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An annually-resolved stem growth tool based on 3D laser scans and 2D tree-ring data


Key message

The combination of terrestrial laser scans and tree-ring data allows for a highly precise reconstruction of annual stem growth, and thus complex tree-growth analyses independent of species and site characteristics.


Reliable carbon pools data are needed to quantify the carbon stored in ecosystems and for effective forest management. Terrestrial laser scanning allows researchers to quickly acquire data about forest structure and to derive tree parameters and volume data automatically. However, accurate models of the development of tree volume over time are still lacking. In contrast to terrestrial laser scanning, tree-ring data show the annual growth development of trees, but do not contain information about tree volume. The fusion of terrestrial laser scanning and tree-ring data may, therefore, lead to reliable stem development data, and thus annually resolved models of volume increment of trees. The aim of this study is to combine these data and apply a root-development model to the aboveground part of trees. Three spruce trees (Picea abies) and two firs (Abies alba) which were part of a long-term forest monitoring survey were scanned using a terrestrial 3D-laser-scanner. Combining these data with tree-ring measurements, we were able to reconstruct stem volume at an annual resolution. Results provide robust annually resolved volume data along with ring-width measurements at any point within the modeled tree stem, which present great potential for complex growth analyses. Stem volume, estimated with a bole volume function, deviated between − 1.65 and 1.9% from our model for four out of five trees. For the fifth tree deviations of 13% were observed. The agreement between the function and our model demonstrates the robustness of the presented approach.

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  1. Ǻkerblom M, Raumonen P, Kaartinen H (2012) Comprehensive quantitative tree models from TLS data. In: Proceedings of the geoscience and remote sensing symposium (IGARRS) 2012 IEEE international, pp 6507–6510. doi:10.1109/IGARSS.2012.6352751

  2. Aschoff T, Thies M, Spiecker H (2004) Describing forest stands using terrestrial laser-scanning. Int Arch Photogramm Remote Sens Spat Inf Sci 35:237–241

  3. Babst F, Bouriaud O, Papale D, Gielen B, Janssens IA, Nikinmaa E, Ibrom A, Wu J, Bernhofer C, Köstner B, Grünwald T, Seufert G, Ciais P, Frank D (2014) Above-ground woody carbon sequestration measured from tree rings is coherent with net ecosystem productivity at five eddy-covariance sites. New Phytol 201:1289–1303. doi:10.1111/nph.12589

  4. Bienert A, Scheller S, Keane E, Mulloly G, Mohan F (2006) Application of terrestrial laser scanners for the determination of forest inventory parameters. In: Maas H-G, Schneider D (eds) Proceedings of ISPRS commission V symposium ‘Image engineering and vision metrology’ 36. Part 5. ISPRS, Dresden

  5. Bottai L, Arcidiaco L, Chiesi M, Maselli F (2013) Application of a single-tree identification algorithm to LiDAR data for the simulation of stem volume current annual increment. J Appl Remote Sens 7:073699–073699. doi:10.1117/1.JRS.7.073699

  6. Brenner C (2007) Interpretation terrestrischer Scandaten. In: Beiträge zum 74.DVW-Seminar Terrestrisches Laser-Scanning, Band 53. Fulda, Germany, pp 170–179

  7. Burt A, Disney MI, Raumonen P, Armston J, Calders K, Lewis P (2013) Rapid characterization of forest structure from TLA and 3D modelling. In: Proceedings of the geoscience and remote sensing symposium (IGARRS) 2013 IEEE international, pp 3387–3390. doi:10.1109/IGARSS.2013.6723555

  8. Calders K, Newnham G, Burt A, Murphy S, Raumonen P, Herold M, Culvenor D, Avitabile V, Disney M, Armston J, Kaasalainen M (2015) Nondestructive estimates of above-ground biomass using terrestrial laser scanning. Meth Ecol Evol 6:198–208. doi:10.1111/2041-210X.12301

  9. Carmean WH (1972) Site index curves for upland oaks in the central states. For Sci 18:109–120

  10. Cignoni P, Callieri M, Corsini M, Dellepiane M, Ganovelli F, Ranzuglia G (2008) MeshLab: an open-source mesh processing tool sixth Eurographics Italian chapter conference, pp 129–136. doi:10.2312/LocalChapterEvents/ItalChap/ItalianChapConf2008/129-136

  11. Cook ER, Kairiukstis A (1990) Methods of dendrochronology—applications in the environmental science. Kluwer, Dordrecht

  12. Dassot M, Constant T, Fournier M (2011) The use of terrestrial LiDAR technology in forest science: application fields, benefits and challenges. Ann For Sci 68:959–974. doi:10.1007/s13595-011-0102-2

  13. Dassot M, Colin A, Santenoise P, Fournier M, Constant T (2012) Terrestrial laser scanning for measuring the solid wood volume, including branches, of adult standing trees in the forest environment. Comput Electron Agr 89:86–93. doi:10.1016/j.compag.2012.08.005

