Computers and Electronics in Agriculture 37 (2002) 185 /198
www.elsevier.com/locate/compag
Information systems in support of wildland fire
management decision making in Canada
B.S. Lee a,, M.E. Alexander a, B.C. Hawkes b, T.J. Lynham c,
B.J. Stocks c, P. Englefield a
a
Natural Resources Canada, Canadian Forest Service, Northern Forestry Centre, 5320-122 Street,
Edmonton, Alta., Canada T6H 3S5
b
Natural Resources Canada, Canadian Forest Service, Pacific Forestry Centre, 506 West Burnside Road,
Victoria, BC, Canada V8Z 1M5
c
Natural Resources Canada, Canadian Forest Service, Great Lakes Forestry Centre, P.O. Box 490, Sault
Sainte Marie, Ont., Canada P6A 5M7
Abstract
This paper provides an overview of four national forest fire management information
systems currently used in Canada. The Canadian forest fire danger rating system (CFFDRS) is
a non-spatial system, which provides the science framework for fire danger rating in Canada.
The spatial fire management system (sFMS) is a geographic information system based fire
management information system that implements two core subsystems of the CFFDRS, along
with other models and systems. The sFMS is the spatial engine that has been used to
implement both of Canada’s national forest fire management information systems, the
Canadian wildland fire information system (CWFIS) and the fire monitoring, mapping and
modeling system (Fire M3). The CWFIS is Canada’s national fire management information
system; it presents daily information on fire weather, fire behavior potential and selected upper
atmospheric conditions. Fire M3 integrates the use of satellite technology for monitoring and
mapping large fire occurrence in Canada. Fire M3 also incorporates information from CWFIS
to model the impacts of large forest fires based on fire weather conditions and potential fire
behavior.
Crown Copyright # 2002 Published by Elsevier Science B.V. All rights reserved.
Keywords: Satellite technology; Information systems; Geographic information; Wildland fire
Corresponding author
E-mail address: blee@nrcan.ge.ca (B.S. Lee).
0168-1699/02/$ - see front matter. Crown Copyright # 2002 Published by Elsevier Science B.V. All rights
reserved.
PII: S 0 1 6 8 - 1 6 9 9 ( 0 2 ) 0 0 1 2 0 - 5
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1. Introduction
The Canadian forest service (CFS) has a history of over 75 years of forest fire
science research (Beall, 1990). Two major contributions from this work have been
the development of a national system for forest fire danger rating and accompanying
forest fire management information systems. This paper provides an overview of
four fire management information systems currently in use in Canada.
Canada has 244 million ha of productive forest that produced $27 billion in forest
product exports in 1994, amounting to more than agriculture, mining and fisheries
combined, (Canadian Council of Forest Ministers, 1994). Ninety percent of
Canada’s forests are owned by the public, represented mainly by the 10 provincial
and three territorial governments. Forest fire management is thus largely the
responsibility of each province and territory and huge areas fall under the
jurisdiction of a single land management agency.
Wildfires are a significant agent of change in Canadian forest ecosystems. On
average, 10 000 fires burn 2.5 million ha of wildland areas in Canada each year. The
variation in number of fires and area burned varies widely from year to year (Fig. 1).
Not all fires are attacked, let alone suppressed. There are two distinct causes of forest
fire in Canada: people and lightning. On average, lightning is responsible for onethird of the fires in Canada, yet results in 90% of the area burned. Typically fires
Fig. 1. Annual forest fire statistics for Canada (from Stocks, 2000). (Illustrations in colour on line in
Science Direct.)
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started by lightning occur in remote areas of Canada where detection and
suppression are more difficult.
The systems described in this paper have been developed to provide a systematic
approach to the assessment of forest fire danger rating in Canada. These systems
have been successfully adopted by Canadian fire management agencies to help
mitigate the impacts of fire both regionally and nationally.
2. Canadian forest fire danger rating system
Fire danger rating constitutes the process of systematically evaluating and
integrating the individual and combined factors that determine the ease of a fire
starting and spreading, difficulty of control and some immediately evident fire
impacts (e.g. crown scorch, depth of burn) based on an assessment of ignition risk,
the fire environment (i.e., fuels, weather, topography) and values-at-risk (Countryman, 1966). Fire danger rating systems produce qualitative and/or numerical indexes
of fire potential that are used as a guide in a wide variety of fire management
applications involving both wildfires and prescribed fires.
