The Earth Engine version of the Fire Information for Resource Management System (FIRMS) dataset contains the LANCE fire detection product in rasterized form. The near real-time (NRT) active fire locations are processed by LANCE using the standard MODIS MOD14/MYD14 Fire and Thermal Anomalies product. Each active fire location represents the centroid of a 1km pixel that is flagged by the algorithm as containing one or more fires within the pixel. The data are rasterized as follows: for each FIRMS active fire point, a 1km bounding box (BB) is defined; pixels in the MODIS sinusoidal projection that intersect the FIRMS BB are identified; if multiple FIRMS BBs intersect the same pixel, the one with higher confidence is retained; in case of a tie, the brighter one is retained. The data in the near-real-time dataset are not considered to be of science quality. Additional information can be found here. NOTE: VIIRS FIRMS datasets from NOAA20 and SUOMI are also available: NASA/LANCE/NOAA20_VIIRS/C2 NASA/LANCE/SNPP_VIIRS/C2

PLEASE NOTE:

_ GEEBAM is an interim product and there is no ground truthing or assessment of accuracy. Fire Extent and Severity Mapping (FESM) data should be used for accurate information on fire severity and loss of biomass in relation to bushfires._

The intention of this dataset was to provide a rapid assessment of fire impact.

In collaboration with the University of NSW, the NSW Department of Planning Infrastructure and Environment (DPIE) Remote Sensing and Landscape Science team has developed a rapid mapping approach to find out where wildfires in NSW have affected vegetation. We call it the Google Earth Engine Burnt Area Map (GEEBAM) and it relies on Sentinel 2 satellite imagery. The product output is a TIFF image with a resolution of 15m. Burnt Area Classes:

1. Little change observed between pre and post fire

2. Canopy unburnt - A green canopy within the fire ground that may act as refugia for native fauna, may be affected by fire

3. Canopy partially affected - A mix of burnt and unburnt canopy vegetation

4. Canopy fully affected -The canopy and understorey are most likely burnt

Using GEEBAM at a local scale requires visual interpretation with reference to satellite imagery. This will ensure the best results for each fire or vegetation class.

Important Note: GEEBAM is an interim product and there is no ground truthing or assessment of accuracy. It is updated fortnightly.

Please see Google Earth Engine Burnt Area Factsheet

This project (funded by the Joint Fire Science Program through the Seney Natural History Association) contains prescribed and wildfire point and polygon data covering 1944-2012 that occurred within the boundaries of the Seney National Wildlife Refuge (SNWR) and point data from the dendrochronological work of Drobyshev et al. (2008, Canadian J. Forest Research). The intention of the project was to create a data set to provide a single source for users of GIS to access point and area fire information. Prior to this project a separate polygon data set created by the refuge covering fires from 2003-2009 was available as was the Drobyshev et al. (2008) data in a separate file. All other records of fire events on the refuge were in various forms ranging from table sets in the Fire Management Information System (FMIS) to references in refuge annual narratives. There was a clear need to establish a system showing basic location and historical information in one format. GIS using ESRI shapefiles was chosen since it shows the most promise and flexibility in future planning and modeling use. This data format should allow for less duplication of future efforts and increase the usability of fire data to not only to refuge staff but also partner agencies and researchers.

Suomi NPP Visible Infrared Imaging Radiometer Suite (VIIRS) Active Fire detection product is based on the instrument's 375m nominal resolution data. Compared to other coarser resolution (≥ 1km) satellite fire detection products, the improved 375 m data provide greater response over fires of relatively small areas, as well as improved mapping of large fire perimeters. Consequently, the data are well suited for use in support of fire management (e.g., near real-time alert systems), as well as other science applications requiring improved fire mapping fidelity. The data in the near-real-time dataset are not considered to be of science quality. Additional information can be found here.

Context

Predicting wildfire spread is critical for land management and disaster preparedness. To this end, we present `Next Day Wildfire Spread,' a curated, large-scale, multivariate data set of historical wildfires aggregating nearly a decade of remote-sensing data across the United States. In contrast to existing fire data sets based on Earth observation satellites, this data set combines 2D fire data with many explanatory variables (e.g., topography, vegetation, weather, drought index, population density) aligned over 2D regions, providing a feature-rich data set for machine learning applications. This data set can be used as a benchmark for developing wildfire propagation models based on remote sensing data for a lead time of one day.

