Fitting forestry models using generalized additive models: a taper model example

Abstract

Nonparametric and semiparametric modelling methods are commonly applied in many fields. However, such methods have not been widely adopted in forestry, other than the most similar neighbour and nearest neighbor methods. Generalized additive modelling is a flexible semiparametric regression method that is useful when model-based prediction is the main goal and the parametric form of the model is unknown and possibly complex. Routines to fit generalized additive models (GAMs) are now readily available in much statistical software, making them an attractive option for forest modelling. Here, the use of GAMs is demonstrated by the construction of a taper model for six tree species in British Columbia, Canada. We compare the results with an existing flexible parametric taper model. We assess the performance of the models using the 0.632+ bootstrap method according to five key attributes: whole-stem volume, merchantable volume, number of logs, small-end diameter of the first log, and volume of the first log. The results show that the GAMs and the flexible taper function yielded similar accuracy for all attributes and all species.