The Canadian forest fire danger rating system (CFFDRS), as developed by the
CFS, is the national system of fire danger rating used in Canada (Stocks et al., 1989;
Alexander et al., 1996; Van Nest and Alexander, 1999). The CFFDRS comprises two
primary subsystems or modules (Fig. 2a) */ the Canadian forest fire weather index
(FWI) System (Canadian Forestry Service, 1984; Van Wagner, 1987) and the
Canadian forest fire behavior prediction (FBP) system (Forestry Canada Fire
Danger Group, 1992; Taylor et al., 1997). The other two elements of the CFFDRS,
the accessory fuel moisture system and the Canadian forest fire occurrence
prediction (FOP) system, have not been entirely developed for national use although
various regional versions exist (e.g., Martell et al., 1989; Kourtz and Todd, 1992).
The latter subsystem is intended to predict the number of lightning and humancaused fires while the purpose of the former is to support applications of the other
three subsystems of the CFFDRS (Lawson et al., 1996). The CFFDRS forms one of
the basic building blocks for other guides and systems developed by either
operational fire management personnel or other wildland fire researchers (e.g.,
Wiltshire et al., 1995; Hawkes and Beck, 1999).
The output of the FWI System consists of six relative numerical ratings for various
aspects of fire danger for a reference fuel type (i.e., mature pine stand) on level
terrain based on a continuous record of four weather observations (Turner and
Lawson, 1978) taken daily at 1300 h daylight time (Fig. 2b and d). The FBP System
on the other hand provides for actual quantitative estimates of certain physical fire
behavior characteristics (e.g., head fire rate of spread in m/min or km/h and head fire
intensity in kW/m) for 16 benchmark fuel types (De Groot, 1993) and topographic
situations based on certain components of the FWI System (Fig. 2d). A simple
elliptical fire growth model is used for estimating the size (area and perimeter) and
shape as well as the flank and back fire characteristics of free-burning fires
originating from a single ignition source.
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Fig. 2. Simplified structure diagrams for (a) CFFDRS illustrating the linkages to fire management actions,
(b) the FWI System, (c) the FBP System, and (d) the definitions of the six standard components of the
FWI System.
Conceptually, the CFFDRS deals with the prediction of fire occurrence and
behavior from point-source weather measurements (i.e., a single fire weather
network station). The CFFDRS deals primarily with variation in weather from
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189
day to day, but it can account for diurnal variation in fire danger as well. The system
does not account for spatial variation in weather elements between points of
measurement. Models and other systems external to the CFFDRS must handle such
interpolation. Spatial variation in fuels and terrain is a fire management information
problem not easily handled by the CFFDRS or any fire danger rating system for that
matter unless linked by computer technology to a geographic information system
(GIS), which stores, updates, and displays such land base information in ways
directly usable to wildland fire managers. It is worth emphasizing that the inherent
difficulty of obtaining sufficiently accurate and timely forecasts of the fire weather
elements (most notably wind speed) used in calculating the various components of
the CFFDRS does place a real limitation on the system and any computerized
decision support systems that depend in whole or in part on the CFFDRS as a means
of predicting wildland fire occurrence and behavior potential.
3. Spatial fire management system
Computer-based fire management systems have been used in Canada since the
early 1970s. All regional fire management agencies in Canada employ some form of
computer-based fire management information systems in their operational fire
management programs. The minicomputer-based Fire Management System
(Kourtz, 1984) and personal computer-based Intelligent Fire Management Information System (Lee and Anderson, 1989, 1991; Lee, 1990; Lee and Buckley, 1992), both
designed and developed by the CFS, provide the bases for most of these systems.
In 1992, the CFS began investigating the use of GISs as an enabling technology
for constructing fire management information systems. These efforts culminated in
the development of the spatial fire management system (sFMS). The sFMS is a
spatially referenced forest fire management information system that builds on
previous systems, while incorporating the latest enabling technologies (Englefield et
al., 2000).
3.1. sFMS architecture and system integration
The sFMS can operate as a stand-alone system or be integrated into existing
information systems. Enabling technologies include a GIS and one or more
relational database management systems. Depending on the tasks to be performed,
data inputs consist of a variety of temporal and spatial data including weather,
elevation, and fuel type.
Fire management functions are supported by an integrated set of system modules.
