A rapid urban site index for assessing the quality of street tree planting sites
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Urban Forestry & Urban Greening
Urban trees experience site-induced stress and this leads to reduced growth and health. A site assessment tool would be useful for urban forest managers to better match species tolerances and site qualities, and to assess the efficacy of soil management actions. Toward this goal, a rapid urban site index (RUSI) model was created and tested for its ability to predict urban tree performance. The RUSI model is field-based assessment tool that scores 15 parameters in approximately five minutes. This research was conducted in eight cities throughout the Midwest and Northeast USA to test the efficacy of the RUSI model. The RUSI model accurately predicted urban tree health and growth metrics (P < 0.0001; R2 0.18–0.40). While the RUSI model did not accurately predict mean diameter growth, it was significantly correlated with recent diameter growth. Certain parameters in the RUSI model, such as estimated rooting area, soil structure and aggregate stability appeared to be more important than other parameters, such as growing degree days. Minimal improvements in the RUSI model were achieved by adding soil laboratory analyses. Field assessments in the RUSI model were significantly correlated with similar laboratory analyses. Other users may be able to use the RUSI model to assess urban tree planting sites (< 5 min per site and no laboratory analyses fee), but training will be required to accurately utilize the model. Future work on the RUSI model will include developing training modules and testing across a wider geographic area with more urban tree species and urban sites.
Scharenbroch, Bryant C.; Carter, David; Bialecki, Margaret; Fahey, Robert; Scheberl, Luke; Catania, Michelle; Roman, Lara A.; Bassuk, Nina; Harper, Richard W.; Werner, Les; Siewert, Alan; Miller, Stephanie; Hutyra, Lucy; Raciti, Steve. 2017. A rapid urban site index for assessing the quality of street tree planting sites. Urban Forestry & Urban Greening. 27: 279-286. https://doi.org/10.1016/j.ufug.2017.08.017.