Andrew P. Robinson, Timothy G. Gregoire, Harry T. Valentine. (1997). Cut-off importance sampling of bole volume. Silva Fennica vol. 31 no. 2 article id 5615. https://doi.org/10.14214/sf.a8516

Keywords: Monte Carlo methods; forest mensuration; mean-square error; two-stage sampling

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Cut-off importance sampling (CIS) is introduced as a means of sampling individual trees for the purpose of estimating bole volume. The novel feature of this variant of importance sampling is the establishment on the bole of a cut-off height, H_C, above which sampling is precluded. An estimator of bole volume between predetermined heights H_L and H_U > H_C is proposed, and its design-based bias and mean square error are derived. In an application of CIS as the second stage of a two-stage sample to estimate aggregate bole volume, the gain in precision realized from CIS more than offset its bias when compared to the precision of importance sampling when H_C = H_U.

Robinson, E-mail: ar@mm.unknown
Gregoire, E-mail: tg@mm.unknown
Valentine, E-mail: hv@mm.unknown

Category : Research article

article id 486, category Research article

Arto Haara. (2003). Comparing simulation methods for modelling the errors of stand inventory data. Silva Fennica vol. 37 no. 4 article id 486. https://doi.org/10.14214/sf.486

Keywords: measurement error; simulation; stand-level inventory; non-parametric estimation; Monte Carlo methods

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Forest management planning requires information about the uncertainty inherent in the available data. Inventory data, including simulated errors, are infrequently utilised in forest planning studies for analysing the effects of uncertainty on planning. Usually the errors in the source material are ignored or not taken into account properly. The aim of this study was to compare different methods for generating errors into the stand-level inventory data and to study the effect of erroneous data on the calculation of specieswise and standwise inventory results. The material of the study consisted of 1842 stands located in northern Finland and 41 stands located in eastern Finland. Stand-level ocular inventory and checking inventory were carried out in all study stands by professional surveyors. In simulation experiments the methods considered for error generation were the 1nn-method, the empirical errors method and the Monte Carlo method with log-normal and multivariate log-normal error distributions. The Monte Carlo method with multivariate error distributions was found to be the most flexible simulation method. This method produced the required variation and relations between the errors of the median basal area tree characteristics. However, if the reference data are extensive the 1nn-method, and in certain conditions also the empirical errors method, offer a useful tool for producing error structures which reflect reality.

Haara, Finnish Forest Research Institute, Joensuu Research Centre, P.O.Box 68, FIN-80101 Joensuu, Finland E-mail: arto.haara@metla.fi

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