iForest - Biogeosciences and Forestry


Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy

Mauro Maesano (1)   , Giovanni Santopuoli (2), Federico Valerio Moresi (1), Giorgio Matteucci (3), Bruno Lasserre (4), Giuseppe Scarascia Mugnozza (1)

iForest - Biogeosciences and Forestry, Volume 15, Issue 6, Pages 451-457 (2022)
doi: https://doi.org/10.3832/ifor3781-015
Published: Nov 03, 2022 - Copyright © 2022 SISEF

Research Articles

Collection/Special Issue: Research Project PRIN-MIUR 2015
The forest-wood value chain: biomass supply, traceability, C-footprint. Innovation for bioarchitecture and energy efficiency
Guest Editors: Scarascia Mugnozza G, Maesano M, Romagnoli M

Knowledge of forest biomass is an essential parameter for managing the forest in a sustainable way, as forest biomass data availability and reliability are necessary for forestry and forest planning, but also for the carbon market as well as to support the local economy in the mountain and inner areas. However, the accurate quantification of the above-ground biomass (AGB) is still a challenge both at the local and global levels. The use of remote sensing techniques with Unmanned Aerial Vehicle (UAV) platforms can be an excellent trade-off between resolution, scale, and frequency data of AGB estimation. In this study, we evaluated the combined use of RGB images from UAV, LiDAR data and ground truth data to estimate AGB in a forested watershed in Southern Italy. A low-cost AGB estimation method was adopted using a commercial fixed-wing drone equipped with an RGB camera, combined with the canopy information derived by LiDAR and validated by field data. Two modelling methods (stepwise regression, SR and random forest, RF) were used to estimate forest AGB. The output was an accurate maps of AGB for each model. The RF model showed better accuracy than the Steplm model, and the R2 increased from 0.81 to 0.86, and the RMSE and MAE values were decreased from 45.5 to 31.7 Mg ha-1 and from 34.2 to 22.1 Mg ha-1 respectively. We demonstrated that by increasing the computing efficiency through a machine learning algorithm, readily available images can be used to obtain satisfactory results, as proven by the accuracy of the Random forest above biomass estimation model.


Above Ground Biomass, UAV, Random Forest, Forest Biomass, Machine Learning

Authors’ address

Mauro Maesano 0000-0002-4325-951x
Federico Valerio Moresi 0000-0003-4648-4373
Giuseppe Scarascia Mugnozza 0000-0003-0357-4360
Department of Innovation in Biological, Agro-food and Forest Systems, University of Tuscia, v. San Camillo de Lellis snc 01100 Viterbo (Italy)
Giovanni Santopuoli 0000-0002-5373-5970
Department of Agricultural, Environmental and Food Sciences, University of Molise, v. Francesco de Sanctis, 86100, Campobasso (Italy)
Giorgio Matteucci 0000-0002-4790-9540
Institute of Bioeconomy, National Research Council of Italy, v. Madonna del Piano 10, I-80056 Sesto Fiorentino, FI (Italy)
Bruno Lasserre 0000-0003-1150-8064
Department of Bioscience and Territory, University of Molise, c.da Fonte Lappone, 86090 Pesche, IS (Italy)

Corresponding author

Mauro Maesano


Maesano M, Santopuoli G, Moresi FV, Matteucci G, Lasserre B, Scarascia Mugnozza G (2022). Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy. iForest 15: 451-457. - doi: 10.3832/ifor3781-015

Academic Editor

Carlotta Ferrara

Paper history

Received: Feb 10, 2021
Accepted: Sep 18, 2022

First online: Nov 03, 2022
Publication Date: Dec 31, 2022
Publication Time: 1.53 months

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