Model-assisted estimation of domain totals, areas, and densities in two-stage sample survey designs

Abstract

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.

Figures (5)

• Figure 1: Tanana inventory unit, interior Alaska, USA. Black lines indicate location of airborne G-LiHT flight lines. Dots indicate (approximate) location of FIA field plots (colored by biomass). Underlying raster indicates stratification, red outline indicates the domain sampled by G-LiHT.
• Figure 2: Marginal distributions from Observed Plots (left column) and Simulated Population (right column).
• Figure 3: Spatial distribution of simulated population within Tanana study area
• Figure 4: Simulated samples at different sampling intensities with simple random sampling. Black lines indicate random sampled of airborne lidar flight lines. Dots indicate simulated field plots, color-coded by biomass
• Figure 5: Simulated samples at different sampling intensities with systematic sampling. Black lines indicate random sampled of airborne lidar flight lines. Dots indicate simulated field plots, color-coded by biomass