Content

We aggregate the data across the contiguous United States from 2012 to 2020. The data set has a total of 18,445 samples. Each sample is a 64 km x 64 km region at 1 km resolution from a location and time at which a fire occurred. We represent the fire information as a fire mask over each region, showing the locations of ‘fire’ versus ‘no fire’, with an additional class for uncertain labels (i.e., cloud coverage or other unprocessed data). To capture the fire spreading pattern, we include both the fire mask at time t (which we call ‘previous fire mask’) and at time t + 1 day (which we call ‘fire mask’). Using Google Earth Engine (GEE), we aggregate data from different data sources and overlay the fire data in location and time with other variables relevant to wildfire predictions. In addition to the fire data, this data set contains the following features: elevation, wind direction and wind speed, minimum and maximum temperatures, humidity, precipitation, drought index, normalized difference vegetation index (NDVI), energy release component (ERC), and population density.

The following figure shows examples from this data set. In the fire masks, red corresponds to fire, while grey corresponds to no fire. Black indicates uncertain labels (i.e., cloud coverage or other unprocessed data).

https://i.postimg.cc/bYxHNVVV/data-visualization.png" alt="data-visualization.png">

The published notebook provides an example of how to read and plot the data.

A detailed description of this data set is provided here: Arxiv paper.

Data Source

• The historical wildfire data are from the MOD14A1 V6 data set [1].
• Topography data are from the Shuttle Radar Topography Mission (SRTM) [2].
• Weather data are from the Gridded Surface Meteorological data set (GRIDMET) [3].
• Drought data are from the GRIDMET Drought data set [4].
• Vegetation data are from the NASA VIIRS Vegetation Indices (VNP13A1) data set [5].
• Population density data are from the Gridded Population of World Version 4 (GPWv4) data set [6].

Inspiration

Some potential questions that this data set can be used to answer include: - Given a fire on a given day, where will the fire spread the following day? - What are the main variables related to fire spreading?

Citation

[1] L. Giglio and C. Justice, “Mod14a1 modis/terra thermal anomalies/fire daily l3 global 1km sin grid v006,” 2015, https://doi.org/10.5067/MODIS/MOD14A1.006.

[2] T. G. Farr, P. A. Rosen, E. Caro, R. Crippen, R. Duren, S. Hensley, M. Kobrick, M. Paller, E. Rodriguez, L. Roth, D. Seal, S. Shaffer, J. Shimada, J. Umland, M. Werner, M. Oskin, D. Burbank, and D. Alsdorf, “The shuttle radar topography mission,” Reviews of Geophysics, vol. 45, no. 2, 2007, https://doi.org/10.1029/2005RG000183.

[3] J. T. Abatzoglou, “Development of gridded surface meteorological data for ecological applications and modelling,” International Journal of Climatology, vol. 33, no. 1, pp. 121–131, 2013, https://doi.org/10.1002/joc.3413.

[4] J. T. Abatzoglou, D. E. Rupp, and P. W. Mote, “Seasonal climate variability and change in the pacific northwest of the united states,” Journal of Climate, vol. 27, no. 5, pp. 2125–2142, 2014, https://doi.org/10.1002/joc.3413.

[5] K. Didan and A. Barreto, “Viirs/npp vegetation indices 16-day l3 global 500m sin grid v001,” 2018, [https://doi.o...

