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Wildfire Operations Research
FINAL REPORT
May 2014
Long-term monitoring programs and data-collection protocols for fuel
treatment sites: a literature review
Steve Hvenegaard
Introduction
The escalating severity of wildfires is widely recognized and the threat to expanding
communities and other values in the wildland-urban interface is increasing. As the frequency
and severity of wildfires increase, fuels management programs strive to reduce the risk of
catastrophic wildland fires that threaten people, communities, and natural resources. General
vegetation management strategies have been widely accepted (Agee and Skinner 2005) and
specific fuel treatment prescriptions (Partners in Protection 2003) are implemented across a
broad range of forest fuels to mitigate the risk of wildfire.
Wildfire management agencies across Canada have been actively conducting extensive forest
fuel treatments for the past two decades. Although most fuel treatments are likely effective in
altering fire behaviour in the short term, the capability of treatments to effectively moderate fire
behaviour and reduce wildfire risk in the long term is not well documented or understood.
Objective
Our advisory members had asked us to develop a universal long-term monitoring program for
fuel treatment sites and a standard data-collection protocol that would generate the data needed
to determine fuel treatment maintenance schedules. A standard data-collection protocol would
allow more efficient and effective sharing of information across districts and, possibly, across
provinces.
Methods
Our first step in this project was to review the available literature. While the project proposal was
worded to address FireSmart sites, our advisory members suggested at the March 2013
advisory meeting that the term fuel treatments would be more appropriate because it would
include all treatments whether or not they strictly adhered to FireSmart standards.
We searched the Internet for published and unpublished (grey) literature using the key words
wildland fuel treatment effectiveness and wildland fuel-treatment monitoring. We then examined
the reference section of the documents we found from our initial search to uncover several more
relevant documents. We sorted the documents into three categories: (1) long-term monitoring of
fuel treatments; (2) data-collection protocols; (3) methods of assessing fuel treatment
effectiveness. We then addressed each of the following topics:
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
goals and objectives of a universal, long-term monitoring program

data needs of a universal, long-term monitoring program

existing programs and data-collection protocols
Results
Monitoring is the collection and analysis of repeated observations or measurements to evaluate
changes in condition and progress toward meeting a management objective. Monitoring can
demonstrate that the current management approach is working and provide evidence supporting
the continuation of current management.
(Elzinga et al. 1998)
Monitoring can also identify problems with a current management practice and provide alternate
solutions that will mitigate concerns and/or increase efficiencies.
Goals and Objectives for Long-Term Monitoring Programs
The success of a long-term monitoring program is often the result of a large, up-front investment
to define program objectives, to optimize sampling design, and to determine the appropriate use
of the data. As Oakley (2003) stated, “Designing a monitoring program is like getting a tattoo:
you want to get it right the first time because making major changes later can be messy and
painful”. Identifying the key questions that are driving the development of a fuel-treatment
monitoring program are important for developing goals and objectives, and for developing
data-collection protocols that clearly address these questions.
Goals of Long-Term Monitoring Programs
The term fuel treatment is used ubiquitously to represent a broad range of modifications to a
forest environment. There is also a wide range of underlying goals and objectives either implied
in the treatment or stated explicitly in the fuel treatment prescription. Fuel treatments are
designed and conducted to achieve specific goals. These goals can include ecosystem health
and restoration, disturbance regime restoration, or more commonly the reduction of forest fuel
accumulation and the mitigation of wildfire risk (Omi and Martinson 2002).
The length of time that fuel treatments are effective in moderating fire behaviour is not well
understood (Agee and Skinner 2005). Extensive research has been conducted to assess the
effectiveness of fuel treatments in the first two years after a treatment (Stephens and
Moghaddas 2005, Vaillant et al. 2009), but there has been limited research conducted to assess
the long-term effectiveness. Wildland fuels managers question the long-term capability of fuel
treatments to moderate fire behaviour effects and to protect values-at-risk. Monitoring programs
can help identify trends in fuel accumulation and distribution as treatments age (Vaillant et al.
2013). A better understanding of these trends and the changing fire behaviour potential can help
managers estimate the longevity of a fuel treatment and develop re-treatment schedules.
