The news is replete with images of wildfire, among them the Grand Prix and Cedar Fires in Southern California, the Biscuit Fire in Oregon, and the Hayman Fire in Colorado (Figure 1). The loss of millions of acres and thousands of homes have significantly affected countless lives. Because of the devastating economic and human impacts of these events, there is high value in addressing how to mitigate the effects of wildland fires.
Figure 1: Hayman Fire, Colorado
One of the most important and proactive ways to identify areas most likely to be impacted by wildland fires is to perform a wildland fire risk assessment. After a wildland fire risk assessment is complete, planners and fire professionals can identify the locations of likely impacts, and can analyze the value of mitigation measures. With accompanying analytical tools, the results of a wildland fire risk assessment can be used in simulations to determine what activities have the highest potential benefit, such as adding another suppression resource in a specific location, or implementing a fuels treatment program. Wildland fire risk assessments can also serve as a baseline for monitoring change in fire susceptibility and effects over time.
Assessments can be conducted at different scales. The appropriate assessment scale depends on the decisions individuals are trying to make. At one end of the spectrum are landscape scale wildland fire risk assessments. These can be followed by community level prioritization and assessments resulting in mitigation options based on either scale. Assessment is frequently used to prioritize fire effects in Wildland Urban Interface (WUI) areas. Each assessment type is a component of an overall wildland fire risk mitigation strategy, and it is important that these components be consistent and compatible across time and space. Temporal and geographic compatibility in the wildland fire risk assessment approach is important to allow for comparability between areas in support of cost-efficiency.
In this article, a wildland fire risk assessment approach is presented which is compatible over time and geography. The details upon implementation may change, but the overall framework for the assessment is consistent. This approach has previously been applied in the State of Florida, and is being used currently in the Southern Wildland Fire Risk Assessment (SWFRA) project.
General Risk/Hazard Framework
A general approach to wildland fire risk assessment was developed by the Florida Division of Forestry and Space Imaging. The project deliverables included a wildland Fire Risk Assessment (FRA) documenting the current situation. An ArcView extension called the wildland Fire Risk Assessment System (FRAS) was then developed to allow users to view the current situation data layers and also to model the effectiveness of some mitigation options. The wildland fire risk assessment framework was originally developed by Donald Carlton of Fire Program Solutions LLC, who was employed by Space Imaging as a consultant for the project. The methodology used was based on the Lake Tahoe Fire Risk Assessment completed for the USDA Forest Service in 1999 by Donald Carlton and Dr. Mark Finney.
In this article, several words will be used in a specific context. Frequently, fire professionals will define what starts fires as “risk.” In this article, fire ignition potential will be referred to as fire occurrence, not risk. In the same context, fire professionals frequently refer to what burns as “hazard.” In this article, the fuels that will burn in a wildland fire will be referred to as fuels, not hazard. Lastly, fire professionals frequently refer to what the effects of a fire will be as “values at risk.” In this article, values that could be affected by a wildland fire will be referred to as values effects, not values at risk. Webster’s dictionary defines risk as “the possibility of suffering harm or loss.” The analytical integration of the likelihood of a wildland fire, the potential fire behavior, the success of fire suppression forces given the fire behavior and the resultant fire’s effects will be defined as “risk” in this article. This use of the term risk is consistent with its accepted definition.
The wildland fire risk assessment framework is presented in four columns (Figure 2). The first column of the model represents Compiled Inputs, and largely consists of locating or creating data sets that form the basis for the wildland fire risk assessment. This is the most critical, and in some ways, the most difficult step in the wildland fire risk assessment process. Fuels can be particularly difficult and expensive to quantify because they are typically geographically diverse and highly specific. It is common for some type of imagery to be used in the development of fuels data, with the specific imagery types and processes depending on the scale of the assessment. Another critical data set is a listing of historic wildland fire ignitions based on past fire occurrence records. Fire occurrence data can be difficult to gather and aggregate, primarily when non-federal government fire protection agencies are involved. Two well-defined wildland fire reporting systems are used by the five federal wildland fire protection agencies (USDA Forest Service, the USDA Bureau of Land Management, the Bureau of Indian Affairs, the National Park Service and the Fish and Wildlife Service). Fire reporting systems used by each of the 50 States are highly variable. In addition, large tracts of land can be managed by other agencies such as the Department of Defense where there is no common fire reporting format. Other data needed includes infrastructure (roads, resource locations), terrain, forest canopy closure, historic weather, wildland fire suppression capabilities, historic fire suppression costs, and values affected. The compiling of the necessary data sets needed to quantify wildland fire risk is not a trivial process, and presents unique challenges.
