Summary:
Published in Forestry 91(3): 259-270. doi: 10.1093/forestry/cpw059
Long-term success of forest management requires knowledge of standing tree characteristics and, an estimation of their evolution over time. In this study, hardwood stem quality was assessed using a specifically designed, non-destructive quality classification system that comprises four categorical output classes based on stem size and occurrence of external defects. We used data from national forest inventory sample plots distributed across Quebec (Canada) to predict hardwood stem quality and its evolution over time. We used ordinal logistic regression to model multiple stem quality classes. Hardwood stem quality was strongly related to stem harvest priority class and bioclimatic subdomain. Stem quality generally improved with d.b.h. and stand basal area. Changes in hardwood stem quality were strongly related to initial stem quality, with most trees retaining their initial quality over time. Stem quality evolution was also positively related to diameter growth. Overall, both initial and future stem quality were estimated with acceptable precision and minimal bias. Our results suggest that the equations could predict hardwood stem quality distribution and evolution among groups of forest stands.
Sector(s):
Forests
Catégorie(s):
Scientific Article
Theme(s):
Forest Growth and Yield Modelling, Forestry Research, Forests
Departmental author(s):
Author(s):
POWER, Hugues and Filip HAVRELJUK
Year of publication:
2016
How to get the publication:
Demander aux chercheurs
Keyword(s):
modélisation, forêt tempérée nordique, érable à sucre, bouleau jaune, qualité des tiges, modélisation de la croissance et du rendement des forêts, article scientifique de recherche forestière, Acer saccharum, Betula alleghaniensis, forest growth and yield modelling, modelling, broadleaf forest, sugar maple, yellow birch, stem quality