OPEN Data View service. The Wildland Fire Risk Assessment project was developed by the National Park Service's Fire and Aviation Management program as a response to the devastating 2011 wildfire season. This project developed a consistent assessment method that has been applied to NPS units nationwide regardless of variations in climate, fuels, and topography.The assessment, based on Firewise® assessment forms, evaluates access, surrounding environment, construction design and materials, and resources available to protect facilities from wildland fire. The data collected during the assessment process can be used for:Identifying, planning, prioritizing and tracking fuels treatments at unit, regional and national levels, and Developing incident response plans for facilities and communities within NPS units.The original spatial data for the assessments comes from a variety of sources including the NPS Buildings Enterprise Dataset, WFDSS, NPMap Edits, manually digitized points using Esri basemaps as a reference at various scales, and GPS collection using a multitude of consumer and professional grade GPS devices. The facilities that have been assessed and assigned a facility risk rating have been ground-truthed and field verified. (In some rare occasions, facilities have been verified during remote assessments. Those that have been remotely assessed are marked as such). The resulting data is stored in a centralized geodatabase, and this publicly available feature layer allows the user to view that data.The NPS Facilities feature layer includes the following layers and related tables:Facility - A facility is defined by the NPS as an asset that the NPS desires to track and manage as a distinct identifiable entity. In the case of wildland fire risk assessments, a facility is most often a structure but in special instances, a park unit may wish to identify and assess other at-risk features such as a historic wooden bridge or an interpretive display. The facilities are assessed based on access, the surrounding environment, construction design, and protection resources and limitations, resulting in a numerical score and risk adjective rating for each facility. These ratings designate the likelihood of ignition during a wildland fire. The facilities are symbolized by their respective risk rating.Community - A community is a group of five or more facilities, a majority of which are within 600 feet of each other, that share common access and protection attributes. The community concept was developed to facilitate data collection and entry in areas with multiple facilities and where it made sense to apply treatments and tactics at a scale larger than individual facilities. Most of the community polygons are created using models in ArcMap, but some may have been created or edited in the field using a Trimble GPS unit. *The NPS Facilities layer is updated continually as new wildfire risk assessments are conducted and the Wildland Fire Risk Assessment project progresses. The assessment data contained here is the most current data available.*More information about the NPS Wildland Fire Risk Assessment Project, and the NPS Facilities data itself, can be found at the New Wildland Fire Risk Assessments website. This site provides information on the data collection process, additional ways to access the data, and how to conduct assessments yourself (for both NPS and non-NPS facilities).FACILITY ATTRIBUTES
Unit_ID NWCG Unit ID, Two letter state code and three letter unit abbreviation, for example UTZIP for Zion National Park in Utah.

Fire_Bldg_ID User maintained unique ID for Facility layer.

Building ID Unique Id from the NPS Enterprise Buildings dataset.

FMSS ID Unique ID for the facility in the NPS FMSS database.

Community ID Unique ID linking facility to a community

Assess Scale Indicates if the facility is part of a community/ will be included in a community assessment. Communities are pre-defined by regional GIS staff and visible in this map as a blue perimeter.
Answer "Yes" if you are adding a facility point within a predefined community.

Common Name Name of the structure. In most cases, the name comes from the NPS FMSS database.

Map Label Numerical label used for mapping purposes.

Owner Indicates who owns the structure being assessed.

Facilty Type Indicates the facility type OR if the facility has been REMOVED, DESTROYED, has NO WILDLAND RISK, is PRIVATE - NO SURVEY REQUIRED or DOES NOT REQUIRE A SURVEY (because it is planned for removal).

Facility Use What is the primary use of the facility?

Building Occupied Is the building occupied?

Community Name Name of the community the facility is located within, if any.

Field Crew Field crew completing the assessment.

Last Site Visit Date Date which the facility was visited and assessment data reviewed/updated.

Location General location within the unit – may use FMUs, watersheds, or other identifier. One location may contain multiple communities and individual facilities. Locations are used to filter data for reports and map products.

PrimaryAccess Primary method of accessing the facility.

IngressEgress Number of routes into and away from the facility.

AccessWidth Width of the road or driveway used to access the facility.

AccessCond Grade and surface material of the road or driveway used to access the facility.

BridgeCond Condition, based on load limits and construction.

Turnaround Describes how close can a fire apparatus drive to the facility and once there, whether it can turnaround.

BldgNum Is the facility clearly signed or numbered?

FuelLoad Fuel loading within 300 ft of the facility (see appendix D of the Wildfire Risk Assessment User Guide)

FuelType Predominant fuel type within 300 ft of the facility.

DefensibleSpace Amount of defensible space around the facility, see criteria for evaluating defensible space in the Wildfire Risk Assessment User Guide.

Topography Predominant slope within 300 ft of facility.

RoofMat Roofing material used on the facility.

SidingMat Siding material used on the facility.

Foundation Describes the facility’s foundation.

Fencing Indicates presence of any wooden attachments, fencing, decking, pergola, etc. and fuels clearance around those attachments.

Firewood Firewood distance from facility.

Propane Inidicates if a propane tank exists within 200 feet of a structure and if there is any fuels clearance around the propane tank(s).

Hazmat List of hazmat existing on the site.

WaterSupply Water supply available to the facility.

OverheadHaz Identifies the presence of overhead hazards that will limit aerial firefighting efforts.

SafetyZone Identifies the presence of any potential safety zones.

SZRadius Radius of any potential safety zones.

Obstacles Additional obstacles, not already included in assessment, that will limit firefighting efforts- to include items such as UXO, hazmat,etc. If there are additional obstacles, be sure to comment in Assessment Comments or Tactic descriptions where appropriate.

TriageCategory Refer to IRPG for descriptions of each category. This information will be displayed in the NIFS Structure Triage layer for incident response.

Score Sum of attribute values for all assessment elements including access, environment, structure and protection portions of the assessment.

Rating Wildland fire risk rating based on score. Ratings are No Wildland Risk, Low, Moderate and High. Rating indicates likelihood if facility igniting if a wildland fire occurs.

ProtectionLevel Inidcates structures which are priority for protection during a wildfire. For Alaska Region data, indicates identified protection level for structure. For lower 48, enter ‘Unknown’ unless specified by local unit.

