Skip to content

User Guide

This guide covers PyFIA's core concepts and workflows for FIA data analysis.

Overview

PyFIA implements design-based estimation following the methodology described in Bechtold & Patterson (2005). This ensures statistically valid estimates that match official USFS results.

Key Concepts

EVALID (Evaluation Identifier)

FIA data is organized by evaluation groups. Each EVALID represents a complete inventory cycle for a state or region. PyFIA automatically selects the most recent evaluation when you filter by state.

from pyfia import download, FIA

db_path = download("GA")  # Download Georgia data
db = FIA(db_path)
db.clip_by_state("GA")    # Auto-selects most recent EVALID

Domain Filtering

Domains define the population of interest:

  • Land Type: Forest land, timberland, or all sampled land
  • Tree Type: Growing stock, all live, or sawtimber
  • Custom Domains: SQL-like conditions for specialized analysis

Expansion Factors

FIA uses a stratified sampling design. Each plot/tree has an expansion factor indicating how many similar plots/trees it represents. PyFIA handles these automatically.

Guides