TWIG Data Resources

You can access the interactive online TWIG Viewer here.
TWIG layer information and metadata at ArcGIS Online are available here.
The TWIG treatment index REST API is located here.

Source Code and Database Schema

The code used to create the TWIG treatment index is available on GitHub.

If you wish to use or modify this code to build your own version of the TWIG treatment index “from scratch,” you will need a template file geodatabase to define the database schema and lookup tables referenced in the source code. This template is available as a zipped file geodatabase here:

Treatment Index Schema and Lookup Tables

Note that this download does not contain any fuel treatment records. It is simply a template for constructing new or modified versions of TWIG from the source code.

License

These resources are licensed under Creative Commons Attribution 4.0 International (data) and GNU General Public License v3 (code).

In August 2025, we published an article in the journal Scientific Data that described the TWIG treatment index in detail. This paper also includes a link to a static version of the TWIG treatment index as it existed in August 2025.

Access the Journal Article Here

As TWIG continues to evolve, the most up-to-date data and metadata are always available on this website. Still, the article contains some timeless background and history regarding the development and intended uses of TWIG.

We ask that all users of TWIG data cite this paper in their final publications.

Suggested Citation:

Call, A., Tomczyk, N., Withnall, K.A., Dappen, P., Heusinkveld, D., Mueller, S.E., Holloway, B., Shennan, K., Herring, J.A., Franko, A., Colavito, M.M., Kimple, A.D., Sánchez Meador, A.J., Stevens-Rumann, C.S. A new geodatabase of fuel treatments across federal lands in the USA. Sci Data 12, 1485 (2025). https://doi.org/10.1038/s41597-025-05859-z

Full Treatment Index

The full TWIG treatment index is available to download as a zipped file geodatabase here:

Full Treatment Index

The database currently contains ~1.4 million records. If you need fewer than 10,000 records for offline use, we suggest using the interactive TWIG Viewer to identify and download only the subset of the data you need. If you need more than 10,000 records, download the full treatment index. If you don’t know how many treatment records to expect in your region of interest, here are some benchmarks:

• Full treatment index: ~1.4 million records
• Colorado (all years): ~40,000 records
• Nebraska (all years): ~ 1,400 records
• Eagle County, Colorado (all years): ~1,400 records

License

TWIG data are licensed under Creative Commons Attribution 4.0 International.

This section contains detailed information regarding the attributes of each record. For each attribute, we focus on the following elements:

• Name, including a human-readable name and the snake_case name used in the database.
  1. Definition: a technical description of what information the attribute contains.
  2. Data source: the relationship between the TWIG attribute and attributes of the source databases (FACTS or NFPORS) from which the TWIG attribute is derived. In most cases, TWIG attribute values are directly copied from the source databases. However, some TWIG attributes are derived through some transformation of the source database. This section describes any transformation that has occurred.
  3. Data structure and accuracy: Information regarding the type or structure and estimated accuracy or precision of the attribute, if such information is available.
  4. Key considerations: Important notes regarding applicability and potential use cases.

Attributes are presented in column order. Recall the following abbreviations: FACTS CA (Forest ACtivity Tracking System, Common Attributes), FACTS HFT (FACTS Hazardous Fuels Treatments), and NFPORS (National Fuel Plan Operations and Reporting System).

Treatment Index Metadata Table

After collecting all FACTS CA- and NFPORS-derived data, both tables are inserted into a single, comprehensive table using a schema crosswalk. The crosswalk unites the equivalent or comparable attribute data from both sources into common attribute fields and generates additional attribute fields to note the original source of each attribute value.

This section will be extended to address the incorporation of data from IFPRS and the NFT when this data becomes available.

The Treatment Index currently contains data from two source databases: FACTS (Forest ACtivity Tracking System, maintained by the U.S. Forest Service) and NFPORS (National Fire Plan Operations and Reporting System, maintained by the U.S. Department of the Interior). All data in the Treatment Index were initially recorded in one of these two source databases, and each individual record can be traced to their original copies in these systems.