Modules can be selected as required, depending on the task. Extensive parameterization has been incorporated into the overall design of sFMS to ease implementation. Users also have the option of incorporating their own components or even
entire modules into the system.
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3.2. sFMS implementations and functionality
The sFMS has been designed to support a range of fire management functions
from policy formulation to fire suppression decision support. The system is primarily
used with current weather data and short-term forecasts to provide daily or hourly
maps of fire weather, fire behavior, ignition probability, and attack time. In most
cases, the maps are distributed via the world wide web (WWW). These maps are used
by fire managers to assist in making decisions regarding initial attack response,
alertness levels and distribution of personnel and equipment. The system is also used
with long-term climate forecasts to predict the impacts of climate change on fire
danger, area burned, fuel consumption and greenhouse gas emissions.
The spatial fire climatology and wildfire threat rating modules use historical
weather to determine normals and trends in fire danger levels. These analyses can be
used to support the setting or review of policy, allocation of national or regional
priorities, establishment of suppression goals, and budget estimation. The system
also includes utilities for setting analysis parameters, displaying maps, managing and
querying spatial data, assessing and optimizing aircraft deployment plans, and
setting up automatic batch processing.
The sFMS can be used at any scale, from local to global. It is used by several fire
management agencies in Canada, notably British Columbia, Alberta, Saskatchewan,
Ontario and the Yukon, as well as internationally (e.g. Florida, Mexico, and
Southeast Asia; see for example Brenner et al., 1997). It is also the engine behind
Canada’s national forest fire management information systems. For links to these
examples on the WWW, see http://fms.nofc.cfs.nrcan.gc.ca/sfms/.
4. Canada’s national forest fire management information systems
Natural resources Canada currently operates two national forest fire management
information systems: the Canadian wildland fire information system (CWFIS) and
the fire monitoring, mapping and modeling system (Fire M3). Both of these systems
incorporate components of the CFFDRS and use the sFMS spatial engine for data
capture, management, modeling, analysis and presentation. The CWFIS is operated
by the CFS, while Fire M3 is a joint initiative of the CFS and the Canada centre for
remote sensing (CCRS). Both the CFS and CCRS are sectors of Natural resources
Canada.
4.1. Canadian wildland fire information system
The CWFIS is Canada’s national level fire management information system. The
system has been operational since 1994 and was developed to provide a national
overview of daily wildland fire conditions and for national reporting (Lee, 1995). It is
used by regional fire management agencies, fire interagency coordination centers, the
public, researchers, and the media. The system employs sFMS as the underlying
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Fig. 3. Examples of daily (a) Fire Weather Index and (b) head fire intensity maps for Canada for October
5, 1999, produced by the CWFIS.
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Fig. 4
B.S. Lee et al. / Computers and Electronics in Agriculture 37 (2002) 185 /198
193
Fig. 4. Sample monitoring and mapping products from Fire M3: (a) a NOAA /AVHRR derived
monitoring product showing hotspots, smoke and cloud in central Alberta during early May 1998 and (b)
a SPOT VGT derived mapping product for the 1998 Virginia Hills Fire in central Alberta, Canada. Images
courtesy of the CCRS. (Illustrations in colour on line in Science Direct).
software and operates in a fully automatic mode from the CFS’s Northern Forestry
Centre, located in Edmonton, Alberta, Canada.
The CWFIS collects hourly weather data from more that 650 weather stations
located across Canada. These data are processed using sFMS to produce daily maps
of the various components comprising the two major subsystems (i.e. FWI and FBP)
of the CFFDRS (Fig. 3). Weather data is interpolated between weather stations to
produce surfaces for each FWI System input (temperature, relative humidity, wind
speed, and 24 h precipitation amount) as well as wind direction. Procedures include
adjustments to compensate for change in elevation. Cartographic modeling is then
Fig. 5. An example of the Fire M3 modeling component. With the interactive map server on the Fire M3
web site, hotspot attributes can be displayed by clicking on hotspot locations. The attributes include fire
weather conditions (Temp, air temperature (8C); RH, relative humidity (%); Wind Dir, wind direction (8);
Wind Sp, 10-m open wind speed (km/h)), the six FWI System components, and selected FBP System
outputs (CFB, crown fraction burned (%); TFC, total fuel consumption (kg/m2); HFI, head fire intensity
(kW/m); ROS, head fire rate of spread (m/min)) for the fuel types involved (M2, boreal mixedwood; C2,
boreal spruce; C1, spruce-lichen woodland) at the hotspot locations as predicted by the CWFIS. In this
particular example, attributes of four hotspots from a fire in northeastern Manitoba are displayed. The fire
burned from August 5 /8, 2000.
