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Mervi Talvitie (email), Olli Leino, Markus Holopainen

Inventory of sparse forest populations using adaptive cluster sampling

Talvitie M., Leino O., Holopainen M. (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

Abstract

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.

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

Author Info
  • 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 (email)
  • 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

Received 1 February 2005 Accepted 17 November 2005 Published 31 December 2006

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Available at https://doi.org/10.14214/sf.354 | Download PDF

Creative Commons License CC BY-SA 4.0

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