  The United States (US) Forest Service Forest Inventory and Analysis (FIA)
program operates the national forest inventory of the US. Traditionally, the
FIA program has relied on sample-based approaches -- permanent plot networks
and associated design-based estimators -- to estimate forest variables across
large geographic areas and long periods of time. These approaches generally
offer unbiased inference on large domains but fail to provide reliable
estimates for small domains due to low sample sizes. Rising demand for small
domain estimates will thus require the FIA program to adopt non-traditional
estimation approaches that are capable of delivering defensible estimates of
forest variables at increased spatial and temporal resolution, without the
expense of collecting additional field data. In light of this challenge, the
development of small area estimation (SAE) methods for FIA data has become an
active and highly productive area of research. Yet, SAE methods remain
difficult to apply to FIA data, due in part to the complex data structures and
inventory design used by the FIA program. Thus, we argue that a new suite of
estimation tools (i.e., software) will be required to accommodate shifts in
demand for inference on large geographic areas and long time periods to
inference on small spatial and/or temporal domains. Herein, we present rFIA, an
open-source R package designed to increase the accessibility of FIA data, as
one such tool. Specifically, we present two case studies chosen to demonstrate
rFIA's potential to simplify the application of a broad suite of SAE methods to
FIA data: (1) estimation of contemporary county-level forest carbon stocks
across the conterminous US using a spatial Fay-Herriot model; and (2)
temporally-explicit estimation of multi-decadal trends in merchantable wood
volume in Washington County, Maine using a Bayesian mixed-effects model.
