Growth Rate Prediction of Ornamental Trees using Regression Functions in Urban Landscapes

Abstract The present study aimed to predict the growth rate of the common urban trees using mathematical equations for the right plantation location in the landscape. Ginkgo biloba, Liquidambar styraciflua L., Parrotia persica, Zelkova carpinifolia, Acer negundo, and Fraxinus rotundifolia Mill. were selected from city of Rasht, Guilan province, in the northern part of Iran. The study used age as the independent variables and height, trunk height, and crown diameter as the dependent variables. The correlation among the variables were analyzed by different regressions (linear, logarithmic, exponential, power, and polynomial). The results showed that polynomial regression functions provided the best determination coefficient to predict the growth parameters with respect to age in the urban green spaces for aesthetic purposes and also, to reduce costs, tackle interference with transit, urban equipment, and finally developing ecosystems sustainably. Polynomial regression functions can be used to predict tree growth in new urban green spaces in terms of environmental factors and age. Keywords: Ecosystems sustainably, Green space, Growth rate, Urban landscape, Ornamental trees