ProtLevelApprovalName Name of person who designated Protection Level

ProtLevelApprovalDate Date Protection Level Designated

ResourcesOfConcern Indicates if it is necessary to contact park staff before engaging in suppression activities because special resources (natural, cultural, historic) of concern are present?

AssessComments Explain any aspects of the assessment that require extra detail.

RegionCode NPS Region Code - AKR, IMR, NER, NCR, MWR, PWR or SER

UnitCode

NPS Unit Code

ReasonIncluded Why is the point in the dataset – NPS owned, Treatment Planning, Protection Responsibility, Planning (other than treatments). Intent of the dataset is to document wildfire risk for NPS owned structures. Other structures or facilities may be included at the discretion of the unit's fire management staff.

Restriction How can the data be shared – Unrestricted, Restricted - No Third Party Release, Restricted – Originating Agency Concurrence, Restricted – Affected Cultural Group Concurrence, Restricted - No Release, Unknown. Only unrestricted data is included in this dataset.

Local_ID Field which can be used to store unique ids linking back to any local datasets.

RevisitInterval How many years will it take for the fuels to change significantly enough to change the score and rating for this facility?

IsVisited Use this field to keep track of what you have done during a field session. Filter on this field to see what has been assessed and what still needs visited during a field data collection session.

DeleteThis Users enter yes if this is this a duplicate or was no facility found.
If you know the facility was REMOVED or DESTROYED, go back to Facility Type and enter that information there.

Data_Source

FirewiseZone1 List of treatments needed to

The wildland fire potential layer provides information on the relative potential for wildfire that would be difficult for fire crews to contain. Areas with higher wildland fire potential values represent fuels with a higher likelihood of experiencing high-intensity fire with torching, crowning, and other forms of extreme fire behavior.Dataset SummaryThis layer provides access to a 270m cell size raster derived from the Large Fire Simulation System (LFSim) produced as part of the Fire Program Analysis System by the USDA Forest Service’s Fire Modeling Institute. The data covers the contiguous U.S.The layer is useful for analyses of wildfire risk, hazardous fuels prioritization and strategic planning across large landscapes (hundreds of square miles) up through regional and national scales. When paired with spatial data depicting highly valued resources, land managers can use these data to create value-specific risk maps. Examples of published research using these data include:Integrated national-scale assessment of wildfire risk to human and ecological valuesA simulation of probabilistic wildfire risk components for the continental United StatesThis layer is derived from the USA Wildland Fire Potential service produced by the US Forest Service.

The current dataset is appropriate for displaying the overall pattern of WUI development at the county level, and comparing counties in terms of development patterns. Until the dataset is refined through a field review and quality assurance process, it is not suited for WUI designations for individual houses or neighborhoods.The data can be downloaded as a file geodatabase here: GIS Mapping and Data Analytics | CAL FIREThree WUI classes are mapped:Wildland Urban Interface – dense housing adjacent to vegetation that can burn in a wildfire,Wildland Urban Intermix - housing development interspersed in an area dominated by wildland vegetation subject to wildfire, and Wildfire Influence Zone - wildfire susceptible vegetation up to 1.5 miles from Wildland Urban Interface or Wildland Urban Intermix. Wildland Urban Interface, Wildland Urban Intermix, and Wildfire Influence Zones. The model uses residential structure density and vegetative cover to define areas within the Fire Hazard Severity Zones. Primary Data Inputs:Fire Hazard Severity Zones (FHSZ_Assessment25_1)Housing Unit Density (HousingUnit_Density2020_DEN4)Secondary Data Inputs:(used to determine vegetation dominance)State Wildland Zones (FHSZ_State_Wildland_Zones_v17_1)Canopy Cover (CanopyCoverSALO2020)Fire Hazard Severity Zones: This source raster dataset represents Fire Management Analysis Zones as adopted officially on April 1, 2024 for State Responsibility Area (SRA) and as distributed to local governments in February and March of 2025 for Local Responsibility Areas (LRA). The source data for the LRA release also contains FHSZ coverage for Federal Responsibility Areas, but these data are used to fill out the state for assessment purposes only. Data sources: FHSZALL_v25_1 (SRA Approved and LRA Recommended) Statewide_v17_4 (Federal Responsibility Areas)Housing unit density classes for California derived from 30-m rasters extracted from Wildfire Risk to Communities: Spatial datasets of wildfire risk for populated areas in the United States and reprojected to California Teale Albers NAD87. DEN4 Description Less than 1 Housing Unit per 20 acres1 Housing Unit per 20 acres to 1 Housing Unit per 5 acresMore than 1 Housing Unit per 5 acres to 1 Housing Unit per acre More than 1 Housing Unit per acre After classifying, clusters of DEN4 values less than 80 cells (just under 18 acres) were nibbled to the nearest adjacent DEN4 class.State Wildland Zones and Canopy Cover: State Wildland Zones are used to determine the dominance of vegetation. Areas which would otherwise be classed as Wildland Urban Interface.area reclassed to Intermix if the vegetation cover is 50% or more. These canopy cover data are used in concert with SALO Canopy Cover to determine vegetation dominance in areas both within and outside of the the extent of the wildland zones.State Wildland Zones: State wildland zones are determined by the attribute flame class, which was derived as part of fire hazard modeling used in the determination of Fire Hazard Severity Zones and represent areas where wildland fire behavior can be assessed using common fire behavior tools. It is derived from fuel model attributes slope, and local fire weather conditions as processed through the NEXUS Fire Behavior platform, and reflects flame front characterization of intensity (flame length) that was then aggregated to fire environment polygons, averaged across the polygon area, and finally classified nominally according to quantile distributions with some threshold adjustments to reflect realistic class breaks for marginal areas of widely accepted hazard levels. All wildland zones are 50% or more vegetated.Canopy Cover: The horizontal cover fraction occupied by tree canopies. 2020 SALO Canopy cover data was downloaded for all California counties from here: https://forestobservatory.com/ on 5/17/2022 and mosaiced into one statewide dataset, reprojected from UTM 10 to Teale Albers NAD83 and resampled to 30m. Note: Vegetation dominance is determined as either FHSZ Wildland Zone 1-3 or SALO > 50% cover. A 3X3 cell circular focal mean is applied and areas with 0.5 or greater are considered at least 50% vegetated.-----------------------------------------------------------------These data are analyzed according to the following parameters:Interface:DEN4 Class 3 or 4 In Moderate, High, or Very High Fire Hazard Severity ZoneLess than 50% vegetation coverSpatially contiguous groups of 30m cells that are approximately 20 acres in size or largerIntermix:DEN4 Class 2 or 3In Moderate, High or Very High Fire Hazard Severity ZoneReclassed Interface:Interface cell groups less than 20 acresInterface that is 50% or more vegetated and in spatially contiguous groups of 30m cells that are at least 20 acres in size Intermix is spatially contiguous groups of 30m cells approximately 100 acres in size or largerInterface and Intermix are then combined. After combining, any cell group with fewer than 80 cells is classed to the value of its neighbor.Influence Zone:Up to 1.5 miles from Interface or IntermixIn Moderate, High, or Very High Fire Hazard Severity ZoneNot Interface or IntermixInterface, Intermix and Influence Zones are then combined. After combining, any cell group with fewer than 80 cells is classed to the value of its neighbor.A final step in the model addressed an inadvertent error invoked by the processing of potential interface conversion to Intermix for small fragments (<20 acres of Interface) and larger areas of Interface that were covered by a majority (>50%) vegetation within the areas otherwise defined as Interface because of meeting housing unit density and hazard requirements. When these lands were then subject to the final size minimum of 100 acres they then reverted to being potential buffer areas. This was remedied by selecting all lands that met the criteria of DEN4 values 3 and 4 (i.e.,all areas with housing density greater than 1 HU/ac) and reverting them to Interface designation. These previously eliminated but now reverted Intermix areas did not meet the 100 acre requirement and did not produce additional buffer zone influence areas from them. In most cases they are sufficiently embedded within Influence Zone buffers to be consistent with map objectives describing the land in terms of exposure and opportunity for community protection and risk mitigation.

