A Growth-Model-Driven Technique for Tree Stem Diameter Estimation by Using Airborne LiDAR Data

Claudia Paris, Lorenzo Bruzzone*

*Corresponding author for this work

Research output: Contribution to journalArticleAcademicpeer-review

7 Citations (Scopus)


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.
Original languageEnglish
Article number8428490
Pages (from-to)76-92
Number of pages17
JournalIEEE transactions on geoscience and remote sensing
Issue number1
Early online date7 Aug 2018
Publication statusPublished - Jan 2019
Externally publishedYes


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