Wildfire Ignition Probability Data

from the Pyrologix Team at Vibrant Planet
Tech Details & Access

Predicting the Cause of Wildfires

Wildfires are natural parts of many ecosystems, and they can present substantial challenges to land management and community safety. Understanding the likelihood of wildfire ignition is crucial for crafting strategies to navigate the unpredictable nature of wildfires. Developing spatial models of ignition likelihood becomes a key component in this effort, offering vital insights for risk-aware decision making in preparedness, prevention, fuels management, and response planning.

By distinguishing between spatial patterns of ignition, especially the significant impact of human activities, targeted prevention measures can more effectively mitigate risks to life and property. Amidst rapid climate change, the varied responses of human and natural ignition sources underscore the importance of refining these models for climate adaptation. Moreover, these models are indispensable for enhancing fire simulation tools and their subsequent use in detailed assessments of risk to communities and landscapes.

© Diaconescu / Adobe Stock

Data-driven insights

Natural-Caused: In the Western U.S., lightning-caused fires display greater spatial diversity, with the landscape's slope and topographic features (such as valleys, ridges, or mid-slopes) playing a significant role in their distribution. This is in contrast to the Southeast, particularly in Florida, where lightning ignitions are notably concentrated. 
Human-Caused:  Human activities significantly increase wildfire risk. This data highlights how the likelihood of wildfire ignitions in both the Western and Southeastern regions of the U.S. is notably higher near roads and urban areas. It also shows that human-caused ignitions outnumber natural (lightning-caused) ignitions by 8.5 times for fires exceeding 20 acres in the Southeast.

What is Wildfire Probability Ignition Data?

This data story explores the ignition probability datasets created in 2023 by Christopher J. Moran, Joe H. Scott, and Kevin Vogler, prominent wildfire scientists at the Pyrologix Wildfire Modeling Team, a division of Vibrant Planet. Their work focused on modeling wildfire ignition probabilities in the most wildfire-prone regions of the United States offers critical insights into managing these unpredictable and often devastating occurrences

Prescribed fires are intentional burns used by professionals to manage forests and reduce wildfire risks. In contrast, unintentional human-caused wildfires (as modeled in this data) are accidental and unpredictable, often sparked by careless activities and posing significant risks to wildlife, property, and human safety.
Dr. Kevin McGarigal

Landscape Ecologist, Vibrant Planet

Kevin is an applied research landscape ecologist, having earned a BS degree in Forestry and Wildlife from Virginia Tech, an MS. degree in Wildlife Science from Oregon State University, and a PhD in Forest Science from Oregon State University. He was a professor of landscape ecology at the University of Massachusetts, Amherst from 1997 until 2019.  Kevin’s applied research focuses on understanding how landscapes are structured physically and biologically and the agents responsible for those patterns, how these patterns affect the distribution and dynamics of plant and animal populations, how these patterns and processes change over time, and how to apply this information to better manage natural resources over multiple spatial and temporal scales. Accordingly, a major component of his work has been the development and technical support of software tools for both scientists and managers (e.g., FRAGSTATS) for quantifying landscape patterns and landscape dynamics. In particular, Kevin is the developer of a landscape disturbance-succession simulation model (LDSim) for simulating the range of variation in landscape patterns under natural and anthropogenic disturbance regimes to help land managers gain a quantitative understanding of the landscape under historical reference period conditions and alternative future scenarios. Kevin is eager to bring his substantial experience, knowledge, and prolific set of tools and software to enhance Land Tender and the future suite of decision support applications that are currently being developed by Vibrant Planet.

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Geography

The analysis focuses on two wildfire-prone regions in the United States: the Western states, known for their arid deserts and dense forests, and the humid, subtropical Southeastern states. These two regions, spanning 24 states, were chosen for their distinct fire regimes and environmental conditions. Understanding how wildfire ignition patterns vary from one region to the next aids managers in tailoring their strategies.

2.1M
Square Miles
1.35
Billion Acres

Combined Wildfire Ignition Probability Across Western US Firesheds

This map displays the combined likelihood of human and natural wildfire ignitions across USFS firesheds in the Western U.S.. Hexagons color-coded from purple (low probability) to yellow (high probability) represent the summarized ignition likelihood, informing strategic fire management decisions.

