%0 Research article
%T Inventory of sparse forest populations using adaptive cluster sampling
%A Talvitie, Mervi
%A Leino, Olli
%A Holopainen, Markus
%D 2006
%J Silva Fennica
%V 40
%N 1
%R doi:10.14214/sf.354
%U https://silvafennica.fi/article/354
%X 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.