ForestTaxator: A tool for detection and approximation of cross-sectional area of trees in a cloud of 3D points

Main Article Content

Maciej Małaszek
Andrzej Zembrzuski
Krzysztof Gajowniczek


Keywords : Point Cloud, Genetic Algorithms, Trees, 3D Scan
Abstract

In this paper we propose a novel software, named ForestTaxator, supporting terrestrial laser scanning data processing, which for dendrometric tree analysis can be divided into two main processes: tree detection in the point cloud and development of three-dimensional models of individual trees. The usage of genetic algorithms to solve the problem of tree detection in 3D point cloud and its cross-sectional area approximation with ellipse-based model is also presented. The detection and approximation algorithms are proposed and tested using various variants of genetic algorithms. The work proves that the genetic algorithms work very well: the obtained results are consistent with the reference data to a large extent, and the time of genetic calculations is very short. The attractiveness of the presented software is due to the fact that it provides all necessary functionalities used in the forest inventory field. The software is written in C# and runs on the .NET Core platform, which ensures its full portability between Windows, MacOS and Linux. It provides a number of interfaces thus ensuring a high level of modularity. The software and its code are made freely available.

Article Details

How to Cite
Małaszek, M., Zembrzuski, A., & Gajowniczek, K. (2022). ForestTaxator: A tool for detection and approximation of cross-sectional area of trees in a cloud of 3D points. Machine Graphics and Vision, 31(1/4), 19–48. https://doi.org/10.22630/MGV.2022.31.1.2
References

J. Elseberg, D. Borrmann, and A. Nuchter. Efficient processing of large 3D point clouds. In Proc. XXIII Int. Symp. Information, Communication and Automation Technologies ICAT 2011, pages 1-7, Sarajevo, Bosnia Herzegovina, 27-29 Oct 2011. https://doi.org/10.1109/icat.2011.6102102. (Crossref)

G. Krok, B. Kraszewski, and K. Stereńczak. Application of terrestrial laser scanning in forest inventory - an overview of selected issues. Forest Research Papers, 81(4):175-194, 2020. https://doi.org/10.2478/frp-2020-0021. (Crossref)

A. Bienert, S. Scheller, E. Keane, et al. Tree detection and diameter estimations by analysis of forest terrestrial laser scanner point clouds. In Proc. Workshop Laser Scanning and SilviLaser 2007 ISPRS 2007, pages 50-55, Espoo, Finland, 12-14 Sep 2007. https://www.isprs.org/proceedings/xxxvi/3-w52/final_papers/Bienert_2007.pdf

A. Konieczny, and B. Neroj. Projekt działania programu do obliczania miąższości drzew na podstawie danych skanowania naziemnego (TLS). Presentation from "Narada Koordynatorów SIP", Zakopane, 23–25 Feb 2016. https://www.geomatyka.lasy.gov.pl/documents/25999395/0/Konieczny-TLS.pdf/b13219cc-1608-4004-8692-2de4d0d44a5e. [Online; accessed 12 Nov 2020].

M. Małaszek. ForestTaxator library. https://github.com/maciej-malaszek/forest-taxator. [Online; accessed 10 Dec 2020].

X. Liang, V. Kankare J. Hyyppä, et al. Terrestrial laser scanning in forest inventories. ISPRS Journal of Photogrammetry and Remote Sensing, 115:63-77, 2016. https://doi.org/10.1016/j.isprsjprs.2016.01.006. (Crossref)

S. Bauwens, H. Bartholomeus, K. Calders, and P. Lejeune. Forest Inventory with terrestrial LiDAR: A comparison of static and hand-held mobile laser scanning. Forests, 7(12):127, 2016. https://doi.org/10.3390/f7060127. (Crossref)

P. Wezyk, K. Koziol, M. Glista, and M. Pierzchalski. Terrestrial laser scanning versus traditional forest inventory first results from the polish forests. In Proc. Workshop Laser Scanning and SilviLaser 2007 ISPRS 2007, pages 12-14, Espoo, Finland, 12-14 Sep 2007. http://foto.hut.fi/ls2007/posters/Wezyk_ls2007_poster.pdf