Human-Caused Wildfire Ignition Probabilty by Firesheds in the West

This map outlines the probability of wildfire ignitions due to human activities summarized within USFS fireshed boundaries across the Western United States. A fireshed that has a "high" probability of wildfire ignition means that, in any given year, there is higher statisticial likelihood that a wildfire will be started within that area.

Natural-Caused Wildfire Ignition Probability by Firesheds in the West

This map outlines the probability of wildfire ignitions due to natural events (e.g., lightning) summarized by USFS firesheds across the Western United States. An area that has a "high" probability of wildfire ignition means that, in any given year, there is higher statisticial likelihood that a wildfire will be started within the fireshed.

Combined Wildfire Ignition Probability by Firesheds in the Southeast

This map displays the combined likelihood of human and natural wildfire ignitions across USFS firesheds in the Southeast U.S.. Hexagons color-coded from purple (low probability) to yellow (high probability) represent the summarized ignition risks, informing strategic fire management decisions.

Human-Caused Wildfire Ignition Probability by Firesheds in the Southeast

This map outlines the probability of wildfire ignitions due to human activities summarized within USFS fireshed boundaries across the Southeast United States. A fireshed that has a "high" probability of wildfire ignition means that, in any given year, there is higher statisticial likelihood that a wildfire will be started within that area.

Natural-Caused Wildfire Ignition Probability by Firesheds in the Southeast

This map outlines the probability of wildfire ignitions due to natural events (e.g., lightning) summarized by USFS firesheds across the Southeast United States. An areas that has a "high" probability of wildfire ignition means that, in any given year, there is higher statisticial likelihood that a wildfire will be started within the fireshed.

Are you interested in applying ignition probability to your research or management project?

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We want to hear from you!  Pyrologix and Vibrant Planet Data Commons are tracking the applied use of this wildfire ignition probability data so that we can demonstrate the impact of good data and research. Share your thoughts and help us improve our resources to better serve the  land management, fire, and research communities.

Understanding Ignition Probability Analysis

In wildfire management, ignition probability analyses are vital tools for predicting where fires are most likely to start. By considering factors like drought conditions, weather patterns, topography, human activities, and broad vegetation characteristics, these analyses help pinpoint high-risk areas.

What sets the work of Pyrologix's team apart from other research and modeling efforts is their modeling ignitions data is cause-specific, analyzing whether a fire has a natural or human-caused origin and highlights regional variations between the Western and Southeastern United States.

Traditional wildfire probability models often rely on the spatial density of past fires. While this offers a basic understanding of potential fire zones, Pyrologix's research goes further.  They incorporate this spatial density layer as one input among many, combining it with climatic, vegetative, topographic, and detailed human influence data. This approach offers greater precision in exploring ignition patterns, revealing new areas with heightened wildfire ignition likelihood and emphasizing the strong correlation between human access (e.g., roads, urban areas) and the occurrence of human-caused fires.

Importantly, this model does not solely rely on historic fire locations. It predicts where high-potential ignitions could occur based on these diverse inputs, making it 'future-looking' while still capturing the spatial variations found in real-world fire patterns.

  • Flammable Vegetation: The presence of dry brush, dead trees, and other easily ignited fuels.
  • Climate Variables: High temperatures, low humidity, and strong winds, creating ideal conditions for fire spread.
  • Topography: Features like steep slopes and high elevations, which influence wind patterns and fuel moisture.
  • Human Activities: Sources like campfires, discarded cigarettes, and power lines that can create accidental ignitions.
Important Note: Data quality significantly impacts the accuracy of ignition probability maps and predictive models. Wildfire prediction inherently has some uncertainty, so predictive analysis offers risk guidance, not guarantees.
Lightning sparked the Lionshead Fire, captured here charring a manzanita tree. The fire started in the fall of 2020 and heavily impacted several surrounding communities. Wildfire ignition probability data helps predict areas susceptible to lightning strikes and other fire triggers. (Photo: Kevin Benedict / USFS)

Predictive Analysis for Wildfire Ignition Forecasting

In wildfire management, predictive analysis enhances conventional approaches by utilizing historical data and machine learning techniques to anticipate the timing and location of future wildfires. The core principle is that patterns observed in previous fire events can be leveraged to forecast future ignition sites. The availability of a comprehensive historical record of wildfires, including details of past occurrences and their contextual conditions, provides the foundation for constructing predictive models.

