Prediction of street tree morphological parameters using artificial neural networks

Street trees positively affect the everyday life of city inhabitants and therefore successful management of this resource is important. A tree inventory is an essential first step towards this end, but this activity can be complex, costly and must be optimized. In order to reduce time and effort required for data acquisition either when using traditional field work or airborne laser systems, a method is proposed to predict the value of essential tree morphological parameters with surrogate variables and artificial intelligence multilayer perceptron networks (MLPs). To evaluate MLPs, seven different models were tested on Acer platanoides L., Acer saccharinum L., Celtis occidentalis L., Fraxinus pennsylvanica Marsh., Gleditsia triacanthos L., Tilia cordata Mill., and Ulmus pumila L. data sets. Three models were intended to predict diameter at breast height (DBH), annual DBH increment and crown volume with a minimal number of input measurements to be extracted from aerial LIDAR (Light Detection and Ranging) data. The last four were associated with traditional ground inventory methods and aimed to predict height, crown volume and their respective annual increments using less labour-intensive variables. The prediction performance was assessed with the Pearson r correlation coefficient, computed between the measured and estimated output values for each of the cross-validation test files per tree species and model. By using carefully selected biotic and abiotic input parameters, the prediction performance of multilayer perceptron showed robustness and precision despite different age-class distribution per species, dissimilar species morphological characteristics, uneven distribution of species within defined urban ecological zones, and varied abiotic growth conditions. More precisely, prediction coefficients were greater than 70% for all models with very small probability levels except for two predictions were input data exhibited strongly non-Gaussian distributions. Overall, the average prediction for all scenarios was 91%. Considering these results, it was found that prediction of DBH, annual DBH increment and crown volume is possible with limited aerial LIDAR laser information. Moreover, it was established that traditional field work effort can be further reduced by predicting the value of unmeasured morphological parameters within acceptable levels of precision. These findings can have an impact on future urban tree inventories. Depending on the number of trees to be measured, municipal administrations have the choice to use either airborne or traditional data acquisition methods. In both cases, this research proposes optimized procedures that may reduce the overall inventory costs.