This data publication contains 250 meter raster data depicting the spatial distribution of forest ownership types in the conterminous United States. The data are a modeled representation of forest land by ownership type, and include three types of public ownership: federal, state, and local; three types of private: family (includes individuals and families), corporate, and other private (includes conservation and natural resource organizations, and unincorporated partnerships and associations); as well as Native American tribal lands. The most up-to-date data available were used in creating this data publication. A plurality of the ownership data were from 2014, but some data were as old as 2004.This record was taken from the USDA Enterprise Data Inventory that feeds into the https://data.gov catalog. Data for this record includes the following resources: ISO-19139 metadata ArcGIS Hub Dataset ArcGIS GeoService For complete information, please visit https://data.gov.

Forests are more frequently being managed to store and sequester carbon for the purposes of climate change mitigation. Generally, this practice involves long-term conservation of intact mature forests and/or reductions in the frequency and intensity of timber harvests. However, incorporating the influence of forest surface albedo often suggests that long rotation lengths may not always be optimal in mitigating climate change in forests characterized by frequent snowfall. To address this, we investigated tradeoffs between three ecosystem services: carbon storage, albedo-related radiative forcing, and timber provisioning. We calculated optimal rotation length at 498 diverse Forest Inventory and Analysis forest sites in the state of New Hampshire, USA. We found that the average optimal rotation lengths across all sites is 94 years (s = 44), with a large cluster of short optimal rotation lengths that were calculated at high elevations in the White Mountain National Forest. Using a regression tree approach, we found that timber growth, annual storage of carbon, and the difference between annual albedo in mature forest versus a post-harvest landscape were the most important variables that influenced optimal rotation. Additionally we found that the choice of a baseline albedo value for each site significantly altered the optimal rotation lengths across all sites, lowering the mean rotation to 59 years with a high albedo baseline, and increasing the mean rotation to 112 years given a low albedo baseline. Given these results, we suggest that utilizing temperate forests in New Hampshire for climate mitigation purposes through carbon storage and the cessation of harvest is appropriate at a site-dependent level that varies significantly across the state.

The US Forest Service manages 193 million acres including the nation's 154 National Forests and 20 National Grasslands. These lands provide a wide variety of recreational opportunities, protect sources of clean water, and supply timber and forage.Dataset SummaryPhenomenon Mapped: United States lands managed by the US Forest Service Coordinate System: Web Mercator Auxiliary SphereExtent: Contiguous United States, Alaska, and Puerto RicoVisible Scale: The data is visible at all scales.Source: USFS Surface Ownership Parcels layerPublication Date: February 2024This layer is a view of the USA Federal Lands layer. A filter has been used on this layer to eliminate non-Forest Service lands. For more information on layers for other agencies see the USA Federal Lands layer.What can you do with this layer? This layer is suitable for both visualization and analysis across the ArcGIS system. This layer can be combined with your data and other layers from the ArcGIS Living Atlas of the World in ArcGIS Online and ArcGIS Pro to create powerful web maps that can be used alone or in a story map or other application.Because this layer is part of the ArcGIS Living Atlas of the World it is easy to add to your map:In ArcGIS Online you can add this layer to a map by selecting Add then Browse Living Atlas Layers. A window will open. Type "forest service" in the search box and browse to the layer. Select the layer then click Add to Map.In ArcGIS Pro open a map and select Add Data from the Map Tab. Select Data at the top of the drop down menu. The Add Data dialog box will open on the left side of the box expand Portal if necessary then select Living Atlas. Type "forest service" in the search box, browse to the layer then click OK.In both ArcGIS Online and Pro you can change the layer's symbology and view its attribute table. You can filter the layer to show subsets of the data using the filter button in Online or a definition query in ProThe data can be exported to a file geodatabase, a shape file or other format and downloaded using the Export Data button on the top right of this webpage..This layer can be used as an analytic input in both Online and Pro through the Perform Analysis window Online or as an input to a geoprocessing tool, model, or Python script in Pro.The ArcGIS Living Atlas of the World provides an easy way to explore many other beautiful and authoritative maps on hundreds of topics like this one.

