# pyFIA

> High-performance Python library for USDA Forest Inventory and Analysis (FIA) data. 10-100x faster than EVALIDator with statistically valid estimates.

## Docs

- [FIA Database](https://pyfia.mintlify.app/api/pyfia-core-fia.md)
- [Settings](https://pyfia.mintlify.app/api/pyfia-core-settings.md)
- [Data Download](https://pyfia.mintlify.app/api/pyfia-downloader.md)
- [area](https://pyfia.mintlify.app/api/pyfia-estimation-estimators-area.md)
- [growth](https://pyfia.mintlify.app/api/pyfia-estimation-estimators-growth.md)
- [mortality](https://pyfia.mintlify.app/api/pyfia-estimation-estimators-mortality.md)
- [removals](https://pyfia.mintlify.app/api/pyfia-estimation-estimators-removals.md)
- [tree_metrics](https://pyfia.mintlify.app/api/pyfia-estimation-estimators-tree_metrics.md)
- [volume](https://pyfia.mintlify.app/api/pyfia-estimation-estimators-volume.md)
- [Validation](https://pyfia.mintlify.app/api/pyfia-evalidator-validation.md)
- [Reference Tables](https://pyfia.mintlify.app/api/pyfia-utils-reference_tables.md)
- [How FIA estimation works](https://pyfia.mintlify.app/concepts/fia-methodology.md): The sampling design, the EVALID system, and the expansion factors behind every pyFIA estimate.
- [Understanding variance](https://pyfia.mintlify.app/concepts/variance.md): How pyFIA computes standard errors using FIA's stratified, design-based variance — and how it's validated against EVALIDator.
- [Examples](https://pyfia.mintlify.app/examples.md): Real, copy-pasteable analyses covering volume, area, mortality, biomass, multi-state work, and validation.
- [Getting started](https://pyfia.mintlify.app/getting-started.md): Install pyFIA, download a state's data, and run your first statistically valid forest estimate — start to finish.
- [Downloading data](https://pyfia.mintlify.app/guides/downloading.md): Download FIA data directly from the USDA Forest Service DataMart, with caching and multi-state support.
- [Domain filtering](https://pyfia.mintlify.app/guides/filtering.md): Define your analysis population with land type, tree type, and custom SQL-like domain filters.
- [Grouping results](https://pyfia.mintlify.app/guides/grouping.md): Break estimates down by forest type, ownership, species, geography, and more — with readable names attached automatically.
- [Spatial filtering](https://pyfia.mintlify.app/guides/spatial.md): Filter and group FIA estimates by polygon boundaries — watersheds, management units, or administrative regions.
- [pyFIA](https://pyfia.mintlify.app/index.md): High-performance Python for USDA Forest Inventory and Analysis (FIA) data — statistically valid estimates, 10-100x faster than EVALIDator.
- [Basic tree counts](https://pyfia.mintlify.app/queries/basic-tree.md): EVALIDator-style tree enumeration with diameter-based adjustment factors and population expansion.
- [Biomass & carbon](https://pyfia.mintlify.app/queries/biomass-carbon.md): Species-specific biomass with moisture content and specific-gravity adjustments, matching EVALIDator.
- [Forest area](https://pyfia.mintlify.app/queries/forest-area.md): Forest area by type group from condition proportions and expansion factors.
- [Forest change](https://pyfia.mintlify.app/queries/forest-change.md): Forest area change over time using the subplot condition-change matrix and remeasurement linkages.
- [Query library](https://pyfia.mintlify.app/queries/index.md): Tested, EVALIDator-validated SQL queries you can run directly against the FIA database — an alternative to the Python API for power users and validation.
- [Mortality](https://pyfia.mintlify.app/queries/mortality.md): Growing-stock mortality from the GRM tables — by cause, by disturbance, and with stratified variance.
- [Volume](https://pyfia.mintlify.app/queries/volume.md): Net merchantable bole volume by diameter class, EVALIDator-validated.