Arboriculture & Urban Forestry 48(4): July 2022 be expected. Consequently, the measurement proto- col of this research would raise the regression coeffi- cient values of DBH. The higher variability rendered more TFD variation unexplainable. The differences in the explained variances between the current and the past studies highlighted the impor- tance of a simple, consistent, and reliable variable for the quantification of trunk flares. Existing sensing technologies, such as unmanned aerial vehicles and LiDAR, help measure DBH and height in forest sites (Birdal et al. 2017; Panagiotidis et al. 2017; Torresan et al. 2017; Kwong and Fung 2020). But technologies are yet to be developed for trunk flare measurement. From the perspective of tree surveyors, work effi- ciency could be enhanced by standardising the mea- surement protocol by specifying that the outermost points of trunk flares should be measured. Although such methods introduced more unexplained varia- tion, DBH-TFD models were still significant. Also, the conflict between tree roots and/or flares and pave- ment surface could be characterised. Therefore, the methods of trunk flare measurement could be updated to the approach as in this research. Effects of Sample Selection on TFD Prediction The performance of DBH-TFD prediction models dropped discernibly if the examined sample was solely composed of trees with protruding roots and/or flares (Table 2b). In fact, TFD values were directly and positively linked to the length of protruding parts which could have rather high variance (Table 3). Such variance would by default influence the varia- tion in TFD, subsequently inflating errors but dimin- ishing the explanatory power of DBH. Protruding parts created variable and disproportionate increase in TFD in relation to DBH. As a result, the model sig- nificance and the regression coefficients of DBH decreased. Landscape architects who need to estimate plant- ing space requirements may be troubled by the less-reliable models containing only trees with pro- truding roots and/or flares. Preventing pavement damage by large-stature species such as A. moluc- canus, C. equisetifolia, and Melaleuca cajuputi may become difficult. Worse still, singling out samples with protrusion resulted in divergent directions of change in regression coefficients. Fearing underesti- mated TFD, landscape architects may refrain from 227 using the reduced regression coefficients caused by the change in sample selection (Table 2b). Without a sufficiently large open soil area, the outward stress caused by the trunk flares of large trees may be detri- mental to the pavement. Despite the uncertainties, the confidence intervals of regression coefficient of DBH could be utilised in various scenarios (Table 2). Along wide pavements, the upper boundary value (CIUP ) of the regression coefficient of DBH could be used to generate larger TFD estimates, justifying the provision of extra buf- fer space. For narrow pavement, TFD could be pre- dicted using the lower boundary value (CILOW ) at the acceptable risk of pavement damage. Unless the open soil area requirement was met as advised by TFD estimates, landscape planners should switch to another suitable species with smaller TFD. This sug- gestion echoes the strategy of planting small-stature trees in cramped spaces (Blunt 2008). The confidence intervals could only be computed when sample size and standard error are available. Therefore, for a more-detailed record-keeping purpose, these critical values should be reported in future studies on DBH- TFD allometry. Recommendations for Landscape Planning In all 3 scenarios, diameter growth significantly increased the likelihood of protrusion (Table 4). Such findings agree with Hilbert et al. (2020). Nonetheless, by using species-specific prediction and comprehen- sive model outputs, the effects of habitat factors on the occurrence of protrusion were captured. There was a difference in the nature of significant predictors of the presence and magnitude of protrusion. Dendro- metric and habitat factors explained the majority of variations in the presence and magnitude of protrud- ing parts, respectively (Tables 4 and 5). Regardless, the apparent contradiction was resolved by reading the correlations among predictors. The highest cor- relations, which were also significant, were found between DBH and soil area (0.376 < r < 0.755). In other words, thick-stemmed trees with higher DBH correlated to larger soil area. Such observations are sensible, as a larger tree pit would be necessary to accommodate larger trees whose larger DBH are in turn linked to higher likelihood of protrusion. More sustainable landscape planning could be enabled with the use of such models. With known ©2022 International Society of Arboriculture
July 2022
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