The data included in this publication depict components of wildfire risk specifically for populated areas in the United States. These datasets represent where people live in the United States and the in situ risk from wildfire, i.e., the risk at the location where the adverse effects take place.National wildfire hazard datasets of annual burn probability and fire intensity, generated by the USDA Forest Service, Rocky Mountain Research 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). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we 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 data products in this publication that represent where people live, reflect 2021 estimates of housing unit and population counts from the U.S. Census Bureau, combined with building footprint data from Onegeo and USA Structures, both reflecting 2022 conditions.The specific raster datasets included in this publication include:Building Count: Building Count is a 30-m raster representing the count of buildings in the building footprint dataset located within each 30-m pixel.Building Density: Building Density is a 30-m raster representing the density of buildings in the building footprint dataset (buildings per square kilometer [km²]).Building Coverage: Building Coverage is a 30-m raster depicting the percentage of habitable land area covered by building footprints.Population Count (PopCount): PopCount is a 30-m raster with pixel values representing residential population count (persons) in each pixel.Population Density (PopDen): PopDen is a 30-m raster of residential population density (people/km²).Housing Unit Count (HUCount): HUCount is a 30-m raster representing the number of housing units in each pixel.Housing Unit Density (HUDen): HUDen is a 30-m raster of housing-unit density (housing units/km²).Housing Unit Exposure (HUExposure): HUExposure is a 30-m raster that represents the expected number of housing units within a pixel potentially exposed to wildfire in a year. This is a long-term annual average and not intended to represent the actual number of housing units exposed in any specific year.Housing Unit Impact (HUImpact): HUImpact is a 30-m raster that represents the relative potential impact of fire to housing units at any pixel, if a fire were to occur. It is an index that incorporates the general consequences of fire on a home as a function of fire intensity and uses flame length probabilities from wildfire modeling to capture likely intensity of fire.Housing Unit Risk (HURisk): HURisk is a 30-m raster that integrates all four primary elements of wildfire risk - likelihood, intensity, susceptibility, and exposure - on pixels where housing unit density is greater than zero.Additional methodology documentation is provided with the data publication download. (https://www.fs.usda.gov/rds/archive/catalog/RDS-2020-0060-2).Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.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.

National burn probability (BP) and conditional fire intensity level (FIL) data were generated for the conterminous United States (US) using a geospatial Fire Simulation (FSim) system developed by the US Forest Service Missoula Fire Sciences Laboratory to estimate probabilistic components of wildfire risk (Finney et al. [2011]). The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current landscape conditions and fire management practices. The data presented here represent modeled BP and FIL for the conterminous US at a 270-meter grid spatial resolution. The six FILs correspond to flame-length classes as follows: FIL1 = < 2 feet (ft); FIL2 = 2 < 4 ft.; FIL3 = 4 < 6 ft.; FIL4 = 6 < 8 ft.; FIL5 = 8 < 12 ft.; FIL6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FIL*_20160830 data must be used in conjunction with the BP_20160830 data for risk assessment.

GIS for Wildfire Analytics Market Outlook

According to our latest research, the Global GIS for Wildfire Analytics market size was valued at $1.2 billion in 2024 and is projected to reach $3.9 billion by 2033, expanding at a CAGR of 14.2% during 2024–2033. The primary driver for this robust growth is the escalating frequency and severity of wildfires globally, which has compelled governments, environmental agencies, and private organizations to invest heavily in advanced Geographic Information System (GIS) solutions for wildfire detection, risk assessment, and resource management. The increasing integration of remote sensing, artificial intelligence, and real-time data analytics into GIS platforms is further accelerating adoption, offering unparalleled situational awareness and decision-making capabilities to stakeholders involved in wildfire management and mitigation.

Regional Outlook

North America currently holds the largest share of the GIS for Wildfire Analytics market, accounting for over 42% of global revenue in 2024. This dominance is attributed to the region’s mature technology ecosystem, high incidence of wildfires in the US and Canada, and proactive government policies that mandate the use of advanced analytics and GIS for disaster management. The United States, in particular, has made significant investments in wildfire prevention and response infrastructure, leveraging GIS technologies for real-time fire detection, predictive modeling, and post-event analysis. The presence of leading GIS software vendors, such as Esri and Trimble, further strengthens the regional market, enabling rapid deployment of cutting-edge solutions and fostering continuous innovation through public-private partnerships.

