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Estimation of the timber quality of scots pine with terrestrial laser scanning / Ville Kankare in Forests, vol 5 n° 8 (August 2014)  

Public [article]  inForests > vol 5 n° 8 (August 2014) . - pp 1879 - 1895 Titre : Estimation of the timber quality of scots pine with terrestrial laser scanning Type de document : Article/Communication Auteurs : Ville Kankare, Auteur ; Marianna Joensuu, Auteur ; Jari Vauhkonen, Auteur ; et al., Auteur Année de publication : 2014 Article en page(s) : pp 1879 - 1895 Langues : Anglais (eng) Descripteur : [Vedettes matières IGN] Lasergrammétrie [Termes IGN] classification par forêts d'arbres décisionnels [Termes IGN] données lidar [Termes IGN] données localisées 3D [Termes IGN] effet d'ombre [Termes IGN] Finlande [Termes IGN] inventaire forestier (techniques et méthodes) [Termes IGN] Pinus sylvestris [Termes IGN] semis de points Résumé : (auteur) Preharvest information on the quality of Scots pine (Pinus sylvestris) timber is required by the forest industry in Nordic countries, due to the strong association between the technical quality and product recovery of this species in particular. The objective of this study was to assess the accuracy of estimating external quality attributes and classifying the quality of mature Scots pine trees by terrestrial laser scanning (TLS). The tree quality was estimated using a random forest approach, based on both field and TLS measurements of stem diameters, tree height and branch heights. The relative root mean squared errors of the TLS measurements for tree height, diameter, diameter at 6 m and the lowest living and dead branch height were 7.1%, 5.9%, 8.9%, 9.6% and 42.9%, respectively. The highest errors of the branch heights were caused by the shadowing effect in the point cloud data. The quality classes were estimated accurately, based on both (field and TLS measured) tree attributes. Trees were classified with 95.0% and 83.6% accuracy into three operationally-important quality classes and with 87.1% and 76.4% accuracy into five classes using, field or TLS measurements, respectively. The obtained quality classification results were promising. The enhanced tree quality information could have a significant effect on planning forest management procedures, wood supply chains and optimizing the flow of raw materials. To fully integrate tree quality measurements in operational forestry, the methods used should be fully automated. Numéro de notice : A2014-760 Affiliation des auteurs : non IGN Thématique : FORET/IMAGERIE Nature : Article DOI : 10.3390/f5081879 En ligne : https://doi.org/10.3390/f5081879 Format de la ressource électronique : URL article Permalink : https://documentation.ensg.eu/index.php?lvl=notice_display&id=76416 [article]