> ## Documentation Index > Fetch the complete documentation index at: https://pyfia.mintlify.site/llms.txt > Use this file to discover all available pages before exploring further. # 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` ```python theme={null} 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` ```python theme={null} component_type(self) -> Literal['growth', 'mortality', 'removals'] ``` Return 'removals' as the GRM component type. #### `get_tree_columns` ```python theme={null} get_tree_columns(self) -> list[str] ``` Required tree columns for removals estimation. #### `load_data` ```python theme={null} load_data(self) -> pl.LazyFrame | None ``` Load GRM data for removals estimation. #### `apply_filters` ```python theme={null} apply_filters(self, data: pl.LazyFrame) -> pl.LazyFrame ``` Apply removals-specific filters. #### `calculate_values` ```python theme={null} 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` ```python theme={null} 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` ```python theme={null} 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` ```python theme={null} format_output(self, results: pl.DataFrame) -> pl.DataFrame ``` Format removals estimation output.