While a variety of sophisticated machine learning algorithms are available for predictive modeling in wildfire management, Pyrologix specifically utilized the Random Forest algorithm for its central role. This choice was driven by the algorithm's exceptional ability to handle complex datasets and was essential to integrate historical fire occurrence data with a wide range of environmental factors, including vegetation types, climate variables, and proximity to human development.

The algorithm works by combining the predictions of multiple decision trees (like a flowchart of questions), each trained on different portions of the data. This approach improves accuracy and prevents the model from focusing too heavily on any single factor. By incorporating insights from their refined ignition probability datasets along with other crucial data, Pyrologix's predictive models consider how these various elements interact to shape wildfire likelihood. The result is a set of probabilistic forecasts for human or natural causes of fire at 120m resolution that pinpoint high-risk areas across the U.S.

  • Flammable Vegetation: The presence of dry brush, dead trees, and other easily ignited fuels.
  • Climate Variables: High temperatures, low humidity, and strong winds, creating ideal conditions for fire spread.
  • Topography: Features like steep slopes and high elevations, which influence wind patterns and fuel moisture.
  • Human Activities: Sources like campfires, discarded cigarettes, and power lines that can create accidental ignitions.
A Random Forest algorithm is a supervised machine learning algorithm that combines the predictions of multiple decision trees trained on different subsets of data and features to improve accuracy and reduce overfitting. While the final output is often determined by majority vote (classification) or averaging (regression), more specialized techniques exist. In this case, a probabilistic Random Forest model was used, with outputs scaled to reflect observed ignition rates for enhanced accuracy.

The History of Applied Machine Learning (ML) in Forest Ecology

The application of machine learning (ML) in forest and ecosystem management has evolved significantly over the past few decades and can be traced back to the late 20th century as computational capabilities began to improve. By the 1990s and early 2000s, initial uses focused on simple statistical methods for forest cover mapping and biomass estimation.  However, by the mid-2000s there was a surge in more advanced ML techniques like decision trees and random forests, fueled by increasing computing power and greater data availability from remote sensing and GIS technology.

Applications broadened to fire risk prediction, disease modeling, and optimizing forest resources.  The 2010s ushered in an explosion of deep learning, allowing for fine-scale object detection and complex pattern recognition, aiding precision conservation, natural disturbance forecasting, and advanced climate change modeling.  The  field continues to evolve rapidly, promising to revolutionize how we manage and understand our forests and ecosystems.

Despite its potential, the expansion of ML in forest ecology faces challenges. Limited suitable data and the complexity of applying these techniques can hinder progress.  However, by combining diverse algorithms, fostering collaboration between ecologists and ML experts, and tackling the challenges of "big data", ML has the potential to become an even more powerful tool for ecologists. The increasing availability of diverse data sources, like sound and video, promises to open new frontiers for research in forest ecology.

Remote Sensing is the process of detecting and monitoring the physical characteristics of an area by measuring its reflected and emitted radiation at a distance (typically from satellite or aircraft). Special cameras collect remotely sensed images, which help researchers "sense" things about the Earth.(What is remote sensing and what is it used for? | U.S. Geological Survey)
The Beachie Creek Fire shown here near Opal Lake, Oregon heavily impacted several communities in the North Fork Santiam River and Little North Fork River drainage and significantly affected highly valued natural and cultural resources. (Photo: InciWeb)

Why Use Predictive Analysis for Wildfire Management?

Predictive analysis in wildfire management is indispensable, particularly in an era where wildfires are becoming more frequent and severe due to climate change and other factors. Several key factors underscore its relevance and urgency:

Adapting to Climate Change and Increasing Fire Risks: Climate change is intensifying wildfire risks, making many regions around the world, including the United States, more susceptible to frequent and severe fires. Predictive analysis helps us understand these shifts, allowing for better preparedness and response.

Safeguarding Lives and Property: Wildfires pose significant threats to communities. Predictive modeling can guide emergency services in strategic planning and resource allocation, potentially saving lives and reducing property damage.

Conserving the Environment: Wildfires can have devastating ecological impacts. Predictive analysis aids in identifying critical areas for conservation, helping mitigate long-term environmental damage.

Optimizing Resource Allocation and Management: Strategic resource allocation is crucial for effective wildfire management. Predictive models inform land managers where to focus efforts like intentional burns, contributing to more efficient use of resources.

Informing Urban Planning and Development: Understanding wildfire risk probabilities is essential for safe development as urban expansion continues. This information is important for shaping building codes and land-use planning.

