Jussi Kuusipalo. (1985). On the use of tree stand parameters in estimating light conditions below the canopy. Silva Fennica vol. 19 no. 2 article id 5239. https://doi.org/10.14214/sf.a15418

Keywords: Pinus sylvestris; basal area; Picea abies; canopy; tree species composition; light conditions; light climate; number of stems; canopy coverage

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Especially in forest vegetation studies, the light climate below the canopy is of great interest. In extensive forest inventories, direct measurement of the light conditions is too time-consuming. Often only the standard tree stand parameters are available. The present study was undertaken with the aim to develop methods for estimation of the light climate on the basis of readily measurable tree stand characteristics. The study material includes 40 sample plots representing different kinds of more or less mature forest stands of Scots pine (Pinus sylvestris L.) and Norway spruce (Picea abies (L.) H. Karst.).

In each forest stand, a set of hemipherical photographs was taken and standard tree stand measurements were performed. A regression approach was applied in order to elaborate linear models for predicting the canopy coverage. The total basal area of the stand explained 63% of variance in the canopy coverage computed from hemipherical photographs. A coefficient representing the relative proportion of Norway spruce in the stand increased the explanatory power into 75%. When either the stand density (stems/unit area) or dominant age of the stand was included into the model, increment of the explanatory power into 80% was achieved. By incorporating both of the preceding predictors, an explanatory power of 85% was reached.

The PDF includes a summary in Finnish.

Kuusipalo, E-mail: jk@mm.unknown

Category : Research article

article id 275, category Research article

Lauri Korhonen, Kari T. Korhonen, Pauline Stenberg, Matti Maltamo, Miina Rautiainen. (2007). Local models for forest canopy cover with beta regression. Silva Fennica vol. 41 no. 4 article id 275. https://doi.org/10.14214/sf.275

Keywords: beta regression; canopy cover; forest canopy

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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.

Korhonen, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: lauri.korhonen@joensuu.fi
Korhonen, Finnish Forest Research Institute, Joensuu Research Unit, P.O. Box 68, FI-80101 Joensuu, Finland E-mail: ktk@nn.fi
Stenberg, Univ. of Helsinki, Dept of Forest Resource Management, P.O. BOX 27, FI-00014 University of Helsinki, Finland E-mail: ps@nn.fi
Maltamo, University of Joensuu, Faculty of Forest Sciences, P.O. Box 111, FI-80101 Joensuu, Finland E-mail: mm@nn.fi
Rautiainen, Univ. of Helsinki, Dept of Forest Resource Management, P.O. BOX 27, FI-00014 University of Helsinki, Finland E-mail: mr@nn.fi

article id 315, category Research article

Lauri Korhonen, Kari T. Korhonen, Miina Rautiainen, Pauline Stenberg. (2006). Estimation of forest canopy cover: a comparison of field measurement techniques. Silva Fennica vol. 40 no. 4 article id 315. https://doi.org/10.14214/sf.315

Keywords: canopy cover; forest canopy; canopy closure; Cajanus tube; line intersect sampling; spherical densiometer; digital photographs

Abstract | View details | Full text in PDF | Author Info

Estimation of forest canopy cover has recently been included in many forest inventory programmes. In this study, after discussing how canopy cover is defined, different ground-based canopy cover estimation techniques are compared to determine which would be the most feasible for a large scale forest inventory. Canopy cover was estimated in 19 Scots pine or Norway spruce dominated plots using the Cajanus tube, line intersect sampling, modified spherical densiometer, digital photographs, and ocular estimation. The comparisons were based on the differences in values acquired with selected techniques and control values acquired with the Cajanus tube. The statistical significance of the differences between the techniques was tested with the nonparametric Kruskall-Wallis analysis of variance and multiple comparisons. The results indicate that different techniques yield considerably different canopy cover estimates. In general, labour intensive techniques (the Cajanus tube, line intersect sampling) provide unbiased and more precise estimates, whereas the estimates provided by fast techniques (digital photographs, ocular estimation) have larger variances and may also be seriously biased.

Korhonen, University of Joensuu, P.O. Box 68, FI-68101 Joensuu, Finland E-mail: lauri.korhonen@joensuu.fi
Korhonen, University of Joensuu, P.O. Box 68, FI-68101 Joensuu, Finland E-mail: ktk@nn.fi
Rautiainen, University of Joensuu, P.O. Box 68, FI-68101 Joensuu, Finland E-mail: mr@nn.fi
Stenberg, University of Joensuu, P.O. Box 68, FI-68101 Joensuu, Finland E-mail: ps@nn.fi

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