The data in the Treatment Index is directly pulled from the following geodatabases and feature services:

• FACTS
  • FACTS Common Attributes
  • FACTS Hazardous Fuels Treatments
• NFPORS

In the near future, the Treatment Index will also contain data from the following sources:

• IFPRS is a recently developed successor to NFPORS. TWIG plans to begin incorporating IFPRS data into the treatment index in November 2025.
• NFT is a database created and maintained by the National Association of State Foresters. It contains records of fuel treatments from state-level agencies and some NGOs. ReSHAPE has partnered with NASF to bring NFT data into TWIG (and vice-versa). This update is planned for early 2026.

The FACTS, NFPORS, IFPRS, and NFT subsections provide some useful background information and a description of how data is extracted from each source.

TWIG currently implements error flagging to assist users in identifying records that likely contain errors. It’s important to remember that TWIG does not modify or remove potentially erroneous attribute values. Rather, TWIG prioritizes harmony with the original source databases. When potential errors are identified, a flag is added to the records “Error” attribute. Multiple flags are concatenated with a semicolon. Users can use the flags to filter or edit the data on their own if they wish to exclude or modify specific records. The following error flags are currently implemented:

Duplicate records: If two or more records are identical in the Activity and Completion Date fields, their spatial geometries are compared. If their geometries match (permitting an XY tolerance of ±10m), the records are flagged as duplicate (but not removed). One of the records is flagged with “DUPLICATE-KEEP” and the other matching records are flagged as “DUPLICATE-DROP”. To determine which record is flagged as “KEEP,” we first consider completeness – if matching records vary in the number of non-null attribute fields, the most complete is chosen. If records are equally complete, we then choose the record that was most recently modified. If records were last modified on the same date, we choose by ordering the matching records by their randomly generated unique ID (alphabetical, descending) and selecting the first.

High cost: Records with improbably high dollar cost values are frequent in FACTS data. Such extreme values can strongly skew the output of some reports and summaries. For example, when calculating the total cost of all management activities in a region, a single extreme value can dramatically alter the final output. To help users identify records with extreme (and likely erroneous) values, records with cost per acre values over $10,000 are flagged with “HIGH_COST”. While valid costs above this threshold are possible, especially in recent years and in difficult-to-access places, they are often indicative of errors.

Nonstandard units of measurement: FACTS records include a “Unit of Measure” or UOM attribute that is necessary to calculate total cost values, as these cost values are recorded “Per UOM.” The UOM is almost always “acres,” and TWIG’s formula for calculating unit costs into total costs is based on this value. If the value of the “Unit of Measure” attribute is not “acres,” the total cost calculation may be erroneous. All such records are flagged with “CHECK_UOM.”

“State” attribute and geometry conflicts: The “State” attribute included in the Treatment Index is derived from a record’s corresponding attribute field in the source database—not from the spatial geometry of the record. In rare cases, the value of this attribute may not match the physical location of the recorded polygon. For example, a record’s polygon may be in Montana, but the record’s “State” attribute value may be “Idaho”. TWIG checks for this type of error and adds the error flag “SPATIAL” to these records.

Error flagging is implemented in SQL and Python. The relevant source code is here. More extensive error checks are planned for future releases of the TWIG database (see Technical Validation below), and more information regarding the Errors column is available in the Metadata section below.

FACTS (Forest ACtivity Tracking System) is a relational database that is used throughout the Forest Service to manage information related to Forest Service-funded operations. The FACTS database contains many linked tables and is not publicly shared in full. However, all records of individual on-the-ground activities are publicly available and distributed as FACTS Common Attributes (FACTS CA): a single large table containing all records of individual activities and their associated “common attributes.” This expansive dataset contains roughly 7.4 million records in total.

