European Journal of Forest Research

, Volume 136, Issue 3, pp 421–432 | Cite as

Predicting tree preferences from visible tree characteristics

  • Mathias HofmannEmail author
  • Tina Gerstenberg
  • Sten Gillner
Original Paper


This paper presents a psychological perspective to the selection of trees for urban residential areas. Sixty tree species suitable for urban planting sites were rated by lay participants regarding preference. We then used outward tree features to predict the preference ratings. Twenty-five different plant characteristics served as possible predictors in a regression model for tree preference. We found that the distinction between conifers and deciduous trees, the maximum tree height, and the crown height-to-width ratio were valuable predictors for preference, explaining more than 70% of the variance. This adds support for evolutionary theories of landscape preference. The regression model presented in this paper can be applied to calculate a preference estimate for other tree species using their known physical data, which may facilitate tree selection tasks in green space planning. By specifying preference-relevant tree characteristics, our findings may also inform the process of selecting diverse species for sites where a homogenous overall appearance is a planning goal.


Tree shapes Urban planning Nature perception Street trees Green spaces 



We wish to thank Anna Neubauer, Joscha Möller, Anne Albinus, and Anne Neumeister for help with data acquisition. We are grateful to the anonymous reviewers for their time and their helpful comments and suggestions. Funding for this research was provided by the European Union and the Free State of Saxony (SAB Grant 100098207).

Supplementary material

10342_2017_1042_MOESM1_ESM.txt (16 kb)
Supplementary material 1 (txt 16 KB)


