Articles by Risto Päivinen

The paper continues an earlier study by Kilkki and Päivinen concerning the use of the Weibull function in modelling the diameter distribution. The data consists of spruces (Picea abies (L.) H. Karst.) measured on angle count sample points of the National Forest Inventory of Finland. First, maximum likelihood estimation method was used to derive the Weibull parameters. Then, regression models to predict the values of these parameters with stand characteristics were calculated. Several methods to describe the Weibull function by a tree sample were tested. It is more efficient to sample the trees at equal frequency intervals than at equal diameter intervals. It also pays to take separate samples for pulpwood and saw timber.

The PDF includes an abstract in Finnish.

• Kilkki, E-mail: pk@mm.unknown
• Maltamo, E-mail: mm@mm.unknown
• Mykkänen, E-mail: rm@mm.unknown
• Päivinen, E-mail: rp@mm.unknown

Theoretical and practical aspects of permanent sample plots are discussed in this paper. A study material of 6,871 permanent sample plots was generated using increment sample plots of the 7th National Forest Inventory of Finland. The effect of measurement errors and use of increment functions as ”a priori” information was studied via simulation experiments. The change in the growing stock volume between two consecutive measurement rounds was divided into the components drain, growth and mortality. Finally, a hypothetical inventory design using permanent sample plots was evaluated.

The PDF includes an abstract in Finnish.

• Päivinen, E-mail: rp@mm.unknown
• Yli-Kojola, E-mail: hy@mm.unknown

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.

• Kilkki, E-mail: pk@mm.unknown
• Päivinen, E-mail: rp@mm.unknown

• Päivinen, European Forest Institute, Torikatu 34, FIN-80101 Joensuu, Finland E-mail: risto.paivinen@efi.fi
• Anttila, European Forest Institute, Torikatu 34, FIN-80101 Joensuu, Finland E-mail: pa@nn.fi