The second component of the wildland fire risk assessment framework is Derived Outputs. In this step, information is derived from the basic data using ranking systems, analytical tools and predictive models. Spatial analysis methods are used to create “Fire Occurrence Rates” from raw historical data on fire ignitions in the local area.
For example, specific weather conditions are generated within derived zones of statistically similar weather and input into a fire behavior model called FlamMap. FlamMap is a set of raster prediction models developed by Dr. Mark Finney. FlamMap uses information on slope, aspect, elevation, wildland surface fuels, and canopy closure, as well as aerial fuels components, together with specific weather conditions to calculate fire behavior values for each cell on the landscape. Specific relationships are developed between fire spread rate and the expected final fire size given a defined level of fire suppression effort. Indices are developed to measure the effect of fire suppression costs and physical fire effects.
The development of Modeled Indices is the third major component of the wildland fire risk assessment framework. In this portion of the process, indices for Fire Response, Wildland Fire Susceptibility and Fire Effects are created from the various Derived Outputs. The goal of this step is to generate ranking information that can be combined into a simple set of outputs for decision makers. The Wildland Fire Susceptibility Index (WFSI) is related to the probability that a specific location will burn in a wildland fire event. WFSI combines information from the fire occurrence rates, the modeled fire behavior based on weather conditions and rate of spread versus final fire size relationships (Figure 3). The Fire Effects Index captures information from the fire suppression costs and the physical fire effects.
Figure 3: Fire Effects Index
The final step in the wildland fire risk assessment process is the generation of products to support fire-planning efforts. Figure 2 lists some of the typical products from a wildland fire risk assessment, including compiled data sets, and standard maps and reports for fire planners and the public. Intermediate information can also be important, such as estimates of ignition potential, maps of fuels, maps of structural hazards, the identification of critical values or resources that might be effected by a fire, and an assessment of the effectiveness of the fire suppression organization. In many cases, the final outcome of a wildland fire risk assessment is some type of mitigation plan that aims to proactively reduce the number of, or effects from wildland fires. A well-designed wildland fire risk assessment provides all the information needed to prioritize areas for analysis of potential mitigation measures.
Scalability of the Wildland Fire Risk Assessment Framework
The scale of a wildland fire risk assessment should be driven by goals and objectives. Is the goal to develop a strategic plan, or a detailed tactical plan? Regardless, the basic wildland fire risk assessment process is the same across geographic and temporal scales. Data is compiled, values are computed or derived, and information is ranked and combined to create final outputs. However, the scale (objective) of a wildland fire risk assessment typically impacts the exact type and resolution of the data required.
The impact of scale, and the scalability of the wildland fire risk assessment framework described in this article can best be demonstrated through specific examples. The unique aspects of three different scales of risk assessment will be presented, beginning with the coarsest scale analysis and progressing to the most detailed.
Landscape Scale Analysis
The Florida Division of Forestry enlisted Space Imaging to conduct a state-wide wildland fire risk assessment on an area of more than 35 million acres. The goals of this project were to create a database and set of application tools that increase the understanding of wildland fire risk across the state, identify the areas of highest concern across the state, and assist efforts by the State of Florida to create a strategic fire mitigation plan.
Fuels are often the most critical and costly data set, and thus tend to dictate the design and resolution of the remaining risk assessment data sets. In the Florida project, fuels were mapped using Landsat 7 (Figure 4), which dictated a 30-meter resolution for the remaining raster data sets and the final project results (Figure 5). Higher resolution imagery would have been more costly to obtain, difficult to store for an entire state, and unwieldy to use. The coarser 30-meter resolution made sense for a strategic, regional analysis. Information on specific structure locations or types, defensible space or other structural hazards is not needed for a project with this objective, eliminating that component of the framework and its associated analyses. However, information on basic infrastructure and the location of housing developments was needed to assess the potential fire effects, although at a lower level of detail than would be needed to develop a mitigation plan for an individual community.
