, Volume 27, Issue 4, pp 1035–1047 | Cite as

Structural crown properties of Norway spruce (Picea abies [L.] Karst.) and European beech (Fagus sylvatica [L.]) in mixed versus pure stands revealed by terrestrial laser scanning

  • Dominik BayerEmail author
  • Stefan Seifert
  • Hans Pretzsch
Original Paper


How tree morphology develops in mixed-species stands is essential for understanding and modelling mixed-stand dynamics. However, research so far focused on the morphological variation between tree species and neglected the variation within a species depending on intra- and interspecific competition. Our study, in contrast, addresses crown properties of nine mature Norway spruces (Picea abies [L.] Karst.) of a pure stand and compares them with ten spruces growing in mixture with European beech (Fagus sylvatica [L.]). The same was done with 11 pure stand beeches and 12 beeches growing in mixture with spruce. Through application of a terrestrial laser scanner and a new skeletonization approach, we deal with both species’-specific morphological traits such as branch angle, branch length, branch bending, crown volume and space occupation of branches within the crown, some of which were hardly accessible so far. Special attention is paid to distinct differences between trees growing in mixed and pure stands: for spruce, our study reveals significantly longer branches and greater crown volumes in the mixed stand when compared to the pure stand. In case of European beech, individuals growing in mixture show flatter branch angles, more distinct ramification, greater crown volumes and a lower share of a single branch’s space occupation in the total crown volume. The results show that the presented methods yield detailed information on the morphological traits analyzed in this study and that interspecific competition on its own may have a significant impact on crown structures. Implications for production ecology and stand dynamics of mixed-species forests are discussed.


Crown allometry Crown plasticity Allometric variability TLS Skeletonization 



We thank the Bavarian State Ministry for Nutrition, Agriculture and Forestry for permanent support of the project W 07 “Long-term experimental plots for forest growth and yield research” (# 781-20400-2012). Thanks are also due to Dr. Peter Biber for advice on the statistical analysis, Gerhard Schütze for participating in the field work and assistance in the data preparation, Ottilie Arz for assistance in field work, the skeletonization work as well as artwork creation in the course of her master thesis and reviewers for their constructive criticism.


