Pekka Hyvönen, Jaakko Heinonen. (2018). Estimating storm damage with the help of low-altitude photographs and different sampling designs and estimators. Silva Fennica vol. 52 no. 3 article id 7710. https://doi.org/10.14214/sf.7710

Keywords: simulation; simple random sampling estimator; ratio estimator; Lindeberg; flight line

Highlights: Digital photographs taken from low altitudes are usable for monitoring storm damage; Simple random sampling and ratio estimators resulted in similar standard errors; Characteristics of the storm influence the optimal flight plan and which variance estimator should be used; The developed model for simulations can be modified and utilized with future storms.

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Climate change has been estimated to increase the risk of storm damage in forests in Finland. There is a growing need for methods to obtain information on the extent and severity of storm damage after a storm occurrence. The first objective of this study was to test whether digital photographs taken from aircrafts flying at low-altitude can be utilized in locating storm-damaged areas and estimating the need for harvesting of wind-thrown trees. The second objective was to test the performance of selected estimators. Depending on distances between flight lines, plots on lines and the used estimator, the relative standard errors of storm area estimates varied between 7.7 and 48.7%. For the area for harvesting and volume of wind-thrown trees, the relative standard errors of estimates varied between 16.8 and 167.3%. Using forest area information from Multisource National Forest Inventory data improved the accuracy of the estimates. However, performance of a simple random sampling estimator and ratio estimator were quite similar. Lindeberg’s method for variance estimation based on adjacent lines was sensitive to line directions in relation to possible trends in storm-damaged area locations. Our results showed that the tested method could be used in estimating storm-damaged area provided that the network of flight lines and photographs on lines are sufficiently dense. The developed model for simulations can be utilized also with forthcoming storms as model’s parameters can be freely adjusted to meet, e.g., the intensity and extent of the storm.

Hyvönen, Natural Resources Institute Finland (Luke), Bioeconomy and environment, Yliopistokatu 6, FI-80100 Joensuu, Finland E-mail: pekka.hyvonen@luke.fi
Heinonen, E-mail: jaakkoheinonen@gmail.com

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 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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