About the benefits of poststratification in forest inventories

Abstract

A large virtual population is created based on the GIS data base of a forest district and inventory data. It serves as a population where large scale inventories with systematic and simple random poststratified estimators can be simulated and the gains in precision studied. Despite their selfweighting property, systematic samples combined with poststratification can still be clearly more efficient than unstratified systematic samples, the gain in precision being close to that resulting from poststratified over simple random samples. The poststratified variance estimator for the conditional variance given the within strata sample sizes served as a satisfying estimator in the case of systematic sampling. The differences between conditional and unconditional variance were negligible for all sample sizes analyzed.

Keywords

• poststratification
• systematic sampling
• simple random sampling
• conditional variance