  1. Assmann E (1961) Waldertragskunde. Organische Produktion, Struktur, Zuwachs und Ertrag von Waldbeständen. BLV Verlagsgesellschaft, MünchenGoogle Scholar
  2. Assmann E (1970) The principles of forest yield study. Pergamon Press, OxfordGoogle Scholar
  3. Badoux E (1946) Krone und Zuwachs. Mitt Schweiz Anst Forstl Versuchswesen 24:405–513Google Scholar
  4. Bauhus J, van Winden AP, Nicotra AB (2004) Above-ground interactions and productivity in mixed-species plantations of Acacia mearnsii and Eucalyptus globulus. Can J For Res 34:686–694CrossRefGoogle Scholar
  5. Bazzaz FA (1975) Plant species diversity in old-field successional ecosystems in southern Illinois. Ecology 56:485–488CrossRefGoogle Scholar
  6. Binkley D, Stape JL, Ryan MG (2004) Thinking about efficiency of resource use in forests. For Ecol Manag 193:5–16CrossRefGoogle Scholar
  7. Binkley D, Campoe OC, Gspaltl M, Forrester DI (2011) Light absorption and use efficiency in forests: Why patterns differ for trees and stands. For Ecol Manage. doi: 10.1016/j.foreco.2011.11.002
  8. Bolte A, Villanueva I (2006) Interspecific competition impacts on the morphology and distribution of fine roots in European beech (Fagus sylvatica L.) and Norway spruce (Picea abies (L.) Karst.). Eur J For Res 125:15–26CrossRefGoogle Scholar
  9. Bremer M, Jochem A, Rutzinger M (2012) Comparison of branch extraction for deciduous single trees in leaf-on and leaf-off conditions—an eigenvector based approach for terrestrial laser scanning point clouds. EARSeL eProc 11(1):33–43Google Scholar
  10. Bucksch AK (2011) Revealing the skeleton from imperfect point clouds. Dissertation, Delft University of Technology Google Scholar
  11. Bucksch AK, Lindenbergh R, Menenti M (2010) SkelTre—Robust skeleton extraction from imperfect point clouds. Vis Comput 26:1283–1300CrossRefGoogle Scholar
  12. Burger H (1939) Holz, Blattmenge und Zuwachs. Mitt Schweiz Anst Forstl Versuchswesen 1939–1953, vol 15–29Google Scholar
  13. Côté JF, Fournier RA, Egli R (2011) An architectural model of trees to estimate forest structural attributes using terrestrial LiDAR. Environ Model Softw 26:761–777CrossRefGoogle Scholar
  14. Edelsbrunner H, Mücke EP (1994) Three-Dimensional Alpha Shapes. ACM Transact Graph 13:43–72CrossRefGoogle Scholar
  15. Enquist BJ, Brown JH, West GB (1998) Allometric scaling of plant energetics and population density. Nature 395:163–165CrossRefGoogle Scholar
  16. Fischer A (1995) Forstliche Vegetationskunde. Pareys Studientexte 82. Blackwell Wissenschaft, Berlin, WienGoogle Scholar
  17. Grams TEE, Lüttge U (2011) Space as a resource. Prog Bot 72:349–370CrossRefGoogle Scholar
  18. Grams TEE, Kozovits AR, Winkler JB, Sommerkorn M, Blaschke H, Häberle K-H, Matyssek R (2002) Quantifying competitiveness in woody plants. Plant Biol 4:153–158CrossRefGoogle Scholar
  19. Hari P (1985) Theoretical aspects of eco-physiolocigal research. In: Tigerstedt PMA, Puttonen P, Koski V (eds) Crop physiology of forest trees. Helsinki Univ Press, Helsinki, pp 21–30 336pGoogle Scholar
  20. Hilker T, van Leeuwen M, Coops NC, Wulder MA, Newnham GJ, Jupp DLB, Culvenor DS (2010) Comparing canopy metrics derived from terrestrial and airborne laser scanning in a Douglas-fir dominated forest stand. Trees 24:819–832CrossRefGoogle Scholar
  21. Huang P, Pretzsch H (2010) Using terrestrial laser scanner for estimating leaf areas of individual trees in a conifer forest. Trees 24:609–619CrossRefGoogle Scholar
  22. Lawton JH (1983) Plant architecture and the diversity of phytophagous insects. Ann Rev Entomol 28:23–39CrossRefGoogle Scholar
  23. MacArthur RH, MacArthur JW (1961) On bird species diversity. Ecology 42:594–598CrossRefGoogle Scholar
  24. Maneewongvatana S, Mount D (1999) It’s okay to be skinny, if your friends are fat. In: Proceedings of the 4th Annual CGC Workshop on Computational GeometryGoogle Scholar
  25. Matyssek R, Agerer R, Ernst D, Munch JC, Oßwald W, Pretzsch H, Priesack E, Schnyder H, Treutter D (2005) The Plant’s Capacity in Regulating Resource Demand. Plant Physiol 7:560–580Google Scholar
  26. McCoy ED, Bell SS (1991) Habitat structure: the evolution and diversification of a complex topic. In: Bell SS, McCoy ED, Mushinsky HR (eds) Habitat structure: the physical arrangement of objects in space. London, Chapman & Hall, pp 3–27CrossRefGoogle Scholar
  27. Niklas KJ (1994) Plant Allometry. Univ Chicago Press, ChicagoGoogle Scholar
  28. Oldemann RAA (1990) Forests: elements of Silvology. Springer, BerlinCrossRefGoogle Scholar
  29. Poorter H, Niklas KJ, Reich PB, Oleksyn J, Poot P, Mommer L (2012) Biomass allocation to leaves, stems and roots: meta-analysis of interspecific variation and environmental control. New Phytol 193:30–50PubMedCrossRefGoogle Scholar
  30. Pretzsch H (1992) Modellierung der Kronenkonkurrenz von Fichte und Buche in Rein- und Mischbeständen. AFJZ 163(11/12):203–213Google Scholar
