Fire Recovery & Resilience

5 datasets

Post-fire recovery, watershed rehabilitation, community resilience, and reforestation.

Fire Recovery & Resilience datasets

Showing 1–5 of 5 datasets

Harvest Sources

CAL FIRE Fuels Reduction Projects - Main Application

This application was developed to provide public information about CAL FIRE Fuel Reduction Projects with work within the current and previous fiscal years. This item is the main application, which makes use of several supporting items in AGOL.


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The Department of Forestry and Fire Protection (CAL FIRE) collects, manages, and distributes information systematically across its Wildland Fuels Reduction programs. Projects are funded through state and federal funding mechanisms and administered by several departmental programs and collaborating partner agencies statewide.  The CAL FIRE Management Activity Project Planning & Event Reporter (CalMAPPER) is the Department’s mechanism to capture map based data about project activities. This information can then be distributed to internal or external stakeholders for purposes of planning, accountability, management, and emergency response.


What's Included in this Application?

 

The data in this application represents a static view of the CalMAPPER data as of the date indicated in the lower right corner of the application. The application data is updated monthly. 


This application only includes data for the current and previous fiscal years (as indicated on the Overview tab's headers). Fiscal Year refers to the State fiscal calendar and runs from July 1 - June 30. As such, the current fiscal year represents an incomplete reporting period, and caution should be exercised when comparing to the previous fiscal year. The first reports of the new fiscal year begin in August, which is when there is a full month of data (July) available for the new fiscal year.

 

What is included in this application:

What's not included in this application:

  • Planned activities.
  • Pest management/control, reforestation, ecological restoration, fire prevention education, and other objectives not listed above.
  • Projects planned, managed, and implemented by the Federal Government on Federal property without CAL FIRE involvement.

* Wildfire Prevention and Forest Health Grant Program and CFIP accomplishments are based on invoices received & entered in CalMAPPER. Invoices are often received a month or more after the activity occurred, resulting in a potential delay before projects and acres are represented in this application.
HTMLArcGIS GeoServices REST API
#CAL FIRE#CAOpenData#CalMAPPER

Updated Oct 7, 2026

Harvest Sources

California Seed Bank Assessment of Needs App

This app is a tool designed for individuals interested in California's reforestation efforts. Users can look up local reforestation information or select broader geographic regions. Mapped seed zone and elevation band areas are color-coded by collection priority. Priority ranges from the highest colored in red (greater than 100 cone bushels needed) to the lowest in yellow (10 bushels or less). Interactive pop-ups display zone-specific cone bushel need and surplus and natural disturbance data to non-federal conifer forests. Disturbance data includes total and high-severity acreage burned by large wildfires, acreage affected by insects and disease, and tree mortality due to such diseases. Supplementary layers include CAL FIRE timber harvesting data, demonstration state forests, and administrative boundaries, such as parcel information and land ownership.

HTMLArcGIS GeoServices REST API
#CAL FIRE#California#Cone Collection

Updated Oct 7, 2026

Harvest Sources

California Vegetation Burn Severity Data Online Viewer Web App

RdNBR is a remotely sensed index of the pre- to post-fire change in vegetation greenness, in this case the growing seasons in the year prior to and the year after the year in which the fire occurred. The mean composite scene selection method utilizes all valid pixels in all Landsat scenes over a specified date range to calculate the fire severity index. The CBI is a standardized field measure of vegetation burn severity (Key and Benson 2006), which here is predicted from a remotely sensed fire severity index using regression equations developed between CBI field plot data and the remote index, RBR (Parks et al 2019). The dataset featured provides an estimation of fire severity of past fires, with fire severity defined here as fire-induced change to vegetation. The dataset is limited to fires included in CAL FIRE’s Historic Wildland Fire Perimeters database and therefore is subject to the same limitations in terms of missing or erroneous data. 


 

This web app was developed to satisfy the requirements of Senate Bill No. 1101: An act to amend Sections 10295 and 10340 of the Public Contract Code, and to add Section 4114.4 to the Public Resources Code, relating to fire prevention.
 


