Annika Kangas. (1994). Classical and model based estimators for forest inventory. Silva Fennica vol. 28 no. 1 article id 5524. https://doi.org/10.14214/sf.a9158

Keywords: models; forest inventories; estimation; systematic cluster sampling; covariance structure

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In this study, model-based and design-based inference methods are used for estimating mean volume and its standard error for systematic cluster sampling. Results obtained with models are compared to results obtained with classical methods. The data are from the Finnish National Forest Inventory. The variation of volume in ten forestry board districts in Southern Finland is studied. The variation is divided into two components: trend and correlated random errors. The effect of the trend and the covariance structure on the obtained mean volume and standard error estimates is discussed. The larger the coefficient of determination of the trend model, the smaller the model-based estimates of standard error, when compared to classical estimates. On the other hand, the wider the range and level of autocorrelation between the sample plots, the larger the model-based estimates of standard error.

Kangas, E-mail: ak@mm.unknown

Category : Research article

article id 925, category Research article

Steen Magnussen. (2013). An assessment of three variance estimators for the k-nearest neighbour technique. Silva Fennica vol. 47 no. 1 article id 925. https://doi.org/10.14214/sf.925

Keywords: forest inventory; simple random sampling; resampling estimators; bootstrap; jackknife; difference estimator; cluster sampling

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A jackknife (JK), a bootstrap (BOOT), and an empirical difference estimator (EDE) of totals and variance were assessed in simulated sampling from three artificial but realistic complex multivariate populations (N = 8000 elements) organized in clusters of four elements. Intra-cluster correlations of the target variables (Y) varied from 0.03 to 0.26. Time-saving implementations of JK and BOOT are detailed. In simple random sampling (SRS), bias in totals was ≤ 0.4% for the two largest sample sizes (n = 200, 300), but slightly larger for n = 50, and 100. In cluster sampling (CLU) bias was typically 0.1% higher and more variable. The lowest overall bias was in EDE. In both SRS and CLU, JK estimates of standard error were slightly (3%) too high, while the bootstrap estimates in both SRS and CLU were too low (8%). Estimates of error suggested a trend in EDE toward an overestimation with increasing sample size. Calculated 95% confidence intervals achieved a coverage that in most cases was fairly close (± 2%) to the nominal level. For estimation of a population total the EDE estimator appears to be slightly better than the JK estimator.

Magnussen, Canadian Forest Service, Natural Resources Canada, 505 West Burnside Road, Victoria BC V8Z 1M5 Canada E-mail: steen.magnussen@nrcan.gc.ca

article id 456, category Research article

Sam B. Coggins, Nicholas C. Coops, Michael A. Wulder. (2010). Improvement of low level bark beetle damage estimates with adaptive cluster sampling. Silva Fennica vol. 44 no. 2 article id 456. https://doi.org/10.14214/sf.456

Keywords: forest inventory; adaptive cluster sampling; mountain pine beetle; object-based classification; high spatial resolution; satellite; digital aerial imagery

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Detection of low level infestation in forest stands is of principle importance to determine effective control strategies before the attack spread to large areas. Of particular concern is the ongoing mountain pine beetle, Dendroctonus ponderosae (Hopkins) epidemic, which has caused approximately 14 million hectares of damage to lodgepole pine (Pinus contorta Dougl. ex. Loud var. latifolia Engl.) forests in western Canada. At the stand level attacked trees are often difficult to locate and can remain undetected until the infestation has become established beyond a small number of trees. As such, methods are required to detect and characterise low levels of attack prior to infestation expansion, to inform management, and to aid mitigation activities. In this paper, an adaptive cluster sampling approach was applied to very fine-scale (20 cm) digital aerial imagery to locate mountain pine beetle damaged trees at the leading edge of the current infestation. Results indicated a mean number of 7.36 infested trees per hectare with a variance of 18.34. In contrast a non-adaptive approach estimated the mean number of infested trees in the same area to be 61.56 infested trees per hectare with a variance of 41.43. Using a relative efficiency estimator the adaptive cluster sampling approach was found to be over two times more efficient when compared to the non-adaptive approach.

Coggins, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, B.C., Canada V6T 1Z4 E-mail: scoggins@interchange.ubc.ca
Coops, Department of Forest Resources Management, University of British Columbia, 2424 Main Mall, Vancouver, B.C., Canada V6T 1Z4 E-mail: ncc@nn.ca
Wulder, Canadian Forest Service (Pacific Forestry Centre), Natural Resources Canada, 506 West Burnside Rd., Victoria, B.C., Canada V8Z 1M5 E-mail: maw@nn.ca

article id 354, category Research article

Mervi Talvitie, Olli Leino, Markus Holopainen. (2006). Inventory of sparse forest populations using adaptive cluster sampling. Silva Fennica vol. 40 no. 1 article id 354. https://doi.org/10.14214/sf.354

Keywords: coarse woody debris; adaptive cluster sampling; simple random sampling; drought damage; efficiency

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In many studies, adaptive cluster sampling (ACS) proved to be a powerful tool for assessing rare clustered populations that are difficult to estimate by means of conventional sampling methods. During 2002 and 2003, severe drought-caused damage was observed in the park forests of the City of Helsinki, Finland, especially in barren site pine and spruce stands. The aim of the present study was to examine sampling and measurement methods for assessing drought damage by analysing the effectiveness of ACS compared with simple random sampling (SRS). Horvitz-Thompson and Hansen-Hurwitz estimators of the ACS method were used for estimating the population mean and variance of the variable of interest. ACS was considerably more effective than SRS in assessing rare clustered populations such as those resulting from drought damage. The variances in the ACS methods were significantly smaller and the inventory efficiency in the field better than in SRS.

Talvitie, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: mervi.talvitie@helsinki.fi
Leino, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ol@nn.fi
Holopainen, University of Helsinki, Department of Forest Resource Management, P.O. Box 27, FI-00014 University of Helsinki, Finland E-mail: ah@nn.fi

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