iForest - Biogeosciences and Forestry


Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques

Salim Malek (1-3), Franco Miglietta (1), Terje Gobakken (2), Erik Næsset (2), Damiano Gianelle (3), Michele Dalponte (3)   

iForest - Biogeosciences and Forestry, Volume 12, Issue 3, Pages 323-329 (2019)
doi: https://doi.org/10.3832/ifor2980-012
Published: Jun 14, 2019 - Copyright © 2019 SISEF

Research Articles

Knowledge about the aboveground biomass (AGB) and the diameters at breast height (DBH) distribution can lead to a precise estimation of carbon density and forest structure which can be very important for ecology studies especially for those concerning climate change. In this study, we propose to predict DBH and AGB of individual trees using tree height (H) and crown diameter (CD), and other metrics extracted from airborne laser scanning (ALS) data as input. In the proposed approach, regression methods, such us support vector machine for regression (SVR) and random forests (RF), were used to find a transformation or a transfer function that links the input parameters (H, CD, and other ALS metrics) with the output (DBH and AGB). The developed approach was tested on two datasets collected in southern Norway comprising 3970 and 9467 recorded trees, respectively. The results demonstrate that the developed approach provides better results compared to a state-of-the-art work (based on a linear model with the standard least-squares method) with RMSE equal to 81.4 kg and 92.0 kg, respectively (compared to 94.2 kg and 110.0 kg) for the prediction of AGB, and 5.16 cm and 4.93 cm, respectively (compared to 5.49 cm and 5.30 cm) for DBH.


Aboveground Biomass, Diameter at Breast Height, Airborne Laser Scanning (ALS), Remote Sensing (RS), Support Vector Machine for Regression (SVR), Random Forests (RF)

Authors’ address

Salim Malek 0000-0002-7168-8952
Franco Miglietta 0000-0003-1474-8143
Institute of Biometeorology, CNR, 50145 Firenze (Italy)
Terje Gobakken 0000-0001-5534-049X
Erik Næsset 0000-0002-2460-5843
Faculty of Environmental Sciences and Natural Resource Management, Norwegian University of Life Sciences, P.O. Box 5003, NO-1432 s (Norway)
Salim Malek 0000-0002-7168-8952
Damiano Gianelle 0000-0001-7697-5793
Michele Dalponte 0000-0001-9850-8985
Dept. of Sustainable Agro-ecosystems and Bioresources, Research and Innovation Centre, Fondazione E. Mach, v. E. Mach 1, 38010 San Michele all’Adige, TN (Italy)

Corresponding author

Michele Dalponte


Malek S, Miglietta F, Gobakken T, Næsset E, Gianelle D, Dalponte M (2019). Prediction of stem diameter and biomass at individual tree crown level with advanced machine learning techniques. iForest 12: 323-329. - doi: 10.3832/ifor2980-012

Academic Editor

Carlotta Ferrara

Paper history

Received: Oct 22, 2018
Accepted: Apr 06, 2019

First online: Jun 14, 2019
Publication Date: Jun 30, 2019
Publication Time: 2.30 months

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