Auxiliary information resolution effects on small area estimation in plantation forest inventory

Abstract

In forest inventory, traditional ground-based resource assessments are often expensive and time-consuming forcing managers to reduce sample sizes to meet budgetary and logistical constraints. Small area estimation (SAE) is a class of statistical estimators that uses a combination of traditional survey data and linearly related auxiliary information to improve estimate precision. These techniques have been shown to improve the precision of stand-level inventory estimates in loblolly pine plantations using lidar height percentiles and thinning status as covariates. In this study, the effects of reduced lidar point-cloud densities and lower digital elevation model (DEM) spatial resolutions were investigated for total planted volume estimates using area-level SAE models. In the managed Piedmont pine plantation conditions evaluated, lower lidar point-cloud densities and DEM spatial resolutions were found to have minimal effects on estimates and precision. The results of this study are promising to those interested in incorporating SAE methods into forest inventory programs.

Increased Precision in County-Level Volume Estimates in the United States National Forest Inventory With Area-Level Small Area Estimation

“…The domain-direct estimate and its sample variance serve as the source of direct information, while the indirect information -sample estimates and variances from other domains -is then linked to the direct domain via the model y ∼ x, which leverages the relationship between sample and auxiliary data sets. Area-based approaches are often synonymous with the Fay and Herriot (1979) estimator, a well-recognized and widely adopted model used in area-based SAE, including a number of forest inventory applications (Green et al, 2020b).…”

Needs for Small Area Estimation: Perspectives From the US Private Forest Sector

“…Many users of FIA data have expressed expanding needs for more detailed information on smaller spatial domains 1 www.forestfoundation.org/family-forest-carbon-program while maintaining the core field measurement program (Guldin, 2020a,b). The FIA program has responded with increased research activity in SAE (for example, Brooks et al, 2016;Nagle et al, 2019;Green et al, 2020;Coulston et al, 2021). These efforts have shown that precision can be improved using SAE with plot data combined with ancillary data.…”

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“…The domain-direct estimate and its sample variance serve as the source of direct information, while the indirect information -sample estimates and variances from other domains -is then linked to the direct domain via the model y ∼ x, which leverages the relationship between sample and auxiliary data sets. Area-based approaches are often synonymous with the Fay and Herriot (1979) estimator, a well-recognized and widely adopted model used in area-based SAE, including a number of forest inventory applications (Green et al, 2020b).…”