Data acquisitionOccurrence data for bee species were downloaded from ALA60 using ALA4R version 1.8.064 in R version 3.6.265.Floral visitation data were obtained from ALA60, Museums Victoria, the Western Australian Museum66,67, and publications (Tables S1 and S2). Floral visitation records were checked for errors and synonymies using the Australian Plant Name Index68. Life-history traits for bee species were sourced, in most cases, from the most recent taxonomic descriptions, or other publications (Tables S1 and S2). A one-hectare resolution Major Vegetation Subgroup (MVS) map was sourced from Geoscience Australia’s National Mapping Division (NMD)61. Fire frequency data from 1988 to 2016 were downloaded from the Department of Environment and Energy (DEE)69, 2019–20 wildfire occurrence data (National Indicative Aggregated Fire Extent Dataset — NIAFED — version 20200623) were sourced from the Department of Agriculture, Water and the Environment (DAWE)36, and 2019–20 wildfire intensity data (Google Earth Engine Burnt Area Map — GEEBAM) were sourced from the Department of Planning, Industry and Environment (DPIE)62. All raster data sources were matched in resolution to the one-hectare MVS map. These GIS data sources may vary in spatial uncertainty or resolution and their caveats can be found at their respective locations online.Data filtering and analysesOccurrence data from ALA were filtered to include only reliable (“preserved specimens”, “machine observations” — e.g., malaise traps, — and data from published datasets) and “present” (compared to “absent”) records. Records without geographic locations or that did not align with base maps were excluded from GIS analyses. Species were then filtered for minimum sample size (n = 30) and minimum number of unique localities (n = 5). However, if there were 15 or more unique localities and a sample size of less than 30, the species was included.The MVS map was reprojected to a world geodetic system (WGS 1984, EPSG:4326) and clipped to the 2019–20 wildfire map in QGIS version 3.1270. The NIAFED and GEEBAM maps were aligned and matched to the resolution of the MVS map using the package raster version 3.0-1271 in R version 3.6.265. Major vegetation subgroups61, 2019–20 wildfire status36, and fire frequency69 were extracted for each ALA record using raster. The proportion of each MVS burnt was calculated by clipping MVS maps with the 2019–20 burn map in ArcMap Version 10.6.172. All map files used in our analyses are available at (html location to be confirmed upon acceptance) for use with our R script.We complemented species distributional data (ALA60 point data) with spatial information on their associated habitat (MVS61), to avoid reliance on the limited data for some species. To determine the potential distribution of each species we buffered the latitudinal and longitudinal extents of the raster datasets (MVS, fire frequency, NIAFED, and GEEBAM) by 20% in each direction. For geographically-restricted species with latitudinal or longitudinal ranges less than one degree (~111 km), we buffered their extent by one degree in each direction along that axis or axes. These values were chosen as conservative estimates of species distributional extents, but we recognize that this treatment may over-inflate the distribution of some species with highly-localized ranges. These data are broken into four files:Map_data — hosts all of the map files used in the analysesBee-plant_point_data — hosts the ALA download data, combined bee dataset, and the life history and plant data spreadsheetWard_comparison_data — hosts some of the data used for the Ward co-analysis using our methodAll_other_R_data — hosts many of the runfiles from our main analysis

The Terra and Aqua combined MCD64A1 Version 6.1 Burned Area data product is a monthly, global gridded 500m product containing per-pixel burned-area and quality information. The MCD64A1 burned-area mapping approach employs 500m MODIS Surface Reflectance imagery coupled with 1km MODIS active fire observations. The algorithm uses a burn sensitive vegetation …

The California Department of Forestry and Fire Protection's Fire and Resource Assessment Program (FRAP) annually maintains and distributes an historical wildland fire perimeter dataset from across public and private lands in California. The GIS data is developed with the cooperation of the United States Forest Service Region 5, the Bureau of Land Management, California State Parks, National Park Service and the United States Fish and Wildlife Service and is released in the spring with added data from the previous calendar year. Although the dataset represents the most complete digital record of fire perimeters in California, it is still incomplete, and users should be cautious when drawing conclusions based on the data.

This data should be used carefully for statistical analysis and reporting due to missing perimeters (see Use Limitation in metadata). Some fires are missing because historical records were lost or damaged, were too small for the minimum cutoffs, had inadequate documentation or have not yet been incorporated into the database. Other errors with the fire perimeter database include duplicate fires and over-generalization. Additionally, over-generalization, particularly with large old fires, may show unburned "islands" within the final perimeter as burned. Users of the fire perimeter database must exercise caution in application of the data. Careful use of the fire perimeter database will prevent users from drawing inaccurate or erroneous conclusions from the data. This data is updated annually in the spring with fire perimeters from the previous fire season. This dataset may differ in California compared to that available from the National Interagency Fire Center (NIFC) due to different requirements between the two datasets. The data covers fires back to 1878. As of May 2025, it represents fire24_1.

