Data from: Toward spatio-temporal models to support national-scale forest carbon monitoring and reporting

National forest inventory (NFI) programs provide vital information on
forest parameters’ status, trend, and change. Most NFI designs and
estimation methods are tailored to estimate status over large areas but
are not well suited to estimate trend and change, especially over small
spatial areas and/or over short time periods (e.g. annual estimates).
Fine-scale space-time indexed estimates are critical to a variety of
environmental, ecological, and economic monitoring efforts. In the United
States, for example, NFI data are used to estimate forest carbon status,
trend, and change to support national, state, and local user group needs.
Increasingly, these users seek finer spatial and temporal scale estimates
to evaluate existing land use policies and management practices, and
inform future activities. Here we propose a spatio-temporal Bayesian small
area estimation modeling framework that delivers statistically valid
estimates with complete uncertainty quantification for status, trend, and
change. The framework accommodates a variety of space and time dependency
structures, and we detail model configurations for different settings. The
proposed framework is used to quantify forest carbon dynamics at an annual
county-level across a 14 year period for the contiguous United States.
Also, using an analysis of simulated data, we compare the proposed
framework with traditional NFI estimators and offer computationally
efficient algorithms, software, and data to reproduce results for
benchmarking.