Estimation of Forest Variables using the FIA Database
Estimate forest land area from FIADB
Estimate land area change from the FIADB
Estimate tree biomass and carbon stocks from the FIADB
Estimate carbon stocks by IPCC forest carbon pools from the FIADB
Spatial and temporal queries for FIADB
Post-stratified estimator for custom variables in FIA Data
Estimate diversity from FIADB
Estimate volume, biomass, and carbon stocks of down woody material (fu...
Find EVALIDs used in the FIADB
Estimate the Forest Stability Index from the FIADB
Extract design features for FIA inventories
Download FIA Data and Load into R
Estimate growth, recruitment, mortality, and harvest rates from FIADB
Intersect FIA data with spatial polygons
Estimate invasive species coverage from FIADB
Convert numeric variables to class intervals (factor)
Static and animated plots of FIA summaries
Load FIA database into R environment from local directory
Estimate seedling abundance per acre from FIADB
Estimate forest structural stage distribution from FIADB
Estimate trees per acre and basal area per acre from FIADB
Estimate vegation cover by canopy layer from FIADB
Estimate tree growth rates from FIADB
Estimate merchantable tree volume from the FIADB
Write FIA tables to local directory
The goal of 'rFIA' is to increase the accessibility and use of the United States Forest Services (USFS) Forest Inventory and Analysis (FIA) Database by providing a user-friendly, open source toolkit to easily query and analyze FIA Data. Designed to accommodate a wide range of potential user objectives, 'rFIA' simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility inherent to the Enhanced FIA design. Specifically, 'rFIA' improves accessibility to the spatial-temporal estimation capacity of the FIA Database by producing space-time indexed summaries of forest variables within user-defined population boundaries. Direct integration with other popular R packages (e.g., 'dplyr', 'tidyr', and 'sf') facilitates efficient space-time query and data summary, and supports common data representations and API design. The package implements design-based estimation procedures outlined by Bechtold & Patterson (2005) <doi:10.2737/SRS-GTR-80>, and has been validated against estimates and sampling errors produced by FIA 'EVALIDator'. Current development is focused on the implementation of spatially-enabled model-assisted and model-based estimators to improve population, change, and ratio estimates.
Useful links