fast automatic precision tree models from terrestrial laser scanner data. Remote Sens. 5(2), 491–520 (2013). https://doi.org/10.3390/rs5020491
Hackenberg, J., Wassenberg, M., Spiecker, H., Sun, D.: Non destructive method for biomass prediction combining TLS derived tree volume and wood density. Forests 6(4), 1274–1300 (2015). https://doi.org/10.3390/f6041274
Raumonen, P., Casella, E., Calders, K., Murphy, S., Åkerblom, M., Kaasalainen, M.: Massive-scale tree modelling from TLS data. IISPRS Ann. Photogramm. Remote Sens. Spatial Inf. Sci. II-3/W4, 189–196 (2015). https://doi.org/10.5194/isprsannals-II-3-W4-189-2015
Hackenberg, J., Morhart, C., Sheppard, J., Spiecker, H., Disney, M.: Highly accurate tree models derived from terrestrial laser scan data: a method description. Forests 5(5), 1069–1105 (2014). https://doi.org/10.3390/f5051069
Hackenberg, J., Spiecker, H., Calders, K., Disney, M., Raumonen, P.: SimpleTree—an efficient open source tool to build tree models from TLS clouds. Forests 6(11), 4245–4294 (2015). https://doi.org/10.3390/f6114245
Kunz, M., et al.: Comparison of wood volume estimates of young trees from terrestrial laser scan data. iForest 10, 451–458 (2017)
Bienert, A., Georgi, L., Kunz, M., Maas, H.G., Von Oheimb, G.: Comparison and combination of mobile and terrestrial laser scanning for natural forest inventories. Forests 9, 395 (2018). https://doi.org/10.3390/f9070395
Zhang, C., et al.: Apple tree branch information extraction from terrestrial laser scanning and backpack-LiDAR. Remote Sens. 12, 3592 (2020). https://doi.org/10.3390/rs12213592
Hackenberg, J.: The Simple Forest Handbook. A User Guide for QSM Building. Hoboken, Wiley (2019)
Hackenberg J., Calders K., Miro D., Raumonen P., Piboule, A., Disney, M.: SimpleForest-a comprehensive tool for 3D reconstruction of trees from forest plot point clouds (2021). https://doi.org/10.1101/2021.07.29.454344
Rusu, R.B., Cousins, S.: 3D is here: point cloud library (PCL). In: IEEE International Conference on Robotics and Automation, Shanghai, pp. 1–4 (2011). https://doi.org/10.1109/ICRA.2011.5980567
Shinozaki, K., Yoda, K., Hozumi, K., Kira, T.: A quantitative analysis of plant form-the pipe model theory: I. Basic analyses. Jpn. J. Ecol. 14(3), 97–105 (1964)
aRchi. https://cran.r-project.org/web/packages/aRchi/aRchi.pdf. Accessed 24 Mar 2022
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2022 The Author(s), under exclusive license to Springer Nature Switzerland AG
About this paper
Cite this paper
Donati Sarti, G., Busa, M., Garnero, G., Magnani, A., Rossato, I. (2022). An Open-Source Approach to Modelling and Analysing a Tree Detected with a Mobile Laser Scanner. In: Borgogno-Mondino, E., Zamperlin, P. (eds) Geomatics for Green and Digital Transition. ASITA 2022. Communications in Computer and Information Science, vol 1651. Springer, Cham. https://doi.org/10.1007/978-3-031-17439-1_20
Download citation
DOI: https://doi.org/10.1007/978-3-031-17439-1_20
Published:
Publisher Name: Springer, Cham
Print ISBN: 978-3-031-17438-4
Online ISBN: 978-3-031-17439-1
eBook Packages: Computer ScienceComputer Science (R0)