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Wildfire management agencies are concerned with escalating costs of wildfire suppression and
are interested in how fuel treatments can reduce them. Intended outcomes of fuel treatments,
such as reducing fire severity, reducing fire size, and increasing containment probability
generally have the associated benefit of reducing suppression costs. A cost-benefit analysis of
fuel treatments by Thompson et al. (2013) and Snider et al. (2003) showed a long-term
reduction in wildfire suppression costs resulting from reduced fire occurrence and fire size. As
fuel treatments evolve and the effectiveness of the treatment changes, this type of
cost-modelling process could potentially quantify changes in fire suppression costs in relation to
the age of a fuel treatment.
Defining the Objectives for a Long-Term Monitoring Program
A clear statement of the goals of a fuel-treatment program will help to develop the objectives of
the program and define the effects that are to be monitored in the program. Within the context of
wildfire mitigation, different statements of fuel-monitoring programs goals and objectives provide
different approaches to implementing a program. Vaillant et al. (2013) provided an example of
quantifiable objectives:
Determine the length of time that fuel treatments are effective at maintaining
goals of reduced fire behaviour by:
a) measuring effects of treatments on canopy characteristics and surface
fuel loads over time, and
b) modeling potential fire behaviour with custom fuel models
A broad-based monitoring approach (Hayes et al. 2008) was used across the United States to
qualitatively evaluate the effectiveness of fuel treatments. The overlying goal of this monitoring
program was to “qualitatively answer specific monitoring questions about overall fuel treatment
objectives and treatment effects on aquatic and terrestrial habitat and air and water quality”. A
finding from this study provides good guidance for other fuel-treatment monitoring programs.
Fuel treatment objectives need to be clearly stated and “fuel specialists could benefit from
training to describe the fuel treatment objectives more clearly” (Hayes et al. 2008).
Data Needs for Long-Term Monitoring Programs
Approaches to Assessing Fuel Treatment Effectiveness
The objectives of a long-term fuel-monitoring program should provide focus for the approach
adopted to assess fuel treatment effectiveness. Tools used to qualitatively assess effectiveness
will differ from those used to quantify fuel treatment effects. It follows, that different approaches
to assessing fuel treatment effectiveness will use different evaluation tools with different data
requirements and data-collection protocols.
Hawkes (2010) outlined different approaches that can be used to assess fuel treatment
effectiveness. These approaches include experienced judgment, expert opinion, wildfire case
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studies, mathematical model simulations, and experimental fires. Each of these approaches has
limitations, advantages, and disadvantages with varying degrees of rigor in the associated
data-collection protocols.
In a review of research methods, Carey and Schumann (2003) categorized research in the
effectiveness of fuel treatments as: observations, case studies, simulation models, and empirical
studies. This literature review presents two relevant findings regarding these research methods:
first, although empirical studies provide the strongest basis for evaluating treatments, the
quantity of research in this category is most limited; second, personal observations are very
plentiful but are the least reliable class of research.
Qualitative Approaches
A qualitative approach to fuel-treatment monitoring requires the least amount of hard data. Using
experienced judgment and expert opinion has the advantage of minimal fuel sampling and
data-collection time, but has the inherent requirement for trained and experienced fuel
management personnel.
Expert opinions are subjective evaluations by qualified personnel of the hazard observed in a
fuel stand or treated area. At a local level, this qualitative approach can be informally applied
when assessing re-treatment needs. On a larger scale, Hayes et al. (2008) used standardized
worksheets to conduct national surveys, which prompted participants to provide a measure of
how well site-specific fuel treatment objectives were met. These protocols were intended to
“ensure results could be aggregated nationally” and could be “easily repeated at low cost by
many agencies”, ultimately achieving a long-term strategy of identifying trends in fuel treatment
effectiveness.
Photo documentation is commonly used to illustrate pre- and post-treatment fuel conditions.
Qualitative descriptors and quantified fuel load data are valuable additions to a photo series
(Lavoie et al. 2010), which can provide a means of estimating fuel loads in a fuel complex. A
national photo series (Ottmar and Vihnanek 2009) provides a standardized approach to
collecting, storing, and accessing photos along with quantified fuels data for major fuel types in
the United States. Fuel data associated with a photo can be used for several fuel management
applications. An adaptation of a data management system such as this could present a photo
series of a representative fuel complex that would allow users to evaluate fire behaviour
potential at different age classes and help develop re-treatment schedules.