  31. Pretzsch H (2003) Diversität und Produktivität von Wäldern. AFJZ 174:88–98Google Scholar
  32. Pretzsch H (2006) Species-specific allometric scaling under self-thinning. Evidence from long-term plots in forest stands. Oecologia 146:572–583PubMedCrossRefGoogle Scholar
  33. Pretzsch H (2009) Forest dynamics, growth and yield: From measurement to model. Springer, BerlinGoogle Scholar
  34. Pretzsch H, Dieler J (2012) Evidence of variant intra- and interspecific scaling of tree crown structure and relevance for allometric theory. Oecologia. doi: 10.1007/s00442-011-2240-5
  35. Pretzsch H, Schütze G (2005) Crown allometry and growing space efficiency of Norway spruce (Picea abies (L.) Karst.) and European beech (Fagus sylvatica L.) in pure and mixed stands. Plant Biol 7:628–639PubMedCrossRefGoogle Scholar
  36. Pretzsch H, Schütze G (2009) Transgressive overyielding in mixed compared with pure stands of Norway spruce and European beech in Central Europe: evidence on stand level and explanation on individual tree level. Eur J For Res 128:183–204CrossRefGoogle Scholar
  37. Pretzsch H, Seifert S, Huang P (2011) Beitrag des terrestrischen Laserscannings zur Erfassung der Struktur von Baumkronen. Schweiz Z Forstwes 162:186–194Google Scholar
  38. Pretzsch H, Block J, Dieler J, Dong PH, Kohnle U, Nagel J, Spellmann H, Zingg A (2010) Comparison between the productivity of pure and mixed stands of Norway spruce and European beech along an ecological gradient. Ann For Sci 67. doi: 10.1051/forest/2010037
  39. Price CA, Gilooly JF, Allen AP, Weitz JS, Niklas KJ (2010) The metabolic theory of ecology: prospects and challenges for plant biology. New Phytol 188:696–710PubMedCrossRefGoogle Scholar
  40. Purves DW, Lichstein JW, Pacala SW (2007) Crown plasticity and competition for canopy space: a new spatially implicit model parameterized for 250 North American tree species. PLoS ONE 2:e870. doi: 10.1371/journal.pone.0000870 PubMedCrossRefGoogle Scholar
  41. Ramachandran P, Varoquaux G (2008) Mayavi: making 3D data visualization reusable. In: Varoquaux G, Vaught T, Millman J (eds) Proceedings of the 7th Python in Science Conference. Pasadena, CA USA, pp 51–56Google Scholar
  42. Richards AE, Forrester DI, Bauhus J, Scherer-Lorenzen M (2010) The influence of mixed tree plantations on the nutrition of individual species: a review. Tree Physiol 30:1192–1208PubMedCrossRefGoogle Scholar
  43. Röhle H, Huber W (1985) Untersuchungen zur Methode der Ablotung von Kronenradien und der Berechnung von Kronengrundflächen. Forstarchiv 56:238–243Google Scholar
  44. Roloff A (2001) Baumkronen. Verständnis und praktische Bedeutung eines komplexen Natur-phänomens, UlmerGoogle Scholar
  45. Seifert T (2003) Integration von Holzqualität und Holzsortierung in behandlungssensitive Waldwachstumsmodelle. Dissertation, Technical University of Munich Google Scholar
  46. Tews J, Brose U, Grimm V, Tielbörger K, Wichmann MC, Schwager M, Jeltsch F (2004) Animal species diversity driven by habitat heterogeneity/diversity: the importance of keystone structures. J Biogeogr 31:79–92CrossRefGoogle Scholar
  47. van Leeuwen M, Hilker T, Coops NC, Frazer G, Wulder MA, Newnham GJ, Culvenor DS (2011) Assessment of standing wood and fiber quality using ground and airborne laser scanning: a review. For Ecol Manag 261:1467–1478CrossRefGoogle Scholar
  48. von Droste zu Hülshoff B (1969) Struktur und Biomasse eines Fichtenbestandes auf Grund einer Dimensionsanalyse an oberirdischen Baumorganen. Ph.D thesis, LMU München, 209 pGoogle Scholar
  49. Vosselman G, Maas HG (2010) Airborne and terrestrial laser scanning. Whittles Publishing, DunbeathGoogle Scholar
  50. Walter H (1931) Die Hydratur der Pflanzen und ihre physiologisch-ökologische Bedeutung. Gustav Fischer Verlag, JenaGoogle Scholar
  51. West GB, Enquist BJ, Brown JH (2009) A general quantitative theory of forest structure and dynamics. PNAS 106:7040–7045PubMedCrossRefGoogle Scholar
  52. Wilhelmsson L, Arlinger J, Spångberg K, Lundqvist SO, Grahn T, Hedenberg Ö, Olsson L (2002) Models for predicting wood properties in Stems of picea abies and pinus sylvestris in Sweden. Scand J For Res 17:330–350CrossRefGoogle Scholar
  53. Zeide B (1998) Fractal analysis of foliage distribution in loblolly pine crowns. Can J For Res 28:106–114CrossRefGoogle Scholar
  54. Zobel B, van Buijtenen J (1989) Wood Variation—its causes and control. Springer, BerlinCrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2013

Authors and Affiliations

  1. 1.Department of Ecology and Ecosystem Management, Chair for Forest Growth and Yield ScienceTechnische Universität MünchenFreisingGermany
  2. 2.Department of Forest and Wood ScienceStellenbosch UniversityMatielandSouth Africa

Personalised recommendations