Title
Influence of stand, site and meteorological variables on the maximum leaf area index of beech, oak and Scots pine Influence of stand, site and meteorological variables on the maximum leaf area index of beech, oak and Scots pine
Author
Faculty/Department
Faculty of Sciences. Biology
Publication type
article
Publication
Berlin
Subject
Biology
Source (journal)
European journal of forest research. - Berlin
Volume/pages
131(2012) :2 , p. 283-295
ISSN
1612-4669
ISI
000301088000002
Carrier
E
Target language
English (eng)
Full text (Publishers DOI)
Affiliation
University of Antwerp
Abstract
Different multiple linear regression models of maximum leaf area index (LAImax) based on stand characteristics, site quality, meteorological variables and their combinations were constructed and cross-validated for three economically important tree species in Flanders, Belgium: European beech (Fagus sylvatica L.), Pedunculate oak (Quercus robur L.) and Scots pine (Pinus sylvestris L.). The models were successfully tested on similar datasets of experimental sites across Europe. For each species, ten homogeneous and mature stands were selected, covering the species entire stand productivity range based on an a priori site index classification. LAImax was derived from measurements of leaf area index (LAI) made by means of hemispherical digital photography over the whole growing season (mid-April till end October 2008). Species-specific models of LAImax for beech and oak were mostly driven by management practice affecting stand characteristics and tree growth. Tree density and dominant height were main predictors for beech, while stand age and tree-ring growth were important in the oak models. Scots pine models were more affected by site quality and meteorological variables. The beech meteorological model showed very good agreement with LAI at several European sites. Scots pines stand model predicted well LAI across Europe. Since the species-specific models did not share common predictors, generic models of LAImax were developed for the 30 studied sites. Dominant height was found to be the best predictor in those generic models. As expected, they showed a lower predictive performance than species-specific ones.
E-info
https://repository.uantwerpen.be/docman/iruaauth/b30d93/632345bb7db.pdf
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