Quantitative Approaches
Post-wildfire case studies (Hudak et al. 2011, Prichard et al. 2010) provide good insight into how
a treated area influences fire behaviour and moderates the impacts of wildfire. Given the
unpredictability of wildfires overrunning selected fuel treatments, this may not be a reliable
method for collecting data. However, valuable insights can be gained regarding how treatments
of different ages can change fire behaviour.
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Experimental fires (Schroeder 2010) have provided valuable data, which supports the
implementation of fuel reduction to reduce fire behaviour. This research method could be
extended to include experimental burns in aging treatments to assess the effectiveness of
treatments with evolving vegetation.
Assessment of fuel treatment effectiveness (Stephens et al. 2009, Fernandes 2009) is often
conducted using pre-treatment and post-treatment fuel sampling inputs to fire behaviour models
to quantify potential fire behaviour. As fuel treatments age, regrowth of vegetation and
accumulation of woody debris will change the fire behaviour potential. Long-term fuel-treatment
monitoring programs would include annual fuel sampling in treatments and use fire models to
quantify temporal changes in fire behaviour potential.
Understanding and quantifying the source of the fire hazard from a specific fuel component will
help fuels managers design more effective fuel treatments (Stephens et al. 2009). Some fire
models are better at assessing these specific fuel components and should be considered when
choosing a model for evaluating the potential fire behaviour. There is a broad variance in the fire
effect, fuel components, and specific data requirements between the different programs, but
generally, fuel load and distribution are the key fuel attributes of concern. The data inputs
required by specific fire behaviour prediction models will dictate the data-collection protocols for
a fuel-monitoring program.
Potential fire behaviour predictions generated through fire models are often tempered with
caveats of assumptions and model limitations. Variations in fuel load and structure, compounded
by changes in continuity, topography, and weather (Fule et al. 2001) create real-world fire
behaviour that may not be consistently represented by fire models using fuel load averages from
a plot-based inventory (Vaillant et al. 2013).
While fire behaviour models may not produce absolutes in quantified fire potential, models
indicate relative estimates that can be used to “rank fire hazard conditions and assess the
effectiveness or temporal persistence of a fuel treatment” (Fernandes 2009).
Choosing a suitable fire behaviour model from the plethora of available software tools is a
daunting task. This selection process will be easier if a fuel-treatment monitoring strategy has
focused objectives and identified specific fire behaviour effects that are to be modified. Some
models are designed specifically to model specific fire behaviour and it is critical to understand a
model’s strengths and weakness.
Metrics for Assessing Fuel Treatment Objectives
A universal goal of mitigating wildfire risk can include a broad range of fuel treatment objectives
to modify specific fire effects. Reducing the potential for active crown fire spread is a common
objective, which is addressed by modifying the fuel components (Agee and Skinner 2005) that
contribute to crown fire initiation and spread. Several fire behaviour prediction models
(Alexander et al. 2006, Andrews 2013, DeGroot 2012) have the capacity to quantify the potential
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for crown fire initiation and crown fire spread potential by processing specific fuel attributes and
weather inputs.
Reducing the fire intensity to provide for safe and effective suppression operations (Kerr 2007) is
another common fuel treatment objective. Implications for wildfire suppression in different fuel
types (Alexander and DeGroot 1988, Alexander and Lanoville 1989, Alexander and Cole 1995)
provide fire intensity values at which specific fire suppression activities may be ineffective or
unsafe. Ongoing fuel-treatment monitoring and fuel sampling coupled with fire behaviour
modelling may provide indications of fuel loads that will produce fire intensity above a tolerable
level.
Improved access and egress for fire suppression crews created by fuel treatments can enhance
safety and provide for alternative suppression strategies (Omi and Martinson 2002). It may be
difficult to measure how well these objectives are maintained within fuel treatments over time
and ongoing qualitative evaluation of fuel treatments will likely be required. Qualitative
evaluations of fires impinging on fuel treatments (Kerr 2007) may provide valuable data to
identify the fuel treatment characteristics that helped suppression efforts and how the treatment
enhanced firefighter safety.
