Diameter at breast height (DBH) is one of the most important tree parameter for forest
inventory. In this paper, we present a novel method for the adaptive and the accurate
DBH estimation of trees characterized by small and large stems. The method automatically
discriminates among different tree growth models by means of a data-driven technique
based on a clustering procedure. First, the method detects young trees belonging to
the lowest forest layer by simply considering the vertical structure of the forest.
Then, different clusters of mature trees that are expected to share the same growth-model
are identified by analyzing the environmental factors that can affect the stem expansion
(e.g., topography and forest density). For each detected growth-model cluster, a tailored
regression analysis is performed to obtain accurate DBH estimation results. Experiments
have been carried out in an homogeneous coniferous forest located in the Alpine mountainous
scenario characterized by a complex topography and a wide range of soil fertility.
The method was tested on two data sets characterized by different light detection
and ranging (LiDAR) point densities and different forest properties. The results obtained
demonstrate the effectiveness of having multiple regression models adapted to the
different growth models.