 Methods:To develop these datasets, a feature service for fire perimeters was created from the CAL FIRE Fire and Resource Assessment Program’s Historic Wildland Fire Perimeters database (firep23_1) for fires or fires that were a part of complexes >= 1,000 acres from 2015 to 2023. Each proceding publication of the fire perimeter dataset was used to produce the next year's data (e.g. firep24_1 was used to produce 2024 data). This feature service is viewable on the California Vegetation Burn Severity Viewer and is used to discover the RdNBR and CBI vegetation burn severity datasets. The feature service is titled VegetationBurnSeverity_FirePerimeters. After this feature service was uploaded to Google Earth Engine (GEE) as an asset, the Parks et al. 2018 script was used to generate RdNBR values with offset (rdnbr_w_offset) data for each individual fire and the Parks et al. 2019 script was used to generate bias corrected Composite Burn Index values (cbi_bc) data for each individual fire using 30m resolution Landsat Collection 2 data. To specify the date range of Landsat satellite images to be queried to create the one-year pre-fire and one-year post-fire mean composite image scenes in both scripts, the variable 'startday' was set to 152 (June 1st) and the variable 'endday' was set to 258 (September 15th) for all fires, as specified in Parks et al. (2019). These variables were used to define the ranges of Landsat scenes that were queried to create the one-year-pre-fire and one-year-post-fire mean composite Landsat scenes. These values were used, as they were detailed as the leaf-on period for the State of California in Parks et al. 2019.  The three 2025 fires processed with this update used one-year pre-fire and same-year post-fire mean composite image scenes (i.e. June 1st to September 15th, 2025).
 Once the RdNBR raster data for each fire had been produced using Parks et al. 2018's GEE script and the CBI raster data for each fire had been produced using Parks et al. 2019's GEE script, a Python script (run in a Jupyter Notebook embedded in the ArcGIS Pro software) was used to clip each fire-specific, continuous feature class to the extent of its fire perimeter. Each CBI feature class was additionally clipped to the extent of Conifer Forest and Hardwood Forest classes (defined in FVEG15's WHR13 Lifeform class for fires from 2015 to 2021 and defined in FVEG22's WHR13 Lifeform class for fires from 2022 to 2025). Once each continuous feature class had been clipped, values were reclassified to create a discrete RdNBR and CBI feature classes. Classes for RdNBR were arbitrarily chosen and do not correspond to meaningful categories of burn severity. Higher RdNBR values do indicate greater loss of vegetation greenness and negative values indicate an increase in greenness, but there is not necessarily a direct or linear correlation between RdNBR values and impacts to vegetation or ecological effects. Remotely sensed fire severity indices are translated into CBI using regression equations developed between CBI field plot data and the remote indices. Very few CBI plots exist in California or elsewhere in the U.S. for vegetation types other than forest. We therefore chose to include only forest vegetation in our CBI dataset. 
 Classes for RdNBR were as follows: Code | Lower Limit (RdNBR) | Upper Limit (RdNBR) 1           < -1,000                           -1,000 2           -1,000                               -800 3           -800                                  -600 4           -600                                  -400 5           -400                                  -200 6           -200                                   0 7            0                                        200 8            200                                   400 9            400                                   600 10          600                                   800 11          800                                   1,000 12          1,000                                1,200 13          1,200                                1,400 14          1,400                                1,600 15          1,600                            > 1,600  Classes for CBI were as follows: Code | Lower Limit (CBI) | Upper Limit (CBI) | Burn Severity 1           0.00                           0.10                           Unburned 2           0.10                           1.25                           Low Vegetation Burn Severity 3           1.25                           2.25                           Moderate Vegetation Burn Severity 4           2.25                           3.00                           High Vegetation Burn Severity 
 The discrete raster feature classes were then converted to vector feature classes. Finally, all individual discrete vector feature classes for individual fires were merged into two vector datasets, RdNBR Burn Severity Data (VegetationBurnSeverity_RdNBR) and CBI Burn Severity Data (VegetationBurnSeverity_CBI_ConiferHardwoodForest). These feature services are viewable on this web app, the California Vegetation Burn Severity Viewer.
HTMLArcGIS GeoServices REST API
#Burn Severity#CAL FIRE#Fire Perimeters