Surface fire intensity and the associated grass loads will be the most likely metric to use in
evaluating potential fire behaviour in grass fuel types. Models used to quantify fire intensity in
grass fuel types will need to have the capacity to categorize the fuel as matted or standing, and
the ability for the user to input the grass fuel load and cure percentage.
Existing Programs and Data-Collection Protocols
There is a scarcity of literature presenting the overall extent of fuel-treatment monitoring
programs implemented by fuel management programs. We identified a few rigorous
fuel-monitoring programs that use standard data-collection protocols to feed fire behaviour
models to assess potential fire behaviour (Fites-Kaufman et al. 2007, Vaillant et al. 2013). The
extent and nature of fuel-treatment monitoring programs across wildfire management agencies
is variable. It is likely that most agencies conduct fuel-treatment monitoring through qualitative
approaches such as experienced judgment or photo-documentation. The limited number of longterm fuel-treatment monitoring programs may be the result of the large commitment of resources
and time required to develop and implement these programs.
Fites-Kaufman et al. (2007) presented preliminary results from a monitoring program designed to
“measure the effectiveness of fuel reduction treatments” (prescribed burn and mechanical) within
fuel types of the Pacific Southwest Region. That monitoring program will assess changes in fuel
load components from pre-treatment to years 1, 2, 5, 10 and 20 post-treatment, but they have
only presented results from the first few years.
Vaillant et al. (2013) continued monitoring these fuel treatments and analyzed annual fuel
sampling data to quantify changes in fuel loads and distribution. They modelled the potential fire
behaviour by using the fuel load data in these plots to evaluate the ability of the various fuel
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treatments to remain effective in reducing fire behaviour characteristics. A key finding from that
monitoring program was that fuel treatments in these forest types often recover to pre-treatment
loads within ten years of treatment. A key fuel management implication is that understanding
trends in post-treatment fuel loading can aid in determining treatment priorities and re-treatment
schedules.
Alberta Permanent Sample-Plot Program
The Alberta Forest Management Branch monitors permanent sample plots to acquire a better
understanding of forest stand growth and change over time (Alberta 2009). Since this program
was established in 1960, 650 plots have been established. A re-measurement schedule
describes the timelines that are implemented to maintain consistent monitoring of these plots.
Extensive data-collection protocols to measure and monitor stand dynamics are described in the
field procedures manual. The Alberta Wildland Fuel Inventory Program has adopted some of
these protocols.
Alberta Wildland Fuel Inventory Program
This program, formerly called the Alberta Fire and Vegetation Monitoring Program, was
developed in 2006 to collect fuel load and fire behaviour data. A provincial database stores the
data and contributes to a decision-support system used in prescribed burn planning and
FireSmart planning in Alberta. Several fuel treatment surveys are conducted throughout Alberta
annually and case studies have been developed using the data collected to demonstrate the
effectiveness of specific treatments in a given fuel type. The Alberta Wildland Fuel Inventory
program incorporates data-collection protocols from several well-established sampling and
inventory programs including the Alberta Prescribed Burn Sampling Handbook (Alexander 2006)
and the Fire Effects Monitoring and Inventory System (Lutes et al. 2005).
Photo Series
Photo series (Blonski and Schramel 1981, Stocks et al. 1990) have been developed to present
common fuels within a geographic area using photographic documentation and inventoried
stand characteristics. These visual and quantified representations help users visually associate
a fuel stand with quantified fuel load. The fuel load data can be used as inputs in fire behaviour
models to evaluate fire behaviour potential in these stand types.
The USDA Forest Service Digital Photo Series1 is a web-based platform that allows users to
access data from the Natural Fuels Photo Series (Ottmar and Vihnanek 2009). This photo series
provides fuel sampling inventories and photos of representative fuel types and sites across the
United States. With data stored in an electronic database, there is potential to extract fuel-load
data to be used in software programs, including fire behaviour models.
