> ## 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.

# Domain filtering

> Define your analysis population with land type, tree type, and custom SQL-like domain filters.

A *domain* is the population you want to estimate — forest land, growing-stock trees, a single species, trees above a diameter threshold, and so on. pyFIA gives you three layers of control: `land_type`, `tree_type`, and free-form domain expressions.

## Land type

`land_type` controls which land conditions are included:

| Value      | Description      | Filter applied                                  |
| ---------- | ---------------- | ----------------------------------------------- |
| `"forest"` | All forest land  | `COND_STATUS_CD = 1`                            |
| `"timber"` | Timberland only  | `COND_STATUS_CD = 1` and productive, unreserved |
| `"all"`    | All sampled land | none                                            |

```python theme={null}
import pyfia

pyfia.volume(db, land_type="forest")   # forest land
pyfia.volume(db, land_type="timber")   # productive timberland
```

## Tree type

`tree_type` filters which trees are counted:

| Value    | Description                        | Filter         |
| -------- | ---------------------------------- | -------------- |
| `"live"` | All live trees                     | `STATUSCD = 1` |
| `"dead"` | Standing dead trees                | `STATUSCD = 2` |
| `"gs"`   | Growing stock (live, merchantable) | `TREECLCD = 2` |
| `"all"`  | All trees regardless of status     | none           |

```python theme={null}
pyfia.volume(db, tree_type="gs")      # growing stock
pyfia.tpa(db, tree_type="live")       # all live trees
```

<Note>
  Defaults vary by estimator to match the most common use case — for example,
  `mortality()` defaults to growing stock on timberland. Check each function's
  [API reference](/api/pyfia-estimation-estimators-mortality) for its defaults.

  The growth/removal/mortality estimators (`growth()`, `mortality()`,
  `removals()`) additionally accept `"al"` (all live) and `"sl"` / `"sawtimber"`
  (sawtimber-size), which select the matching FIA GRM tree populations.
</Note>

## Custom domain filters

For anything more specific, pass SQL-like expressions. These are evaluated against the underlying FIA columns.

### Tree domain

Filters individual trees by their attributes:

```python theme={null}
# Large trees only
pyfia.volume(db, tree_domain="DIA >= 12.0")

# Specific species
pyfia.volume(db, tree_domain="SPCD IN (110, 111, 121)")

# Combined conditions
pyfia.volume(db, tree_domain="DIA >= 5.0 AND SPCD = 316")
```

### Area domain

Filters conditions/plots by their attributes:

```python theme={null}
# A specific forest type
pyfia.area(db, area_domain="FORTYPCD = 201")

# Gentle slopes only
pyfia.volume(db, area_domain="SLOPE < 30")

# Private land
pyfia.area(db, area_domain="OWNGRPCD = 40")
```

## Putting it together

Domain filters compose, so you can target a precise population:

```python theme={null}
# Pine growing stock on private timberland, gentle slopes
pyfia.volume(
    db,
    land_type="timber",
    tree_type="gs",
    tree_domain="SPGRPCD = 10",     # softwood group
    area_domain="OWNGRPCD = 40 AND SLOPE < 30",
)
```

## Common patterns

<AccordionGroup>
  <Accordion title="Species-specific volume">
    ```python theme={null}
    pyfia.volume(
        db,
        land_type="timber",
        tree_type="gs",
        tree_domain="SPGRPCD = 10",   # softwoods
    )
    ```
  </Accordion>

  <Accordion title="Sawtimber volume">
    ```python theme={null}
    pyfia.volume(
        db,
        vol_type="sawlog",
        land_type="timber",
        grp_by="SPCD",
    )
    ```
  </Accordion>

  <Accordion title="Private forest land">
    ```python theme={null}
    pyfia.volume(
        db,
        land_type="forest",
        area_domain="OWNGRPCD = 40",
    )
    ```
  </Accordion>
</AccordionGroup>

## See also

* [Grouping results](/guides/grouping) — break estimates down by category
* [How FIA estimation works](/concepts/fia-methodology) — land and tree classifications in depth