Volume Estimation¶

Estimate standing tree volume with various volume types and filters.

Overview¶

The volume() function calculates tree volume estimates following EVALIDator methodology.

import pyfia

db = pyfia.FIA("georgia.duckdb")
db.clip_by_state("GA")

# Total net volume
total = pyfia.volume(db, land_type="forest")

# Volume by species
by_species = pyfia.volume(db, grp_by="SPCD")

Function Reference¶

volume ¶

volume(db: Union[str, FIA], grp_by: Optional[Union[str, List[str]]] = None, by_species: bool = False, by_size_class: bool = False, land_type: str = 'forest', tree_type: str = 'live', vol_type: str = 'net', tree_domain: Optional[str] = None, area_domain: Optional[str] = None, plot_domain: Optional[str] = None, totals: bool = True, variance: bool = False, most_recent: bool = False, eval_type: Optional[str] = None) -> DataFrame

Estimate tree volume from FIA data.

Calculates volume estimates using FIA's design-based estimation methods with proper expansion factors and stratification. Automatically handles EVALID selection to prevent overcounting from multiple evaluations.

PARAMETER	DESCRIPTION
`db`	Database connection or path to FIA database. Can be either a path string to a DuckDB/SQLite file or an existing FIA connection object. TYPE: `Union[str, FIA]`
`grp_by`	Column name(s) to group results by. Can be any column from the TREE, COND, and PLOT tables. Common grouping columns include: Tree Characteristics: - 'SPCD': Species code (see REF_SPECIES table) - 'SPGRPCD': Species group code (hardwood/softwood groups) - 'DIA': Diameter at breast height (continuous, use with caution) - 'HT': Total tree height in feet - 'CR': Crown ratio (percent of bole with live crown) - 'CCLCD': Crown class code (1=Open grown, 2=Dominant, 3=Codominant, 4=Intermediate, 5=Overtopped) - 'TREECLCD': Tree class code (2=Growing stock, 3=Rough cull, 4=Rotten cull) - 'DECAYCD': Decay class for standing dead trees Ownership and Management: - 'OWNGRPCD': Ownership group (10=National Forest, 20=Other Federal, 30=State/Local, 40=Private) - 'OWNCD': Detailed ownership code (see REF_RESEARCH_STATION) - 'ADFORCD': Administrative forest code - 'RESERVCD': Reserved status (0=Not reserved, 1=Reserved) Forest Characteristics: - 'FORTYPCD': Forest type code (see REF_FOREST_TYPE) - 'STDSZCD': Stand size class (1=Large diameter, 2=Medium diameter, 3=Small diameter, 4=Seedling/sapling, 5=Nonstocked) - 'STDORGCD': Stand origin (0=Natural, 1=Planted) - 'STDAGE': Stand age in years Site Characteristics: - 'SITECLCD': Site productivity class (1=225+ cu ft/ac/yr, 2=165-224, 3=120-164, 4=85-119, 5=50-84, 6=20-49, 7=0-19) - 'PHYSCLCD': Physiographic class code - 'SLOPE': Slope in percent - 'ASPECT': Aspect in degrees (0-360) Location: - 'STATECD': State FIPS code - 'UNITCD': FIA survey unit code - 'COUNTYCD': County code - 'INVYR': Inventory year Disturbance and Treatment: - 'DSTRBCD1', 'DSTRBCD2', 'DSTRBCD3': Disturbance codes - 'TRTCD1', 'TRTCD2', 'TRTCD3': Treatment codes For complete column descriptions, see USDA FIA Database User Guide. TYPE: `str or list of str` DEFAULT: `None`
`by_species`	If True, group results by species code (SPCD). This is a convenience parameter equivalent to adding 'SPCD' to grp_by. TYPE: `bool` DEFAULT: `False`
`by_size_class`	If True, group results by diameter size classes. Size classes are defined as: 1.0-4.9", 5.0-9.9", 10.0-19.9", 20.0-29.9", 30.0+". TYPE: `bool` DEFAULT: `False`
`land_type`	Land type to include in estimation: 'forest': All forestland (COND_STATUS_CD = 1) 'timber': Timberland only (unreserved, productive forestland with SITECLCD < 7 and RESERVCD = 0) 'all': All land types including non-forest TYPE: `(forest, timber, all)` DEFAULT: `'forest'`
`tree_type`	Tree type to include: 'live': All live trees (STATUSCD = 1) 'dead': Standing dead trees (STATUSCD = 2) 'gs': Growing stock trees (live, TREECLCD = 2, no defects) 'all': All trees regardless of status TYPE: `(live, dead, gs, all)` DEFAULT: `'live'`
`vol_type`	Volume type to estimate: 'net': Net cubic foot volume (VOLCFNET) - gross minus defects 'gross': Gross cubic foot volume (VOLCFGRS) - total stem volume 'sound': Sound cubic foot volume (VOLCFSND) - gross minus rot 'sawlog': Sawlog board foot volume (VOLBFNET) - net board feet TYPE: `(net, gross, sound, sawlog)` DEFAULT: `'net'`
`tree_domain`	SQL-like filter expression for tree-level attributes. Examples: "DIA >= 10.0": Trees 10 inches DBH and larger "SPCD IN (131, 110)": Specific species (loblolly and Virginia pine) "DIA BETWEEN 10 AND 20": Mid-sized trees "HT > 50 AND CR > 30": Tall trees with good crowns TYPE: `str` DEFAULT: `None`
`area_domain`	SQL-like filter expression for COND-level attributes. Examples: "STDAGE > 50": Stands older than 50 years "FORTYPCD IN (161, 162)": Specific forest types "OWNGRPCD == 40": Private lands only "SLOPE < 30 AND ASPECT BETWEEN 135 AND 225": Gentle south-facing slopes TYPE: `str` DEFAULT: `None`
`totals`	If True, include total volume estimates expanded to population level. If False, only return per-acre values. TYPE: `bool` DEFAULT: `True`
`variance`	If True, return variance instead of standard error. TYPE: `bool` DEFAULT: `False`
`most_recent`	If True, automatically select the most recent evaluation for each state/region. Equivalent to calling db.clip_most_recent() first. TYPE: `bool` DEFAULT: `False`
`eval_type`	Evaluation type to select if most_recent=True. Options: 'ALL', 'VOL', 'GROW', 'MORT', 'REMV', 'CHANGE', 'DWM', 'INV'. Default is 'VOL' for volume estimation. TYPE: `str` DEFAULT: `None`