Perhaps unsurprisingly, most of these 7.4 million records are unrelated to fuel treatments or wildfire. As a response to demands for better, more focused tracking of fuel treatments, the Forest Service now publishes a subset of FACTS CA known as FACTS Hazardous Fuel Treatment Reduction data (FACTS HFT). FACTS HFT ostensibly includes all records of “vegetative manipulation designed to create and maintain resilient and sustainable landscapes, including burning, mechanical treatments, and/or other methods that reduce the quantity or change the arrangement of living or dead fuel so that the intensity, severity, or effects of wildland fire are reduced within acceptable ecological parameters and consistent with land management plan objectives, or activities that maintain desired fuel conditions.” In practice, FACTS HFT is a subset of FACTS CA based on two attribute fields: activity and fund code. While these inclusion criteria fit the needs of some users, they exclude many records of activities that cause substantive change in fuel and forest structure but lack specific funding from hazardous fuels budgets, such as the National Forest System Hazardous Fuels (NFHF) Account of the Forest Service or the Wildland Fire Management Account of the DOI.

In contrast with FACTS HFT, TWIG contains a more comprehensive subset of FACTS CA. TWIG includes all records from FACTS HFT plus additional records of activities that may have created substantive changes in fuel but are not associated with the activity or fund code values required for inclusion in FACTS HFT. This is a major point of contrast between TWIG and other databases that collate fuel treatment data, such as IFPRS and FTEM. These other databases do collate data from FACTS HFT, but do not attempt to collect any additional records from FACTS CA.

The inclusion of additional records from FACTS CA ensures users are provided with the most complete information possible. ReSHAPE recognizes that many management activities, such as commercial logging, are not intended to act as fuel treatments or to affect wildfire outcomes. Nevertheless, these management actions can still change fuel loads and alter wildfire behavior. Knowledge of when and where these activities have occurred is essential to understanding the causes of variation in wildfire outcomes across landscapes.

Not all FACTS CA records are useful, since many records describe activities that likely had no impact on fuel loads or wildfire outcomes (e.g. seed collection, animal damage surveys, or tree marking/timber cruising activities). To identify and include only the records that are most likely to be relevant (i.e., most likely to affect fuel and wildfire outcomes), we developed a conservative, rules-based approach that considers multiple attributes of each original record: treatment age, footprint size, and the activity, method, and equipment attributes. The specific rules are defined as SQL queries and are implemented in this section of TWIG’s source code.

At the time of writing, our criterion identified approximately 1M additional records that are not intended as fuel treatments but may be considered as such – a 156% increase beyond what is available in FACTS HFT. These additional records are clearly identified in the “Data Source” attribute field of the treatment index. To exclude these additional FACTS CA records from TWIG, simply filter based on this attribute.

FACTS data is collected from the Forest Service by downloading the full FACTS HFT polygon file geodatabase and FACTS CA file geodatabases for each Forest Service region. FACTS also contains point and polyline data, distributed in separate file geodatabases, but these records are not included in TWIG.

Because all FACTS HFT records are also contained in FACTS CA, duplicates must be identified and removed as these data sources are consolidated. To accomplish this, FACTS CA records from each region are combined into a single large file, and records from FACTS HFT are matched to their corresponding records in FACTS CA using a full outer join on their unique identifiers.

In rare cases, anomalies in the region attribute of FACTS HFT cause some records to have no match in FACTS CA: an erroneous or non-standard value in the region attribute will cause the record to be absent from all region-wide queries, and because the distributed version of FACTS CA is comprised of region-wide data subsets, the erroneous records will not be found in any one region. Such records are included in TWIG but flagged as potential errors.

More commonly, records are successfully matched by their unique identifier, but one or more attribute values are not identical. We assume these discrepancies arise due to differences in the update frequency of the published geodatabases. In all such cases, preference is given to the value recorded in FACTS HFT. After joining all records into a single, consolidated table, this table is subset to include all records originating from FACTS HFT or meeting our rules-based inclusion criterion. This subset of records is then combined with data from NFPORS before further processing.

Note: There can be slight differences between the different distributed versions of FACTS data due to processing artifacts (such as trimmed trailing whitespaces in *.csv files). If you were to compare the attributes of a TWIG record with the corresponding FACTS version, you may not find an exact match unless you are using the file geodatabase (*.gdb) version of the FACTS records.

