References
  1. Bayer, D., Seifert, S., Pretzsch, H. 2013. 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. Trees 27: 1035-1047. https://doi.org/10.1007/s00468-013-0854-4
  2. Bergeron, Y., Leduc, A., Hervery, B.D., Gauthier, S. 2002. Natural fore regime: A guid for sustainable management of the Canadian boreal forest. Silva Fennica 36: 553. https://doi.org/10.14214/sf.553
  3. Butler, B.J., Leatherberry, E.C. 2004. America’s family forest owners. Journal of Forestry 102: 4-14. https://doi.org/10.1093/jof/102.7.4
  4. Cajander, A.K. 1913. Ueber Waldtypen. Acta Forestalia Fennica 1: article id 7526. https://doi.org/10.14214/aff.7526
  5. Calders, K., Adams, J., Armston, J., Bartholomeus, H., Bauwens, S., Bentley, L.P., Chave, J., Danson, F.M., Demol, M., Disney, M., Gaulton, R., Krishna Moorthy, S.M., Levick, S., Saarinen, N., Schaaf, C., Stovall, A., Terryn, L., Wilkes, P., Verbeeck, H. 2020. Terrestrial laser scanning in forest ecology: Expanding the horizon. Remote Sensing of Environment 251: 112102. https://doi.org/10.1016/j.rse.2020.112102
  6. Calders, K., Lewis, P., Disney, M., Verbesselt, J., Herold, M. 2013. Investigating assumptions of crown archetypes for modelling LiDAR retursn. Remote Sensing of Environment 134: 39-49. https://doi.org/10.1016/j.rse.2013.02.018
  7. Calders, K., Origo, N., Disney, M., Nightingale, J., Woodgate, W., Armston, J., Lewis, P. 2018. Variability and bias in active and passive ground-based measurements of effective plant, wood and leaf area index. Agricultural and Forest Meteorology 252: 231-240. https://doi.org/10.1016/j.agrformet.2018.01.029
  8. Camarretta, N., Harrison, P.A., Bailey, T., Potts, B., Lucieer, A., Davidson, N., Hunt, M. 2020. Monitoring forest structure to guide adaptive management of forest restoration: a review of remote sensing approaches. New Forests 51: 573-596. https://doi.org/10.1007/s11056-019-09754-5
  9. Dorji, Y., Annighöfer, P., Ammer, C., Seidel, D. 2019. Response of Beech (Fagus sylvatica L.) Trees to Competition—New Insights from Using Fractal Analysis. Remote Sensing 11: 2656. https://doi.org/10.3390/rs11222656
  10. Ehbrecht, M., Schall, P., Ammer, C., Seidel, D. 2017. Quantifying stand structural complexity and its relationship with forest management, tree species diversity and microclimate. Agricultural and Forest Meteorology 242: 1-9. http://dx.doi.org/10.1016/j.agrformet.2017.04.012
  11. Fedrowitz, K., Koricheva, J., Baker, S.C., Lindenmayer, D.B., Palik, B., Rosenvald, R., Beese, W., Franklin, J.F., Kouki, J., MacDonald, E., Messier, C., Svedrup-Thygeson, A., Gustafsson, L. 2014. Can retention forestry help conserve biodiversity? A meta-analysis. Journal of Applied Ecology 51: 1669-1679. https://doi.org/10.1111/1365-2664.12289
  12. Georgi, L., Kunz, M., Fichtner, A., Härdtle, W., Reich, K.F., Strum, K., Welle, T., von Oheimb, G. 2018. Long-term abandonment of forest management has a strong impact on tree morphology and wood volume allocation pattern of European beech (Fagus sylvatica L.). Forests 9: 704. https://doi.org/10.3390/f9110704
  13. Gough, C.M., Atkins, J.W., Fahey, R.T., Hardiman, B.S. 2019. High rates of primary production in structurally complex forests. Ecology 100: e02864. https://doi.org/10.1002/ecy.2864
  14. Hardiman, B.S., Bohrer, G., Gough, C.M., Vogel, C.S., Curtis, P.S. 2011. The role of canopy structural complexity in wood net primary production of a maturing northern deciduous forest. Ecology 92: 1818-1827. https://doi.org/10.1890/10-2192.1
  15. Hugosson, M., Ingermason, F. 2004. Objectives and motivations of small-scale forest owners; theoretical modelling and qualitative assessment. Silva Fennica 38: 430. https://doi.org/10.14214/sf.430
  16. Isenburg, M. 2019. LAStools—Efficient LiDAR Processing Software, (version 181001 academic); rapidlasso GmbH: Gilching, Germany. http://rapidlasso.com/LAStools
  17. Ishii, H.T., Tanabe, S., Hiura, T. 2004. Exploring the relationship among canopy structure, stand productivity, and biodiversity of temperate forest ecosystems. Forest Science 50: 342-355.