M. Zasada, K. Stereńczak, W. M. Dudek, and A. Rybski. Horizon visibility and accuracy of stocking determination on circular sample plots using automated remote measurement techniques. Forest Ecology and Management, 302:171-177, 2013. https://doi.org/10.1016/j.foreco.2013.03.041. (Crossref)

R. Astrup, M. J. Ducey, A. Granhus, et al. Approaches for estimating stand-level volume using terrestrial laser scanning in a single-scan mode. Canadian Journal of Forest Research, 44(6):666-676, 2014. https://doi.org/10.1139/cjfr-2013-0535. (Crossref)

P. Raumonen, M. Kaasalainen, M. Åkerblom, et al. Fast automatic precision tree models from terrestrial laser scanner data. Remote Sensing, 5(2):491-520, 2013. https://doi.org/10.3390/rs5020491. (Crossref)

P. Wilkes, A. Lau, M. Disney, et al. Data acquisition considerations for Terrestrial Laser Scanning of forest plots. Remote Sensing of Environment, 196:153-520, 2017. https://doi.org/10.1016/j.rse.2017.04.030. (Crossref)

A. M. Kim, R. C. Olsen, and M. Béland. Simulated full-waveform lidar compared to Riegl VZ-400 terrestrial laser scans. Laser Radar Technology and Applications XXI,9832:242-255, 2016. https://doi.org/10.1117/12.2223929. (Crossref)

M. T. Vaaja, J. P. Virtanen, M. Kurkela, et al. The effect of wind on tree steam parameter estimation using terrestrial laser scanning. International Society for Photogrammetry and Remote Sensing Workshop on Laser Scanning, III-8:117-122, 2016. https://doi.org/10.5194/isprsannals-iii-8-117-2016. (Crossref)

D. Seidel, S. Fleck, and C. Leuschner. Analyzing forest canopies with ground-based laser scanning: A comparison with hemispherical photography. Agricultural and Forest Meteorology, 154:1-8, 2012. https://doi.org/10.1016/j.agrformet.2011.10.006. (Crossref)

M. Dassot, T. Constant, and M. Fournier. The use of terrestrial LiDAR technology in forest science: Application fields, benefits and challenges. Annals of Forest Science, 68(5):959-974, 2011. https://doi.org/10.1007/s13595-011-0102-2. (Crossref)

L. J. Chmielewski, M. Bator, M. Zasada, et al. Fuzzy Hough transform-based methods for extraction and measurements of single trees in large-volume 3d terrestrial LIDAR data. In Computer Vision and Graphics: Proc. ICCVG 2010, Part I, volume 6374 of Lecture Notes in Computer Science, pages 265-274, Warsaw, Poland, 20-22 Sep 2010. Springer. https://doi.org/10.1007/978-3-642-15910-7_30. (Crossref)

E. Lindberg, J. Holmgren, K. Olofsson, and H. Olsson. Estimation of stem attributes using a combination of terrestrial and airborne laser scanning. European Journal of Forest Research, 131(6):1917-1931, 2012. https://doi.org/10.1007/s10342-012-0642-5. (Crossref)

W. Zhang, P. Wan, T. Wang, et al. A novel approach for the detection of standing tree stems from plot-level Terrestrial Laser Scanning data. Remote Sensing, 11(2):211, 2019. https://doi.org/10.3390/rs11020211. (Crossref)

A. T. Albrecht, M. Fortin, U. Kohnle, and F. Ningre. Coupling a tree growth model with storm damage modeling–conceptual approach and results of scenario simulations. Environmental Modelling & Software, 69:63-76, 2015. https://doi.org/10.1016/j.envsoft.2015.03.004. (Crossref)

D. E. Goldberg. Genetic Algorithms. Pearson Education India, 2013.

T. Bäck. Evolutionary Algorithms in Theory and Practice: Evolution Strategies, Evolutionary Programming, Genetic Algorithms. Oxford University Press, 1996. (Crossref)