  1. Akaike H (1987) Factor analysis and AIC. Psychometrika 52(3):317–332CrossRefGoogle Scholar
  2. Autorengruppe Bildungsberichterstattung (2014) Bildung in Deutschland 2014. Ein indikatorengestützter Bericht mit einer Analyse zur Bildung von Menschen mit Behinderungen, Bertelsmann Stiftung, BielefeldGoogle Scholar
  3. Balling JD, Falk JH (1982) Development of visual preference for natural environments. Environ Behav 14(1):5–28CrossRefGoogle Scholar
  4. Bates D, Mächler M, Bolker BM, Walker SC (2014) lme4: fitting linear mixed-effects models using lme4. ArXiv.
  5. Belsley DA (1991) A guide to using the collinearity diagnostics. Comput Sci Econ Manag 4(1):33–50Google Scholar
  6. Belsley DA, Kuh E, Welsch RE (1980) Regression diagnostics: identifying influential data and sources of collinearity. Wiley, New YorkCrossRefGoogle Scholar
  7. Berkowitz AR, Nilon CH, Hollweg KS (eds) (2003) Understanding urban ecosystems. Springer, New YorkGoogle Scholar
  8. Berman MG, Jonides J, Kaplan S (2008) The cognitive benefits of interacting with nature. Psychol Sci 19(12):1207–1212CrossRefPubMedGoogle Scholar
  9. Bowler DE, Buyung-Ali LM, Knight TM, Pullin AS (2010) A systematic review of evidence for the added benefits to health of exposure to natural environments. BMC Public Health 10(1):456CrossRefPubMedPubMedCentralGoogle Scholar
  10. Bréda NJJ (2003) Ground-based measurements of leaf area index: a review of methods, instruments and current controversies. J Exp Bot 54(392):2403–2417CrossRefPubMedGoogle Scholar
  11. Burns RM, Honkala BH (1990) Hardwoods. Agriculture handbook 654. No. 2 in Silvics of North America. U.S. Department of Agriculture, Forest Service, Washington, DCGoogle Scholar
  12. Camacho-Cervantes M, Schondube JE, Castillo A, MacGregor-Fors I (2014) How do people perceive urban trees? Assessing likes and dislikes in relation to the trees of a city. Urban Ecosyst 17(3):761–773CrossRefGoogle Scholar
  13. Canty A, Ripley BD (2015) Boot: bootstrap R (S-plus) functions. R package version 1.1-9Google Scholar
  14. Cekstere G, Nikodemus O, Osvalde A (2008) Toxic impact of the de-icing material to street greenery in Riga, Latvia. Urban For Urban Green 7(3):207–217CrossRefGoogle Scholar
  15. Chen JM, Rich PM, Gower ST, Norman JM, Plummer S (1997) Leaf area index of boreal forests: theory, techniques, and measurements. J Geophys Res 102(D24):29,429CrossRefGoogle Scholar
  16. Conway TM (2007) Impervious surface as an indicator of pH and specific conductance in the urbanizing coastal zone of New Jersey, USA. J Environ Manag 85(2):308–316CrossRefGoogle Scholar
  17. Conway TM, Urbani L (2007) Variations in municipal urban forestry policies: a case study of Toronto, Canada. Urban For Urban Green 6(3):181–192CrossRefGoogle Scholar
  18. Davison AC, Hinkley DV (1997) Bootstrap methods and their applications. Cambridge University Press, CambridgeCrossRefGoogle Scholar
  19. Donovan GH, Butry DT, Michael YL, Prestemon JP, Liebhold AM, Gatziolis D, Mao MY (2013) The relationship between trees and human health. Am J Prev Med 44(2):139–145CrossRefPubMedGoogle Scholar
  20. Euostat (2013) Rural development statistics by urban–rural typology. Online resource, Eurostat.
  21. Falk JH, Balling JD (2009) Evolutionary influence on human landscape preference. Environ Behav 42(4):479–493CrossRefGoogle Scholar
  22. Fraver S, D’Amato AW, Bradford JB, Jonsson BG, Jönsson M, Esseen PA (2013) Tree growth and competition in an old-growth Picea abies forest of boreal Sweden: influence of tree spatial patterning. J Veg Sci 25(2):374–385CrossRefGoogle Scholar
  23. Gerstenberg T, Hofmann M (2016) Perception and preference of trees: a psychological contribution to tree species selection in urban areas. Urban For Urban Green 15:103–111CrossRefGoogle Scholar
  24. Gillner S, Bräuning A, Roloff A (2014) Dendrochronological analysis of urban trees: climatic response and impact of drought on frequently used tree species. Trees 28(4):1079–1093CrossRefGoogle Scholar