The Asia Pacific region is poised to be the fastest-growing market, projected to register a CAGR of 17.8% through 2033. Rapid urbanization, increasing forest cover, and heightened vulnerability to climate-induced wildfires are driving substantial investments in GIS-enabled wildfire analytics across countries like Australia, China, and India. Governments and forestry departments are actively collaborating with international technology providers to implement large-scale wildfire monitoring systems, supported by significant funding and policy reforms. Australia, in particular, has emerged as a leader in deploying satellite-based GIS platforms for early fire detection and real-time response coordination, following devastating wildfire seasons. The region’s burgeoning tech sector and growing awareness of environmental risks are expected to sustain high growth rates over the forecast period.

In emerging economies such as those in Latin America, the Middle East, and Africa, adoption of GIS for wildfire analytics is gaining momentum but remains constrained by limited infrastructure, funding challenges, and varying levels of regulatory support. While countries like Brazil and South Africa have begun integrating GIS technologies into their national disaster management frameworks, widespread deployment is hindered by budgetary constraints and a shortage of skilled personnel. Nevertheless, international aid programs and partnerships with global technology providers are helping to bridge these gaps, enabling localized pilot projects and capacity-building initiatives. As awareness of the economic and environmental impacts of wildfires grows, these regions are expected to gradually increase their adoption of advanced GIS solutions, albeit at a slower pace compared to developed markets.

Report Scope

This data provides information on forest fire spread risk areas in Chungju, North Chungcheong Province, developed through the 2022 Public Data Enterprise Matching Support Project. It was created to proactively identify areas at high risk of forest fire spread and to facilitate disaster response strategies and prevention initiatives. It includes risk assessment results for each region. The data consists of administrative numbers, townships, and districts, road addresses, latitude and longitude coordinates, forest fire spread scores (risk), and ratings. These ratings are based on the scores and indicate relative risk levels. Risk scores are calculated by considering factors that influence forest fire spread, such as wind speed, topography, vegetation, and nearby access roads. These ratings are typically categorized as 1 to 5 or high/low risk. This data can be utilized for various disaster management purposes, such as prioritizing forest fire prevention by region, prioritizing disaster management budget allocation, securing firefighting response bases, and providing guidance to residents in forest-adjacent areas. Latitude and longitude information can be utilized for GIS-based spatial information analysis and visualization, and is also useful as a reference for developing real-time risk prediction models and planning forestry policies.

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)

Perimeter Fire Risk Mapping via Satellite Market Outlook

According to our latest research, the global Perimeter Fire Risk Mapping via Satellite market size reached USD 1.47 billion in 2024, reflecting robust growth driven by increasing wildfire incidents and the expanding need for real-time monitoring solutions. The market is projected to grow at a CAGR of 12.3% during the forecast period, reaching approximately USD 4.18 billion by 2033. This impressive growth trajectory is underpinned by heightened adoption of satellite-based technologies, rising investments in disaster management infrastructure, and a growing emphasis on environmental sustainability worldwide.

A significant growth driver for the Perimeter Fire Risk Mapping via Satellite market is the alarming increase in wildfire frequency and intensity across various continents, particularly in North America, Australia, and Southern Europe. Climate change has exacerbated drought conditions and elevated temperatures, creating an environment where wildfires can ignite and spread rapidly. As a result, governments, forestry agencies, and private sector stakeholders are investing heavily in advanced fire detection and risk assessment solutions. Satellite-based perimeter mapping provides crucial, timely data that enables proactive wildfire management, resource allocation, and rapid response, making it an indispensable tool in the fight against catastrophic fire events.

Technological advancements in satellite imaging, artificial intelligence, and geospatial analytics are further propelling the market forward. Modern satellites equipped with high-resolution sensors and thermal imaging capabilities can detect even small-scale fire outbreaks and monitor vast, remote regions with unparalleled accuracy. The integration of AI-driven analytics with satellite data allows for predictive modeling and risk mapping, empowering stakeholders to anticipate fire spread patterns and prioritize at-risk areas. These innovations are not only enhancing the precision of fire risk mapping but also reducing operational costs and response times, thereby expanding the market’s appeal to a broader range of end-users.

Another vital growth factor is the increasing regulatory emphasis on disaster preparedness and environmental conservation. International agencies, national governments, and local authorities are mandating the adoption of advanced fire risk assessment and monitoring systems as part of comprehensive disaster management strategies. Additionally, insurance companies are leveraging satellite-derived data to evaluate fire risk exposure and optimize policy pricing, contributing to the market’s expansion. The synergy between regulatory requirements, insurance risk management, and technological innovation is fostering a dynamic ecosystem where satellite-based fire risk mapping is rapidly becoming a standard practice.

From a regional perspective, North America currently dominates the global market, accounting for the largest revenue share in 2024 due to its vast forested areas, frequent wildfire incidents, and substantial investments in space and remote sensing technologies. Europe follows closely, driven by stringent environmental regulations and cross-border wildfire management initiatives. Meanwhile, the Asia Pacific region is witnessing the fastest growth, fueled by rising awareness, expanding government budgets for disaster management, and the increasing vulnerability of densely populated regions to wildfires. Latin America and the Middle East & Africa are also emerging as significant markets, supported by international aid and collaborative projects aimed at enhancing fire risk monitoring infrastructure.

Solution Analysis

The Perimeter Fire Risk Mapping via Satellite market is segmented by solution into Software, Hardware, and Services, each playing a pivotal role in the overall ecosystem. The software segment encompasses fire risk modeling platforms, geospatial analytics tools, and AI-powered dashboards that process satellite data to generate actionable insights. These solutions are increasingly cloud-based, offering scalability, interoperability, and real-time collaboration for diverse stakeholders. Software providers are focusing on user-friendly interfaces, integration with existing GIS systems, and advanced visualization features, ensuring that even non-technical users can derive value from complex satellite datasets.

