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Ari OkunFollow


Access Type

Open Access Thesis

Document Type


Degree Program

Environmental Conservation

Degree Type

Master of Science (M.S.)

Year Degree Awarded


Month Degree Awarded



Tree risk assessment is an important arboricultural practice used to determine a tree’s risk of failing and causing damage to property or injury to people. Risk assessment includes three assessments: the likelihood of failure, the likelihood of striking a target, and the severity of consequences. To assess the likelihood of tree failure, arborists consider factors that affect the loads a tree experiences (for example, whether it is sheltered or exposed to strong winds, whether the crown is porous or dense, and how tall and wide is the crown) and factors that affect its load-bearing capacity (for example, what is the strength of wood in the tree and how large are the cross-sectional areas of branches, trunk and roots). They also visually inspect a tree for defects that reduce the tree’s load-bearing capacity. Symptoms of decay include cavities, dead/sloughing bark, depressions, cracks, resin/sap flow, excessive basal tapering and canopy dieback. Signs of pathogens may also indicate the presence of decay, including fruiting bodies (mushrooms/conks), fungal mycelia, bacterial ooze, and nematodes. But the presence of symptoms or signs alone does not necessarily correlate with the full extent of decay. When visual inspections are inconclusive, additional tools (such as mallets to sound a decayed area) or advanced diagnostic technologies (such as resistance drilling and sonic tomography) may be needed. The industry consensus is that tools and advanced technologies provide more accurate information regarding the internal condition of a tree, however there are comparatively few 3 | P a g e rigorous data to confirm whether the tools provide meaningful information for arborists assessing tree risk. The goal of this study was to understand better whether the additional information provided by advanced assessment techniques altered an experienced risk assessor’s rating of the likelihood of failure compared to their visual assessment. To investigate this question, I compared likelihood of failure ratings for 30 trees assessed by 18 arborists who used visual and advanced decay detection techniques. Mean likelihood of failure ratings for assessments derived using sonic tomography were higher compared to the other assessment techniques. However, when assessors consulted with a peer about their likelihood of failure ratings after performing each of the assessment techniques, mean likelihood of stem failure ratings were lower. Additionally, no assessment technique consistently reduced variability in ratings among arborists. Tree risk management programs can use the findings of this study to enhance the systematic procedures they use to assess tree risk in the urban landscape.


First Advisor

Brian Kane