Current issue: 56(1)
Under compilation: 56(2)
The relascope method, introduced by Bitterlich, has been mostly used in estimating the basal area of tree stands or growing stock. Volume estimation requires, in addition, mean height and form. The purpose of this study is to work out a method for calculating the volume, bark and increment of the stand from the measurements of sample trees taken on a plot determined with the relascope. All trees of the same diameter have their own plot size and the stand characteristics are the sum of all tree characteristics multiplied by a blow-up factor which is a function of the diameter.
Accurate determination of a sample plot with the relascope requires checking the boundary trees with a tape. In an average forest there are 10 to 20 unit trees on each plot if the opening of the relascope is 2 cm. Because all trees of equal diameter to be tallied on a sample plot represent an equal share of the total basal area, the number of trees to be tallied is very economical from the stand point of volume estimation. Objective selection of the sample trees can easily be done. The unit volume per hectare represented by each tallied tree, or by each sample tree, is directly proportional to the tree height. Thus, the estimates of the stock characteristics can be calculated as arithmetic means from the sample tree characteristics. The calculation procedure which gives the ordinary stock table, volume, bark and increment is also easily carried out with punched cards.
The PDF includes a summary in Finnish.
The paper demonstrates the possibility of using data from small relascope sample plots in the derivation of the regression models which predict the Weibull function parameters for the dbh-distribution. The Weibull parameters describing the basal area dbh-distribution were estimated for relascope sample plots from the Finnish National Forest Inventory. In the first stage of the estimation nonlinear regression analysis was employed to derive initial parameter estimates for the second stage, in which the maximum likelihood method was used. The parameter estimates were employed as dependent variables for the derivation of the regression models; the independent variables comprised of the compartment-wise stand variables generally estimated in ocular inventories.
The PDF includes an abstract in Finnish.