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Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy / Mauro Maesano in iForest, biogeosciences and forestry, vol 15 n° 6 (December 2022)  

Public [article]  iniForest, biogeosciences and forestry > vol 15 n° 6 (December 2022) . - pp 451-457 Titre : Above ground biomass estimation from UAV high resolution RGB images and LiDAR data in a pine forest in Southern Italy Type de document : Article/Communication Auteurs : Mauro Maesano, Auteur ; Giovanni Santopuoli, Auteur ; Federico Valerio Moresi, Auteur ; et al., Auteur Année de publication : 2022 Article en page(s) : pp 451-457 Note générale : bibliographie Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Applications photogrammétriques [Termes IGN] apprentissage automatique [Termes IGN] biomasse aérienne [Termes IGN] Calabre [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] gestion forestière durable [Termes IGN] image captée par drone [Termes IGN] image RVB [Termes IGN] modèle numérique de surface de la canopée [Termes IGN] régression [Termes IGN] semis de points [Termes IGN] structure-from-motion Résumé : (auteur) 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. Numéro de notice : A2022-903 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3832/ifor3781-015 Date de publication en ligne : 03/11/2022 En ligne : https://doi.org/10.3832/ifor3781-015 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=102299 [article]