Please help improve this dataset by filling out this survey with feedback:

Historic Fire Perimeter Dataset Feedback (arcgis.com)

Current criteria for data collection are as follows:

CAL FIRE (including contract counties) submit perimeters ≥10 acres in timber, ≥50 acres in brush, or ≥300 acres in grass, and/or ≥3 impacted residential or commercial structures, and/or caused ≥1 fatality.

All cooperating agencies submit perimeters ≥10 acres.

Version update:

Firep24_1 was released in April 2025. Five hundred forty-eight fires from the 2024 fire season were added to the database (2 from BIA, 56 from BLM, 197 from CAL FIRE, 193 from Contract Counties, 27 from LRA, 8 from NPS, 55 from USFS and 8 from USFW). Six perimeters were added from the 2025 fire season (as a special case due to an unusual January fire siege). Five duplicate fires were removed, and the 2023 Sage was replaced with a more accurate perimeter. There were 900 perimeters that received updated attribution (705 removed “FIRE” from the end of Fire Name field and 148 replaced Complex IRWIN ID with Complex local incident number for COMPLEX_ID field). The following fires were identified as meeting our collection criteria but are not included in this version and will hopefully be added in a future update: Addie (2024-CACND-002119), Alpaugh (2024-CACND-001715), South (2024-CATIA-001375). One perimeter is missing containment date that will be updated in the next release.

Cross checking CALFIRS reporting for new CAL FIRE submissions to ensure accuracy with cause class was added to the compilation process. The cause class domain description for “Powerline” was updated to “Electrical Power” to be more inclusive of cause reports.

Includes separate layers filtered by criteria as follows:

California Fire Perimeters (All): Unfiltered. The entire collection of wildfire perimeters in the database. It is scale dependent and starts displaying at the country level scale.

Recent Large Fire Perimeters (≥5000 acres): Filtered for wildfires greater or equal to 5,000 acres for the last 5 years of fires (2020-January 2025), symbolized with color by year and is scale dependent and starts displaying at the country level scale. Year-only labels for recent large fires.

California Fire Perimeters (1950+): Filtered for wildfires that started in 1950-January 2025. Symbolized by decade, and display starting at country level scale.

Detailed metadata is included in the following documents:

Wildland Fire Perimeters (Firep24_1) Metadata

For any questions, please contact the data steward:

Kim Wallin, GIS Specialist

CAL FIRE, Fire & Resource Assessment Program (FRAP)

kimberly.wallin@fire.ca.gov

Google Earth Engine used to compute the NDVI statistics added to Globe-LFMC. The input of the program is a point shapefile (“samplePlotsShapefile”, extensions .cpg, .dbf, .prj, .shp, .shx) representing the location of each Globe-LFMC site. This shapefile is available as additional data in figshare (see Code Availability). To run this GEE code the shapefile needs to be uploaded into the GEE Assets and, then, imported into the Code Editor with the name “plots” (without quotation marks).Google Earth Engine codeChange Notice - GEE_script_for_GlobeLFMC_ndvi_stats_v2.jsThe following acknowledgements have been added at the beginning of the code: “Portions of the following code are modifications based on work created and shared by Google in Earth Engine Data Catalog and Earth Engine Guides under the Apache 2.0 License. https://www.apache.org/licenses/LICENSE-2.0”Change Notice - samplePlotsShapefile_v2The shapefile describing the database sites has been corrected and updated with the correct coordinates.

