Identification of Significant Street Tree Inventory Parameters Using Multivariate Statistical Analyses

Street tree inventories are costly procedures that must be designed to optimally meet management and operation- al requirements. To assess the importance of several low-technology inventory parameters, a three-step multivariate statistical analysis was designed and tested on growth models of Norway maple (Acer platanoides), silver maple (Acer saccharinum), common hackberry (Celtis occidentalis), green ash (Fraxinus pennsylvanica), honeylocust (Gleditsia triacanthos), littleleaf linden (Tilia cordata), and Siberian elm (Ulmus pumila). The first step appraised and compared the significance of qualitative and quantitative parameters. Results revealed that using qualitative indices decreased the explanatory power of models. Ac- cordingly, it was proposed that quantitative parameters be preferred for urban tree inventory. The second step aimed at reduc- ing the volume of necessary information needed for urban tree growth estimation. Various simple and complex combinations of quantitative parameters were tested. Results were conclusive and species independent: the simplified models were sta- tistically non-significant. The best model was composed of multiple parameters. The third step looked for the identification of an inventory parameter that could be used to assess any urban tree physiological stage. It was found that no single param- eter can adequately delineate the complexity of all tree physiological stages. The optimal model is rather multidimensional.

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