The treatment index is the core geodatabase of forest management activities at the heart of TWIG. It contains unified and standardized records from multiple source databases. Each record represents a single management activity with a discrete spatial footprint and associated attributes. The spatial footprint is always a polygon or multipart polygon – no records containing point or line geometries are included. Attribute data contain information such as the activity’s type, completion date, funding source, estimated cost, and much more. Only records of activities completed in 1984 or later are included, though earlier records exist in the source databases.

IFPRS (Interior Fuels and Post-fire Reporting System) is the recently created successor to NFPORS. Most DOI agencies began recording fuel treatment information in IFPRS in early 2025. IFPRS is still in active development, complicating the task of incorporating IFPRS data into TWIG. However, as of October 2025, we expect that TWIG will include all IFPRS data in November 2025. When this update occurs, the Data Source attribute domain will be expanded to include “IFPRS” in addition to “FACTS CA”, “FACTS HFT”, and “NFPORS”.

Sometime in the near future (likely late 2025 or early 2026), all historical NFPORS data will be migrated into IFPRS, and NFPORS will be decommissioned. When this occurs, TWIG will solely rely on IFPRS for access to this historical data, and “NFPORS” will be dropped from the Data Source attribute domain.

NFPORS (National Fuel Plan Operations and Reporting System) is maintained by the DOI and contains records of activities “funded primarily by the DOI and designed with the primary objective of mitigating wildfire risks and its potential effects on communities and their values.” NFPORS is currently the most up-to-date source of information regarding fuels treatments on DOI-managed lands but will soon be replaced by a new system known as the Interior Fuels and Post-fire Reporting System (IFPRS). TWIG will transition to IFPRS when this resource becomes available. NFPORS was created in the early 2000s in response to the now-defunct National Fire Plan (NFP), and although the NFP has been superseded, NFPORS records are categorized by three of the five NFP “key points”: restoration and rehabilitation (key point 2), hazardous fuels reduction (key point 3), and community assistance (key point 4). This contrasts with TWIG data from FACTS, as FACTS HFT also recognizes the NFP “key points” categorization, but only includes activities specifically related to reducing hazardous fuels (key point 3). Thus, TWIG data sourced from NFPORS includes many records of restoration, rehabilitation, and community assistance activities completed shortly after wildfire occurrence (e.g. postfire seeding). To reflect the historical uses of these record-keeping systems, activities associated with key points 2 and 4 are generally excluded from TWIG data sourced from FACTS, but not NFPORS. NFPORS data is publicly accessed through an ESRI feature service REST API. This API is queried weekly to check for updates, and new or updated records are inserted into an extant, locally stored copy.

The NFT (National Fuels Treatment Initiative) is a project of the National Association of State Foresters. The NFT includes data captured through the NFT’s Grant Accomplishment Reporting Portal and through NASF’s wide-ranging efforts to work directly with state agencies to catalog their existing geospatial data.

ReSHAPE has partnered with NASF to bring TWIG data into the Treatment Index. We expect this update to occur in Q1 or Q2 of 2026. Meanwhile, this important state-level dataset is already accessible via the NFT’s own online data explorer.

The NFT data explorer also currently contains records from FACTS HFT, NFPORS, and IFPRS, but does not contain additional records from FACTS CA, and focuses only on more recent (>2019) records from these systems. ReSHAPE and NASF are currently working together to extend and harmonize the scope of the federal-level data available through NFT.

NFPORS

treatment index treatment_date and status are set in tandem.

if NFPORS act_comp_dt is not null:

treatment_date = act_comp_dt

status = ‘Completed’

if NFPORS act_comp_dt is null, but plan_int_dt is not null:

treatment_date = plan_int_dt

status = ‘Planned’

if NFPORS act_comp_dt and plan_int_dt are null:
treatment_date = col_date

status = ‘Other’

FACTS Hazardous Fuels

if FACTS Hazardous Fuels date_completed is not null:
treatment_date = date_completed

status = ‘Completed’

if FACTS Hazardous Fuels date_completed is null:
treatment_date = date_planned

status = ‘Planned’