This layer displays change in US land cover between 2001 and 2011. Pixels that changed during this period display the land cover value that they changed to. Pixels with no change are transparent.The National Land Cover Database 2011 (NLCD 2011) is the most recent national data product created by the United States Multi-Resolution Land Characteristics (MRLC) Consortium. The MRLC is a group of federal agencies who coordinate and generate consistent and relevant land cover information at the national scale for a wide variety of environmental, land management, and modeling applications. NLCD 2011 provides - for the first time - the capability to assess wall-to-wall, spatially explicit, national land cover changes and trends across the United States from 2001 to 2011. As with two previous NLCD land cover products NLCD 2011 keeps the same 16-class land cover classification scheme that has been applied consistently across the United States at a spatial resolution of 30 meters. NLCD 2011 is based primarily on a decision-tree classification of circa 2011 Landsat satellite data.The 2001/2011 land cover change layer is one of five primary data products produced as part of the NLCD 2011: 1) NLCD 2011 Land Cover 2) NLCD 2006/2011 Land Cover Change Pixels labeled with the 2011 land cover class 3) NLCD 2011 Percent Developed Imperviousness 4) NLCD 2006/2011 Percent Developed Imperviousness Change Pixels 5) NLCD 2011 Tree Canopy Cover.Land cover class categories include forest, planted/cultivated lands, wetland, grassland, water, developed areas and barren land. Land cover information is critical for local, state, and federal managers and officials to assist them with issues such as assessing ecosystem status and health, modeling nutrient and pesticide runoff, understanding spatial patterns of biodiversity, land use planning, deriving landscape pattern metrics, and developing land management policies

This feature class depicts the boundaries of Land Survey features called sections, defined by the Public Lands Survey System Grid. Normally, 36 sections make up a township. The entire extent of each of these units should be collected, not just the portion on National Forest System lands. This dataset is derived from the USFS Southwestern Region ALP (Automated Lands Program) data Project. This is one of six layers derived from ALP for the purpose of supplying data layers for recourse GIS analysis and data needs within the Forest Service. The six layers are Surface Ownership, Administrative Forest Boundary, District Boundary, Townships, Sections, and Wilderness. There were some gapes in the ALP data so a small portion of this dataset comes from CCF (Cartographic Feature Files) datasets and the USFS Southwestern Region Core Data Project. ALP data is developed from data sources of differing accuracy, scales, and reliability. Where available it is developed from GCDB (Geographic Coordinate Data Base) data. GCDB data is maintained by the Bureau of Land Management in their State Offices. GCDB data is mostly corner data. Not all corners and not all boundaries are available in GCDB so ALP also utilizes many other data sources like CFF data to derive its boundaries. GCDB data is in a constant state of change because land corners are always getting resurveyed. The GCDB data used in this dataset represents a snapshot in time at the time the GCDB dataset was published by the BLM and may not reflect the most current GCDB dataset available. The Forest Service makes no expressed or implied warranty with respect to the character, function, or capabilities of these data. These data are intended to be used for planning and analyses purposes only and are not legally binding with regards to title or location of National Forest System lands.

A feature class describing the spatial location of the administrative boundary of the lands managed by the Forest Supervisor's office. An area encompassing all the National Forest System lands administered by an administrative unit. The area encompasses private lands, other governmental agency lands, and may contain National Forest System lands within the proclaimed boundaries of another administrative unit. All National Forest System lands fall within one and only one Administrative Forest Area. This dataset is derived from the USFS Southwestern Region ALP (Automated Lands Program) data Project. This is one of six layers derived from ALP for the purpose of supplying data layers for recourse GIS analysis and data needs within the Forest Service. The six layers are Surface Ownership, Administrative Forest Boundary, District Boundary, Townships, Sections, and Wilderness. There were some gapes in the ALP data so a small portion of this dataset comes from CCF (Cartographic Feature Files) datasets and the USFS Southwestern Region Core Data Project. ALP data is developed from data sources of differing accuracy, scales, and reliability. Where available it is developed from GCDB (Geographic Coordinate Data Base) data. GCDB data is maintained by the Bureau of Land Management in their State Offices. GCDB data is mostly corner data. Not all corners and not all boundaries are available in GCDB so ALP also utilizes many other data sources like CFF data to derive its boundaries. GCDB data is in a constant state of change because land corners are always getting resurveyed. The GCDB data used in this dataset represents a snapshot in time at the time the GCDB dataset was published by the BLM and may not reflect the most current GCDB dataset available. The Forest Service makes no expressed or implied warranty with respect to the character, function, or capabilities of these data. These data are intended to be used for planning and analyses purposes only and are not legally binding with regards to title or location of National Forest System lands.