RETURNS DESCRIPTION

DataFrame

Volume estimates with the following columns:

YEAR : int Inventory year
[grouping columns] : varies Any columns specified in grp_by parameter
VOLCFNET_ACRE : float (if vol_type='net') Net cubic foot volume per acre
VOLCFGRS_ACRE : float (if vol_type='gross') Gross cubic foot volume per acre
VOLCFSND_ACRE : float (if vol_type='sound') Sound cubic foot volume per acre
VOLBFNET_ACRE : float (if vol_type='sawlog') Net board foot volume per acre
VOLCFNET_ACRE_SE : float (if variance=False) Standard error of per-acre volume estimate
VOLCFNET_ACRE_VAR : float (if variance=True) Variance of per-acre volume estimate
N_PLOTS : int Number of plots in estimate
N_TREES : int Number of trees in estimate
AREA_TOTAL : float Total area (acres) represented by the estimation
VOLCFNET_TOTAL : float (if totals=True) Total volume expanded to population level
VOLCFNET_TOTAL_SE : float (if totals=True and variance=False) Standard error of total volume

See Also

pyfia.area : Estimate forest area pyfia.biomass : Estimate tree biomass pyfia.tpa : Estimate trees per acre pyfia.mortality : Estimate annual mortality pyfia.growth : Estimate annual growth pyfia.constants.SpeciesCodes : Species code definitions pyfia.constants.ForestTypes : Forest type code definitions pyfia.constants.StateCodes : State FIPS code definitions pyfia.utils.reference_tables : Functions for adding species/forest type names

Notes

The volume estimation follows USDA FIA's design-based estimation procedures as described in Bechtold & Patterson (2005). The basic formula is:

Volume per acre = Σ(VOLCFNET × TPA_UNADJ × ADJ_FACTOR × EXPNS) / Σ(AREA)

