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Double sampling for post-stratification in forest inventory

Authors
  • Westfall, James A.1
  • Lister, Andrew J.1
  • Scott, Charles T.
  • Weber, Thomas A.1
  • 1 Northern Research Station, U.S. Forest Service, Newtown Square, PA, 19073, USA , Newtown Square (United States)
Type
Published Article
Journal
European Journal of Forest Research
Publisher
Springer Berlin Heidelberg
Publication Date
Mar 14, 2019
Volume
138
Issue
3
Pages
375–382
Identifiers
DOI: 10.1007/s10342-019-01171-9
Source
Springer Nature
Keywords
License
Yellow

Abstract

Many national forest inventories (NFI) use auxiliary data to increase the precision of estimates. Typically, this is accomplished via stratified estimation techniques that rely on assignment of similar sample plot observations to strata constructed with the goal of lowering the variance of estimates. While early applications of stratification used strata constructed from photo-interpretation of aerial photography, current technology makes using wall-to-wall digital map information more appealing due to automated processing capabilities; however, there is generally a reduction in classification accuracy in comparison with photo-interpretation and a concomitant decrease in the precision of estimates. While most established NFI have permanent plots and employ post-stratification (PS) with stratum weights known from a map, it is unclear what are the compromises compared to using a photo-interpretation (PI) approach. In this study, differences in cost and precision were evaluated for post-stratification using strata derived from a digital map and double sampling for post-stratification (DSPS) with strata created from PI of aerial imagery. It was found that DSPS consistently provided better precision than PS for estimates of total biomass and forestland area with approximately 13 PI points per sample plot, which incurred a cost increase equivalent to 0.5% per ground plot. Increasing the number of PI points per plot resulted in further gains in precision, with cost increases proportional to the PI intensity. To attain specific precision goals, DSPS was generally less costly than increasing the sample size under PS, although the PS design was more cost-effective if the PI intensity was too low. The results of this study provide a decision framework for inventory planners considering sampling designs that rely on post-stratified estimation.

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