  25. Gillner S, Vogt J, Tharang A, Dettmann S, Roloff A (2015) Role of street trees in mitigating effects of heat and drought at highly sealed urban sites. Landsc Urban Plan 143:33–42CrossRefGoogle Scholar
  26. Ginau A, Opp C, Sun Z, Halik Ü (2013) Influence of sediment, soil, and micro-relief conditions on vitality of Populus euphratica stands in the lower Tarim riparian ecosystem. Quat Int 311:146–154CrossRefGoogle Scholar
  27. Gundersen VS, Frivold LH (2008) Public preferences for forest structures: a review of quantitative surveys from Finland, Norway and Sweden. Urban For Urban Green 7(4):241–258CrossRefGoogle Scholar
  28. Gómez-Baggethun E, Barton DN (2013) Classifying and valuing ecosystem services for urban planning. Ecol Econ 86:235–245CrossRefGoogle Scholar
  29. Hägerhäll CM, Ode Å, Tveit MS, Velarde MD, Colfer CJP, Sarjala T (2010) Forests, human health and well-being in light of climate change and urbanisation. In: Mery G, Katila P, Galloway G, Alfaro RI, Kanninen M, Lobovikov M, Varjo J (eds) Forests and society: responding to global drivers of change, IUFRO world series, chapter 12, vol 25. International Union of Forestry Research Organizations, Wien, pp 223–234Google Scholar
  30. Haluza D, Schönbauer R, Cervinka R (2014) Green perspectives for public health: a narrative review on the physiological effects of experiencing outdoor nature. Int J Environ Res Public Health 11(5):5445–5461CrossRefPubMedPubMedCentralGoogle Scholar
  31. Hartig T, Evans G, Jamner L, Davis D, Gärling T (2003) Tracking restoration in natural and urban field settings. J Environ Psychol 23(2):109–123CrossRefGoogle Scholar
  32. Hartig T, Mitchell R, de Vries S, Frumkin H (2014) Nature and health. Annu Rev Public Health 35(1):207–228CrossRefPubMedGoogle Scholar
  33. Hofmann M, Westermann JR, Kowarik I, van der Meer E (2012) Perceptions of parks and urban derelict land by landscape planners and residents. Urban For Urban Green 11(3):303–312CrossRefGoogle Scholar
  34. Honold J, Lakes T, Beyer R, van der Meer E (2015) Restoration in urban spaces: nature views from home, greenways, and public parks. Environ Behav 48(6):796–825CrossRefGoogle Scholar
  35. IPCC (2014) Climate change 2014. Synthesis report, United Nations Intergovernmental Panel on Climate Change, GenèveGoogle Scholar
  36. James P, Tzoulas K, Adams M, Barber A, Box J, Breuste J, Elmqvist T, Frith M, Gordon C, Greening K, Handley J, Haworth S, Kazmierczak A, Johnston M, Korpela K, Moretti M, Niemelä J, Pauleit S, Roe M, Sadler J, Thompson CW (2009) Towards an integrated understanding of green space in the European built environment. Urban For Urban Green 8(2):65–75CrossRefGoogle Scholar
  37. Jo HK, Ahn TW (2012) Landscape preferences for greenspace structures. J For Sci 28(1):56–62Google Scholar
  38. Johnson A (1995) The good, the bad and the ugly: science, aesthetics and environmental assessment. Biodivers Conserv V 4(7):758–766CrossRefGoogle Scholar
  39. Jucker T, Bouriaud O, Avacaritei D, Dănilă I, Duduman G, Valladares F, Coomes DA (2014) Competition for light and water play contrasting roles in driving diversity–productivity relationships in Iberian forests. J Ecol 102(5):1202–1213CrossRefGoogle Scholar
  40. Keniger L, Gaston K, Irvine K, Fuller R (2013) What are the benefits of interacting with nature? Int J Environ Res Public Health 10(3):913–935CrossRefPubMedPubMedCentralGoogle Scholar
  41. Koffka K (1922) Perception: an introduction to the Gestalt-Theorie. Psychol Bull 19(10):531–585CrossRefGoogle Scholar
  42. Kuo FE, Sullivan WC (2001) Environment and crime in the inner city. Environ Behav 33(3):343–367CrossRefGoogle Scholar
  43. Lohr VI, Pearson-Mims CH (2006) Responses to scenes with spreading, rounded, and conical tree forms. Environ Behav 38(5):667–688CrossRefGoogle Scholar
  44. Maas J, Verheij RA, Groenewegen PP, de Vries S, Spreeuwenberg P (2006) Green space, urbanity, and health: how strong is the relation? J Epidemiol Community Health 60(7):587–592CrossRefPubMedPubMedCentralGoogle Scholar
  45. Mitchell R, Popham F (2008) Effect of exposure to natural environment on health inequalities: an observational population study. Lancet 372(9650):1655–1660CrossRefPubMedGoogle Scholar
