PyFIA¶

High-performance Python library for USDA Forest Inventory and Analysis (FIA) data.

PyFIA provides statistically valid estimation methods following Bechtold & Patterson (2005) methodology, enabling researchers and analysts to work with FIA data efficiently.

Features¶

Direct Data Download: Download FIA data directly from the DataMart with download("GA")
Statistical Rigor: Design-based estimation with proper variance calculations
High Performance: Built on Polars and DuckDB for fast data processing
EVALIDator Compatible: Results validated against official USFS estimates
Simple API: Intuitive functions like volume(), mortality(), and tpa()
Lazy Evaluation: Memory-efficient processing for large datasets

Quick Example¶

from pyfia import download, FIA, area

# Download Georgia FIA data directly
db_path = download("GA")

# Connect and analyze
with FIA(db_path) as db:
    db.clip_most_recent()
    result = area(db, land_type="forest")
    print(result)

Installation¶

pip install pyfia

Getting Started¶

Download FIA Data¶

from pyfia import download

# Download a single state
db_path = download("GA")

# Download multiple states (merged into single database)
db_path = download(["GA", "FL", "SC"])

# Download to specific directory
db_path = download("GA", dir="./data")

Run Analysis¶

from pyfia import FIA, volume

with FIA(db_path) as db:
    db.clip_by_state("GA")
    result = volume(db, grp_by="SPCD")
    print(result)

Documentation Overview¶

- :material-download: **[Downloading Data](guides/downloading.md)** Download FIA data directly from the DataMart - :material-rocket-launch: **[Getting Started](getting-started.md)** Installation, setup, and your first analysis - :material-book-open-variant: **[User Guide](guides/index.md)** Domain filtering, variance estimation, and workflows - :material-api: **[API Reference](api/index.md)** Complete function and class documentation - :material-file-code: **[Examples](examples.md)** Real-world analysis examples

Estimation Functions¶

Function	Description
`area()`	Estimate forest area by land type and categories
`area_change()`	Estimate annual changes in forest area
`volume()`	Estimate standing tree volume
`tpa()`	Calculate trees per acre and basal area
`biomass()`	Estimate tree biomass and carbon
`mortality()`	Calculate annual mortality rates
`growth()`	Estimate annual growth
`removals()`	Estimate timber removals

Why PyFIA?¶

FIA data analysis traditionally requires complex SQL queries and careful attention to statistical methodology. PyFIA handles:

Direct data access with automatic downloads from FIA DataMart
Proper stratified estimation with expansion factors
Variance calculation following ratio-of-means methodology
EVALID filtering for consistent evaluation groups
Domain-specific filtering (forest land, timberland, growing stock)

Comparison with rFIA¶

PyFIA brings the ease of rFIA's getFIA() to Python:

Feature	rFIA (R)	PyFIA (Python)
Download data	`getFIA(states='GA')`	`download("GA")`
Multiple states	`getFIA(states=c('GA','FL'))`	`download(["GA", "FL"])`
Estimate area	`area(fiaData)`	`area(db)`
Estimate volume	`biomass(fiaData)`	`volume(db)`

License¶

PyFIA is released under the MIT License.