Leveraging wildfire to augment forest management and amplify forest resilience
Data files
May 22, 2025 version files 217.62 MB
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FTE_Severity_allyears_revision_28Jan2025.csv
47.32 KB
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FTE_Treatment_allyears_revision_28Jan2025.csv
60.09 KB
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README_Workflow.txt
3.46 KB
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README.md
8.65 KB
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RefinedTreatmentData_Shive_etal2025.zip
66.86 MB
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ResistanceClassification_Shive_etal2025.R
6.23 KB
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Severity_Shive_etal2025.zip
88.77 MB
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SoS_Code_Final.ipynb
55.63 KB
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SummaryTrends_Shive_etal2025_Final.R
11.59 KB
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Treatment_data_cleaning_Shive_etal2025.R
3.01 KB
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YPMC_2001.zip
61.79 MB
Abstract
Successive catastrophic wildfire seasons in western North America have escalated the urgency around reducing fire risk to communities and ecosystems. In historically frequent-fire forests, fuel buildup as a result of fire exclusion is contributing to increased fire severity, but the probability of high severity fire can be reduced by active forest management that reduces fuels, prompting federal and state agencies have committed significant resources to increase the pace and scale of fuel reduction treatments. However, wildfires also have the potential to act as “treatments” in areas that burn at lower severity, but even catastrophic fires with large areas of high severity can still have substantial area of lower severity fire that may be improving forest conditions. We quantified active management and wildfire severity across yellow pine and mixed conifer forests (YPMC) in the Sierra Nevada of California over a 22-year period (2001-2022). We did not detect clear increases in the area treated through time but the area of beneficial wildfire (low to moderate severity) increased substantially, exceeding active treatment area in 9 of 22 years. Overall, beneficial wildfire treated ~20% more area than all treatments combined, and nearly seven times more area than fire-related treatments alone. We then used disturbance history to evaluate resistance to high severity wildfire and forest loss across the YPMC range. Of the 2.3 million ha that were YPMC forests in 2001, 19% lost mature forests due to high severity fire by 2022, nearly half of all YPMC area burned. Most of the landscape (47%) remains at risk of high severity fire because it had no restorative disturbances, but 33% of the study area has some level of resistance to high severity wildfire. In these areas, resistance will need to be enhanced and maintained over time via active management or managed wildfire. These treatment needs will likely outpace capacity even under optimistic implementation scenarios. Given limited resources for implementing active management and the likelihood of a more fiery future, incorporating beneficial wildfire into landscape-level treatment planning has the potential to amplify active management treatments, expanding the forest area that is resistant to high severity wildfire.
Dataset DOI: 10.5061/dryad.ttdz08m7d
Description of the data and file structure
This file describes the data and code available for the publication: “Leveraging wildfire to augment forest management and amplify forest resilience,” abstract below. Note that the workflow can be found in the README_Workflow.txt.
Successive catastrophic wildfire seasons in western North America have escalated the urgency around reducing fire risk to communities and ecosystems. In historically frequent-fire forests, fuel buildup as a result of fire exclusion is contributing to increased fire severity. The probability of high severity fire can be reduced by active forest management that reduces fuels, prompting federal and state agencies to commit significant resources to increase the pace and scale of fuel reduction treatments. However, lower-severity areas of wildfires also have the potential to act as “treatments,” and even catastrophic fires with large areas of high severity can still have substantial area of lower severity fire that may be improving forest conditions locally. We quantified active management and wildfire severity across yellow pine and mixed conifer forests (YPMC) in the Sierra Nevada of California over a 22-year period (2001-2022). We did not detect increases in the area treated through time but the area of beneficial wildfire (low to moderate severity) increased substantially, exceeding active treatment area in 8 of 22 years. Overall, beneficial wildfire treated ~17% more area than all treatments combined, and roughly four times more area than fire-related treatments alone. We then used disturbance history to evaluate resistance to high severity wildfire and forest loss across the YPMC range. Of the 2.3 million ha YPMC of forests in 2001, 20% lost mature forests due to high severity fire by 2022, which is nearly half of all YPMC area burned. Most of the landscape (47%) remains at risk of high severity fire because it had no restorative disturbances, but 33% of the study area has some level of resistance to high severity wildfire. In these areas, resistance will need to be enhanced and maintained over time via active management or managed wildfire, but these treatment needs will likely outpace capacity even under optimistic implementation scenarios. Given limited resources for implementing active management and the likelihood of a more fiery future, incorporating beneficial wildfire into landscape-level treatment planning has the potential to amplify the impact of active management treatments.