Updated Oct 7, 2026

California Department of Conservation

Earthquake Zones of Required Investigation
The California Earthquake Hazards Zone Application ("EQ Zapp") is an onlin​e map that allows anyone with a computer, tablet or smartphone to conveniently check whether a property is in an earthquake hazard zone.
With EQ Zapp, you can type in an address or use the location capability of your computer or mobile device to determine whether a property lies within any of CGS’s mapped earthquake hazard zones.​ It will also tell you if CGS has not yet evaluated the hazards in that area.
Earthquake hazard zones define areas subject to three distinct types of geologic ground failures: 
  1. fault rupture, where the surface of the earth breaks along a fault;
  2. liquefaction, in which the soil temporarily turns to quicksand and cannot support structures; and
  3. earthquake-induced landslides.
Although strong ground shaking is responsible for most earthquake-related damage, these zones identify areas where earthquake hazards other than structural shaking — specifically ground failures during an earthquake — are more likely.​ The zones trigger geologic and engineering investigations that can identify and mitigate the ground failure hazard before construction begins, thereby making the structure itself more resilient to potential shaking.
In California, there are environmental and earthquake hazard disclosure requirements for sellers of residential real estate. EQ Zapp can be used by prospective sellers and buyers of both residential and commercial properties to learn more about potential earthquake hazards. Additionally, for new construction or significant remodels, site-specific geologic studies are required so builders can avoid known hazards or incorporate mitigation features.
The earthquake hazard zone data are also available to be viewed/downloaded as PDF maps and reports, or as geographic information system (GIS) shapefiles through the CGS Information Warehouse. You can also explore our available GIS data through the Geologic Hazards Data Viewer and Data List.
HTMLArcGIS GeoServices REST API
#alquist-priolo#california#california department of conservation

Updated Sep 4, 2026

Harvest Sources

CAL FIRE Green Schoolyards Grants Project Information App

App showing schools and childcare facilities included in California Department of Forestry and Fire Protection (CAL FIRE) Urban and Community Forestry Program's Green Schoolyards Grants. Green Schoolyards Grants are designed to protect the health, well-being, and educational opportunity of children most vulnerable to increasing temperatures and extreme heat across California. Projects shall be centered around improving the environmental conditions and experiences for school children with the highest levels of co-benefits. Projects will invest in nature-based climate solutions that deliver multiple benefits such as helping to alleviate extreme heat, improving the immediate environment for students as well as supporting outdoor learning and environmental literacy, while also reducing greenhouse gas (GHG) emissions, improving functionality of urban forests, arresting the decline of urban forest resources, increasing climate change resilience, improving the quality of the environment in urban areas, and optimizing co-benefits to school children and surrounding urban residents. Such projects shall include the planting of trees and may include converting pavement to green spaces on school campuses with a focus on child-accessible areas of campus. Projects may also include strategies such as the installment of natural features for learning and recess such as pocket forests, rain gardens, botanical gardens, natural playgrounds, food producing gardens and landscaping, outdoor classrooms as well as maintenance of planted vegetation for the duration of their expected life span. Public access is encouraged after school hours to areas identified by the campus for a specific purpose such as recreation or growing food. 


More information can be found at Urban and Community Forestry Grants Program.


California schools maintain a closed campus policy. For the safety of students, visitors are restricted from entering until receiving official permission from each school to enter a campus. Access to the data on this map in no way implies permission to enter any school campus.

HTMLArcGIS GeoServices REST API
#CAL FIRE#Urban Forestry#cal fire

Updated Oct 7, 2026