%0 Research article
%T Local models for forest canopy cover with beta regression
%A Korhonen, Lauri
%A Korhonen, Kari T.
%A Stenberg, Pauline
%A Maltamo, Matti
%A Rautiainen, Miina
%D 2007
%J Silva Fennica
%V 41
%N 4
%R doi:10.14214/sf.275
%U https://silvafennica.fi/article/275
%X Accurate field measurement of the forest canopy cover is too laborious  to be used in extensive forest inventories. A possible alternative to  the separate canopy cover measurements is to utilize the correlations  between the percent canopy cover and easier-to-measure forest variables,  especially the basal area. A fairly new analysis technique, the beta  regression, is specially designed for modelling percentages. As an  extension to the generalized linear models, the beta regression takes  into account the distribution of the model residuals, and uses a  logistic link function to ensure logical predictions. In this study, the  beta regression method was found to perform well in conifer dominated  study area located in central Finland. The same model shape, with basal  area, tree height and an additional predictor (Scots pine: site  fertility, Norway spruce: percentage of hardwoods) as independent  variables, produced good results for both pine and spruce dominated  sites. The models had reasonably high pseudo R-squared values (pine:  0.91, spruce: 0.87) and low standard errors (pine: 6.3%, spruce: 5.9%)  for the fitting data, and also performed well in a cross validation  test. The models were also tested on separate test plots located in a  different geographical area, where the prediction errors were slightly  larger (pine: 8.8%, spruce: 7.4%). In pine plots, the model fit was  further improved by introducing additional predictors such as stand age  and density. This improved also the performance of the models in the  cross validation test, but weakened the results for the external data  set. Our results indicated that the beta regression method offers a  noteworthy alternative to separate canopy cover measurements, especially  if time is limited and the models can be applied in the same region  where the modelling data were collected.