  46. Orians GH (2001) An evolutionary perspective on aesthetics. Bull Psychol Arts 2:25–29Google Scholar
  47. R Core Team (2015) R: a language and environment for statistical computing. R Foundation for Statistical Computing, WienGoogle Scholar
  48. Radoglou K, Dobrowolska D, Spyroglou G, Nicolescu V (2009) A review on the ecology and silviculture of limes (Tilia cordata Mill., Tilia platyphyllos Scop. and Tilia tomentosa Moench.) in Europe. Die Bodenkult 60(3):9–19Google Scholar
  49. Rambow R, Bromme R (1995) Implicit psychological concepts in architects’ knowledge: how large is a large room? Learn Instr 5(4):337–355CrossRefGoogle Scholar
  50. Säumel I, Weber F, Kowarik I (2016) Toward livable and healthy urban streets: roadside vegetation provides ecosystem services where people live and move. Environ Sci Policy 62:24–33CrossRefGoogle Scholar
  51. Schroeder H, Flannigan J, Coles R (2006) Residents’ attitudes toward street trees in the UK and US communities. Arboric Urban For 32(5):236–246Google Scholar
  52. Sommer R (1997) Further cross-national studies of tree form preference. Ecol Psychol 9(2):153–160CrossRefGoogle Scholar
  53. Sommer R, Summit J (1995) An exploratory study of preferred tree form. Environ Behav 27(4):540–557CrossRefGoogle Scholar
  54. Sommer R, Summit J (1996) Cross-national rankings of tree shape. Ecol Psychol 8(4):327–341CrossRefGoogle Scholar
  55. Stamps AE III (2010) Use of static and dynamic media to simulate environments: a meta-analysis. Percept Mot Skills 111(2):355–364CrossRefPubMedGoogle Scholar
  56. Statistisches Bundesamt (2015) Bildungsstand der Bevölkerung. Technical report, Statistisches Bundesamt, WiesbadenGoogle Scholar
  57. Summit J, Sommer R (1999) Further studies of preferred tree shapes. Environ Behav 31(4):550–576CrossRefGoogle Scholar
  58. Sæbø A, Borzan Ž, Ducatillion C, Hatzistathis A, Lagerström T, Supuka J, García-Valdecantos JL, Rego F, Van Slycken J (2005) The selection of plant materials for street trees, park trees and urban woodland. In: Konijnendijk C, Nilsson K, Randrup T, Schipperijn J (eds) Urban forests and trees. Springer, Heidelberg, pp 257–280Google Scholar
  59. Thomsen P, Bühler O, Kristoffersen P (2016) Diversity of street tree populations in larger Danish municipalities. Urban For Urban Green 15:200–210CrossRefGoogle Scholar
  60. Tinio PPL, Leder H (2009) Natural scenes are indeed preferred, but image quality might have the last word. Psychol Aesthet Creativity Arts 3(1):52–56CrossRefGoogle Scholar
  61. Todorovic D (2008) Gestalt principles. Scholarpedia 3(12):5345CrossRefGoogle Scholar
  62. United Nations (2014) World urbanization prospects: the 2014 revision. United Nations, Department of Economic and Social Affairs, Population Division, New YorkGoogle Scholar
  63. Vogt J, Gillner S, Hofmann M, Tharang A, Dettmann S, Gerstenberg T, Schmidt C, Gebauer H, van de Riet K, Berger U, Roloff A (2017) Citree: a database supporting tree selection for urban areas in temperate climate. Lands Urban Plan 157:14–25CrossRefGoogle Scholar
  64. von Döhren P, Haase D (2015) Ecosystem disservices research: a review of the state of the art with a focus on cities. Ecol Ind 52:490–497CrossRefGoogle Scholar
  65. Wertheimer M (1923) Untersuchungen zur Lehre von der Gestalt. II. Psychol Res 4(1):301–350CrossRefGoogle Scholar
  66. Zensusdatenbank (2013) Zensus 2011. Online resource, Statistische Ämter des Bundes und der Länder.
  67. Zheng B, Zhang Y, Chen J (2011) Preference to home landscape: wildness or neatness? Landsc Urban Plan 99(1):1–8CrossRefGoogle Scholar

Copyright information

© Springer-Verlag Berlin Heidelberg 2017

Authors and Affiliations

  1. 1.Swiss Federal Institute for Forest, Snow and Landscape Research WSLBirmensdorfSwitzerland
  2. 2.Forest Research Institute Baden-WürttembergFreiburgGermany
  3. 3.Centre for Interdisciplinary Research in Technological Development (ZIT)TU DresdenDresdenGermany
  4. 4.Institute of Forest Botany and Forest ZoologyTU DresdenTharandtGermany

Personalised recommendations