DOI: 10.5061/dryad.4mw6m90kf
title: Data from: Toward spatio-temporal models to support national-scale forest carbon monitoring and reporting

description: 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.
description: # Data from: Toward spatio-temporal models to support national-scale
 forest carbon monitoring and reporting Here we provide the simulated and
 FIA datasets along with R code to fit candidate models and provide model
 output summaries. ## Simulated datasets conus_pop_plots.tar.gz is the
 compressed conus_pop_plots.csv file that holds plot-level population
 values. Columns are: * *state*: State name. * *county*: County name. *
 *fips*: State and county code. * *ha*: County hectare. * *poly_id*: Unique
 county code. * *pop_id*: Unique plot code. * *tcc_2008 - tcc_2021*: NLCD
 tree canopy cover (%) by year. * *n_2008 - n_2021*: Number of plots in the
 county by year. * *carbon_2008 - carbon_2021*: Simulated carbon values
 (Mg/ha) by year. * *geometry*: Simple feature point coordinates.
 conus_pop_counties.csv is a file that holds county-level population values
 computed from conus_pop_plots.csv. Columns are: * *state*: State name. *
 *county*: County name. * *fips*: State and county code. * *ha*: County
 hectare. * *poly_id*: Unique county code. * *n_plots - n_2021*: Number of
 plots observed in the actual FIA data by year. * *carbon_2008 -
 carbon_2021*: Simulated carbon values (Mg/ha) averaged over plot values by
 year. * *tcc_2008 - tcc_2021*: NLCD tree canopy cover (%) averaged over
 plot values by year. * *geometry*: Simple feature polygon geometry.
 conus_rep_1_counties.csv is a file that holds county-level direct
 estimates computed using the first replicate's sample data drawn from
 conus_pop_plots.csv. Columns are: * *poly.id*: Unique county code. *
 *year*: Year. * *state*: State name. * *county*: County name. * *fips*:
 State and county code. * *ha*: County hectare. * *n*: Sample size. *
 *hat.mu*: Direct estimate of the mean for carbon (Mg/ha). *
 *hat.sigma.sq*: Standard error of *hat.mu*. * *tcc*: County-level mean
 NLCD tree canopy cover (%). * *mu.true*: Population carbon (Mg/ha) which
 matches *carbon_2008* - *carbon_2021* in conus_pop_counties.csv. ## FIA
 datasets conus_counties.geojson a spatial polygon file that holds
 county-level values. Columns include: * *state*: State name. * *county*:
 County name. * *fips*: State and county code. * *tcc_2008* - *tcc_2021*:
 County-level NLCD tree canopy cover (%) by year. * *hat_mu_2008* -
 *hat_mu_2021*: Direct estimate of the mean for carbon (Mg/ha) from
 observed FIA plot data by year. * *hat_sigma_sq_2008* -
 *hat_sigma_sq_2021*: Standard error of *hat_mu_2008* - *hat_mu_2021* by
 year. * *n_2008* - *n_2021*: Sample size by year. * *geometry*: Simple
 feature polygon geometry. conus_carbon_county_data.csv is a file that
 holds annual county-level direct estimates from observed FIA plot data.
 Rows are ordered by year 2008 - 2021 within county (follows stacking
 described in paper Section S4). County order matches that of
 conus_counties.geojson . Columns are: * *n*: Sample size. * *hat.mu*:
 Direct estimate of the mean for carbon (Mg/ha). * *hat.sigma.sq*: Standard
 error of the mean. * *tcc*: County-level NLCD tree canopy cover (%).
 conus_car_D_matrix.txt is a file that holds the diagonal values for the
 diagonal matrix D defined in Section 2.2. Ordering matches county order in
 conus_counties.geojson. conus_car_W_matrix.txt is a file that holds the
 values for the matrix W defined in Section 2.2. Ordering matches county
 order in conus_counties.geojson. ## Missing values NA values in the
 simulated and FIA data represent missing values (i.e., no data collected
 for the given county and year). ## Code Files fullmodel.R, submodel_1.R,
 and submodel_2.R implement the MCMC samplers for the Full model, Submodel
 1, and Submodel 2 defined in Section 2.2 and described in Section S4.
 mkREX.cpp and mkREX.R is code used to efficiently create sparse design
 matrices and used by fullmodel.R, submodel_1.R, and submodel_2.R. Given R
 build tools, mkREX.cpp can be built into a .so or .dll shared library
 using R CMD SHLIB mkREX.cpp. ## Running code For illustration,
 fullmodel.R, submodel_1.R, and submodel_2.R are set up to run one chain
 and save resulting MCMC samples and create summaries used for tables and
 figures in the paper. By setting the `type` variable in these files, you
 can fit the models to the simulated or actual FIA data. To run, create a
 shared library using mkREX.cpp (the code is currently set up for a
 mkREX.so, adjustments will need to be made for Windows operating system
 such that a mkREX.dll is created). Create a directory called
 "samples" to receive the resulting MCMC samples. Rmarkdown files
 Simulated_summaries_and_figures.Rmd and FIA_summaries_and_figures.Rmd can
 be used to summarize the MCMC samples for the first replicate's data
 and actual FIA data, respectively. ## Sharing/Access information Data were
 derived from the following sources. Housman, I., Schleeweis, K., Heyer,
 J., Ruefenacht, B., Bender, S., Megown, K., Goetz, W., and Bogle, S.
 (2023). National land cover database tree canopy cover methods
 v2021.4.611. GTAC-10268-RPT1. Salt Lake City, UT: U.S. Department of
 Agriculture, Forest Service, Geospatial Technology and Applications
 Center. USDA Forest Service. 2019. Forest Inventory and Analysis database.
 St. Paul, MN: USDA Forest Service, Northern Research Station.
 [https://doi.org/10.2737/RDS-2001-FIADB](https://doi.org/10.2737/RDS-2001-FIADB)