FACTS Common Attributes

if FACTS Common Attributes date_completed is not null:
treatment_date = date_completed

status = ‘Completed’

if FACTS Common Attributes date_completed is null:
treatment_date = act_created_date

status = ‘Planned’

IFPRS

Since IFPRS has its own status field, status and treatment_date are set independently for this source.

treatment_date:
if IFPRS completiondate is not null:
treatment_date = completiondate

if IFPRS completiondate is null:
treatment_date = originalinitiationdate

status:

IFPRS has a status field that is populated within its database. This status field is consolidated to a more simplified form for the TWIG treatment index status field.

‘Completed’ status in IFPRS creates ‘Completed’ status in the Treatment Index.

‘Planned’ is assigned to the following status values:
‘Draft’,
‘Approved (Local)’,
‘Approved (Regional)’,
‘Ready for Approval’,
‘Approved (Department)’,
‘Approved (Agency)’,
‘Approved’,
‘Not Started’

‘Other’ is assigned to the following status values:

‘UnApproval Requested’

‘Cancelled’

Because the treatment index is designed to represent the underlying systems of record accurately, errors in those systems are not modified or removed. However, the treatment index includes an Errors attribute that can be used to identify many of the records that likely contain errors. The various types of error flags are described in Error Flagging.

In this section, we describe our efforts (and some independent efforts) to assess the accuracy TWIG’s source databases. This information is organized around three of the most frequent and problematic sources of error: duplicate records, completeness and accuracy of spatial geometries, and accuracy of cost estimates. In addition, information regarding the accuracy of each Treatment Index attribute field is included in the Attribute Metadata section (if such information is available).

Duplicate Records

Duplicate records occur when a single management activity is recorded multiple times in a single database or when the activity is recorded in separate databases which are subsequently merged. TWIG considers records to be erroneously duplicated when the completion date and activity code exactly match, and the spatial geometries intersect with 95% or more overlap. Because the same management activity can be repeated multiple times and different management activities may co-occur in the same area, records with non-matching completion dates and activity codes are not considered duplicates. As of January 2025, approximately 57,000 records (3%) were identified as potential duplicates.

When duplicate records are identified, all records in the matching set are flagged in the Errors attribute. The most complete record (or, if records are equally complete, the record with the most recent edits) is flagged with “DUPLICATE-KEEP”, and other matches are flagged with “DUPLICATE-DROP”, allowing users to exclude these records with a simple query.

Completeness and Accuracy of Spatial Geometries

The completeness and accuracy of spatial geometries have not been independently assessed by ReSHAPE, though some errors are known to exist. Published accuracy assessments are rare, but not absent: in a landmark study of FACTS HFT data in California, trained observers compared FACTS polygons with those from a Landsat-based change detection algorithm. The observers noted that just 54% of FACTS polygons were matched with a polygon from the change detection algorithm, and of this 54%, only 79% were closely matched (±25% error in total area). Thus, over half of all examined FACTS HFT records were either undetectable or were poorly matched to detectable changes. Though not all management activities are expected to create detectable changes in land cover, a parallel analysis of CALFIRE (California’s state forestry and wildfire agency) fuel treatments showed that 80% of activities were detectable. In sum, this study suggests that both spatial inaccuracies and errors of commission (i.e., records of management actions in areas where they did not occur, or false positives) occur within FACTS data, though this may vary depending on region and ecosystem type.

Errors of omission (i.e., missing records of management actions that did occur, or false negatives) are more difficult to identify, but a recent study of forest management activity in New Mexico lends some insight: omission errors were quantified by comparing data collected from regional- or state-level offices to data collected from requests and in-person visits to local offices. Omission error rates from the regional- and state-level ranged from 0% to 57.4%, averaging 7.4% of the treated area across the five HUC-10 watersheds where error was quantified 22. Though these error rates encompass federal, state, and private data (whereas TWIG only includes federal data), this result does provide a benchmark for the frequency of omission errors in TWIG.