Guiding Insurance and Economic Decisions: The insurance sector relies on predictive wildfire analysis for risk assessment and premium settings. This analysis also helps in economic planning and recovery strategies.

Building Adaptation and Resilience: Predictive analysis can enhance societal resilience against wildfires. It provides data for better preparedness and adaptive strategies in various sectors.

“Whether the particular situation calls for wildfire to be promoted, prevented, or mitigated, understanding and quantifying wildfire risk provides useful information for its management."

Moran, C. J., Scott, J. H., & Vogler, K. (2023). Wildfire ignition probability datasets by cause for the Western and Southeastern United States.

The human-ignition probability layer provides excellent information for planning fire prevention activities as part of a Community Wildfire Protection Plan, for example, and also to support fine-grained wildfire simulation modeling in the Wildland-Urban Interface. Having separate input layers for human and natural ignitions allows fire modelers to calibrate those two types of wildfire separately, allowing us to learn how those two origins contribute to hazard and risk across the landscape.
— Joe Scott - Pyrologix

Applications: Enhanced Fire Management and Risk Assessment

As we've discussed, predictive analysis of wildfire risk probabilities marks a shift from a reactive stance to a proactive one, where actions and strategies are informed, refined, and can be implemented before crises occur. This dataset provides a valuable tool that supports managers and communities in efforts to reduce fire risk and severity via strategic mitigation before wildfires occur. For example, by accurately pinpointing areas more prone to human ignitions, prevention efforts - such as public education, regulations, and infrastructure upgrades - can become even more effective.

Similarly, land managers gain the detailed information necessary to prioritize prescribed burns, fuel reduction, and revegetation where they will be most impactful. Emergency services can leverage the data to optimize resource placement and preparedness, while land managers gain insights for long-term adaptive planning amidst factors like climate change. In every application, these datasets become vital tools, aligning with a holistic approach that prioritizes wildfire prevention, community safety, and sustainable land use.

Human-Caused Wildfire Exposure Index across various firesheds: This map visualizes the potential impact of human-caused wildfires in the western US. The index was calculated by multiplying the summarized populations within areas likely to be affected by wildfire (firesheds) with the mean probability of human-caused wildfire ignitions within those same areas. Areas with higher index values represent a greater potential for human communities to be exposed to the effects of a human-caused wildfire. This information can be used to prioritize fire prevention efforts and resource allocation.
Human-Caused Wildfire Ignition Risk by Firesheds in the Western US: This map outlines the probability of wildfire ignitions due to human activities summarized within USFS fireshed boundaries across the Western United States. A fireshed that has a "high" risk of wildfire ignition means that, in any given year, there is higher statisticial likelihood that a wildfire will be started within that area.Slide 2 of 6.
Population Density of the United States (2018) in Relation to Wildfire Risk: This map highlights population density in the United States (2018).  Yellow and green colors represent higher density areas, with yellow indicating urban or high-density population centers (US Census Bureau: urban ≥ 386 people/sq mi, high-density ≥ 3,861 people/sq mi). Population density is a key factor in wildfire risk assessment. Use alongside a wildfire ignition probability map for a comprehensive view. (Source: USDA Forest Service https://www.fs.usda.gov/rds/archive/Catalog/RDS-2020-0060) 
Human-Caused Wildfire Ignition Risk by Firesheds in the Southeast US: This map outlines the probability of wildfire ignitions due to human activities summarized within USFS fireshed boundaries across the Southeast United States. A fireshed that has a "high" risk of wildfire ignition means that, in any given year, there is higher statisticial likelihood that a wildfire will be started within that area.
The scientific evidence keeps growing that human activity has an important role in shaping modern fire regimes. The ability to isolate spatial patterns of human-caused ignitions and input them into fire simulation models has enormous potential to support proactive ignition prevention and community risk reduction efforts.
— Matt Thompson - Pyrologix

Human-Caused Wildfire Exposure Index by Firesheds

This map visualizes the Human-Caused Wildfire Exposure Index within the Western U.S.  Color represents the combined impact of population density and the probability of human-caused wildfire ignition within each fireshed. Areas with darker shades indicate a greater potential exposure of human communities to wildfires.

Natural-Caused Wildfire Exposure Index by Firesheds

This map highlights areas where human population density intersects with the likelihood of natural wildfires, such as those caused by lightning strikes.  Firesheds with larger populations and higher probabilities of natural ignition are shown in darker colors, indicating greater potential risk to communities. This information helps target fire prevention and preparedness efforts.