This data set contains site characteristics, stand descriptors, and measured and calculated above-ground biomass, above-ground net primary production (ANPP), and woody detritus input data for an old Sequoia sempervirens stand at Bull Creek in Humboldt Redwoods State Park, California. There is one data file (.csv format) with this data set. Productivity of the Sequoia stand was studied via tree re-measurement (1972 and 2001) and allometric relationships. Measurements of tree circumference at 1.7 m above ground were made at the beginning and the end of the study. A 1972 stem map of the stand allowed the investigators to identify and re-measure trees >10 cm in diameter. ANPP was estimated using a range of specific gravities and several allometric relationships for tree volume. Estimation procedures were outlined by Busing and Fujimori (2005). Tree loss to mortality over the study interval was included in the analysis. Estimates of total tree ANPP ranged from 600 to 1,400 g/m2/yr. However, ANPP values in the range of 700-1,000 g/m2/yr were considered to be the most reasonable estimate because of the accuracy of the particular equations, specific gravities, and assumptions used to obtain them (Busing and Fujimori, 2005). Above-ground total tree biomass was extremely high (> 300,000 g/m2). Revision Notes: Only the documentation for this data set has been modified. The data files have been checked for accuracy and are identical to those originally published in 2005.

This dataset provides maps of aboveground forest biomass (AGB) of living trees and standing dead trees in Mg/ha across portions of Northwestern United States, including Washington, Oregon, Idaho, and Montana, at a spatial resolution of 30 m. Forest inventory data were compiled from 29 stakeholders that had overlapping lidar imagery. The collection totaled 3805 field plots with lidar imagery for 176 collections acquired between 2002 and 2016. Plot-level AGB estimates were calculated from tree measurements using the default allometric equations found in the Fire Fuels Extension (FFE) of the Forest Vegetation Simulator (FVS). The random forest algorithm was used to model AGB from lidar height and density metrics that were generated from the lidar returns within fixed-radius field plot footprints, gridded climate metrics obtained from the Climate-FVS Ready Data Server, and topographic estimates extracted from Shuttle Radar Topography Mission (SRTM) 1 Arc-Second Global elevation rasters. AGB was then mapped from the same lidar metrics gridded across the extent of the lidar collections at 30-m resolution. The standard deviation of estimated AGB of the terminal nodes from the random forest predictions was also mapped to show pixel-level model uncertainty. Note that the AGB estimates are, for the most part, a single snapshot in time and that the forest conditions are not necessarily representative of the larger study area.

CAL FIRE's Fire and Resource Assessment Program (FRAP) annually maintains and distributes an historical 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, the 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 app contains three pages of maps and documentation of the historical fire perimeter metadata:

Historical Fire Perimeters: The landing page highlights the recent large fires (≥5,000 acres) on a backdrop of all of the dataset's documented fire perimeters dating back to 1878. This map includes perimeters symbolized by decade, county boundaries, California Vegetation, and NAIP imagery back to 2005. This page provides users the ability to add their own data or filter the fire perimeter data. It cleanly lists fire perimeters shown on the map with their name, year, and GIS calculated acreage. The user can navigate to the CAL FIRE current incident webpage or provide comments to the dataset's steward.

Times Burned: The second page provides a map showing an analysis performed annually on the fire perimeter dataset to show case burn frequency from 1950 to present for fires greater than one acre.

Fire Across Time: This third page provides a time enabled layer of the fire perimeter dataset, featuring a time slider to allow users to view the perimeter dataset across time.