  14. Dixon RK, Brown S, Houghton RA, Solomon AM, Trexler MC, Wisniewski J (1994) Carbon pools and flux of global forests ecosystems. Science 263:185–190. doi:10.1126/science.263.5144.185

  15. Dobbs C, Escobedo FJ, Zipperer WC (2011) A framework for developing urban forest ecosystem services and goods indicators. Landsc Urban Plan 99:196–206. doi:10.1016/j.landurbplan.2010.11.004

  16. Eysn L, Pfeifer N, Ressl C, Hollaus M, Grafl A, Morsdorf F (2013) A practical approach for extracting tree models in forest environments based on equirectangular projections of terrestrial laser scans. Remote Sens 5:5424–5448. doi:10.3390/rs5115424

  17. Fehrmann L, Kleinn C (2006) General considerations about the use of allometric equations for biomass estimation on the example of Norway spruce in central Europe. For Ecol Manag 236:412–421. doi:10.1016/j.foreco.2006.09.026

  18. Führer E (2000) Forest functions, ecosystem stability and management. For Ecol Manag 132:29–38. doi:10.1016/S0378-1127(00)00377-7

  19. Girardeau-Montaut D (2016) Cloud Compare: 3D point cloud and mesh processing software, open source project. Accessed 7 July 2017

  20. Gooddale CL, Apps MJ, Birdsey RA, Field CB, Heath LS, Houghton RA, Jenkins JC, Kohlmaier GH, Kurz W, Liu S, Nabuurs GJ, Nilsson S, Shvidenko AZ (2002) Forest Carbon Sinks in the northern Hemisphere. Ecol Appl 12:891–899. doi:10.1890/1051-0761(2002)012[0891:FCSITN]2.0.CO;2

  21. Hackenberg J, Morhart C, Sheppard J, Spiecker H, Disney M (2014) Highly accurate tree models derived from terrestrial laser scan data: a method description. Forests 5:1069–1105. doi:10.3390/f5051069

  22. Hackenberg J, Wassenberg M, Spiecker H, Sun D (2015a) Non destructive method for biomass prediction combining TLS derived tree volume and wood density. Forests 6:1274–1300. doi:10.3390/f6041274v

  23. Hackenberg J, Spiecker H, Calders K, Disney M, Raumonen P (2015b) SimpleTree—an efficient open source tool to build tree models from TLS clouds. Forests 6:4245–4294. doi:10.3390/f6114245

  24. Henning JG, Radtke PJ (2006) Detailed stem measurements of standing trees from ground-based scanning lidar. For Sci 52:67–80 (ISSN 0015-749X)

  25. Kankare V, Holopainen M, Vastaranta M, Puttonen E, Yu X, Hyyppä J, Vaaja M, Hyyppä H, Alho P (2013) Individual tree biomass estimation using terrestrial laser scanning. ISPRS J Photogramm Remote Sens 75:64–75. doi:10.1016/j.isprsjprs.2012.10.003

  26. Kankare V, Vauhkonen J, Tanhuanpää T, Holopainen M, Vastaranta M, Joensuu M, Krooks A, Hyyppä J, Hyyppä H, Alho P, Viitala R (2014) Accuracy in estimation of timber assortments and stem distribution—a comparison of airborne and terrestrial laser scanning techniques. ISPRS J Photogramm Remote Sens 97:89–97. doi:10.1016/j.isprsjprs.2014.08.008

  27. Kaufmann E (2001) Estimation of standing timber, growth and cut. In: Brassel P, Lischke H (eds) Swiss National Forest Inventory: methods and models of the second assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp 162–192

  28. Kleinn C (2002) New technologies and methodologies for national forest inventories. Unasylva 53:10–15

  29. Köhl M (2001) Inventory Concept NFI2. In: Brassel P, Lischke H((eds) Swiss National Forest Inventory: methods and models of the second assessment. Swiss Federal Research Institute WSL, Birmensdorf, pp 19–44

  30. Lefsky M, McHale M (2008) Volume estimates of trees with complex architecture from terrestrial laser scanning. J Appl Remote Sens 2(1):19. doi:10.1117/1.2939008

  31. Liang X, Hyyppä J, Kaartinen H, Holopainen M, Melkas T (2012) Detecting changes in forest structure over time with bi-temporal terrestrial laser scanning data. Int J Geo-Inf 1:242–255. doi:10.3390/ijgi1030242

  32. Liang X, Kankare V, Yu X, Hyyppä J, Holopainene M (2014) Automated stem curve measurement using terrestrial laser scanning geoscience and remote sensing. IEEE Trans Geosci Remote Sens 52:1739–1748. doi:10.1109/TGRS.2013.2253783