  18. Jacobs, M., Rais, A., Pretzsch, H. 2020. Analysis of stand density effects on the stem form of Norway spruce trees and volume miscalculation by traditional form factor equations using terrestrial laser scanning (TLS). Canadian Journal of Forest Research 50: 51-64. https://doi.org/10.1139/cjfr-2019-0121
  19. Juchheim, J., Annighöfer, P., Ammer, C., Calders, K., Raumonen, P., Seidel, D. 2017. How management intensity and neighborhood composition affect the structure of beech (Fagus sylvatica L.) trees. Trees 31(5): 1723-1735. https://doi.org/10.1007/s00468-017-1581-z
  20. Kozlowski, T. 1964. Shoot growth in woody plants. Botanical Review 30(3): 335-392. Available http://www.jstor.org/stable/4353695 [cited October 8, 2020]
  21. Kuuluvainen, T. 2009. Forest management and biodiversity conservation based on natural ecosystem dynamics in Northern Europe: The complexity challenge. AMBIO: A Journal of the Human Environment 38: 309-315. https://doi.org/10.1579/08-A-490.1
  22. Liang, X., Litkey, P., Hyyppä, J., Kaartinen, H., Vastaranta, M., Holopainen, M. 2012. Automatic stem mapping using single-scan terrestrial laser scanning. IEEE Transactions on Geoscience and Remote Sensing 50: 661-670. https://doi.org/10.1109/TGRS.2011.2161613
  23. Mandelbrot, B. B. 1977. The fractal geometry of nature . New York, NY: W.H. Freeman Company.
  24. Mäkinen, H., Isomäki, A. 2004. Thinning intensity and long-term changes in increment and stem form of Scots pine trees. Forest Ecology and Management 203: 21-34. https://doi.org/10.1016/j.foreco.2004.07.028
  25. McElhinny, C., Gibbons, P., Brack, C., Bauhus, J. 2005. Forest and woodland stand structural complexity: Its definition and measurement. Forest Ecology and Management 218: 1-24. https://doi.org/10.1016/j.foreco.2005.08.034
  26. Messier, C., Puettmann, K.J., Coates, K.D. (Eds) 2013. Managing forests as complex adaptive systems – Building resilience to the challenge of global change. Routledge, Oxford, UK. 353 p.
  27. Metz, J., Seidel, D., Schall, P., Scheffer, D., Schulze, E.-D. 2013. Crown modelling by terrestrial laser scanning as an approach to assess the effect of aboveground intra- and interspecific competition on tree growth. Forest Ecology and Management 310: 275-288. https://doi.org/10.1016/j.foreco.2013.08.014
  28. Meyer, F., Beucher, S. 1990. Morphological segmentation. Journal of Visual Communication and Image Representation 1:21–46. https://doi.org/10.1016/1047-3203(90)90014-M
  29. Laasasenaho, J. 1982. TaperCurve and Volume Functions forPine, Spruce and Birch ; Metsäntutkimuslaitos: Vantaa, Finland.
  30. Osawa, A., Kurachi, N. 2004. Spatial leaf distribution and self-thinning exponent of Pinus banksiana and Populus tremuloides . Trees 18: 327-338. DOI 10.1007/s00468-003-0310-y
  31. Pickover, C.A. 2009. The Math Book – From Pythagoras to the 57th Dimension, 250 Milestones in the History of Mathematics. Sterling Publishing. 532 p.
  32. Pinheiro, J., Bates, M. D., DebRoy, S.S., Sarkar, D. 2013. Nlme: Linear and nonlinear mixed effects models. R package.
  33. Popescu, S.C., Wynne, R.H. 2004. Seeing the Trees in the Forest. Photogrammetric Engineering & Remote Sensing 70: 589–604. https://doi.org/10.14358/PERS.70.5.589
  34. Pretzsch, H. 2014. Canopy space filling and tree crown morphology in mixed-species stands compared with monocultures. Forest Ecology and Management 327: 251-264. https://doi.org/10.1016/j.foreco.2014.04.027
  35. Puettmann, K.J., Wilson, S.McG., Baker, S.C., Donoso, P.J., Drössler, L., Amente, G., Harvey, B.D., Knoke, T., Lu, Y., Nocentini, S., Putz, F.E., Yoshida, T. Bauhus, J. 2015. Silvicultural alternatives to conventional even-aged forest management – what limits global adoption? Forest Ecosystems 2: 8. https://doi.org/10.1186/s40663-015-0031-x
  36. Ritter, T., Schwarz, M., Tockner, A., Leisch, F., Nothdurft, A. 2017. Automatic mapping of forest stands based on three-dimensional point clouds derived from terrestrial laser-scanning. Forests 8 (8): 265. https://doi.org/10.3390/f8080265
