Comparing Traditional Methods and Modern Statistical Techniques for Tree Height Prediction

Forest mensuration is important to gain knowledge and information about forest stands. Because tree height often proves more difficult to measure than diameter, different statistical models are used for their estimation instead. In this paper, the data of 986 spruce trees (Picea abies KARST. (L.)), measured in the federal states of Salzburg and Tyrol (Austria), were used to train and compare random forest with more traditional approaches such as linear and non-linear mixed models and a classical uniform height curve. For model comparison, RMSE, percent RMSE, percent bias, and bias are used. For further visualization of the differences, residual plots, partial dependence plots, and conditional dependence plots are shown. The results show that random forest (RMSE 2.23 m) can compete with more traditional methods, such as non-linear (RMSE 2.14 m) and linear (RMSE 2.24 m) mixed models or uniform height curves (RMSE 2.92 m), but is not able to outperform those methods, especially when it comes to extrapolation or prediction in areas where training data are sparse or not available. Furthermore, the results show that the incorporation of additional covariates can improve the prediction of certain models.