The final page provides the user with the dataset's metadata, including its most current data dictionary.

For any questions, please contact the data steward:

Kim Wallin, GIS Specialist

CAL FIRE, Fire & Resource Assessment Program (FRAP)

kimberly.wallin@fire.ca.gov

Data on Forest Inventory and Analysis (FIA) includes information on Palau's forests 2013-2014. The Pacific Northwest Forest Inventory and Analysis (PNW-FIA) program measures and compiles data on plots in coastal Alaska, California, Hawaii, Oregon, Washington, and U.S.- affiliated Pacific Islands. Most data are available in Access databases and can be downloaded by clicking one of the links below. PNW data are combined with data from all states in the U.S. and stored in the national FIADB. Data for any state can be accessed on the national website (see links to national tools below). Please be aware that some documents may be very large. The PNW-FIA Program shifted from a periodic to an annual inventory system in 2001. Periodic inventories sampled primarily timberland plots outside of national forests and most reserved areas, in a single state within a 2- or 3-year window. Typically, re-assessments occurred every ten years in the West. For the annual inventory in the Pacific Northwest all forested plots are now sampled, with one-tenth of the plots in any given state being visited annually. A full annual inventory cycle is complete in ten years. To download and use the FIA Database, follow this link https://www.fs.fed.us/pnw/rma/fia-topics/inventory-data

This layer contains the fire perimeters from the previous calendar year, and those dating back to 1878, for California. Perimeters are sourced from the Fire and Resource Assessment Program (FRAP) and are updated shortly after the end of each calendar year. Information below is from the FRAP web site. There is also a tile cache version of this layer.About the Perimeters in this LayerInitially CAL FIRE and the USDA Forest Service jointly developed a fire perimeter GIS layer for public and private lands throughout California. The data covered the period 1950 to 2001 and included USFS wildland fires 10 acres and greater, and CAL FIRE fires 300 acres and greater. BLM and NPS joined the effort in 2002, collecting fires 10 acres and greater. Also in 2002, CAL FIRE’s criteria expanded to include timber fires 10 acres and greater in size, brush fires 50 acres and greater in size, grass fires 300 acres and greater in size, wildland fires destroying three or more structures, and wildland fires causing $300,000 or more in damage. As of 2014, the monetary requirement was dropped and the damage requirement is 3 or more habitable structures or commercial structures.In 1989, CAL FIRE units were requested to fill in gaps in their fire perimeter data as part of the California Fire Plan. FRAP provided each unit with a preliminary map of 1950-89 fire perimeters. Unit personnel also verified the pre-1989 perimeter maps to determine if any fires were missing or should be re-mapped. Each CAL FIRE Unit then generated a list of 300+ acre fires that started since 1989 using the CAL FIRE Emergency Activity Reporting System (EARS). The CAL FIRE personnel used this list to gather post-1989 perimeter maps for digitizing. The final product is a statewide GIS layer spanning the period 1950-1999.CAL FIRE has completed inventory for the majority of its historical perimeters back to 1950. BLM fire perimeters are complete from 2002 to the present. The USFS has submitted records as far back as 1878. The NPS records date to 1921.About the ProgramFRAP compiles fire perimeters and has established an on-going fire perimeter data capture process. CAL FIRE, the United States Forest Service Region 5, the Bureau of Land Management, and the National Park Service jointly develop the fire perimeter GIS layer for public and private lands throughout California at the end of the calendar year. Upon release, the data is current as of the last calendar year.The fire perimeter database represents the most complete digital record of fire perimeters in California. However it is still incomplete in many respects. Fire perimeter database users must exercise caution to avoid inaccurate or erroneous conclusions. For more information on potential errors and their source please review the methodology section of these pages.The fire perimeters database is an Esri ArcGIS file geodatabase with three data layers (feature classes):A layer depicting wildfire perimeters from contributing agencies current as of the previous fire year;A layer depicting prescribed fires supplied from contributing agencies current as of the previous fire year;A layer representing non-prescribed fire fuel reduction projects that were initially included in the database. Fuels reduction projects that are non prescribed fire are no longer included.All three are available in this layer. Additionally, you can find related web maps, view layers set up for individual years or decades, and tile layers here.Recommended Uses There are many uses for fire perimeter data. For example, it is used on incidents to locate recently burned areas that may affect fire behavior (see map left).Other uses include:Improving fire prevention, suppression, and initial attack success.Reduce and track hazards and risks in urban interface areas.Provide information for fire ecology studies for example studying fire effects on vegetation over time. Download the Fire Perimeter GIS data hereDownload a statewide map of Fire Perimeters hereSource: Fire and Resource Assessment Program (FRAP)