Combined Wildfire Exposure Index by Firesheds

This map highlights areas where human population density intersects with the likelihood of wildfires, considering both natural (like lightning) and human-caused ignition sources.  Firesheds with larger populations and higher overall ignition probabilities are shown in darker colors, indicating greater potential risk to communities. This information helps in comprehensive fire prevention efforts and  resource allocation.

Human-Caused Wildfire Exposure Index by Firesheds

This map visualizes the Human-Caused Wildfire Exposure Index within the Southeastern U.S.  Color represents the combined impact of population density and the probability of human-caused wildfire ignition within each fireshed. Areas with darker shades indicate a greater potential exposure of human communities to wildfires.

Natural-Caused Wildfire Exposure Index by Firesheds

This map highlights areas where human population density intersects with the likelihood of natural wildfires, such as those caused by lightning strikes.  Firesheds with larger populations and higher probabilities of natural ignition are shown in darker colors, indicating greater potential risk to communities. This information helps target fire prevention and preparedness efforts.

Combined Wildfire Exposure Index by Firesheds

This map highlights areas where human population density intersects with the likelihood of wildfires, considering both natural (like lightning) and human-caused ignition sources.  Firesheds with larger populations and higher overall ignition probabilities are shown in darker colors, indicating greater potential risk to communities. This information helps in comprehensive fire prevention efforts and  resource allocation

Applied Use Case:

Enhancing FSim Modeling and the Land Tender Tool with Wildfire Probability Ignition Data

The Archie Creek Fire burned a total of 125,500 acres, with 95,000 of those acres burned as medium and high severity. Overall, 49% of forested acres burned were on private lands. (Sources: 2020 Labor Day Fires – Economic Impacts to Oregon’s Forest Sector, Inciweb)
Location
Western U.S.
Management Teams
Pyrologix
Data Used
Ignition Probility Datasets

Pyrologix, a Vibrant Planet company, has developed a valuable set of wildfire ignition probability data layers. The immediate application of this data is to enhance their Fire Simulation (FSim) models. FSim, a cornerstone in fire management and planning, simulates wildfire spread and behavior, providing vital information for effective wildfire risk management.

Application in FSim Modeling:

The integration of Pyrologix's ignition probabilities into FSim modeling marks a significant advancement. The updated Ignition Density Grid (IDG) input data transforms how fire simulations are conducted. This enhancement leads to more accurate placement of potential wildfire ignitions in the model, resulting in simulations that more realistically represent potential fire spread scenarios. For fire managers and decision-makers, this means an ability to predict and prepare for wildfires with unprecedented precision.

Impact on Vibrant Planet's Land Tender Tool:

This refined data also plays a pivotal role in improving Vibrant Planet's Land Tender tool. Land Tender, designed to optimize land management and restoration decisions, relies heavily on accurate fire behavior predictions. The enhanced FSim model, powered by Pyrologix's data described here, provides Land Tender with more reliable inputs. As a result, Land Tender can offer more precise recommendations for land treatments, resource allocations, and fire mitigation strategies, aligning more closely with the actual risks and behavior of potential wildfires.

Contribution to Quantitative Wildfire Risk Assessment (QWRA):

Pyrologix's team is a leader in providing quantitative wildfire risk assessments (QWRAs). Their improved ignition probability analysis builds upon their strong foundation of fire modeling innovation, which is essential for accurate QWRAs.  This advancement provides a more detailed and 'future-looking' understanding of ignition likelihood, resulting in QWRAs with greater precision and long-term relevance for strategic fire planning. Additionally, modeling ignition likelihood by cause unlocks exciting new avenues in risk assessment. This allows for quantifying the potential impact of previously overlooked fire management strategies, such as targeted ignition prevention campaigns and infrastructure upgrades.

Charting the Future: Your Role in Shaping Wildfire Resilience

This ignition probability dataset presents a powerful tool for proactive wildfire management, offering researchers, data scientists, and land managers crucial data-driven insights.

We encourage you to:

By harnessing the power of these ignition probability datasets, we can move beyond reactive firefighting and embrace a proactive approach to wildfire management, better protecting communities and ecosystems from the devastating impacts of these unpredictable yet predictable events.

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Acknowledgements

This wildfire ignition probability data was made possible in part by funding from the Strategic Analytics Branch, Fire and Aviation Management, National Headquarters, United States Forest Service.

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