A. Burt, M. Disney, and K. Calders. Extracting individual trees from lidar point clouds using treeseg. Methods in Ecology and Evolution, 10(3):438-445, 2018. https://doi.org/10.1111/2041-210x.13121. (Crossref)

K. Olofsson, and J. Holmgren. Single tree stem profile detection using Terrestrial Laser Scanner data, flatness saliency features and curvature properties. Forests, 7(12):207, 2016. https://doi.org/10.3390/f7090207. (Crossref)

M. B. Vicari, M. Disney, P. Wilkes, et al. Leaf and wood classification framework for terrestrial LiDAR point clouds. Methods in Ecology and Evolution, 10(5):680-690, 2019. https://doi.org/10.1111/2041-210x.13144. (Crossref)

H. Yoon, H. Song, and K. Park. A phase-shift laser scanner based on a time-counting method for high linearity performance. Review of Scientific Instruments, 82(7):075108, 2011. https://doi.org/10.1063/1.3600456. (Crossref)

J. D. Schaffer, R. Caruana, L. J. Eshelman, and R. Das. A study of control parameters affecting online performance of genetic algorithms for function optimization. In Schaffer J. D. (ed.), Proc. 3rd Int. Conf. Genetic Algorithms, pages 51-60, Morgan Kaufmann Publishers Inc., San Francisco, CA, 1989.

K. Stereńczak, S. Miścicki, M. Zasada et al. Project Remote sensing based assessment of woody biomass and carbon storage in forests (REMBIOFOR). Funded by the Polish National Centre for Research and Development (NCBiR) within the program Natural Environment, Agriculture and Forestry BIO-STRATEG 2015, under the agreement no. BIOSTRATEG1/267755/4/NCBR/2015. http://rembiofor.pl/en/305-2/. [Online; accessed 10 Dec 2020].

M. Małaszek, A. Zembrzuski, and K. Gajowniczek. Supplementary Materials to this paper - file Detailedresults.xlsx. Published together with this paper. https://doi.org/10.22630/MGV.2022.31.1.2. (Crossref)

M. Małaszek. GeneticToolkit library. https://github.com/maciej-malaszek/GeneticToolkit. [Online; accessed 10 Dec 2020].

CloudCompare. 3D point cloud and mesh processing software. Open Source Project. https://www.danielgm.net/cc/. [Online; accessed 10 Feb 2021].

T. de Conto, Jean-Romain, C. Hamamura and A. Marcozzi. TreeLS. https://github.com/tiagodc/TreeLS. [Online; accessed 10 Feb 2021].

J. Hackenberg, H. Spiecker, K. Calders, et al. 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. (Crossref)

J. Trochta, M. Krůček, T. Vrška, and K. Král. 3D Forest: An application for descriptions of three-dimensional forest structures using terrestrial LiDAR. PLOS ONE, 12(5):e0176871, 2017. https://doi.org/10.1371/journal.pone.0176871. (Crossref)

A. Othmani, L. F. C. Lew Yan Voon, C. Stolz, and A. Piboule. Single tree species classification from Terrestrial Laser Scanning data for forest inventory. Pattern Recognition Letters, 34(16):2144-2150, 2017. https://doi.org/10.1016/j.patrec.2013.08.004. (Crossref)

S. Du, R. Lindenbergh, H. Ledoux, et al. AdTree: Accurate, detailed, and automatic modelling of laser-scanned trees. Remote Sensing, 11(18):2074, 2019. https://doi.org/10.3390/rs11182074. (Crossref)

D. Wang. Unsupervised semantic and instance segmentation of forest point clouds. ISPRS Journal of Photogrammetry and Remote Sensing, 165:86-97, 2020. https://doi.org/10.1016/j.isprsjprs.2020.04.0. (Crossref)

T. De Conto, K. Olofsson, E. B. Görgens, et al. Performance of stem denoising and stem modelling algorithms on single tree point clouds from terrestrial laser scanning. Computers and Electronics in Agriculture, 143:165-176, 2017. https://doi.org/10.1016/j.compag.2017.10.019. (Crossref)

Statistics

Downloads

Download data is not yet available.
Recommend Articles
Most read articles by the same author(s)