This data publication contains urban tree growth data collected over a period of 14 years (1998-2012) in 17 cities from 13 states across the United States: Arizona, California, Colorado, Florida, Hawaii, Idaho, Indiana, Minnesota, New Mexico, New York, North Carolina, Oregon, and South Carolina.

Measurements were taken on over 14,000 urban street and park trees. Key information collected for each tree species includes bole and crown size, location, and age. Based on these measurements, 365 sets of allometric equations were developed for tree species from around the U.S. Each “set” consists of eight equations for each of the approximately 20 most abundant species in each of 16 climate regions. Tree age is used to predict a species diameter at breast height (dbh), and dbh is used to predict tree height, crown diameter, crown height, and leaf area. Dbh is also used to predict age. For applications with remote sensing, average crown diameter is used to predict dbh. There are 171 distinct species represented within this database. Some species grow in more than one region. The Urban Tree Database (UTD) contains foliar biomass data (raw data and summarized results from the foliar sampling for each species and region) that are fundamental to calculating leaf area, as well as tree biomass equations (compiled from literature) for carbon storage estimates. An expanded list of dry weight biomass density factors for common urban species is made available to assist users in using volumetric equations.Information on urban tree growth underpins models used to calculate effects of trees on the environment and human well-being. Maximum tree size and other growth data are used by urban forest managers, landscape architects and planners to select trees most suitable to the amount of growing space, thereby reducing costly future conflicts between trees and infrastructure. Growth data are used to develop correlations between growth and influencing factors such as site conditions and stewardship practices. Despite the importance of tree growth data to the science and practice of urban forestry, our knowledge is scant. Over a period of 14 years scientists with the U.S. Forest Service recorded data from a consistent set of measurements on over 14,000 trees in 17 U.S. cities.These data were originally published on 03/02/2016. The metadata was updated on 10/06/2016 to include reference to a new publication. Minor metadata updates were made on 12/15/2016. On 01/07/2020 this data publication was updated to correct a few species' names and systematic errors in the data that were found. A complete list of these changes is included (\Supplements\Errata_Jan2020_RDS-2016-0005.pdf). In addition, we have included a list of changes for the General Technical Report associated with these data (\Supplements\Errata_Jan2020_PNW-GTR-253.pdf).

This feature class contains data that depicts National Forest System (NFS) Lands within 37 States designated under section 602 and 603 of the Healthy Forest Restoration Act. Designated areas were selected based on a set of eligibility criteria regarding forest health and do not include any areas coinciding with Wilderness and Wilderness Study Areas. The data is comprised of selected HUC-6 units or other areas of similar size and scope clipped to Proclaimed National Forest System lands. Non-Forest Service land ownership areas (inholdings) are also removed. In some cases, entire National Forests were designated. Some state designations' methodologies may differ from the national standard.Please note that this data is current as of January 15, 2016, and changes to designated areas will be republished and archived on weekly basis. Therefore, the most current layer along with previous years' data are available, and the user should ensure that they understand which layer is being accessed. Previous years' data will have a date stamp in the file name, while the most current layer will not. Further, the attribute, Latest_Revision_Date contains the date of the most recent designation layer for each state. Metadata

Analysis of ‘Surface Drinking Water Importance - Forests on the Edge (Feature Layer)’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://catalog.data.gov/dataset/b7591774-f55d-46e4-9cfa-65310be52473 on 11 February 2022.