This dataset depicts components of wildfire risk for all lands in the United States that: 1) are landscape-wide (i.e., measurable at every pixel across the landscape); and 2) represent in situ risk - risk at the location where the adverse effects take place on the landscape. National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Researc Station and Pyrologix LLC, form the foundation of the Wildfire Risk to Communities data. Vegetation and wildland fuels data from LANDFIRE 2020 (version 2.2.0) were used as input to two different but related geospatial fire simulation systems. Annual burn probability was produced with the USFS geospatial fire simulator (FSim) at a relatively coarse cell size of 270 meters (m). The burn probability raster data was upsampled to the native 30 m resolution of the LANDFIRE fuel and vegetation data, to bring the data down to a finer resolution more useful for assessing hazard and risk to communities. In this upsampling process, the provider also spread values of modeled burn probability into developed areas represented in LANDFIRE fuels data as non-burnable. Burn probability rasters represent landscape conditions as of the end of 2020. Fire intensity characteristics were modeled at 30 m resolution using a process that performs a comprehensive set of FlamMap runs spanning the full range of weather-related characteristics that occur during a fire season and then integrates those runs into a variety of results based on the likelihood of those weather types occurring. Before the fire intensity modeling, the LANDFIRE 2020 data were updated to reflect fuels disturbances occurring in 2021 and 2022. As such, the fire intensity datasets represent landscape conditions as of the end of 2022.

The Landscape Fire Scars Database for Chile makes publicly available for the first time a historical high-resolution (~30 m) burned area and fire severity product for the country. The georeferenced database is a multi-institutional effort containing information on more than 8,000 fires events between July 1984 and June 2018. Using Google Earth Engine (GEE), we reconstructed the fire scar area, perimeter, and severity for each fire. We also provide the Landsat mosaic image of pre- and post-fire events, including the NDVI and NBR indexes. In the related paper, we release the GEE code to reproduce our database or enable the international community to reconstruct another individual burned areas and fire severity data, with minimum input requirements. In the summary file is the list of reconstructed fire events. The identification number (ID) relates the initial information of the wildfires with fire scar and severity data.

Fire boundaries based on the MODIS dataset MCD64A1. The data were computed based on an algorithm that relies on encoding in a graph structure a space-time relationship among patches of burned areas. Each fire has a unique number identifying the event.

Participants in this course will learn about remote sensing of wildfires from instructors at the University of Alaska Fairbanks, located in one of the world’s most active wildfire zones. Students will learn about wildfire behavior, and get hands-on experience with tools and resources used by professionals to create geospatial maps that support firefighters on the ground.

Upon completion, students will be able to:

Access web resources that provide near real-time updates on active wildfires, Navigate databases of remote sensing imagery and data, Analyze geospatial data to detect fire hot spots, map burn areas, and assess severity, Process image and GIS data in open source tools like QGIS and Google Earth Engine.

This data package includes 481 geospatial vector polygons of historic fire perimeters associated with National Wildlife Refuges in Alaska. These polygons, originally held within the Alaska Large Fire Database (ALFD), were reviewed and found to have geospatial inaccuracies with respect to the fire they represent. They were corrected and updated based on a variety of remote sensing resources; the Monitoring Trends in Burn Severity (MTBS) database (https://www.mtbs.gov/viewer/index.html), geospatial rasters of historic fire activity derived from Landsat 1-9 imagery in the Google Earth Engine(https://earthengine.google.com) environment, and historical air photos acquired through EarthExplorer (https://earthexplorer.usgs.gov/). Wildfire records occurring within Alaska Wildlife Refuge units and considered for necessary updates, spanned the 1943-2022 time period and comprised 1,229 recorded fires. After reviewing all fire records, 400 fire perimeters were updated and 81 previously unrecorded fires were added to the database. These updated records spanned 1954-2021.

The Portuguese burn severity atlas (1984 -2022) provides satellite-derived burn severity estimates of each historical fire with its start and end dates recorded and equal to or larger than100 ha for fires from 1984 to 2022 in Portugal. The fire perimeters are provided by the Instituto da Conservação da Natureza e das Florestas (ICNF) (https://sig.icnf.pt/portal/home/item.html?id=983c4e6c4d5b4666b258a3ad5f3ea5af). Landsat imagery is applied for the creation of this atlas. The burn severity indices applied within this atlas are differenced Normalized Burn Ratio (dNBR), Relative differenced Normalized Burn Ratio (RdNBR), Relative Burn Ratio (RBR), and index combining dNBR with enhanced vegetation index (dNBR-EVI).