Where: - VOLCFNET: Net cubic foot volume per tree (or VOLCFGRS, VOLCFSND, VOLBFNET) - TPA_UNADJ: Unadjusted trees per acre factor - ADJ_FACTOR: Size-based adjustment factor (MICR, SUBP, or MACR) - EXPNS: Expansion factor from stratification - AREA: Total area from condition proportions

Adjustment Factors: Trees are adjusted based on their diameter and sampling method: - Trees < 5.0" DBH: Microplot adjustment (ADJ_FACTOR_MICR) - Trees 5.0" to MACRO_BREAKPOINT_DIA: Subplot adjustment (ADJ_FACTOR_SUBP) - Trees ≥ MACRO_BREAKPOINT_DIA: Macroplot adjustment (ADJ_FACTOR_MACR)

EVALID Handling: If no EVALID is specified, the function automatically selects the most recent EXPVOL evaluation to prevent overcounting from multiple evaluations. For explicit control, use db.clip_by_evalid() before calling volume().

Valid Grouping Columns: The function joins TREE, COND, and PLOT tables, so any column from these tables can be used for grouping. Not all columns are suitable - continuous variables like DIA should be used with caution or binned first.

NULL Value Handling: Some grouping columns may contain NULL values (e.g., HT ~30% NULL for some species). NULL values are handled safely by Polars and will appear as a separate group in results if present.

Growing Stock Definition: Growing stock trees (tree_type='gs') are defined as live trees of commercial species that meet minimum merchantability standards: - Must be live (STATUSCD = 1) - Must be growing stock (TREECLCD = 2) - Excludes rough and rotten culls - Typically ≥ 5.0" DBH for hardwoods and softwoods

Board Foot Conversion: Sawlog volume (vol_type='sawlog') uses board foot measurements which apply only to sawtimber-sized trees: - Softwoods: ≥ 9.0" DBH - Hardwoods: ≥ 11.0" DBH

Trees below these thresholds will have NULL or 0 board foot volume.

Examples:

Basic net volume on forestland:

>>> from pyfia import FIA, volume
>>> with FIA("path/to/fia.duckdb") as db:
...     db.clip_by_state(37)  # North Carolina
...     results = volume(db, land_type="forest", vol_type="net")

Volume by species on timberland:

>>> results = volume(
...     db,
...     by_species=True,
...     land_type="timber",
...     tree_type="gs"  # Growing stock only
... )
>>> # Find top species by volume
>>> if not results.is_empty():
...     top_species = results.sort(by='VOLCFNET_ACRE', descending=True).head(5)

Large tree volume by ownership:

>>> results = volume(
...     db,
...     grp_by="OWNGRPCD",
...     tree_domain="DIA >= 20.0",
...     variance=True
... )

Sawlog volume by forest type:

>>> results = volume(
...     db,
...     grp_by="FORTYPCD",
...     vol_type="sawlog",
...     tree_type="gs",
...     tree_domain="DIA >= 11.0"  # Hardwood sawtimber size
... )

Volume by multiple grouping variables:

>>> results = volume(
...     db,
...     grp_by=["STATECD", "OWNGRPCD", "STDSZCD"],
...     land_type="forest",
...     totals=True
... )

Complex filtering with domain expressions:

>>> # High-value timber on productive sites
>>> results = volume(
...     db,
...     grp_by="SPCD",
...     land_type="timber",
...     tree_domain="DIA >= 16.0 AND TREECLCD == 2",
...     area_domain="SITECLCD <= 3 AND SLOPE < 35"
... )

Dead tree volume assessment:

>>> results = volume(
...     db,
...     tree_type="dead",
...     by_species=True,
...     tree_domain="DIA >= 10.0 AND DECAYCD IN (1, 2)"  # Sound dead trees
... )

Notes

Variance calculations follow Bechtold & Patterson (2005) stratified ratio-of-means methodology. A ValueError is raised if required tree-level data is unavailable for variance calculation.

RAISES	DESCRIPTION
`ValueError`	If invalid parameter values are provided, if required tables (TREE, COND, PLOT) are not found in the database, or if variance is requested but tree-level data is unavailable.
`KeyError`	If specified columns in grp_by don't exist in the joined tables.