1
http://depts.washington.edu/nwfire/dps/
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A sequence of photos of a forest stand provides a good visual representation of changing stand
characteristics. Lavoie et al. (2010) presented photos and fuel load data for eight different
Jack Pine-Black Spruce forest stands in varying degrees of recovery after stand-replacing crown
fires. Photo series are typically used in fuel-treatment monitoring studies to illustrate changes in
fuel accumulation and stand structure (Fites-Kaufmann et al. 2007, Vaillant et al. 2013).
Discussion
Future Directions and Challenges
The Flat Top Complex Wildfire Review Committee stated in their report (2012) that “further
research and monitoring of fuel treatment effectiveness, along with the development of
appropriate decision support tools, will support FireSmart investments”; that it will be important
to monitor the results of fuel treatments to “gain a clearer understanding of the relative benefits
of different treatments and approaches”; and that results of long-term fuel-treatment monitoring
can be used to “adjust treatment methods and priorities“.
Each wildfire management agency has varying resource capacity to implement and maintain a
fuels management program. This is a critical factor in designing a monitoring program and
determining the degree of rigor in a data-collection and management systems. An agency with
limited resources for fuel sampling and data management may opt for a less rigorous monitoring
program, such as photo documentation and a database filing system.
Across Canada and within each wildfire management agency there are a multitude of fire
environments with diverse fuel complexes, weather patterns, and topographic features. Given
this diversity in wildland fuels across regions and provinces, the first step in long-term monitoring
will be to choose representative sites to conduct long-term fuel sampling.
Distinct fuel complexes have specific fuel components that drive fire behaviour and will influence
the focus of a monitoring program and the data-collection protocols. Ogden (2008) describes the
Yukon forestry monitoring program with a section developed to assess potential fire hazard in
the forests of southwest Yukon that have been impacted by spruce-beetle. While this
assessment protocol shares a universal objective of reducing the potential for crown fire, the
author cautions that some modifications to methods for assessing crown structure would be
need to be revised for the protocol to be applied elsewhere in the Yukon.
While data-collection protocols for unique fuel complexes such as chaparral (Fites-Kaufmann et
al. 2007) may not be applicable to Canadian fuel complexes, grass fuel types require special
considerations in measuring fuel load and assessing fire hazard (Baxter 2006).
Pilot Programs
Elzinga et al. (1998) stated that, “the challenges of successful monitoring involve efficient and
specific design, and a commitment to the implementation of the monitoring project, from data
collection, to reporting and using results”. Fuel sampling and data management are
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time-consuming activities. The development of a fuel-monitoring program will be limited by
management support and available resources (people and equipment). A pilot program would
help agencies assess the time and personnel commitments required to continue annual fuel
sampling and manage data. Findings from a pilot program will help to determine the appropriate
scale and complexity of an ongoing monitoring program.
It is likely not possible for an agency to continue regular re-sampling and long-term data
collection for every combination of representative fuel type and fuel treatment. A manageable
pilot program would need to narrow the focus of the data collection to one or two of the most
commonly implemented fuel treatments in the most prevalent hazardous fuel types.
Fuel-Monitoring Photo Series
Most fuel-sampling protocols include photo documentation of the different fuel layers within a
fuel complex. When combined with fuel load data, a user has a good visual representation of a
fuel complex that can be associated with a fuel environment with a measured fuel load. A photo
guide arranged chronologically can effectively illustrate the changes in fuel load and structure
within a fuel complex.
Many agencies use photo documentation to illustrate changes in fuel loads over time. Adopting
established photo-documentation protocols could enhance comparative analysis of year-overyear changes in fuel treatments. A universal photo-documentation protocol would contribute to
efficient file management and sharing. Alberta ESRD Foothills Area has implemented a
database that stores data on treatment location, area, fuel type, and treatment type. It would be
possible to attach an annual photo of fuel treatments to a file and a database query could
produce a photo series to assess ongoing changes in the fuel complex.
While much of the data-collection and management processes are by electronic means, the
value of hard copies of photo series illustrating the changes in a fuel treatment should not be
discounted.