Since 1984 to 2022, the total burned area recorded in Portugal is 4.85 million ha. Only valid fires, which are fires equal to or larger than 100 ha with known dates, were considered for the burn severity estimates. The total area of valid fires is 3.29 million ha. The Portuguese Burn Severity Atlas provides estimates for 3.17 million ha, accounting for 65% of all fires and 96% of valid fires.

The Portuguese burn severity atlas is organized in subfolders, each entitled as the corresponding year and containing shapefile, maps and a table with details on pairs of images used for burn severity estimates. Within each subfolder, the following data are stored:

• the annual fires’ perimeters shapefile (.dbf, .prj, .shp, .shx)
• dNBR map (.tiff)
• RdNBR map (.tiff)
• RBR map (.tiff)
• dNBR-EVI map (.tiff)
• confidence map: average “SUITABILITY in each pixel within the area of the fire (.tiff)
• comma separated value (.csv) file containing details on the pair of images used for burn severity estimates (year, ID, iteration number, pre-fire time lag(day), pre-fire cloud%, pre-fire suitability (%), post-fire time lag (day), post-fire cloud%, post-fire suitability(%), confidence in the iteration(%), area with dNBR estimation(ha), area of fire (ha), fire i number (to refer in GEE code), dNBR offset value, RdNBR offset value, RBR offset value, and dNBR-EVI offset value).

To the best of our knowledge, Portuguese Burn Severity Atlas is the first open access atlas providing burn severity estimates of historical fires for an entire European country, in this case, Portugal, with 38 years of coverage. Target audience who can benefit from this atlas can be policymakers at national levels, field managers, project managers, and agency and academic researchers.

*** The second version of this atlas provides updated fire data and burn severity estimates of fires from 1984 to 2000 with corrected dates area equal or larger than 100 ha. Moreover, for 2012, aside from burn severity estimates provided via imagery from Terra abroad Moderate Resolution Imaging Spectroradiometer (MODIS), Landsat7-derived maps are included.

The Monitoring Trends in Burn Severity (MTBS) burned area boundaries dataset contains the extent polygons of the burned areas of all currently completed MTBS fires for the continental United States, Alaska, Hawaii, and Puerto Rico. Below NBR stands for "Normalized Burn Ratio", while dNBR stands for "delta NBR", or "PreFire NBR - PostFire NBR". Notes on the threshold values: dNBR is used when available, but sometimes NBR must be used. NBR and dNBR, in this situation, have an inverse relationship Therefore, thresholds are determined based both on the type of incoming data and the range of the data The 9999 and -9999 values are fill values representing the cases when an analyst did not use a threshold (for example, a low severity incident would not warrant the use of a high severity threshold). In some cases values of 999 and -999 were entered (instead of 9999 and -9999). Monitoring Trends in Burn Severity (MTBS) is an interagency program whose goal is to consistently map the burn severity and extent of large fires across all lands of the United States from 1984 to present. This includes all fires 1000 acres or greater in the western United States and 500 acres or greater in the eastern Unites States. The extent of coverage includes the continental U.S., Alaska, Hawaii and Puerto Rico. The program is conducted by the U.S. Geological Survey Center for Earth Resources Observation and Science (EROS) and the USDA Forest Service Geospatial Technology and Applications Center (GTAC). MTBS was first enacted in 2005, primarily to meet the information needs of the Wildland Fire Leadership Council (WFLC). The primary objective at that time was to provide data to the WFLC for monitoring the effectiveness of the ten-year National Fire Plan. The scope of the program has grown since inception and provides data to a wide range of users. These include national policy-makers such as WFLC and others who are focused on implementing and monitoring national fire management strategies; field management units such as national forests, parks and other federal and tribal lands that benefit from the availability of GIS-ready maps and data; other federal land cover mapping programs such as LANDFIRE which utilizes burn severity data in their own efforts; and academic and agency research entities interested in fire severity data over significant geographic and temporal extents. MTBS data are freely available to the public and are generated by leveraging other national programs including the Landsat satellite program, jointly developed and managed by the USGS and NASA. Landsat data are analyzed through a standardized and consistent methodology, generating products at a 30 meter resolution dating back to 1984. One of the greatest strengths of the program is the consistency of the data products which would be impossible without the historic Landsat archive, the largest in the world. You can visit the MTBS Project Website for more information. You can also visit the MTBS Data Explorer to learn more and interact with the data.