  33. Maas H-G, Bienert A, Scheller S, Keane E (2008) Automatic forest inventory parameter determination from terrestrial laser scanner data. Int J Remote Sens 29:1579–1593. doi:10.1080/01431160701736406

  34. Miura S, Amacher M, Hofer T, San-Miguel-Ayanz J, Thackway R (2015) Protective functions and ecosystem services of global forests in the past quarter-century. For Ecol Manag 352:35–46. doi:10.1016/j.foreco.2015.03.039

  35. Muukkonen P (2007) Generalized allometric volume and biomass equations for some tree species in Europe. Eur J For Res 126:157–166. doi:10.1007/s10342-007-0168-4

  36. Othmani A, Piboule A, Krebs M, Stolz C, Lew Yan Voon LFC (2011) Towards automated and operational forest inventories with T-Lidar. In: 11th international conference on LiDAR applications for assessing forest ecosystems (SilviLaser 2011), Oct 2011. Hobart

  37. Perez D (2008) Growth and volume equations developed from stem analysis for tectona grandis in Costa Rica. J Trop For Sci 20:66–75 (ISSN 0128-1283)

  38. Pfeifer N, Winterhalder D (2004) 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

  39. Raumonen P, Kaasalainen M, Åkerblom M, Kaasalainen S, Kaartinen H, Vastaranta M, Holopainen M, Disney M, Lewis P (2013) Fast automatic precision tree models from terrestrial laser scanner data. Remote Sens 5:491–520. doi:10.3390/rs5020491

  40. Raumonen P, Casella E, Calders K, Murphy S, Åkerblom M, Kaasalainen M (2015) Massive-scale tree modelling from TLS data. ISPRS Ann Photogramm Remote Sens Spat Inf Sci 43:189–196. doi:10.5194/isprsannals-II-3-W4-189-2015

  41. Remondino F (2003) From point cloud to surface: the modelling and visualization problem. Int Arch Photogramm Remote Sens Spat Inf Sci 34:5/W10

  42. Santini S, Wagner B (2010) RootLAB: a matlab framework for the modeling of tree roots. Technical report no. 696. Department of Computer Science, ETH Zurich, Zurich, Oct 2010

  43. Schmid-Haas P, Werner J (1969) Kontroll-Stichproben: Aufnahmeinstruktion. Bericht Eidgenössischer. Forsch Wald Schnee Landsch 27:22

  44. Seidel D, Fleck S, Leuschner C, Hammett T (2011) Review of ground-based methods to measure the distribution of biomass in forest canopies. Ann For Sci 68:225–244. doi:10.1007/s13595-011-0040-z

  45. Sheppard J, Morhart C, Hackenberg J, Spiecker H (2017) Terrestrial laser scanning as a tool for assessing tree growth. iForest 1:172–179. doi:10.3832/ifor2138-009

  46. Srinivasan S, Sorin C, Popescu M, Eriksson R, Sheridan D, Ku NW (2015) Terrestrial laser scanning as an effective tool to retrieve tree level height, crown width, and stem diameter. Remote Sens 7:1877–1896

  47. Trochta J, Krůček M, Vrška T, Král K (2017) 3D forest: an application for descriptions of three-dimensional forest structures using terrestrial LiDAR. PLoS One 12:e0176871. doi:10.1371/journal.pone.0176871

  48. Wagner B, Santini S, Ingensand H, Gärtner H (2011a) A tool to model 3D coarse-root development with annual resolution. Plant Soil 346:79–96. doi:10.1007/s11104-011-0797-8

  49. Wagner B, Gärtner H, Santini S, Ingensand H (2011b) Cross-sectional interpolation of annual rings within a 3D root model. Dendrochronologia 29:201–210. doi:10.1016/j.dendro.2010.12.003

  50. Wang D, Kankare V, Puttonen E, Hollaus M, Pfeifer N (2017) Reconstructing stem cross section shapes from terrestrial laser scanning. IEEE Geosci Remote Sens Lett 2:272–276. doi:10.1109/LGRS.2016.2638738

  51. Zianis D, Muukkonen P, Mäkipää R, Menuccini M (2005) Biomass and stem volume equations for tree species in Europe. Silva Fenn Monogr 4:63 (ISBN 951-40-1984-9)

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The authors would like to thank Patrick Thee (WSL) for scanning the forest stands. Furthermore, the authors would like to thank Esther Thürig (WSL) for fruitful discussions of the data. Finally we would like to gratefully acknowledge Bronwyn Price (WSL) for the English revision of the manuscript.

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Correspondence to Bettina Wagner.

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The authors declare that they have no conflict of interest.

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Communicated by E. van der Maaten.

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Wagner, B., Ginzler, C., Bürgi, A. et al. An annually-resolved stem growth tool based on 3D laser scans and 2D tree-ring data. Trees 32, 125–136 (2018).

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  • Terrestrial laser scanning
  • Tree rings
  • Annually resolved volume
  • Stem growth model