Wildfire Risk Mapping Software Market Outlook

According to our latest research, the wildfire risk mapping software market size reached USD 1.42 billion in 2024 globally. Demonstrating robust momentum, the market is expected to expand at a CAGR of 15.7% from 2025 to 2033, ultimately achieving a forecasted value of USD 5.3 billion by 2033. This rapid growth is primarily driven by the increasing frequency and severity of wildfires worldwide, escalating demand for advanced risk assessment tools, and the integration of artificial intelligence and geospatial analytics into wildfire management systems. As per our latest research, the wildfire risk mapping software market is poised to transform how governments, insurance companies, utilities, and forestry organizations prepare for and respond to wildfire threats over the coming decade.

One of the most significant growth drivers for the wildfire risk mapping software market is the alarming rise in wildfire incidents globally. Climate change has led to prolonged droughts, higher temperatures, and unpredictable weather patterns, all of which contribute to more frequent and intense wildfires. These environmental changes have put immense pressure on governments and organizations to adopt sophisticated risk management technologies. Wildfire risk mapping software leverages advanced data analytics, satellite imagery, and predictive modeling to provide real-time risk assessments, helping stakeholders make informed decisions regarding resource allocation, evacuation planning, and post-fire recovery. As wildfires increasingly threaten both rural and urban areas, the adoption of these technologies is seen as not just beneficial, but essential for public safety and environmental preservation.

Another crucial factor propelling market growth is the surge in regulatory requirements and insurance industry demands. Governments worldwide are implementing stricter mandates for wildfire risk assessment and mitigation, particularly in regions prone to catastrophic fires such as California, Australia, and Southern Europe. Insurance companies are also leveraging wildfire risk mapping software to accurately price policies, assess liabilities, and minimize financial losses. The ability of these solutions to integrate with Geographic Information Systems (GIS), weather forecasting, and historical fire data enables insurers and policymakers to proactively identify high-risk zones and implement preventive measures. This regulatory and commercial push is significantly accelerating the adoption of wildfire risk mapping software across a diverse range of end-users.

Technological advancements play a pivotal role in shaping the wildfire risk mapping software market. The integration of artificial intelligence, machine learning, and cloud-based platforms has revolutionized the capabilities of these solutions. Modern wildfire risk mapping software can now process vast datasets in real time, offering predictive insights and automated alerts to stakeholders. The growing adoption of drones and remote sensing technologies further enhances the accuracy and granularity of risk assessments. Moreover, the increasing availability of open-source geospatial data and APIs is fostering innovation and reducing entry barriers for new market entrants. These technological trends are expected to continue driving market expansion, as organizations seek more efficient and scalable solutions for wildfire risk management.

From a regional perspective, North America dominates the global wildfire risk mapping software market, accounting for the largest share in 2024. The region's leadership is attributed to frequent wildfire incidents in the United States and Canada, significant government investments in disaster management infrastructure, and a mature insurance sector. Europe follows closely, with growing adoption in Mediterranean countries and the implementation of EU-wide wildfire prevention policies. The Asia Pacific region is witnessing the fastest growth, driven by increasing wildfire activity in Australia, Southeast Asia, and parts of China, coupled with rising awareness about environmental sustainability. Latin America and the Middle East & Africa are also emerging as promising markets, fueled by expanding forestry management initiatives and international cooperation on wildfire mitigation.

Component Analysis

The component segment of the wildfire risk mapping software market is bifurcated into software and se

Protecting Forests and Communities from Wildfire Risk (FIGURE 1b-3) The Protecting Forests and Communities from Wildfire Risk map shows areas of North Carolina where wildfire mitigation and preparedness efforts can reduce loss of life and property, and prevent degradation of the forest resource due to intense fires typical of southern forests. These lands rank high for wildfire susceptibility in the Southern Wildfire Risk Assessment System (ArcGIS software). Many of these areas are considered to be within the wildland-urban interface, and many are owned by individuals who may be unfamiliar with the role of fire in southern forests and firewise building principles.

Note: This image service has been consolidated into a unified service that now includes Alaska (AK), the Continental United States (CONUS), and Hawaii (HI). For access to the updated and combined image service, please visit the new item page: https://usfs.maps.arcgis.com/home/item.html?id=ba507f07fa4e4ea1b512d915db75dd13.National data on burn probability (BP) and conditional flame-length probability (FLP) were generated for the conterminous United States (CONUS), Alaska, and Hawaii using a geospatial Fire Simulation (FSim) system developed by the USDA Forest Service Missoula Fire Sciences Laboratory. The FSim system includes modules for weather generation, wildfire occurrence, fire growth, and fire suppression. FSim is designed to simulate the occurrence and growth of wildfires under tens of thousands of hypothetical contemporary fire seasons in order to estimate the probability of a given area (i.e., pixel) burning under current landscape conditions and fire management practices. The data presented here represent modeled BP and FLPs for the United States (US) at a 270-meter grid spatial resolution. Flame-length probability is estimated for six standard Fire Intensity Levels. The six FLPs correspond to flame-length classes as follows: FLP1 = < 2 feet (ft); FLP2 = 2 < 4 ft.; FLP3 = 4 < 6 ft.; FLP4 = 6 < 8 ft.; FLP5 = 8 < 12 ft.; FLP6 = 12+ ft. Because they indicate conditional probabilities (i.e., representing the likelihood of burning at a certain intensity level, given that a fire occurs), the FLP data must be used in conjunction with the BP data for risk assessment.

This Dataset contains 1,217 simulated wildfire scenarios generated using FARSITE, a widely used wildfire behavior model developed by the U.S. Forest Service. Simulations integrate topography, fuel types, and weather data to produce GIS-compatible fire spread outputs.

Fires are modeled within central California (lat 37.6°–38.1° N, long -120.7°–-120.0° W), covering ~842,000 acres across diverse landscapes, including oak woodlands, grasslands, and coniferous forests. The region's Mediterranean climate and complex terrain offer realistic wildfire conditions.

The IEEE 30-bus transmission network is overlaid onto this region, with random ignition points placed along power lines. Each scenario assumes a 96-hour containment period. The dataset captures varied fire behaviors influenced by terrain and infrastructure, making it suitable for wildfire risk analysis and grid resilience studies.

