PyFIA: analyzing and visualizing forest attributes using the United States Forest Inventory and Analysis database

BACKGROUND: The United States Forest Service Forest Inventory and Analysis (FIA) database is extensive and complex, requiring significant processing to link its multiple tables. In addition, integrating FIA data with external datasets, such as climate data and remote sensing products, involves substantial preprocessing and computational effort, posing challenges for users without advanced programming expertise. RESULTS: To efficiently process, analyze, and visualize forest attributes using the FIA database, we developed PyFIA, an open-source Python-based tool. PyFIA provides a suite of functions, including statistical analyses, spatial mapping, and a bookkeeping model for tracking forest biomass dynamics at different scales. Additionally, it can acquire climate information for each inventory plot, enabling in-depth investigations of how climate conditions influence the spatial and temporal patterns of forest attributes. CONCLUSIONS: This program enhances the use of FIA inventory data in forest related studies, particularly for forest carbon. It also incorporates raster datasets, providing a valuable resource for research on forest ecosystems. PyFIA is designed with a modular structure and is openly available on GitHub, enabling easy access, customization, and continuous improvement. Users can contribute to its development, ensuring long-term sustainability. In addition, its flexible architecture allows for the integration of new functions, making it highly adaptable to diverse research needs.