Skip to main content

Potential for wider application of 3P sampling in forest inventory

Buy Article:

$50.00 plus tax (Refund Policy)

Abstract:

Sampling with probability proportional to prediction (3P sampling) is useful where the variable of interest to a forest inventory is costly to measure and where there exists a cheaper to measure auxiliary variable, which correlates positively with the variable of interest. Two forms of 3P sampling, termed “classical” and “point-” 3P sampling, have received some use in forest inventory. However, both have limitations that have restricted their use mainly to estimation of tree stem wood volume for timber sales over small forest areas in North America. A more general form of 3P sampling, termed here “ordinary” 3P sampling, has been all but ignored to date. It has potential for use in inventory of a broad range of forest attributes, both floral and faunal and both commercial and environmental, across large or small forest areas. Using a common mathematical approach, the present work derives the estimators of the population mean for these three forms of 3P sampling. Their properties are compared with simple random sampling through Monte Carlo simulations based on two example forest populations. The work lays a basis from which 3P sampling might develop further and enjoy wider application in forest inventory than has been the case previously.

Document Type: Research Article

DOI: https://doi.org/10.1139/x11-062

Publication date: 2011-07-05

More about this publication?
  • Published since 1971, this monthly journal features articles, reviews, notes and commentaries on all aspects of forest science, including biometrics and mensuration, conservation, disturbance, ecology, economics, entomology, fire, genetics, management, operations, pathology, physiology, policy, remote sensing, social science, soil, silviculture, wildlife and wood science, contributed by internationally respected scientists. It also publishes special issues dedicated to a topic of current interest.
  • Information for Authors
  • Submit a Paper
  • Subscribe to this Title
  • Terms & Conditions
  • Sample Issue
  • Reprints & Permissions
  • Ingenta Connect is not responsible for the content or availability of external websites
  • Access Key
  • Free ContentFree content
  • Partial Free ContentPartial Free content
  • New ContentNew content
  • Open Access ContentOpen access content
  • Partial Open Access ContentPartial Open access content
  • Subscribed ContentSubscribed content
  • Partial Subscribed ContentPartial Subscribed content
  • Free Trial ContentFree trial content
Cookie Policy
X
Cookie Policy
Ingenta Connect website makes use of cookies so as to keep track of data that you have filled in. I am Happy with this Find out more