252 Walker and Dahle: Likelihood of Failure of Trees Along Utility Rights-of-Way exceeding 108 km/h at the top of the canopy, for a period of 10 minutes, without sustaining some amount of damage (Peltola 1996). Canham and Loucks (1984) postulated that as the severity of damage increases, the differences between species, size, and other factors diminish, until a threshold at which most trees over a certain diameter fail is reached. This idea is one with which Francis and Gillespie (1993) unknowingly concurred, positing their own idea of “storm build-up.” Storm build-up describes a process where there exists a wind speed at which any tree will shed its crown or will be windthrown. The authors go on to describe how time, too, has a role, such that storms with a slow build-up to their maximum wind speed should cause less windthrow because of the increased time for trees to defoliate and thus decrease wind-load interception (Francis and Gillespie 1993). Likewise, storms with a fast build-up should see more windthrow due to the decreased time to defoliate and thus increased wind-load interception (Francis and Gillespie 1993). Furthermore, the complete dynamic process of windthrow has never been verified in field experiments, and the assumption that the maximum wind load produced by a particular event is the key factor in whether damage to trees occurs has never been confirmed (Hale et al. 2012; James et al. 2014). In summary, the removal of a tree will eliminate the risk associated with that tree but may increase the risk of windthrow of neighboring trees due to changes in the wind regime and exposure (Kane 2008). While tree properties and wind are likely the 2-largest fac- tors contributing to windthrow, the 2 combined do not explain all observed variation in the windthrow of trees (Francis and Gillespie 1993; Kane 2008). More- over, understanding how to manage the interaction of wind and trees is as crucial to utility vegetation man- agement as it is to society during times of inclement weather when the population is most dependent upon the electrical grid. CONCLUDING REMARKS Each of the reviewed methodologies aided in build- ing our collective knowledge about the nature of tree failure. Explanatory approaches have helped illumi- nate the key factors which influence failures and their relations (Peterson 2007; Kane 2008). Mechanistic studies have revealed monotonous response to soil- root plate inclination and the existence of damping responses in trees, and have even predicted individual tree failures as a function of location, the critical wind ©2022 International Society of Arboriculture speed for the tree to fail, and the likelihood of that location experiencing a wind speed greater than or equal to the critical wind speed (Peltola et al. 1999; Ancelin et al. 2004; Gardiner et al. 2008; Lundström et al. 2009; James et al. 2014). Statistical methods have demonstrated that tree failure may be somewhat predictable given the correct method and variables (Kabir et al. 2018). Yet, despite each of these contri- butions to our body of knowledge, none are perfectly suited to UVM. Even so, GIS-implemented predictive mechanistic models such as GALES, HWIND, and FOREOLE may prove to be adaptable to UVM, particularly if these models can refine their tree-based inputs (i.e., species, height, DBH, etc.), such that they are better suited to and integrated with modern remote-sensing technologies. Vegetation managers can then utilize remotely sensed imagery of a ROW or service area to inform a predictive mechanistic model. Moreover, these models may prove useful during storm harden- ing efforts and major storm-response planning by modeling where tree failures would be likely given 10-, 50-, and 100-year storm wind speeds. Furthermore, the use of remote sensing to inven- tory the utility forest and monitor individual trees and stands may have applications in tree contractor work auditing and work planning. As an additional benefit to the field of arboriculture overall, the development of a baseline likelihood of tree failure may be possi- ble through successive imaging and change-detection techniques of electric ROWs. Suggestions for future work include incorporating inputs from remotely sensed products into predictive mechanistic models of tree failure and the continued validation of predictive mechanistic models such as GALES, HWIND, FOREOLE, and their derivatives. The use of more advanced statistical techniques within the study of likelihood of tree failure may be required to make accurate conclusions about the rela- tionships between the factors which influence failure, as well as how to predict failure on the whole. Fur- thermore, the standardization of vegetation-related distribution interruption and outage-reporting meth- ods and codes would be beneficial for the study of UVM generally and would be useful for the identifi- cation of the regionality and phenology of vegetation- related conflicts with distribution systems. Our current standard, the ISA’s UTRA BMP, has proven to be a powerful tool in an adept arborist’s hands. Yet, the UTRA is limited by physical access to
July 2022
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