Files and variables
File: FTE_Severity_allyears_revision_28Jan2025.csv
Description: Fire severity per year and landowner
Variables
- Year: Year burned
- FTE: Landowner type
- Severity Category: Low, moderate, high, or unburned
- Acres: area impacted in acres
File: FTE_Treatment_allyears_revision_28Jan2025.csv
Description: Treatment area by reclassified treatment types and landowner
Variables
- Year: year treated
- FTE: Landowner
- Treatment: Reclassified treatment types
- Commercial thin/Large hazard tree removal (note: in the code, this treatment type was combined with “mechanical fuels management” because the distinctions were not accurate enough, both represent forest and fuels management with heavy equipment or hand tools)
- Mechanical fuels management (note: in the code, this treatment type was combined with “Commercial thin/Large hazard tree removal” because the distinctions were not accurate enough, both represent forest and fuels management with heavy equipment or hand tools)
- Fire-related treatment: prescribed fire or pile burning treatments
- No treatment: no treatment between 2001-2022
- Acres: area impacted in acres
File: README_Workflow.txt
Description: This file describes the overall workflow for the publication.
File: ResistanceClassification_Shive_etal2025.R
Description: R code for the resistance classification.
File: SummaryTrends_Shive_etal2025_Final.R
Description: R code for summary data.
File: SoS_Code_Final.ipynb
Description: Python code for creating the csv’s included here.
File: Treatment_data_cleaning_Shive_etal2025.R
Description: Python code for an intermediate data cleaning step.
File: RefinedTreatmentData_Shive_etal2025.zip
Description: Shapefile of treatment data that has been cleaned and reclassified to our selected treatment types. See methods section and Supplemental information for further information on data cleaning steps and the original sources for these data.
File: Severity_Shive_etal2025.zip
Description: Shapefile of severity data from the US Forest Service (2000-2017) and generated from Google Earth Engine (2018-2022) as described in the methods. In several years, there was some overlap at theedges of adjacent fires. To reduce these to one severity value per year, we dissolved these and retained the higher severity classification.
File: YPMC_2001.zip
Description: Shapefile of the yellow-pine mixed conifer forest area from a CALVEG classification from 2000 by the US Forest Service which was obtained directly from the agency. A table of forest types that were included can be found in Table 1. Additional information on CALVEG can be found here: https://www.fs.usda.gov/detail/r5/landmanagement/resourcemanagement/?cid=stelprdb5347192.
Code/software
R code (*.R) was generated in R 4.4.2.
Python code (*.ipynb) was generated in Python version 3.12 using Jupyter Notebook 7.2, and uses these packages: os, pandas, arcpy (note that arcpy is not open source).
See the README_Workflow.txt file for workflow information.
Access information
Some treatment data was obtained directly from the institution (University of California Berkeley Research Forests and Bureau of Land Management), and some severity data was generated via Google Earth Engine as described in Parks et al. 2018.
Data was derived from the following sources:
- CAL FIRE, 2023a. CAL FIRE (California Department of Forestry and Fire Protection) Timber Harvesting Plans All WGS 1984 (2010-2023 https://calfire-forestry.maps.arcgis.com/home/item.html?id=e465165103474d14b684b7effb3e1a43&sublayer=0
- CAL FIRE, 2023b. CAL FIRE (California Department of Forestry and Fire Protection) Historical Timber Harvesting Plans All WGS 1984 (1997-2009). https://calfire-forestry.maps.arcgis.com/home/item.html?id=e465165103474d14b684b7effb3e1a43&sublayer=1#overview
- CAL FIRE, 2023c. Historic Fire Perimeters 2022 Geodatabase: prescribed fire layer (rxburn22_1). https://www.fire.ca.gov/what-we-do/fire-resource-assessment-program/fire-perimeters. Accessed 6/1/2023.
- NPS, 2024. National Park Service Complete Treatment Perimeters. https://nifc.maps.arcgis.com/home/item.html?id=51f9750534c64b1d94b65d1fd2ff7d2f (accessed 4.15.24).
- Parks, S.A., Holsinger, L.M., Voss, M.A., Loehman, R.A., Robinson, N.P., 2018. Mean Composite Fire Severity Metrics Computed with Google Earth Engine Offer Improved Accuracy and Expanded Mapping Potential. Remote Sens. 10, 879. https://doi.org/10.3390/rs10060879
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U.S. Forest Service, 2023a. Hazard Fuels Treatments Database: S_USA.Activity_HazFuelTrt_PL.shp. https://data.fs.usda.gov/geodata/edw/datasets.php (accessed 1.1.23).
U.S. Forest Service, 2023b. Timber Harvest Database: S_USA.Activity_TimberHarvest.shp. https://data.fs.usda.gov/geodata/edw/datasets.php (accessed 1.1.23).
- U.S. Forest Service, 2018b. Vegetation burn severity using the Composite Burn Index, 1984-2017 (VegBurnSeverity.shp) (ESRI Shapefile). U.S. Department of Agriculture, Forest Service, Pacific Southwest Research Station, McClellan, CA. https://www.fs.usda.gov/detail/r5/landmanagement/gis/?cid=stelprdb5327833
As detailed in the manuscript, we acquried publicly available data from a variety of sources and created fire severity maps for 2018-2022 via Google Earth Engine.