These data were generated to map spatial burn severity and emissions of each historically observed large wildfires (>404 hectares (ha)) that burned during 1984–2020 in the state of California in the US. Event-based assessments were conducted at 30-m resolution for all fires and daily emissions were calculated at 500-m resolution for fires burned since 2002. A total of 1697 wildfires were assessed using the Wildfire Burn Severity and Emissions Inventory(WBSE) framework developed by Xu et al 2022. The comprehensive, long-term event and daily emissions records described here could be used to study health effects of wildfire smoke, either by combining them with transport modeling to model air quality and estimate exposures, or by incorporating them into statistical models predicting health impacts as a direct function of estimated emissions. These data will also facilitate analyses of changing emissions impacts on the carbon cycle over the last three decades. High resolution severity and emissions raster maps are generated for each fire event to support further spatial analysis. While the emissions calculated for California with WBSE are not a substitute for real-time daily emissions estimates, it is designed to extend the estimated emissions record back to 1984 with a finer spatial resolution and provide more up-to-date estimates on emissions factors reflecting information from California's recent extreme fires. Methods This dataset provides estimates of 30 m resolution burn severity, and emissions of CO2, CO, CH4, non-methane organic compounds (NMOC), SO2, NH3, NO, NO2, nitrogen oxides (NOx = NO + NO2), PM2.5, OC, and BC. WBSE was implemented for California large wildfires on a per-fire event scale since 1984 and also a daily scale since 2002. The inventory implementation steps, input datasets, and output data are summarized in figure 1 in Xu et al, 2022. Burn severity calculation Fire records for California from 1984 to 2019 were retrieved from MTBS (https://mtbs.gov/viewer/index.html) via interactive viewer on 8 May 2021, resulting in a dataset with a total of 1623 wildfires. We also acquired fire perimeters for 74 large wildfires in 2020 from CAL FIRE (https://frap.fire.ca.gov/frap-projects/fire-perimeters/) and calculated dNBR for each 2020 fire using the dNBR calculation tool with Google Earth Engine (GEE). This process first selects either initial assessment or extended assessment for each fire. The initial assessment utilizes Landsat images acquired immediately after a fire to capture first-order fire effects. The extended assessment uses images obtained during the growing season following the fire to identify delayed first-order effects and dominant second-order effects (Eidenshink et al 2007). We utilized LANDFIRE Biophysical Settings (BPS) to determine which assessment type to apply for each fire burned in 2020. After Picotte et al (2021), we used extended assessment if the majority of general vegetation groups within the fire perimeter are forests, while initial assessment is used when the majority of general vegetation groups are grassland/shrubland. By contrast, MTBS uses extended assessment for forest and shrubland types. We did not delineate grasslands into burn severity categories. Instead, we classified them as burned ('grass burn') because of difficulties in assessing vegetation change. Post-fire images for extended assessment were selected during the next peak of the green season (June–September) using the mean compositing approach suggested by Parks et al (2018). Composite post-fire images acquired immediately within two months after the fire containment dates were used for the initial assessment. Composite pre-fire images for extended and initial assessments were acquired with the matching periods from the preceding year. The dNBR images were produced by quantifying the spectral difference between composite pre-fire and post-fire Landsat scenes. We calculated the unitless, continuous CBI variable from dNBR/NBR values using the linear and Sigmoid B regression models developed for the CONUS by Picotte et al (2021). CBI values were then classified following thresholds modified based on Crotteau et al(2014) into six severity classes: unburned, low severity, moderate severity, high severity, grass burn, and non-processing area. Emissions calculation Emissions of all species are calculated as a function of area burned, fuel loading, the fraction of vegetation burned based on burn severity, and an emissions factor specific to each vegetation type using the following equation modified from the FINN model (Wiedinmyer et al 2011). Fuel categories were assigned from LANDFIRE EVT products. For emissions calculations, EVT data were then categorized into five general vegetation categories: grass, shrub, forest under 5500 feet (1676 m), forest between 5500–7500 feet (1676–2286 m), and forest above 7500 feet (2286 m), updated for California ecosystems. Fuel consumption was determined following Hurteau et el 2014 assigning fuel loading and consumption values for each severity class for the five general vegetation categories based on the First Order Fire Effects Model v5 (Reinhardt et al 1997). Emission factors for greenhouse gases, particulate matter, and reactive trace gases were updated with recent data for each general vegetation class using results from recent field campaigns and studies specific for California ecosystems and Western U.S. ecosystems. Day of burning and daily emissions To assign the day of burning for individual pixels, NASA fire information for resource management system (FIRMS) active fire products from MODIS (Collection 6) within 750 m of the fire perimeter shapefiles supplied by MTBS or CAL FIRE were selected for interpolation to account for detections that might be outside the boundary due to detection radius. VIIRS 375 m data, when available since 2012, was added to complement MODIS data with improved performance to assign burn dates using the fire progression raster tool (figure 4). We filtered the MODIS/VIIRS detection points to the date range of interest and created a 500 m buffer around each point. Points were then converted to circle polygons to represent each point's detection extent properly. The average date was selected as the proper date in regions of overlapping buffers. We then calculated daily emissions and assigned them to the centroids of the aggregated daily progression polygons.

