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Jouni Siipilehto (email)

Linear prediction application for modelling the relationships between a large number of stand characteristics of Norway spruce stands

Siipilehto J. (2006). Linear prediction application for modelling the relationships between a large number of stand characteristics of Norway spruce stands. Silva Fennica vol. 40 no. 3 article id 334. https://doi.org/10.14214/sf.334

Abstract

The aim was to produce models for a large number of stand characteristics of Norway spruce dominated stands. A total of 227 national forest inventory based permanent stand plots, dominated by Norway spruce (Picea abies), were used in modelling eight stand variables as a function of the stand mean biological age and site characteristics. The basic models were able to characterize the average development of the modelled stand variables, but resulted in a relatively high RMSE. Basal area (G) and stem number (N) were the most inaccurate, having a RMSE of 34–41%, while that of mean diameter and height characteristics varied between 16–20%. The expectations and error variances of the basic models were calibrated with known stand variables using linear prediction theory. The best linear unbiased predictor (BLUP) with a single stand variable used for calibration proved to be ineffective for unknown G and N, but relatively effective for the unknown mean characteristics. However, calibration with one sum and one mean characteristic proved to be effective, and additional calibration variables enhanced the precision only marginally. The BLUP method provided a flexible approach when characterizing the relationships between a large number of stand variables, thus enabling multiple use of these models because they were not fixed to a specific inventory system.

Keywords
Picea abies; stand characteristics; interdependence; prediction; calibration estimation

Author Info
  • Siipilehto, Finnish Forest Research Institute, P.O. Box 18, FI-01301 Vantaa, Finland ORCID ID:E-mail jouni.siipilehto@metla.fi (email)

Received 22 November 2005 Accepted 29 May 2006 Published 31 December 2006

Available at https://doi.org/10.14214/sf.334 | Download PDF

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