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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

Abstract

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.

Keywords

Tree shapes Urban planning Nature perception Street trees Green spaces 

Notes

Acknowledgements

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)

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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

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