Using fractal analysis of crown images to measure the structural condition of trees

Murray, Jon and Blackburn, George Alan and Whyatt, James Duncan and Edwards, Christopher James (2018) Using fractal analysis of crown images to measure the structural condition of trees. Forestry, 91 (4). pp. 480-491. ISSN 0015-752X

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Observations of tree canopy structure are routinely used as an indicator of tree condition for the purposes of monitoring tree health, assessing habitat characteristics or evaluating the potential risk of tree failure. Trees are assigned to broad categories of structural condition using largely subjective methods based upon ground-based, visual observations by a surveyor. Such approaches can suffer from a lack of consistency between surveyors; are qualitative in nature and have low precision. In this study, a technique is developed for acquiring, processing and analysing hemispherical images of sessile oak (Quercus petraea (Matt.) Liebl.) tree crowns. We demonstrate that by calculating the fractal dimensions of tree crown images it is possible to define a continuous measurement scale of structural condition and to be able to quantify intra-category variance of tree crown structure. This approach corresponds with traditional categorical methods; however, we recognize that further work is required to precisely define interspecies thresholds. Our study demonstrates that this approach has the potential to form the basis of a new, transferable and objective methodology that can support a wide range of uses in arboriculture, ecology and forest science.

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This is a pre-copy-editing, author-produced PDF of an article accepted for publication in Forestry following peer review. The definitive publisher-authenticated version Jon Murray, George Alan Blackburn, James Duncan Whyatt, Christopher Edwards; Using fractal analysis of crown images to measure the structural condition of trees, Forestry: An International Journal of Forest Research, Volume 91, Issue 4, 1 October 2018, Pages 480–491, is available online at:
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17 May 2018 13:34
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22 Jun 2024 00:30