--- Dataset description provided by original source is as follows ---

Note: This is a large dataset. To download, go to ArcGIS Open Data Set and click the download button, and under additional resources select the shapefile or geodatabase option. America's private forests provide a vast array of public goods and services, including abundant, clean surface water. Forest loss and development can affect water quality and quantity when forests are removed and impervious surfaces, such as paved roads, spread across the landscape. We rank watersheds across the conterminous United States according to the contributions of private forest land to surface drinking water and by threats to surface water from increased housing density. Private forest land contributions to drinking water are greatest in the East but are also important in Western watersheds. Development pressures on these contributions are concentrated in the Eastern United States but are also found in the North-Central region, parts of the West and Southwest, and the Pacific Northwest; nationwide, more than 55 million acres of rural private forest land are projected to experience a substantial increase in housing density from 2000 to 2030. Planners, communities, and private landowners can use a range of strategies to maintain freshwater ecosystems, including designing housing and roads to minimize impacts on water quality, managing home sites to protect water resources, and using payment schemes and management partnerships to invest in forest stewardship on public and private lands.This data is based on the digital hydrologic unit boundary layer to the Subwatershed (12-digit) 6th level for the continental United States. To focus this analysis on watersheds with private forests, only watersheds with at least 10% forested land and more than 50 acres of private forest were analyzed. All other watersheds were labeled ?Insufficient private forest for this analysis'and coded -99999 in the data table. This dataset updates forest and development statistics reported in the the 2011 Forests to Faucet analysis using 2006 National Land Cover Database for the Conterminous United States, Grid Values=41,42,43,95. and Theobald, Dr. David M. 10 March 2008. bhc2000 and bhc2030 (Housing density for the coterminous US in 2000 and 2030, respectively.) Field Descriptions:HUC_12: Twelve Digit Hydrologic Unit Code: This field provides a unique 12-digit code for each subwatershed.HU_12_DS: Sixth Level Downstream Hydrologic Unit Code: This field was populated with the 12-digit code of the 6th level hydrologic unit that is receiving the majority of the flow from the subwatershed.IMP1: Index of surface drinking water importance (Appendix Map). This field is from the 2011 Forests to Faucet analysis and has not been updated for this analysis.HDCHG_AC: Acres of housing density change on private forest in the subwatershed. HDCHG_PER: Percent of the watershed to experience housing density change on private forest. IMP_HD_PFOR: Index Private Forest importance to Surface Drinking Water with Development Pressure - identifies private forested areas important for surface drinking water that are likely to be affected by future increases in housing density, Ptle_IMP_HD: Private Forest importance to Surface Drinking Water with Development Pressure (Figure 7), percentile. Ptle_HDCHG: Percentage of each subwatershed to Experience an increase in House Density in Private Forest (Figure 6), percentile. FOR_AC: Acres forest (2006) in the subwatershed. PFOR_AC: Acres private forest (2006) in the subwatershed. PFOR_PER: Percent of the subwatershed that is private forest. HU12_AC: Acreage of the subwatershedFOR_PER: Percent of the subwatershed that is forest. PFOR_IMP: Index of Private Forest Importance to Surface Drinking Water. .Ptle_PFIMP: Private forest importance to surface drinking water(Figure 4), percentile. TOP100: Top 100 subwatersheds. 50 from the East, 50 from the west (using the Mississippi River as the divide.) Metadata

--- Original source retains full ownership of the source dataset ---

An accurate depiction of the spatial distribution of habitat types within California is required for a variety of legislatively-mandated government functions. The California Department of Forestry and Fire Protection's CALFIRE Fire and Resource Assessment Program (FRAP), in cooperation with California Department of Fish and Wildlife VegCamp program and extensive use of USDA Forest Service Region 5 Remote Sensing Laboratory (RSL) data, has compiled the "best available" land cover data available for California into a single comprehensive statewide data set. The data span a period from approximately 1990+. Typically the most current, detailed and consistent data were collected for various regions of the state. Decision rules were developed that controlled which layers were given priority in areas of overlap. Cross-walks were used to compile the various sources into the common classification scheme, the California Wildlife Habitat Relationships (CWHR) system. This service depicts the WHR13 Type from the fveg dataset (with Wildlife Habitat Relationship classes grouped into 13 major land cover types).The full dataset can be downloaded in raster format here: GIS Mapping and Data Analytics | CAL FIREThe service represents the latest release of the data, and is updated when a new version is released. Currently it represents fveg15_1.