Wildfire Mapping from Space Market Outlook

According to our latest research, the global wildfire mapping from space market size has reached USD 1.32 billion in 2024, reflecting robust demand for advanced space-based technologies to combat and monitor wildfires worldwide. The market is projected to grow at a CAGR of 11.7% from 2025 to 2033, reaching an estimated USD 3.67 billion by 2033. This impressive growth trajectory is primarily driven by the increasing frequency and severity of wildfires, the rapid advancement in satellite and remote sensing technologies, and the growing emphasis on disaster preparedness and environmental sustainability across the globe.

One of the primary growth factors for the wildfire mapping from space market is the escalating incidence of wildfires due to climate change and prolonged droughts in various regions. Governments and environmental agencies are under mounting pressure to implement proactive prevention and rapid response strategies. Space-based technologies, such as satellite imaging and remote sensing, have become crucial tools for early detection, real-time monitoring, and post-event assessment of wildfires. These technologies enable authorities to track fire spread, assess affected areas, and allocate resources more efficiently. Furthermore, the integration of artificial intelligence and machine learning with satellite data is enhancing the accuracy and speed of wildfire prediction models, which is further fueling market growth.

Another significant driver is the increasing investment by both public and private sectors in space infrastructure and geospatial intelligence. The launch of new constellations of low-earth orbit (LEO) satellites and the deployment of advanced thermal imaging sensors are expanding the capabilities of wildfire mapping systems. These advancements are reducing the latency and increasing the resolution of wildfire data, making it possible to deliver near real-time information to disaster management teams. Additionally, the collaboration between space agencies, technology providers, and research institutions is leading to the development of more sophisticated wildfire monitoring platforms, which are being adopted by a growing number of end-users, including insurance companies and forestry managers.

The growing awareness of the economic and environmental impact of wildfires is also propelling the adoption of space-based mapping solutions. Wildfires not only cause significant loss of life and property but also lead to long-term ecological damage and substantial financial losses for governments and businesses. The insurance industry, in particular, is leveraging satellite data to assess risks, estimate damages, and streamline claims processing. Moreover, the integration of geospatial information systems (GIS) with remote sensing data is enabling more comprehensive environmental monitoring and forestry management, supporting sustainable development goals and regulatory compliance.

From a regional perspective, North America currently dominates the wildfire mapping from space market, accounting for the largest share in 2024. This leadership position can be attributed to the high incidence of wildfires in the United States and Canada, strong government initiatives, and the presence of leading space technology providers. Europe and Asia Pacific are also witnessing significant growth, driven by rising investments in space-based disaster management infrastructure and increasing awareness about climate change. Meanwhile, Latin America and the Middle East & Africa are emerging markets, with growing adoption of wildfire mapping solutions in response to recent wildfire events and international collaborations.

Technology Analysis

The technology segment of the wildfire mapping from space market encompasses satellite imaging, remote sensing, GIS mapping, and thermal imaging, each playing a pivotal role in the detection, monitoring, and management of wildfires. Satellite imaging is at the forefront, providing high-resolution images of large geographic areas, which are essential for early detection and situational awareness during wildfire outbreaks. The continuous improvement in satellite sensor technology has enabled the capture of multispectral and hyperspectral data, allowing for more precise identification of fire hotspots and assessment of vegetation health. This technological evolution is further supported by the proliferation of small satellites and CubeSats, which offer more frequent revisit times and

The wildfire hazard potential (WHP) is a raster geospatial product at 270-meter resolution covering all lands in the conterminous United States. It can help to inform evaluations of wildfire risk or prioritization of fuels management needs across very large landscapes (millions of acres). Our specific objective with the WHP map is to depict the relative potential for wildfire that would be difficult for suppression resources to contain. For more information, please visit: https://www.firelab.org/project/wildfire-hazard-potential.

This data publication is a second edition. The first edition (https://doi.org/10.2737/RDS-2015-0046) represents WHP mapped in 2014, depicting landscape conditions as of 2010. This second edition is the 2018 version, and depicts landscape conditions as of 2012. (See \Supplements\WHP2014_to_2018_ChangeSummary.pdf for a summary of the changes between the first and second editions of these data.)�To check for the latest version of the WHP geospatial data and map graphics, as well as documentation on the mapping process, see: https://www.firelab.org/project/wildland-fire-potential. Details about the Wildfire Hazard Potential mapping process can be found in Dillon et al. 2015. Steps described in this paper about weighting for crown fire potential have been dropped in the 2018 version due to changes to the FSim modeling products used as the primary inputs to WHP mapping. The FSim products used to create the 2018 version of WHP can be found here in Short et al. 2016. Dillon, Gregory K.; Menakis, James; Fay, Frank. 2015. Wildland fire potential: A tool for assessing wildfire risk and fuels management needs. In: Keane, Robert E.; Jolly, Matt; Parsons, Russell; Riley, Karin. Proceedings of the large wildland fires conference; May 19-23, 2014; Missoula, MT. Proc. RMRS-P-73. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 60-76. https://www.fs.usda.gov/treesearch/pubs/49429 Short, Karen C.; Finney, Mark A.; Scott, Joe H.; Gilbertson-Day, Julie W.; Grenfell, Isaac C. 2016. Spatial dataset of probabilistic wildfire risk components for the conterminous United States. Fort Collins, CO: Forest Service Research Data Archive. This dataset can be downloaded at: https://www.fs.usda.gov/rds/archive/Product/RDS-2015-0046-2

