The research was carried out in unmanaged middle-aged (75–85 years) Northern taiga Scots pine (Pinus sylvestris L.) forests in the Kola peninsula. It was established that forests of green moss-lichen and green moss site types are characterised by a predominance (65–70% by stand volume) of moderately and strongly weakened trees. Trees of differing vitality have significant differences in annual increment. Healthy trees had a radial increment (RI) 70–75% greater than that of dying trees, and a basal area increment (BAI) 85–90% greater. The dynamics of the RI and BAI of Scots pine trees for the 70-year period (from 1945 to 2015) is different. The RI of all individuals in the communities studied decreases consistently. The decrease is expressed more strongly in green moss Scots pine forests (80–95% from 1945 to 2015) compared to green moss-lichen forests (60–80%); it manifests itself more in strongly weakened and dying individuals (75–95%) than in healthy and moderately weakened ones (60–80%). Annual basal area increment in green moss Scots pine forests increases by 45–65% from stand establishment until the trees are 25 to 35 years old and subsequently decreases by 50–80% to 70–80 years of age. In green moss-lichen pine forests the BAI of Scots pine remains rather stable in healthy and moderately weakened trees and decreases in strongly weakened and dying individuals by 45% and 75–80%, respectively throughout the studied period.
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.
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Thinning models are generally based on the density of the stand measured by the average basal area per hectare, for instance. These models are handicapped by the uneven structure of the stands. In uneven stands the averages are inadequate indicators for the need and amount of thinnings.
Small relascope plots were tested in the measurement of the spatial distribution of trees and in the determination of the need and amount of thinnings. The thinning quantity was determined as the difference between the actual distribution of the relascope plots into basal area classes and the ideal distribution after thinning. Sequential sampling was used in the derivation of the decision equations. A respective BASIC-program for a programmable pocket calculator is given.
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Dependence of the growth increase given by fertilization on different stand characteristics is examined in this article. The aim was to determine whether the volume growth increase can be accurately determined beforehand when fertilization is carried out on mineral soil sites at a dosage of 120 kg N/ha. The material consisted mostly on of mature stands ready for cutting, a total of 22 Scots pine (Pinus sylvestris L.) and 20 Norway spruce (Picea abies (L.) H. Karst.) stands. Increase in basal area, height quality class and basal area of the stand were found to best explain the increment and its increase in the regression equations calculated for different types of fertilizer and the control level.
The PDF includes a summary in English.