Tapani Lahti 
Understorey vegetation as an indicator of forest site potential in Southern Finland
Lahti T. (1995). Understorey vegetation as an indicator of forest site potential in Southern Finland. Acta Forestalia Fennica no. 246 article id 7508. https://doi.org/10.14214/aff.7508
Abstract
The relationship between site characteristics and understorey vegetation composition was analysed with quantitative methods, especially from the viewpoint of site quality estimation. Theoretical models were applied to an empirical data set collected from the upland forests of Southern Finland comprising 104 sites dominated by Scots pine (Pinus sylvestris. L.) and 165 sites dominated by Norway spruce (Picea abies (L.) H. Karst.). Site index H100 was used as an independent measure of site quality.
A new model for the estimation of site quality at sites with a known understorey vegetation composition was introduced. It is based on the application of Bayes’ theorem to the density function of site quality within the study area combined with the species-specific presence-absence response curves. The resulting probability density function may be used for calculating an estimate for the site variable
Using this method, a jackknife estimate of site index H100 was calculated separately for pine- and spruce-dominated sites. The results indicated that the cross-validation root mean squared error (RMSEcv) of the estimates improved from 2.98 m down to 2.34 m relative to the ”null” model (standard deviation of the sample distribution) in pine-dominated forests. In spruce-dominated forests RMSEcv decreased from 3.94 m down to 3.19 m.
In order to assess these results, four other estimation methods based on understorey vegetation composition were applied to the same data set. The results showed that none of the methods was clearly superior to the others. In pine-dominated forests RMSEcv varied between 2.34 and 2.47 m, and the corresponding range for spruce-dominated forest was from 3.13 to 3.57 m.
Keywords
Norway spruce;
site quality;
Scots pine;
classification;
forest vegetation;
estimation
Published in 1995
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