The State of Vermont has a long history of acquiring properties for conservation and recreation purposes. Since the first official state forest (L.R. Jones State Forest) was acquired in 1909, the State has acquired over 345,000 acres of land in more than 200 towns across the state. In addition, the Agency has recently acquired conservation easements on over 44,000 acres of privately-owned forest land. These diverse holdings are managed by the Agency of Natural Resources and include state parks, state forests, wildlife management areas, and fishing access areas, pond sites, streambanks, fish culture stations, dams, and other miscellaneous properties.

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 2024, it represents fire23_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:

Firep23_1 was released in May 2024. Two hundred eighty four fires from the 2023 fire season were added to the database (21 from BLM, 102 from CAL FIRE, 72 from Contract Counties, 19 from LRA, 9 from NPS, 57 from USFS and 4 from USFW). The 2020 Cottonwood fire, 2021 Lone Rock and Union fires, as well as the 2022 Lost Lake fire were added. USFW submitted a higher accuracy perimeter to replace the 2022 River perimeter. Additionally, 48 perimeters were digitized from an historical map included in a publication from Weeks, d. et al. The Utilization of El Dorado County Land. May 1934, Bulletin 572. University of California, Berkeley. Two thousand eighteen perimeters had attributes updated, the bulk of which had IRWIN IDs added. A duplicate 2020 Erbes perimeter was removed. The following fires were identified as meeting our collection criteria, but are not included in this version and will hopefully be added in the next update: Big Hill #2 (2023-CAHIA-001020).

YEAR_ field changed to a short integer type. San Diego CAL FIRE UNIT_ID changed to SDU (the former code MVU is maintained in the UNIT_ID domains). COMPLEX_INCNUM renamed to COMPLEX_ID and is in process of transitioning from local incident number to the complex IRWIN ID. Perimeters managed in a complex in 2023 are added with the complex IRWIN ID. Those previously added will transition to complex IRWIN IDs in a future update.

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 (2019-2023), 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-present. Symbolized by decade, and display starting at country level scale.

Detailed metadata is included in the following documents:

Wildland Fire Perimeters (Firep23_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

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 2024, it represents fire23_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:

Firep23_1 was released in May 2024. Two hundred eighty four fires from the 2023 fire season were added to the database (21 from BLM, 102 from CAL FIRE, 72 from Contract Counties, 19 from LRA, 9 from NPS, 57 from USFS and 4 from USFW). The 2020 Cottonwood fire, 2021 Lone Rock and Union fires, as well as the 2022 Lost Lake fire were added. USFW submitted a higher accuracy perimeter to replace the 2022 River perimeter. Additionally, 48 perimeters were digitized from an historical map included in a publication from Weeks, d. et al. The Utilization of El Dorado County Land. May 1934, Bulletin 572. University of California, Berkeley. Two thousand eighteen perimeters had attributes updated, the bulk of which had IRWIN IDs added. A duplicate 2020 Erbes perimeter was removed. The following fires were identified as meeting our collection criteria, but are not included in this version and will hopefully be added in the next update: Big Hill #2 (2023-CAHIA-001020).

YEAR_ field changed to a short integer type. San Diego CAL FIRE UNIT_ID changed to SDU (the former code MVU is maintained in the UNIT_ID domains). COMPLEX_INCNUM renamed to COMPLEX_ID and is in process of transitioning from local incident number to the complex IRWIN ID. Perimeters managed in a complex in 2023 are added with the complex IRWIN ID. Those previously added will transition to complex IRWIN IDs in a future update.

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 (2019-2023), 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-present. Symbolized by decade, and display starting at country level scale.

Detailed metadata is included in the following documents:

Wildland Fire Perimeters (Firep23_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