Model-assisted estimation of domain totals, areas, and densities in two-stage sample survey designs
arxiv(2024)
摘要
Model-assisted, two-stage forest survey sampling designs provide a means to
combine airborne remote sensing data, collected in a sampling mode, with field
plot data to increase the precision of national forest inventory estimates,
while maintaining important properties of design-based inventories, such as
unbiased estimation and quantification of uncertainty. In this study, we
present a comprehensive set of model-assisted estimators for domain-level
attributes in a two-stage sampling design, including new estimators for
densities, and compare the performance of these estimators with standard
poststratified estimators. Simulation was used to assess the statistical
properties (bias, variability) of these estimators, with both simple random and
systematic sampling configurations, and indicated that 1) all estimators were
generally unbiased. and 2) the use of lidar in a sampling mode increased the
precision of the estimators at all assessed field sampling intensities, with
particularly marked increases in precision at lower field sampling intensities.
Variance estimators are generally unbiased for model-assisted estimators
without poststratification, while model-assisted estimators with
poststratification were increasingly biased as field sampling intensity
decreased. In general, these results indicate that airborne remote sensing,
collected in a sampling mode, can be used to increase the efficiency of
national forest inventories.
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