**THIS NEWER 2016 DIGITAL MAP REPLACES THE OLDER 2014 VERSION OF THE GRI FIIS Pre-Hurricane Sandy Map. The Unpublished Digital Pre-Hurricane Sandy Geomorphological Map of Fire Island National Seashore and Vicinity, New York is composed of GIS data layers and GIS tables in a 10.0 file geodatabase (fiis_pre-sandy_geology.gdb), a 10.0 ArcMap (.MXD) map document (fiis_pre-sandy_geology.mxd), and individual 10.0 layer (.LYR) files for each GIS data layer, an ancillary map information (.PDF) document (fiis_geomorphology.pdf) which contains source map unit descriptions, as well as other source map text, figures and tables, metadata in FGDC text (.TXT) and FAQ (.HTML) formats, and a GIS readme file (fiis_gis_readme.pdf). Please read the fiis_gis_readme.pdf for information pertaining to the proper extraction of the file geodatabase and other map files. To request GIS data in ESRI 10.0 shapefile format contact Stephanie O’Meara (stephanie.omeara@colostate.edu; see contact information below). The data is also available as a 2.2 KMZ/KML file for use in Google Earth, however, this format version of the map is limited in data layers presented and in access to GRI ancillary table information. Google Earth software is available for free at: http://www.google.com/earth/index.html. Users are encouraged to only use the Google Earth data for basic visualization, and to use the GIS data for any type of data analysis or investigation. The data were completed as a component of the Geologic Resources Inventory (GRI) program, a National Park Service (NPS) Inventory and Monitoring (I&M) Division funded program that is administered by the NPS Geologic Resources Division (GRD). Source geologic maps and data used to complete this GRI digital dataset were provided by the following: Rutgers University, Institute of Marine and Coastal Sciences. Detailed information concerning the sources used and their contribution the GRI product are listed in the Source Citation section(s) of this metadata record (fiis_pre-sandy_metadata_faq.html; available at http://nrdata.nps.gov/geology/gri_data/gis/fiis/fiis_pre-sandy_metadata_faq.html). Users of this data are cautioned about the locational accuracy of features within this dataset. Based on the source map scale of 1:12,000 and United States National Map Accuracy Standards features are within (horizontally) 6.1 meters or 20 feet of their actual location as presented by this dataset. Users of this data should thus not assume the location of features is exactly where they are portrayed in Google Earth, ArcGIS or other software used to display this dataset. All GIS and ancillary tables were produced as per the NPS GRI Geology-GIS Geodatabase Data Model v. 2.1. (available at: http://science.nature.nps.gov/im/inventory/geology/GeologyGISDataModel.cfm). The GIS data projection is NAD83, UTM Zone 18N, however, for the KML/KMZ format the data is projected upon export to WGS84 Geographic, the native coordinate system used by Google Earth. The data is within the area of interest of Fire Island National Seashore.

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 t..., 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., , # Leveraging wildfire to augment forest management and amplify forest resilience

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 t...,