Inventory of sparse forest populations using adaptive cluster sampling

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.

Efficiency of adaptive cluster sampling and traditional sampling for coastal mangrove in Hainan of China

“…Relative to HT, the amplitude of fluctuation for the results obtained by HH was smaller than HT and the density of HH was closer to the true value (Table 1). This result was similar to that of Talvitie et al (2005).…”

Improvement of low level bark beetle damage estimates with adaptive cluster sampling

“…This approach has been used to idenitify rare tree species (Acharaya et al 2000), and sparse forest populations (Talvitie et al 2006) and to predict forest inventory variables (Roesch 1993). The methodology used in this study is applicable to forests globally, to detect rare and clustered populations on the landscape that may be easily identified on remotely sensed imagery.…”

“…Previous studies have utilised adaptive cluster sampling for a variety of applications including for example, providing estimates of low density mussel populations (Smith et al 2003), estimating the density of wintering waterfowl (Smith et al 1995), and estimating stock size of fish in estuarine rivers (Conners and Schwager 2002). In a forestry context this adaptive cluster sampling approach has also been utilised to assess the presence of rare tree species in Nepal (Acharaya et al 2000), in combination with probability proportional to size sampling to predict forest inventory variables in the United States (Roesch 1993), and to inventory sparse forest populations in Finland (Talvitie et al 2006). …”

Evaluation de la ressource en teck, Tectona grandis L.f., des plantations privées du Sud-Bénin

“…Celle-ci s'apparente à un échantillonnage boule de neige ( Il s'agit d'une technique d'investigation qui est surtout utilisée dans le domaine de la sociologie (MAGNANI et al, 2005 ;KENDALL et al, 2008) et plus particulièrement lorsque la population de base relative au domaine d'étude est considérée comme rare ou difficilement dénombrable (MAGNANI et al, 2005 ;PLATT et al, 2006). Il existe égale-ment des exemples d'application du snowball sampling dans le domaine de la foresterie sociale (KANT, LEE, 2004 ;THOMPSON et al, 2005) et de l'inventaire forestier adaptatif, notamment l'échantillonnage adaptatif par grappes (TALVITIE et al, 2006) et l'échantillonnage adaptatif systé-matique en grappes (ACHARYA et al, 2000).…”