Source code in src/pyfia/estimation/estimators/volume.py

def volume(
    db: Union[str, FIA],
    grp_by: Optional[Union[str, List[str]]] = None,
    by_species: bool = False,
    by_size_class: bool = False,
    land_type: str = "forest",
    tree_type: str = "live",
    vol_type: str = "net",
    tree_domain: Optional[str] = None,
    area_domain: Optional[str] = None,
    plot_domain: Optional[str] = None,
    totals: bool = True,
    variance: bool = False,
    most_recent: bool = False,
    eval_type: Optional[str] = None,
) -> pl.DataFrame:
    """
    Estimate tree volume from FIA data.

    Calculates volume estimates using FIA's design-based estimation methods
    with proper expansion factors and stratification. Automatically handles
    EVALID selection to prevent overcounting from multiple evaluations.

    Parameters
    ----------
    db : Union[str, FIA]
        Database connection or path to FIA database. Can be either a path
        string to a DuckDB/SQLite file or an existing FIA connection object.
    grp_by : str or list of str, optional
        Column name(s) to group results by. Can be any column from the
        TREE, COND, and PLOT tables. Common grouping columns include:

        **Tree Characteristics:**
        - 'SPCD': Species code (see REF_SPECIES table)
        - 'SPGRPCD': Species group code (hardwood/softwood groups)
        - 'DIA': Diameter at breast height (continuous, use with caution)
        - 'HT': Total tree height in feet
        - 'CR': Crown ratio (percent of bole with live crown)
        - 'CCLCD': Crown class code (1=Open grown, 2=Dominant, 3=Codominant,
          4=Intermediate, 5=Overtopped)
        - 'TREECLCD': Tree class code (2=Growing stock, 3=Rough cull, 4=Rotten cull)
        - 'DECAYCD': Decay class for standing dead trees

        **Ownership and Management:**
        - 'OWNGRPCD': Ownership group (10=National Forest, 20=Other Federal,
          30=State/Local, 40=Private)
        - 'OWNCD': Detailed ownership code (see REF_RESEARCH_STATION)
        - 'ADFORCD': Administrative forest code
        - 'RESERVCD': Reserved status (0=Not reserved, 1=Reserved)

        **Forest Characteristics:**
        - 'FORTYPCD': Forest type code (see REF_FOREST_TYPE)
        - 'STDSZCD': Stand size class (1=Large diameter, 2=Medium diameter,
          3=Small diameter, 4=Seedling/sapling, 5=Nonstocked)
        - 'STDORGCD': Stand origin (0=Natural, 1=Planted)
        - 'STDAGE': Stand age in years

        **Site Characteristics:**
        - 'SITECLCD': Site productivity class (1=225+ cu ft/ac/yr,
          2=165-224, 3=120-164, 4=85-119, 5=50-84, 6=20-49, 7=0-19)
        - 'PHYSCLCD': Physiographic class code
        - 'SLOPE': Slope in percent
        - 'ASPECT': Aspect in degrees (0-360)

        **Location:**
        - 'STATECD': State FIPS code
        - 'UNITCD': FIA survey unit code
        - 'COUNTYCD': County code
        - 'INVYR': Inventory year

        **Disturbance and Treatment:**
        - 'DSTRBCD1', 'DSTRBCD2', 'DSTRBCD3': Disturbance codes
        - 'TRTCD1', 'TRTCD2', 'TRTCD3': Treatment codes

        For complete column descriptions, see USDA FIA Database User Guide.
    by_species : bool, default False
        If True, group results by species code (SPCD). This is a convenience
        parameter equivalent to adding 'SPCD' to grp_by.
    by_size_class : bool, default False
        If True, group results by diameter size classes. Size classes are
        defined as: 1.0-4.9", 5.0-9.9", 10.0-19.9", 20.0-29.9", 30.0+".
    land_type : {'forest', 'timber', 'all'}, default 'forest'
        Land type to include in estimation:

        - 'forest': All forestland (COND_STATUS_CD = 1)
        - 'timber': Timberland only (unreserved, productive forestland with
          SITECLCD < 7 and RESERVCD = 0)
        - 'all': All land types including non-forest
    tree_type : {'live', 'dead', 'gs', 'all'}, default 'live'
        Tree type to include:

        - 'live': All live trees (STATUSCD = 1)
        - 'dead': Standing dead trees (STATUSCD = 2)
        - 'gs': Growing stock trees (live, TREECLCD = 2, no defects)
        - 'all': All trees regardless of status
    vol_type : {'net', 'gross', 'sound', 'sawlog'}, default 'net'
        Volume type to estimate:

        - 'net': Net cubic foot volume (VOLCFNET) - gross minus defects
        - 'gross': Gross cubic foot volume (VOLCFGRS) - total stem volume
        - 'sound': Sound cubic foot volume (VOLCFSND) - gross minus rot
        - 'sawlog': Sawlog board foot volume (VOLBFNET) - net board feet
    tree_domain : str, optional
        SQL-like filter expression for tree-level attributes. Examples:

        - "DIA >= 10.0": Trees 10 inches DBH and larger
        - "SPCD IN (131, 110)": Specific species (loblolly and Virginia pine)
        - "DIA BETWEEN 10 AND 20": Mid-sized trees
        - "HT > 50 AND CR > 30": Tall trees with good crowns
    area_domain : str, optional
        SQL-like filter expression for COND-level attributes. Examples:

        - "STDAGE > 50": Stands older than 50 years
        - "FORTYPCD IN (161, 162)": Specific forest types
        - "OWNGRPCD == 40": Private lands only
        - "SLOPE < 30 AND ASPECT BETWEEN 135 AND 225": Gentle south-facing slopes
    totals : bool, default True
        If True, include total volume estimates expanded to population level.
        If False, only return per-acre values.
    variance : bool, default False
        If True, return variance instead of standard error.
    most_recent : bool, default False
        If True, automatically select the most recent evaluation for each
        state/region. Equivalent to calling db.clip_most_recent() first.
    eval_type : str, optional
        Evaluation type to select if most_recent=True. Options:
        'ALL', 'VOL', 'GROW', 'MORT', 'REMV', 'CHANGE', 'DWM', 'INV'.
        Default is 'VOL' for volume estimation.

    Returns
    -------
    pl.DataFrame
        Volume estimates with the following columns:

        - **YEAR** : int
            Inventory year
        - **[grouping columns]** : varies
            Any columns specified in grp_by parameter
        - **VOLCFNET_ACRE** : float (if vol_type='net')
            Net cubic foot volume per acre
        - **VOLCFGRS_ACRE** : float (if vol_type='gross')
            Gross cubic foot volume per acre
        - **VOLCFSND_ACRE** : float (if vol_type='sound')
            Sound cubic foot volume per acre
        - **VOLBFNET_ACRE** : float (if vol_type='sawlog')
            Net board foot volume per acre
        - **VOLCFNET_ACRE_SE** : float (if variance=False)
            Standard error of per-acre volume estimate
        - **VOLCFNET_ACRE_VAR** : float (if variance=True)
            Variance of per-acre volume estimate
        - **N_PLOTS** : int
            Number of plots in estimate
        - **N_TREES** : int
            Number of trees in estimate
        - **AREA_TOTAL** : float
            Total area (acres) represented by the estimation
        - **VOLCFNET_TOTAL** : float (if totals=True)
            Total volume expanded to population level
        - **VOLCFNET_TOTAL_SE** : float (if totals=True and variance=False)
            Standard error of total volume

    See Also
    --------
    pyfia.area : Estimate forest area
    pyfia.biomass : Estimate tree biomass
    pyfia.tpa : Estimate trees per acre
    pyfia.mortality : Estimate annual mortality
    pyfia.growth : Estimate annual growth
    pyfia.constants.SpeciesCodes : Species code definitions
    pyfia.constants.ForestTypes : Forest type code definitions
    pyfia.constants.StateCodes : State FIPS code definitions
    pyfia.utils.reference_tables : Functions for adding species/forest type names

    Notes
    -----
    The volume estimation follows USDA FIA's design-based estimation procedures
    as described in Bechtold & Patterson (2005). The basic formula is:

    Volume per acre = Σ(VOLCFNET × TPA_UNADJ × ADJ_FACTOR × EXPNS) / Σ(AREA)

