Systematic adaptive cluster sampling for the assessment of rare tree species in Nepal

Acharya, Bhoj Kumar; Bhattarai, Gopal Prakash; Gier, A. de; Stein, Alfred

doi:10.1016/s0378-1127(99)00318-7

Cited by 73 publications

(47 citation statements)

References 1 publication

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Adaptive cluster sampling (Thompson 1990) is a useful design for these types of populations and it has been used in a range of applications from surveying waterfowl (Smith et al 1995) and fish larvae (Lo et al 1997) to forest trees (Acharya et al 2000) and herbaceous plants (Philippi 2005). See Smith et al (2004) and Philippi (2005) for a review of these, and other, applications.…”

Section: Introductionmentioning

confidence: 99%

An adaptive two‐stage sequential design for sampling rare and clustered populations

et al. 2008

View full text Add to dashboard Cite

How to design an efficient large-area survey continues to be an interesting question for ecologists. In sampling large areas, as is common in environmental studies, adaptive sampling can be efficient because it ensures survey effort is targeted to subareas of high interest. In two-stage sampling, higher density primary sample units are usually of more interest than lower density primary units when populations are rare and clustered. Two-stage sequential sampling has been suggested as a method for allocating second stage sample effort among primary units. Here, we suggest a modification: adaptive two-stage sequential sampling. In this method, the adaptive part of the allocation process means the design is more flexible in how much extra effort can be directed to higher-abundance primary units. We discuss how best to design an adaptive two-stage sequential sample.

show abstract

Section: Introductionmentioning

confidence: 99%

An adaptive two‐stage sequential design for sampling rare and clustered populations

et al. 2008

View full text Add to dashboard Cite

show abstract

“…Roesch (1993) was the first to combine the probability-proportional-to-size sampling schemes that are commonly used in forestry with an ACS scheme to develop a system that could be applied to many inventory systems. Acharya et al (2000) sampled rare tree species using systematic ACS and determined that for a clustered species the efficiency for density estimation increased by as much as 500%; however, for an unclustered species it decreased by 40%. They also suggested that an optimal group size would relate to design efficiency, because when groups become too large ACS becomes comparable to complete enumeration.…”

Section: Introductionmentioning

confidence: 99%

Inventory of sparse forest populations using adaptive cluster sampling

Talvitie¹,

Leino²,

Holopainen³

2006

Silva Fenn.

View full text Add to dashboard Cite

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.

show abstract

“…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.…”

Section: Resultsmentioning

confidence: 99%

“…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). …”

Section: Role For Samplingmentioning

confidence: 99%

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

Coggins

Coops

Wulder³

2010

Silva Fenn.

View full text Add to dashboard Cite

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.

show abstract

Systematic adaptive cluster sampling for the assessment of rare tree species in Nepal

Cited by 73 publications

References 1 publication

An adaptive two‐stage sequential design for sampling rare and clustered populations

An adaptive two‐stage sequential design for sampling rare and clustered populations

Inventory of sparse forest populations using adaptive cluster sampling

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

Contact Info

Product

Resources

About