A data-driven identification of growth-model classes for the adaptive estimation of single-tree stem diameter in LiDAR data

In this paper we present a growth-model based approach to the accurate estimation of stem diameter at single tree level by using high-density LiDAR data. First, we detect classes of trees characterized by different growth conditions by means of a data-driven inference process. To this end, all the environmental factors that can affect the growth of the tree (i.e., forest density and topography) are modeled and analyzed. Second, for each detected growth-model class a tailored regression function is trained to adapt the model on the considered class. The crown structure, the topography and the forest density are considered to accurately retrieve the stem diameter. Experiments carried out in mountainous scenario characterized by complex morphology and a wide range of soil fertility demonstrate the effectiveness of the proposed method.