Marc Palahí, Timo Pukkala, Antoni Trasobares. (2006). Calibrating predicted tree diameter distributions in Catalonia, Spain. Silva Fennica vol. 40 no. 3 article id 332. https://doi.org/10.14214/sf.332

Keywords: calibration estimation; Weibull function; parameter prediction

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Several probability density functions have been used in describing the diameter distributions of forest stands. In a case where both the stand basal area and number of stems per hectare are assessed, the fitted or predicted distribution is scaled using only one of these variables, with the result that the distribution often gives incorrect values for the other variable. Using a distribution that provides incorrect values for known characteristics means wasting information. Calibrating the distribution so that it is compatible with the additional information on stand characteristics is a way to avoid such wasting. This study examined the effect of calibration on the accuracy of the predicted diameter distributions of the main tree species of Catalonia. The distributions were calibrated with and without considering the prediction errors of the frequencies of diameter classes. When prediction errors were assumed, the calibration was done with and without making allowance for estimation errors in the stand level calibration variables. Calibrated distributions were more accurate than non-calibrated in terms of sums of different powers of diameters. The set of calibration variables that gave the most accurate results included six stand variables: number of trees per hectare, stand basal area, basal-area-weighted mean diameter, non-weighted mean diameter, median diameter, and basal area median diameter. Of the tested three-variable combinations the best was: number of trees per hectare, stand basal area, and basal-area-weighted mean diameter. Means were more useful calibration variables than medians.

Palahí, Centre Tecnológic Forestal de Catalunya. Passeig Lluis Companys, 23, 08010, Barcelona, Spain E-mail: marc.palahi@ctfc.es
Pukkala, University of Joensuu, Faculty of Forestry, P.O. Box 111, 80101 Joensuu, Finland E-mail: tp@nn.fi
Trasobares, Foreco Technologies, Av. Diagonal 416, Estudio 2, Barcelona 08037, Spain E-mail: at@nn.es

article id 524, category Research article

Timo Pukkala, Jari Miina, Marc Palahí. (2002). Thinning response and thinning bias in a young Scots pine stand. Silva Fennica vol. 36 no. 4 article id 524. https://doi.org/10.14214/sf.524

Keywords: Pinus sylvestris; growth model; non-spatial model; spatial model

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

The study analyses the annual post-thinning response and thinning bias of a young Scots pine stand as a function of tree size, competition faced by the tree, and competition that is removed around the tree in the thinning treatment. The thinning response of a tree was defined as the change of tree growth due to a thinning treatment. The thinning bias was defined as the difference between the true growth and model prediction. A distance-dependent (spatial) and a distance-independent (non-spatial) growth model were used in the calculations. The empirical data were measured from a thinning experiment consisting of ten plots, each 40 x 30 m in size, which were thinned to different stand densities. The ten-year post-thinning growth of every remaining tree was measured. The results indicated that the highest thinning response is among medium-sized and co-dominant trees. The thinning response is quite small, and even negative for some trees, for two years after thinning but it becomes clearly positive from the third year onwards. The spatial model underestimated the growth of small trees (which usually face high competition) while the non-spatial model overestimated the growth of trees that are small or face much competition. The spatial model used in this study overemphasized the effect of competition while the non-spatial model underestimated this effect. Both growth models overestimated the growth of trees in heavily thinned places, but this bias disappeared in two years. The negative bias was more pronounced with a spatial growth model because the tendency of the non-spatial model to underestimate the growth of trees facing little competition partly compensated for the negative bias.

Pukkala, University of Joensuu, Faculty of Forestry, P.O. Box 111, FIN-80101 Joensuu, Finland E-mail: timo.pukkala@joensuu.fi
Miina, Finnish Forest Research Institute, Joensuu Research Centre, P.O. Box 68, FIN-80101 Joensuu, Finland E-mail: jm@nn.fi
Palahí, European Forest Institute, Torikatu 34, FIN-80100 Joensuu, Finland E-mail: mp@nn.fi

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