Data Collection and Management
The fuel-sampling protocol used by the Alberta Wildland Fuel Inventory program captures fuel
load data that can be used in fire behaviour models, such as CFIS (Alexander et al. 2006) and
CanFIRE (de Groot 2012). All portions of this sampling protocol may not be necessary to satisfy
the data needs of these or other fire models. An agency could tailor a fuel sampling protocol to
provide essential inputs to a chosen fire behaviour model.
The provincial fuels database maintained by the Alberta Wildland Fuels Inventory Program will
accommodate additional annual fuel load data and photos of fuel treatments that are included in
a fuel-treatment monitoring program. The desired deliverables of a fuel-treatment program
should be identified to design queries within the database that will produce these outputs. For
example, the database could be queried to generate a photo series of an evolving fuel treatment
and include fuel load data for the years that fuel sampling was conducted.
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Capitalizing on Established Programs
Opportunities exist to expand current programs to include ongoing monitoring of fuel treatments.
Currently, the Alberta Wildland Fuels Inventory Program collects and manages pre- and
post-treatment fuel load data and photo documentation from plots across the province. A longterm monitoring pilot program could continue these protocols on a continued resampling
schedule to capture changes in fuel loads and structure in fuel treatments.
Research Sites
Research sites are being established by ESRD in several areas throughout Alberta to explore
fuel treatments of varying nature and intensity in predominant hazardous fuel types. The ESRD
Wildland Fuels Inventory Program will monitor these sites over the long-term with scheduled
resampling. This process will be a good opportunity to assess the time and resource
requirements of a fuel-treatment monitoring program and how existing data-collection protocols
can be streamlined to be implemented by more personnel over a wider land base.
The fuel types studied in these research areas are representative of fuels found in those
geographic areas. Data collected from long-term monitoring programs in these research sites
will provide good direction for fuels management in that area, but should be applied with caution
for similar fuels in different areas.
With a focus on a specific fuel type and fuel treatment prescription, ongoing fuel sampling will
provide data to develop a better understanding of regrowth in these treatments and assess
changing fire behaviour potential in these fuel complexes.
Adaptive Management and Fuels Monitoring
Measuring a trend, or how a resource changes over time, is the most common approach to
environmental monitoring. Although monitoring for a trend identifies how a resource is changing
over time, it does not include the measurements as part of a management cycle (Elzinga et al.
1998). Within the fuel treatment context, monitoring for management would use measurements,
such as fuel load and resulting potential fire behaviour, as a trigger for re-treatment or some
other corrective response.
Many components of a long-term fuels monitoring program (fuel sampling, data management
analysis) are currently being conducted. The challenge for fuels mangers and agencies will be to
identify a structure for a monitoring program and determine how these existing programs can
contribute to a successful monitoring program driven by management objectives.
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Conclusions
For several decades, wildland fuels managers have recognized the need to reduce the build-up
of hazardous forest fuels to mitigate the risk of wildfire. While fuel management programs have
strategically and extensively conducted forest fuel treatments, there have been limited studies of
their long-term effectiveness in maintaining the objectives of modifying fire behaviour.
Generally, fuel-treatment monitoring has been informally conducted through various methods
such as expert opinion observations or photo documentation. Fuels managers have recognized
the need for more formalized long-term fuels monitoring programs that would incorporate
ongoing fuel sampling and data-collection protocols with data management systems to provide
answers to fuel management questions.
A limited number of documented monitoring programs have captured long-term fuel loading data
to identify trends in the evolution of fuel environments. When used in the context of an adaptive
management system, these trends can augment a decision support system to prioritize fuel
treatments, modify fuel treatment practices, and determine appropriate re-treatment schedules.
Documented long-term fuel-treatment monitoring programs provide a good template that can be
adapted for use in other areas. Opportunities to use existing data-collection protocols and data
management systems will reduce start-up costs of a long-term monitoring program. Data
management is an essential component of a long-term monitoring program. Ease of data
access, analysis, and dissemination are important considerations.
Defining and developing a formalized long-term fuel-treatment monitoring program will require
political commitment to initiate a program and secure adequate resources (personnel and
finances) to sustain a program. Currently, strong political mandates and very active fuels
management programs provide a good opportunity and the momentum needed to develop pilot
programs to evaluate the potential for monitoring programs.
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