244 Walker and Dahle: Likelihood of Failure of Trees Along Utility Rights-of-Way James et al. 2014). Simply stated, the theoretical like- lihood of failure of a tree can be determined by the moment capacity of the tree, the anticipated loads the tree will experience, and the anticipated weather- related phenomena which the tree will experience (Dahle et al. 2017). Yet, there is sparse information available for the load-bearing capacity of trees, the anticipated load trees intercept, and the site and envi- ronmental factors that affect failure (Dahle et al. 2014; James et al. 2014; Dahle et al. 2017). The inspection of vegetation in and along electric ROWs for utility vegetation management (UVM) is difficult, as trees with elevated likelihood of failure, such as those with significant internal decay or struc- tural issues, may not be observable or obvious from a foot patrol’s visual inspection (Dahle et al. 2006; Most and Weissman 2012; Goodfellow 2020). The Interna- tional Society of Arboriculture (ISA) recently pub- lished a new Best Management Practices (BMP) for Utility Tree Risk Assessment (UTRA) to provide arborists, urban and utility foresters, and their associ- ated industries with tree work–related guidance and research-based recommendations (Goodfellow 2020). The UTRA is specifically intended to aid utility for- esters and the UVM industry in assessing tree-related risks to utility infrastructures. The application of tree- risk- assessment practices for UVM differs in scale from other users of tree-risk-assessment frameworks. Whereas a commercial tree-risk assessor and a utility forester may both conduct a tree-risk assessment on a singular tree, the risk being managed by the utility forester is managed across a widespread population of trees in proximity to the utility infrastructure, also known as the utility forest. Additionally, UTRA dif- fers from general arboricultural tree-risk assessment in that both direct (damage to the infrastructure) and indirect consequences (power outages, fines, public safety, etc.) are considered (Goodfellow 2020). Due to the scope and spread of the utility systems, utility foresters may not be able to assess each tree individually, either because of time constraints or lack of access to the location. Trees can experience a localized failure (e.g., broken branches or cracks in the branches or stem) without incurring full structural failure and tree fall (or “final failure”)(Dunster et al. 2017). Thus, assessing the number of trees that have experienced final failure and have fallen within a specified time period will be easier than attempting to assess the number of trees which have experienced localized failures. This is particularly true of remotely ©2022 International Society of Arboriculture sensed data, where the presence or absence of a given tree over a time series of images or scans may be detectable. However, there do not currently exist meth- ods to remotely assess whether a given tree has expe- rienced a localized failure. Furthermore, the UVM industry stands to benefit from change-detection techniques and remote-sensing technologies, such as LiDAR data and temporal-image differencing or ratioing (Lillesand et al. 2007; Mati- kainen et al. 2016). With successive scans of the same area, one should be able to visualize vegetation differ- ences along ROWs. In particular, the presence of new vegetation or absence of previously present vegetation should be obvious. Change-detection methodologies would also aid in calculating vegetation growth rates, perhaps down to the individual tree or stem. Addition- ally, remote sensing and change detection could provide a robust set of tools to help monitor a large number of trees over time, which would potentially be useful in the calculation of the likelihood of failure of trees. However, due to the limitations of current remote- sensing technologies, the likelihood of tree failure derived from a change-detection study would be lim- ited to the detection of tree fall, and thus, final failure. METHODOLOGIES Several techniques have been proposed in the litera- ture to assess the likelihood of windthrow of trees (Baker 1995; Peltola et al. 1999; Ciftci et al. 2014a; Kamimura et al. 2016; Suzuki et al. 2016; Virot et al. 2016; Yan et al. 2016; Kamimura et al. 2017). Kabir et al. (2018) separate these research techniques into 3 key methodological groups: explanatory approaches, mechanistic approaches, and statistical approaches, and our review will follow this grouping. In the fol- lowing section we will discuss each of the 3 method- ological approaches, including an in-depth discussion of different methodologies within mechanistic approaches. Furthermore, each of the biomechanical methodologies mentioned have benefits and draw- backs, and all have aided in augmenting the existing knowledge base. Explanatory Approaches Explanatory approaches assess the relationship of tree failure and a variety of physical or geographical parameters, such as tree species, diameter at breast height (DBH), soil characteristics, or mode of failure (Kabir et al. 2018). The primary methodology within explanatory approaches is referred to as a “post-storm
July 2022
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