    Where:
    - VOLCFNET: Net cubic foot volume per tree (or VOLCFGRS, VOLCFSND, VOLBFNET)
    - TPA_UNADJ: Unadjusted trees per acre factor
    - ADJ_FACTOR: Size-based adjustment factor (MICR, SUBP, or MACR)
    - EXPNS: Expansion factor from stratification
    - AREA: Total area from condition proportions

    **Adjustment Factors:**
    Trees are adjusted based on their diameter and sampling method:
    - Trees < 5.0" DBH: Microplot adjustment (ADJ_FACTOR_MICR)
    - Trees 5.0" to MACRO_BREAKPOINT_DIA: Subplot adjustment (ADJ_FACTOR_SUBP)
    - Trees ≥ MACRO_BREAKPOINT_DIA: Macroplot adjustment (ADJ_FACTOR_MACR)

    **EVALID Handling:**
    If no EVALID is specified, the function automatically selects the most
    recent EXPVOL evaluation to prevent overcounting from multiple evaluations.
    For explicit control, use db.clip_by_evalid() before calling volume().

    **Valid Grouping Columns:**
    The function joins TREE, COND, and PLOT tables, so any column from these
    tables can be used for grouping. Not all columns are suitable - continuous
    variables like DIA should be used with caution or binned first.

    **NULL Value Handling:**
    Some grouping columns may contain NULL values (e.g., HT ~30% NULL for
    some species). NULL values are handled safely by Polars and will appear
    as a separate group in results if present.

    **Growing Stock Definition:**
    Growing stock trees (tree_type='gs') are defined as live trees of
    commercial species that meet minimum merchantability standards:
    - Must be live (STATUSCD = 1)
    - Must be growing stock (TREECLCD = 2)
    - Excludes rough and rotten culls
    - Typically ≥ 5.0" DBH for hardwoods and softwoods

    **Board Foot Conversion:**
    Sawlog volume (vol_type='sawlog') uses board foot measurements which
    apply only to sawtimber-sized trees:
    - Softwoods: ≥ 9.0" DBH
    - Hardwoods: ≥ 11.0" DBH

    Trees below these thresholds will have NULL or 0 board foot volume.

    Examples
    --------
    Basic net volume on forestland:

    >>> from pyfia import FIA, volume
    >>> with FIA("path/to/fia.duckdb") as db:
    ...     db.clip_by_state(37)  # North Carolina
    ...     results = volume(db, land_type="forest", vol_type="net")

    Volume by species on timberland:

    >>> results = volume(
    ...     db,
    ...     by_species=True,
    ...     land_type="timber",
    ...     tree_type="gs"  # Growing stock only
    ... )
    >>> # Find top species by volume
    >>> if not results.is_empty():
    ...     top_species = results.sort(by='VOLCFNET_ACRE', descending=True).head(5)

    Large tree volume by ownership:

    >>> results = volume(
    ...     db,
    ...     grp_by="OWNGRPCD",
    ...     tree_domain="DIA >= 20.0",
    ...     variance=True
    ... )

    Sawlog volume by forest type:

    >>> results = volume(
    ...     db,
    ...     grp_by="FORTYPCD",
    ...     vol_type="sawlog",
    ...     tree_type="gs",
    ...     tree_domain="DIA >= 11.0"  # Hardwood sawtimber size
    ... )

    Volume by multiple grouping variables:

    >>> results = volume(
    ...     db,
    ...     grp_by=["STATECD", "OWNGRPCD", "STDSZCD"],
    ...     land_type="forest",
    ...     totals=True
    ... )

    Complex filtering with domain expressions:

    >>> # High-value timber on productive sites
    >>> results = volume(
    ...     db,
    ...     grp_by="SPCD",
    ...     land_type="timber",
    ...     tree_domain="DIA >= 16.0 AND TREECLCD == 2",
    ...     area_domain="SITECLCD <= 3 AND SLOPE < 35"
    ... )

    Dead tree volume assessment:

    >>> results = volume(
    ...     db,
    ...     tree_type="dead",
    ...     by_species=True,
    ...     tree_domain="DIA >= 10.0 AND DECAYCD IN (1, 2)"  # Sound dead trees
    ... )

    Notes
    -----
    Variance calculations follow Bechtold & Patterson (2005) stratified
    ratio-of-means methodology. A ValueError is raised if required tree-level
    data is unavailable for variance calculation.