  37. Roxburgh, S., Noble, I. 2013. Encyclopedia of Biodiversity. 2nd Edition. Academic Press. 5504 p.
  38. Saarinen, N., Kankare, V., Vastaranta, M., Luoma, V., Pyörälä, J., Tanhuanpää, T., Liang, X., Kaartinen, H., Kukko, A., Jaakkola, A., Yu, X., Holopainen, M., Hyyppä, J. 2017. Feasibility of Terrestrial Laser Scanning for Collecting Stem Volume Information from Single Trees. ISPRS Journal of Photogrammetry and Remote Sensing 123:140-158. https://doi.org/10.1016/j.isprsjprs.2016.11.012
  39. Saarinen, N., Kankare, V., Yrttimaa, T., Viljanen, N., Honkavaara, E., Holopainen, M., Hyyppä, J., Huuskonen, S., Hynynen, J., Vastaranta, M. 2020. Assessing the effects of thinning on stem growth allocation of individual Scots pine trees. Forest Ecology and Management 474: 118344. https://doi.org/10.1016/j.foreco.2020.118344
  40. Shenker, O.R. 1994. Fractal geometry is not the geometry of nature. Studies in History and Philosophy of Science Part A. 25: 967-981. https://doi.org/10.1016/0039-3681(94)90072-8
  41. Seidel, D. 2018. A holistic approach to determine tree structural complexity based on laser scanning data and fractal analysis. Ecology and Evolution 8: 128-134. https://doi.org/10.1002/ece3.3661
  42. Seidel, D., Annighöfer, P., Stiers, M., Zemp, C.D., Burkardt, K., Ehbrecht, M., Willim, K., Kreft, H., Hölscher, D., Ammer, C. 2019a. How a measure of tree structural complexity relates to architectural benefit-to-cost ratio, light availability, and growth of trees. Ecology and Evolution 9: 7134-7142. https://doi.org/10.1002/ece3.5281
  43. Seidel, D., Ehbrecht, M. Dorji, Y., Jambay, J., Ammer, C., Annighöfer, P. 2019b. Identifying architectural characteristics that determine tree structural complexity. Trees 33: 911-949. https://doi.org/10.1007/s00468-019-01827-4
  44. Seidel, D., Leuschner, C., Müller, A., Krause, B. 2011. Crown plasticity in mixed forests–Quantifying asymmetry as a measure of competition using terrestrial laser scanning. Forest Ecology and Management 261: 2123-2132. https://doi.org/10.1016/j.foreco.2011.03.008
  45. Tomlinson, P.B. 1983. Tree Architecture: New approaches help to define the elusive biological property of tree form. American Scientist 71: 141-149. Available at: https://www.jstor.org/stable/27851897 [cited July 3, 2020]
  46. Urquhart, J., Cortney, P. 2011. Seeing the owner behind the trees: A typology of small-scale private woodland owners in England. Forest Policy and Economics 13: 535-544. https://doi.org/10.1016/j.forpol.2011.05.010
  47. White, J. 1980. Demographic factors in population of plants. In: Solbrig, O.T. (Ed.) Demography and Evaluation in Plant Populations. Botanical Monographs 15. University of California Press. Berkeley and Los Angeles, USA. 222 p.
  48. von Gadow, K., Zhang, Z.Y., Wehenkel, C., Pommerening, A., Corral-Rivas, J., Korol, M., Myklush, S., Hui, G.Y., Kiviste, A., Zhao, X.H. 2012. Forest Structure and Diversity. In: Pukkala, T., von Gadow, K. (eds) Continuous Cover Forestry. Managing Forest Ecosystems 23. Spronger, Dodrecth, The Netherlands. pp. 29-83. http://doi.org/10.1007/978-94-007-2202-6_2
  49. Yrttimaa, T., Saarinen, N., Kankare, V., Hynynen, J., Huuskonen, S., Holopainen, M., Hyyppä, J., Vastaranta, M. 2020. Performance of terrestrial laser scanning to characterize managed Scots pine (Pinus sylvestris L.) stands is dependent on forest structural variation. EarthArXiv. March 5. https://doi.org/10.31223/osf.io/ybs7c
  50. Yrttimaa, T., Saarinen, N., Kankare, V., Liang, X., Hyyppä, J., Holopainen, M., Vastaranta, M. 2019. Investigating the Feasibility of Multi-Scan Terrestrial Laser Scanning to Characterize Tree Communities in Southern Boreal Forests. Remote Sensing 11: 1423. https://doi.org/10.3390/rs11121423
  51. Zenner, EK. 2015. Differential growth response to increasing growing stock and structural complexity in even- and uneven-sized mixedPicea abies stands in southern Finland. Canadian Journal of forest Research 46: 1195 – 1204. https://doi.org/10.1139/cjfr-2015-0400