The accuracy of the spatial geometry is likely to vary with the age of the recorded activity and the data input method. Some older records (especially pre-2005) were only described by a single point or general administrative boundary and a measure of total area. More recent spatial geometries are more likely to be recorded with handheld GPS or manual digitization of aerial imagery. Furthermore, the spatial extent of activities like thinning, sanitation cuts, or prescribed fires can be hard to define. These activities are typically described by the area encompassing the activity, though the activity may only modify fuels and forest structure on a small, dispersed fraction of the full polygon. The treatment index makes no distinction between spatial geometries recorded in different ways or at different spatial scales.

Accuracy of Cost Estimates

TWIG records contain extensive attribute data (see Metadata section). A large federal workforce manually enters these data; thus, errors are inevitable. The frequency of errors is impossible to assess in most cases. The accuracy of dollar costs (Total Cost attribute) is particularly difficult to assess: accounting practices may differ among agencies and regions, and values are not reconciled with actual expenditures. However, some errors can be identified simply by their extreme values. These errors can arise as typos or when values are mistakenly recorded as total cost rather than cost per unit area(in the Cost Per UOM attribute). All records with cost values exceeding a (conservatively large) value of $10,000 per acre are flagged as “HIGH_COST”.

An error also occurs when the UOM attribute value is EACH or MILES (though these values are rare, comprising just ~70 records as of January 2025). In these cases, the total cost field is likely to be incorrect, because the acreage of the spatial polygon is not the correct multiplier. However, after close examination, we could find no consistent way to correctly calculate total cost estimates for these records without external validation. Furthermore, it seems that the values EACH and MILES are probably incorrect in a substantial fraction of these already-rare cases. So, rather than attempting to manually derive a unique cost estimate for each of these records, we have maintained a consistent total cost calculation across all records and flagged these records with “CHECK_UOM.”

Extreme cost errors are especially problematic for summary statistics: a single erroneous record can bias the summary statistic by orders of magnitude. Consider this area summary report for the Dixie National Forest in Southern Utah. The total funding amount for the entire region of interest is $752m. This is driven almost entirely by a single chaining treatment with a cost per acre of $405,451 and covering 1729 acres—that’s a total cost of over $700m for this single treatment. This (presumed) error has caused the total cost estimate for the region of interest to explode over 10x. For this reason, we strongly urge end users to consider possible errors when calculating cost-related summary statistics.

TWIG is a transformative tool for wildfire management and ecological restoration, offering comprehensive data integration and accessibility. It combines data from federal systems of record, using carefully developed rules to capture treatments that affect forest structure and wildfire behavior. We anticipate that TWIG will facilitate policy development and planning for wildfire risk mitigation and research on the ecological, economic, and societal effects of fuel reduction treatments. By integrating data across jurisdictional boundaries, the treatment index supports shared stewardship and cross-boundary collaboration, with partnerships like the National Association of State Foresters (NASF) extending its reach to state-level data in planned future releases. With its broad scope, the treatment index supports diverse users and applications and evolves through iterative development and user feedback, ensuring it remains adaptable to future needs.

However, TWIG has limitations. Data should not be used for real-time decision support, emergency management, small-scale mapping, planning, or analysis (e.g., of single treatment projects, stands, or trees), or any other use requiring real-time or precise data. The Treatment Index is comprised of data from existing systems of record and thus shares many of the same accuracy and completeness limitations as these systems. Spatial accuracy may vary, particularly for older records, and cost data may be inconsistent due to manual entry and lack of reconciliation with actual expenditures. Though the treatment index includes some error flagging, it does not correct errors in the source data, requiring users to filter and validate information for specific needs. Current coverage primarily includes federal data and does not yet fully incorporate state, private, or NGO data, though we plan to extend the scope of included data in future releases. While versatile, TWIG is not universally applicable, and specialized tools may be better suited for specific local-scale tasks. Other resources that are similar to TWIG and may be of interest are listed on our Other Data and Decision Support Tools page.

More information coming soon! For now, check out the TWIG Tutorial videos on the ReSHAPE Resources page.