    Raises
    ------
    ValueError
        If invalid parameter values are provided, if required tables
        (TREE, COND, PLOT) are not found in the database, or if variance
        is requested but tree-level data is unavailable.
    KeyError
        If specified columns in grp_by don't exist in the joined tables.
    """
    # Import validation functions
    from ...validation import (
        validate_boolean,
        validate_domain_expression,
        validate_grp_by,
        validate_land_type,
        validate_tree_type,
        validate_vol_type,
    )

    # Validate inputs
    land_type = validate_land_type(land_type)
    tree_type = validate_tree_type(tree_type)
    vol_type = validate_vol_type(vol_type)
    grp_by = validate_grp_by(grp_by)
    tree_domain = validate_domain_expression(tree_domain, "tree_domain")
    area_domain = validate_domain_expression(area_domain, "area_domain")
    plot_domain = validate_domain_expression(plot_domain, "plot_domain")
    by_species = validate_boolean(by_species, "by_species")
    by_size_class = validate_boolean(by_size_class, "by_size_class")
    totals = validate_boolean(totals, "totals")
    variance = validate_boolean(variance, "variance")
    most_recent = validate_boolean(most_recent, "most_recent")

    # Ensure db is a FIA instance
    if isinstance(db, str):
        db = FIA(db)
        owns_db = True
    else:
        owns_db = False

    # Handle EVALID selection for volume estimation
    if db.evalid is None and most_recent:
        db.clip_most_recent(eval_type=eval_type or "VOL")
    elif db.evalid is None:
        # Auto-select most recent EXPVOL evaluation for volume
        import warnings

        warnings.warn(
            "No EVALID specified. Automatically selecting most recent EXPVOL evaluations. "
            "For explicit control, use db.clip_most_recent() or db.clip_by_evalid() before calling volume()."
        )
        db.clip_most_recent(eval_type="VOL")

        # If still no EVALID, warn about potential issues
        if db.evalid is None:
            warnings.warn(
                "WARNING: No EXPVOL evaluations found. Results may be incorrect due to "
                "inclusion of multiple overlapping evaluations. Consider using db.clip_by_evalid() "
                "to explicitly select appropriate EVALIDs."
            )

    # Create config
    config = {
        "grp_by": grp_by,
        "by_species": by_species,
        "by_size_class": by_size_class,
        "land_type": land_type,
        "tree_type": tree_type,
        "vol_type": vol_type,
        "tree_domain": tree_domain,
        "area_domain": area_domain,
        "plot_domain": plot_domain,
        "totals": totals,
        "variance": variance,
        "most_recent": most_recent,
    }

    try:
        # Create and run estimator
        estimator = VolumeEstimator(db, config)
        return estimator.estimate()
    finally:
        # Clean up if we created the db
        if owns_db and hasattr(db, "close"):
            db.close()

Volume Types¶

Type	Column	Description
`"net"`	`VOLCFNET`	Net cubic-foot volume
`"gross"`	`VOLCFGRS`	Gross cubic-foot volume
`"sawlog"`	`VOLBFNET`	Board-foot sawlog volume
`"sound"`	`VOLCSNET`	Sound wood volume

Examples¶

Net Volume by Species¶

result = pyfia.volume(db, vol_type="net", grp_by="SPCD")
result = pyfia.join_species_names(result, db)
print(result.sort("estimate", descending=True).head(10))

Sawlog Volume on Timberland¶

result = pyfia.volume(
    db,
    vol_type="sawlog",
    land_type="timber",
    tree_type="sl"  # Sawtimber only
)
print(f"Sawlog Volume: {result['estimate'][0]:,.0f} board feet")

Volume by Diameter Class¶

# Create diameter classes
result = pyfia.volume(
    db,
    grp_by="DIA_CLASS",
    tree_domain="DIA >= 5.0"
)

Pine Volume Only¶

result = pyfia.volume(
    db,
    land_type="forest",
    tree_domain="SPGRPCD = 10"  # Softwood group
)