The data included in this publication depict the 2024 version of 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 Research 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). To bring the burn probability raster data down to a finer resolution more useful for assessing hazard and risk to communities, we upsampled them to the native 30 m resolution of the LANDFIRE fuel and vegetation data. In this upsampling process, we 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. Additional methodology documentation is provided in a methods document (\Supplements\WRC_V2_Methods_Landscape-wideRisk.pdf) packaged in the data download.The specific raster datasets in this publication include:Risk to Potential Structures (RPS): A measure that integrates wildfire likelihood and intensity with generalized consequences to a home on every pixel. For every place on the landscape, it poses the hypothetical question, "What would be the relative risk to a house if one existed here?" This allows comparison of wildfire risk in places where homes already exist to places where new construction may be proposed. This dataset is referred to as Risk to Homes in the Wildfire Risk to Communities web application.Conditional Risk to Potential Structures (cRPS): The potential consequences of fire to a home at a given location, if a fire occurs there and if a home were located there. Referred to as Wildfire Consequence in the Wildfire Risk to Communities web application.Exposure Type: Exposure is the spatial coincidence of wildfire likelihood and intensity with communities. This layer delineates where homes are directly exposed to wildfire from adjacent wildland vegetation, indirectly exposed to wildfire from indirect sources such as embers and home-to-home ignition, or not exposed to wildfire due to distance from direct and indirect ignition sources.Burn Probability (BP): The annual probability of wildfire burning in a specific location. Referred to as Wildfire Likelihood in the Wildfire Risk to Communities web application.Conditional Flame Length (CFL): The mean flame length for a fire burning in the direction of maximum spread (headfire) at a given location if a fire were to occur; an average measure of wildfire intensity.Flame Length Exceedance Probability - 4 ft (FLEP4): The conditional probability that flame length at a pixel will exceed 4 feet if a fire occurs; indicates the potential for moderate to high wildfire intensity.Flame Length Exceedance Probability - 8 ft (FLEP8): the conditional probability that flame length at a pixel will exceed 8 feet if a fire occurs; indicates the potential for high wildfire intensity.Wildfire Hazard Potential (WHP): An index that quantifies the relative potential for wildfire that may be difficult to manage, used as a measure to help prioritize where fuel treatments may be needed.Additional methodology documentation is provided with the data publication download. https://www.fs.usda.gov/rds/archive/Catalog/RDS-2020-0016-2Note: Pixel values in this image service have been altered from the original raster dataset due to data requirements in web services. The service is intended primarily for data visualization. Relative values and spatial patterns have been largely preserved in the service, but users are encouraged to download the source data for quantitative analysis.

