Forest growth, mortality, carbon, and diversity, all at your fingertips.
rFIA simplifies the estimation of forest variables using the Forest Inventory and Analysis Database, putting the power to explore in the hands of the people.
rFIA is an R package aimed at increasing the accessibility and use of the USFS Forest Inventory and Analysis (FIA) Database. Providing a user-friendly, open source toolset to easily query and analyze FIA Data,
rFIA simplifies the estimation of forest variables from the FIA Database and allows all R users (experts and newcomers alike) to unlock the flexibility and potential inherent to the Enhanced FIA design.
rFIA improves accessibility to the spatio-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.,
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), and has been validated against estimates and sampling errors produced by FIA’S EVALIDator.
Current development is focused on the implementation of spatially-enabled model-assisted estimators to improve population, change, and ratio estimation. We enivision
rFIA as a key component in the future of the FIA Program, targetting expansion in small area estimation, timber product monitoring, urban inventory, and the development oflong-term monitoring and reporting tools.
Hunter Stanke is the lead author and maintainer of
rFIA. Hunter is a Gradaute Research Fellow with the National Science Foundation and Master’s student at Michigan State University. He is interested in disturbance and landscape ecology, and plans to pursue a PhD in Quantitative Ecology and Resource Management beginning in 2020. See his personal website here.
MS Forest Science, 2020
Michigan State Univeristy
BS Forestry, 2019
Michigan State University
Andy is a professor at Michigan State University with a joint appointment in the Departments of Forestry and Geography, and is a co-author of
rFIA. A central theme in his research is the use of hierarchical models to integrate information from disparate sources to improve inference and prediction. In terms of application areas, his research focuses on spatial-temporal modeling of changing ecosystem components and systems.
PhD Natural Resources Science and Management, 2006
University of Minnesota
MS Statistics, 2007
University of Minnesota
MS Forestry, 2003
University of Massachusetts
BS Forestry, 2000
The Pennsylvania State University