Figure 4: Mapping fuels using Landsat 7
Rating Communities In the real world, there will very likely be many more communities seeking financial assistance for detailed planning than can be supported by the available resources, creating the need to rate or rank the candidate communities. The process of prioritizing communities begins with a landscape scale analysis, and adds some new data elements and some spatial analysis components, each of which is contained within the fire risk framework illustrated in Figure 2.
The most important new data element is the locations of communities to be ranked. More detail or more accuracy may be needed than in a landscape analysis because the geographic focus has narrowed to individual communities from the larger surrounding landscape. Communities can be located as a “point” on the landscape, or a polygon if the boundary information is available. Point features may be sufficient for small communities, in which case a circular buffer is generated to create an area for the feature. Polygonal boundaries are needed for larger communities with irregular shapes, and a buffer can be added using standard GIS software tools (Figures 6-8, right).
The next analytical step is to overlay these community boundaries with the landscape scale wildland fire risk assessment to determine the situation in and around the communities, in what is commonly called the Wildland Urban Interface. Various mathematical raster analysis procedures can then be used to summarize the situation within the community and the WUI. The details of the raster analysis will likely vary for different clients or in different regions of the county, but this process is objective and repeatable, and could be rerun as the community boundaries change through time and give consistent, comparable results. The final result is simply a rated set of communities. These community ratings are then used with other information in a prioritization or ranking process by local decision makers to determine how resources will be allocated.
Community Hazard Assessments
The most detailed wildland fire risk assessments take place at the individual community level, and are often called community level assessments. This intensive level of assessment may only take place on the communities identified as highest priority after the ranking analysis because of the cost of data collection. At this level, the focus of the analysis is on human constructs (structural hazards), and often requires a combination of high resolution imagery and field-collected information.
Important structural hazard assessment information often can be generated quickly and cost- effectively on imagery. Automated image processing techniques combined with high-resolution satellite imagery such as the natural color IKONOS image can be used to identify and map rooftop footprints more quickly than field visits (Figure 9 – left). Roof types for these structures, such as asphalt shingle, clay tile or wood shake, have also been successfully identified on high-resolution satellite imagery, which may be a critical element of a structural hazard assessment. Distance from the structure to flammable vegetation, also known as “defensible space” can also be derived on high-resolution imagery by combining vegetation layer and structure layers in a GIS (Figure 10).
Figure 10: “defensible space”
The remotely sensed information may need to be combined with data collected in the field to complete the community level assessment. Exterior flammable structures such as decks or outbuildings under vegetation, flammable exterior walls, or unique situations at individual structures, may need to be identified in a house-by-house field visitation. The key is to utilize remote sensing and field data collection optimally in combination to reduce the total cost of data collection.
Once all the required data are compiled, the process outlined in the wildland fire risk assessment framework is again followed—derived information, modeled indices and outputs. The factors affecting the overall ignition potential—fuels, fire behavior, fire suppression resources effectiveness, and fire effects—are the same as in a landscape level wildland assessment. The levels of detail or specific analyses may be different. The output of a community level assessment can vary widely, however. Whereas the focus of a wildland fire risk assessment is usually on the wildland fuels and fire ignition potential, a community hazard assessment is focused on the interaction between wildland fuels and structural hazards. Community level assessment requires very specific information based on the decisions to be made.
While they may appear to be unique on the surface, different types of wildland fire risk assessments have a common kernel that can be used to define the processes. A general wildland fire risk assessment framework can be used to: 1) Conduct a landscape scale wildland fire risk assessment, 2) Rank communities according to the likelihood an acre will burn, and 3) Support a community level assessment and the development of a community-specific mitigation plan. Because wildland fire risk assessment is a spatio-temporal phenomenon, it is important to have a process that is flexible and consistent over time and space. It is also critical that the process use proven analytical processes based on accepted science to integrate adequately the inputs to the intermediate and fine measures of potentials for effects. Planners and fire professionals need decision support tools that give reliable and valid answers, especially for questions relating to the efficiency of fire mitigation programs.
This article was originally published in Imaging Notes, Winter 2004. Entire article copyright (c) 2004, Space Imaging
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