removals

pyfia.estimation.estimators.removals

Removals estimation for FIA data.

Simple implementation for calculating average annual removals of merchantable bole wood volume of growing-stock trees.

Functions

removals

removals(db: str | FIA, grp_by: str | list[str] | None = None, by_species: bool = False, by_size_class: bool = False, size_class_type: str = 'standard', land_type: str = 'forest', tree_type: str = 'gs', measure: str = 'volume', tree_domain: str | None = None, area_domain: str | None = None, totals: bool = True, variance: bool = False, most_recent: bool = False, remeasure_period: float = 5.0) -> pl.DataFrame

Estimate average annual removals from FIA data.

Calculates average annual removals of merchantable bole wood volume of growing-stock trees (at least 5 inches d.b.h.) on forest land.

Args:

• db: Database connection or path
• grp_by: Columns to group by (e.g., “STATECD”, “FORTYPCD”)
• by_species: Group by species code
• by_size_class: Group by diameter size classes
• size_class_type: Type of size class grouping to use (only applies when by_size_class=True):
• “standard”: FIA numeric ranges (1.0-4.9, 5.0-9.9, etc.)
• “descriptive”: Text labels (Saplings, Small, Medium, Large)
• “market”: Timber market categories (Pulpwood, Chip-n-Saw, Sawtimber)
• land_type: Land type: “forest”, “timber”, or “all”
• tree_type: Tree type: “gs” (growing stock), “all”
• measure: What to measure: “volume”, “biomass”, or “count”
• tree_domain: SQL-like filter for trees
• area_domain: SQL-like filter for area
• totals: Include population totals
• variance: Return variance instead of SE
• most_recent: Use most recent evaluation
• remeasure_period: Remeasurement period in years for annualization

Returns:

• Removals estimates with columns:
• REMOVALS_PER_ACRE: Annual removals per acre
• REMOVALS_TOTAL: Total annual removals
• REMOVALS_PER_ACRE_SE: Standard error of per-acre estimate
• REMOVALS_TOTAL_SE: Standard error of total estimate
• Additional grouping columns if specified

Examples:

Basic volume removals on forestland

results = removals(db, measure=“volume”)

Removals by species (tree count)

results = removals(db, by_species=True, measure=“count”)

Biomass removals by forest type

results = removals( … db, … grp_by=“FORTYPCD”, … measure=“biomass” … )

Removals on timberland only

results = removals( … db, … land_type=“timber”, … area_domain=“SITECLCD >= 225” # Productive sites … )

Classes

RemovalsEstimator

Removals estimator for FIA data.

Estimates average annual removals of merchantable bole wood volume of growing-stock trees (at least 5 inches d.b.h.) on forest land.

Methods:

component_type

component_type(self) -> Literal['growth', 'mortality', 'removals']

Return ‘removals’ as the GRM component type.

get_tree_columns

get_tree_columns(self) -> list[str]

Required tree columns for removals estimation.

load_data

load_data(self) -> pl.LazyFrame | None

Load GRM data for removals estimation.

apply_filters

apply_filters(self, data: pl.LazyFrame) -> pl.LazyFrame

Apply removals-specific filters.

calculate_values

calculate_values(self, data: pl.LazyFrame) -> pl.LazyFrame

Calculate removal values.

EVALIDator methodology: TPAREMV_UNADJ * VOLCFNET TPAREMV_UNADJ is already annualized (trees removed per acre per year).

aggregate_results

aggregate_results(self, data: pl.LazyFrame | None) -> AggregationResult

Aggregate removals with two-stage aggregation.

Returns:

• Bundle containing results, plot_tree_data, and group_cols for explicit variance calculation.

calculate_variance

calculate_variance(self, agg_result: AggregationResult) -> pl.DataFrame

Calculate variance for removals estimates using ratio-of-means formula.

Implements Bechtold & Patterson (2005) stratified variance calculation.

Args:

• agg_result: Bundle containing results, plot_tree_data, and group_cols from aggregate_results().

Returns:

• Results with variance columns added.

format_output

format_output(self, results: pl.DataFrame) -> pl.DataFrame

Format removals estimation output.