This web map shows the current wildfires and fire danger zones within Canada. The layers used within this web map are Esri Canada's wildfire live feature services that are updated daily along with NRCan's current fire danger WMS. A description of each layer can be found below along with the link to their respective items on ArcGIS Online. Active Wildfires in Canada Reported active fire locations are updated daily as provided by fire management agencies (provinces, territories and Parks Canada). The fires data is managed through a national Data Integration Project (DIP) coordinated by the Canadian Interagency Forest Fire Centre (CIFFC) and Natural Resources Canada with participation from all partner agencies. The active fires data includes attributes for agency, fire name, latitude, longitude, start date, fire size (ha) and stage of control (fire status). Possible values for stage of control include: OC (Out of Control), BH (Being Held), UC (Under Control), EX (Out). Supplemental InformationThe national Data Integration Project (DIP) is coordinated by the Canadian Interagency Forest Fire Centre (CIFFC) and Natural Resources Canada with participation from all partner agencies. This initiative focuses on development and implementation of data standards and enabling the exchange and access of national fire data. More details are available in the CIFFC IM/IT Strategy, available at: https://ciffc.ca/publications/general-publications. Feux de végétation actifs au Canada Les positions rapportées des feux de végétation actifs sont mises à jour quotidiennement d'après les données fournies par les agences de gestion des feux (provinces, territoires et Parcs Canada). Les données sur les feux sont gérées dans le cadre d'un Projet d'intégration de données national coordonné par le Centre interservices des feux de forêt du Canada (CIFFC) et par Ressources naturelles Canada, avec la participation de tous les organismes partenaires. Les données sur les feux actifs comprennent les champs d'attributs des agences, le nom du feu, la latitude, la longitude, le début du feu, la taille du feu (ha) et le stade de contrôle (état du feu). Les valeurs possibles pour le stade de contrôle sont les suivantes : OC (out of control/hors de contrôle), BH (being held/contenu), UC (under control/maîtrisé) et EX (out/éteint). Renseignements complémentairesLe Projet d'intégration de données national est coordonné par le CIFFC et par Ressources naturelles Canada, avec la participation de tous les organismes partenaires. Cette initiative a pour but d'élaborer et de mettre en œuvre des normes de données, ainsi que de rendre possible l'accès aux données nationales sur les feux et l'échange de ces données. On trouvera plus de détails à ce sujet dans la Stratégie de GI/TI du CIFFC, à l'adresse suivante : https://ciffc.ca/publications/general-publications Active Wildfire Perimeters in CanadaThis dataset displays active wildfire perimeters derived from hotspots identified in satellite imagery provided by the Canadian Wildland Fire Information System (CWFIS) and Natural Resources Canada (NRCan) updated every 3 hours. || Ce jeu de données, mis à jour toutes les trois heures, affiche les périmètres de feux de forêt actifs dérivés des points chauds relevés dans l’imagerie satellite fournie par le Système canadien d’information sur les feux de végétation (SCIFV) et Ressources naturelles Canada (RNCan). Wildfire Smoke Forecast in Canada This layer displays forecasted wildfire smoke across Canada sourced from BlueSky Canada's FireSmoke Canada app, updated every 6 hours. The wildfire smoke layer consists of hourly concentrations of particulate matter 2.5 microns and smaller (PM2.5) in units of micrograms per meter cubed (µg/m3) observed at ground level from wildfires. It is an approximation of when and where wildfire smoke events may occur over the next two days. This layer is sourced from BlueSky Canada's FireSmoke Canada app. Current Fire Danger Fire Danger is a relative index of how easy it is to ignite vegetation, how difficult a fire may be to control, and how much damage a fire may do. Fire Danger is a reclassification of the CFFDRS fire weather index (FWI) which is a numeric rating of fire intensity. These general fire descriptions apply to most coniferous forests. The national fire danger maps show conditions as classified by the provincial and territorial fire management agencies. Choice and interpretation of classes may vary between provinces. For fuel-specific fire behavior, consult the Fire Behavior Prediction maps.• Low: Fires likely to be self-extinguishing and new ignitions unlikely. Any existing fires limited to smoldering in deep, drier layers.• Moderate: Creeping or gentle surface fires. Fires easily contained by ground crews with pumps and hand tools.• High: Moderate to vigorous surface fire with intermittent crown involvement. Challenging for ground crews to handle; heavy equipment (bulldozers, tanker trucks, aircraft) often required to contain fire.• Very High: High-intensity fire with partial to full crown involvement. Head fire conditions beyond the ability of ground crews; air attack with retardant required to effectively attack fire's head.• Extreme: Fast-spreading, high-intensity crown fire. Very difficult to control. Suppression actions limited to flanks, with only indirect actions possible against the fire's head.Forecasted weather data provided by Environment Canada. More information about forecasted weather is available at https://cwfis.cfs.nrcan.gc.ca/background/dsm/fwiMore information about the Canadian Forest Fire Weather Index (FWI) System is available at https://cwfis.cfs.nrcan.gc.ca/background/summary/fwiMaps are produced using Spatial Fire Management System and are updated multiple times per day. Maps updated daily, year-round.Supplemental InformationThe Canadian Forest Fire Danger Rating System (CFFDRS). is a national system for rating the risk of forest fires in Canada. Forest fire danger is a general term used to express a variety of factors in the fire environment, such as ease of ignition and difficulty of control. Fire danger rating systems produce qualitative and/or numeric indices of fire potential, which are used as guides in a wide variety of fire management activities. The CFFDRS has been under development since 1968. Currently, two subsystems–the Canadian Forest Fire Weather Index (FWI) System and the Canadian Forest Fire Behavior Prediction (FBP) System–are being used extensively in Canada and internationally.Risque d'incendie actuel Le risque d'incendie est un indice relatif indiquant le niveau de facilité pour allumer un incendie de végétation, le niveau de difficulté qu'un incendie peut demander pour être contrôlé ainsi que la quantité de dommages qu'un incendie peut causer.Ces descriptions générales des incendies s'appliquent à la plupart des forêts de conifères. Les cartes nationales sur le danger d'incendie illustrent les conditions telles qu'elles sont classées par les agences provinciales et territoriales de gestion des feux. Le choix et l'interprétation des classes peuvent varier entre les provinces. En ce qui a trait au comportement des incendies en regard du combustible spécifique, veuillez consulter les cartes de prédiction du comportement des incendies.• Faible: Incendie possiblement auto-extincteur; de nouveaux allumages sont invraisemblables. Tout incendie existant est limité à couver dans des couches profondes plus sèches.• Modéré: Incendie de surface rampant modéré. Il est facilement circonscrit par les équipes au sol munies de pompes et d'outils manuels.• Élevé: Incendie de surface modéré à vigoureux avec implication intermittente des cimes. Pose des défis aux équipes chargées de le combattre sur le terrain; les équipements lourds (bouteurs, camions-citernes à eau et avions) sont souvent requis pour contenir l'incendie.• Très élevé: Incendie de forte intensité avec implication partielle ou complète des cimes. Les conditions au front de l'incendie sont au-delà de la capacité des équipes sur le terrain à y faire face; les attaques aériennes avec largage de produits ignifugeants sont requises pour combattre effectivement le front de l'incendie.• Extrême: Feu de cimes à forte intensité et à propagation rapide. Très difficile à contrôler. Les actions de suppression sont limitées aux flancs alors que seules des actions indirectes sont possibles au front de l'incendie.Prévisions météorologiques fournies par Environnement Canada. Pour de plus amples renseignements sur les prévisions, consultez la section Renseignements généraux.De plus amples informations sur la Méthode canadienne de l'indice Forêt-Météo (IFM) sont disponibles à la rubrique Renseignements généraux.Les cartes sont produites à l'aide du Système de gestion spatiale des feux de forêt et sont mises à jour plusieurs fois par jour.Les cartes sont mises à jour quotidiennement, tout au long de l'année l'année.Renseignements complémentairesLa Méthode canadienne d'évaluation des dangers d'incendie de forêt (MCEDIF) est une méthode nationale pour classer le risque d'incendie de forêt au Canada.Le danger d'incendie de forêt est un terme général employé pour exprimer une diversité de facteurs dans les conditions de brûlage tels que la facilité d'allumage et la difficulté de contrôle. Les méthodes d'évaluation du danger d'incendie génèrent des indices qualitatifs ou numériques du potentiel d'incendie qui sont utilisés comme guides dans une grande variété d'activités de gestion des incendies de forêt. La MCEDIF est en cours d'élaboration depuis 1968. En ce moment, deux sous-systèmes – la Méthode canadienne de l'indice forêt météo (IFM) et la Méthode canadienne de prévision du comportement des incendies de forêt (PCI) – sont couramment utilisés au Canada et sur

The Fireshed Registry is a geospatial dashboard and decision tool built to organize information about wildfire transmission to buildings and monitor progress towards risk reduction for communities from management investments. The concept behind the Fireshed Registry is to identify and map the source of risk rather than what is at risk across all lands in the United States. While the Fireshed Registry was organized around mapping the source of fire risk to communities, the framework does not preclude the assessment of other resource management priorities and trends such as water, fish and aquatic or wildlife habitat, or recreation. The Fireshed Registry is also a multi-scale decision tool for quantifying, prioritizing, and geospatially displaying wildfire transmission to buildings in adjacent or nearby communities. Fireshed areas in the Fireshed Registry are approximately 250,000 acre accounting units that are delineated based on a smoothed building exposure map of the United States. These boundaries were created by dividing up the landscape into regular-sized units that represent similar source levels of community exposure to wildfire risk. Subfiresheds are approximately 25,000 acre accounting units nested within firesheds. Firesheds for the Conterminous U.S., Alaska, and Hawaii were generated in separate research efforts and are published in incremental versions in the Research Data Archive. They are combined here for ease of use.

ABSTRACT This paper presents a GIS methodological approach for mapping forest landscape multifunctionality. The aims of the present study were: (1) to integrate and prioritize production and protection functions by multicriteria spatial analysis using the Analytic Hierarchy Process (AHP); and (2) to produce a multifunctionality map (e.g., production, protection, conservation and recreation) for a forest management unit. For this, a study area in inner Portugal occupied by forest and with an important protection area was selected. Based on maps for functions identified in the study area, it was possible to improve the scenic value and the biodiversity of the landscape to mitigate fire hazard and to diversify goods and services. The developed methodology is a key tool for producing maps for decision making support in integrated landscape planning and forest management.