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Fig. 6
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195
performed on these weather data surfaces to compute both FWI System and FBP
System values at a 1-km cell resolution.
4.2. Fire monitoring, mapping and modeling system
About 3% of the wildfires in Canada grow larger than 200 ha in size; however,
they account for almost 97% of the annual area burned (Stocks et al., 1996). In an
average year, some 300 fires attain a size greater than 200 ha. This provides an
excellent opportunity to monitor active large fires using low-resolution sensors.
Towards this end, the primary objectives of the Fire M3 are to use satellite
technology to automatically monitor fire activity on a daily basis, to produce end-ofseason fire maps, to model the impacts of fire, and to disseminate this information
daily via the Internet.
The monitoring component of Fire M3 uses 1-km resolution National oceanographic and atmospheric administration (NOAA) advanced very high-resolution
radiometer (AVHRR) satellite data to detect actively burning forest fires. Using an
algorithm developed by the CCRS (Li et al., 1997), AVHRR images are processed
daily from May through October to monitor large fire activity in Canada. The
algorithm effectively identifies pixels containing fire (hotspots) and smoke plumes
from large fires (Fig. 4a).
The mapping component of Fire M3 serves to get better estimates of area burned
using both low and high-resolution sensors. Initial estimates of area burned are
possible using AVHRR and Système Probatoire d’Observation de la Terre (SPOT)
Vegetation (VGT) (Fig. 4b). Medium- to high-resolution sensors such as Landsat
Thematic Mapper and SPOT high resolution visible are being used in the Fire M3
project to calibrate and verify area estimates from AVHRR and SPOT VGT. These
sensors are also being used to demonstrate the advantages of using medium to highresolution sensors over traditional fire mapping approaches.
The modeling component of Fire M3 integrates data from the CWFIS to provide
point data estimates of fire weather conditions, fire danger ratings (i.e., FWI System
components) and potential fire behavior (i.e., selected FBP System components) at
hotspot locations (Fig. 5). Map production includes images (Fig. 6) for public web
site access along with an Internet map server that allows interactive panning,
zooming and querying of satellite images and hotspot maps.
Outputs from both Fire M3 and CWFIS are available publicly via the WWW.
Further information on both of these two systems, as well as the CFFDRS and
sFMS can be found on the Internet (see http://fms.nofc.cfs.nrcan.gc.ca).
Fig. 6. Fire M3 products: (a) AVHRR satellite image for July 12, 1999 (b) a map of all hotspots detected
during the 1999 fire season as of October 6, 1999. Images courtesy of the CCRS.
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5. Benefits of forest fire management information systems
Over the past 30 years, Canadian forest fire management agencies have
demonstrated that information technology has an essential place in tracking fire
weather, predicting fire behavior and monitoring fire occurrence. During this period,
decision support systems have been successfully integrated into daily fire operations
of all Canadian fire management agencies. These systems have extended the utility of
the CFFDRS by exploiting the ability to turn data into information in near real
time. Better information has resulted in more effective and efficient decision making.
While the CFFDRS technically deals with the prediction of fire potential from a
point-source (i.e., a single fire weather station), it is the integration of a fire danger
rating system with spatial information management and technology that accounts
for variations across the landscape. Systems such as sFMS provide a mechanism for
the fire manager to deal with spatial variation in weather, fuels, and terrain.
At the national level, systems like CWFIS and Fire M3 have a much broader role.
These systems were not designed for specific operational fire management purposes,
but rather for more general monitoring and reporting on a national basis. For
example, the Canadian Interagency Forest Fire Centre located in Winnipeg,
Manitoba, uses the CWFIS and Fire M3 to monitor national fire danger and
wildfire activity on a daily basis. This information contributes to decision making
with respect to interagency transfers of fire suppression resources. A developing role
for CWFIS and Fire M3 is in the area of national and international reporting. At the
national level, both of these systems contribute data to the Canada Forest Inventory,
Canada Forest Statistics Data Base and the State of Canada’s Forests annual report.
These data can also been further abstracted to meet international reporting
requirements for criteria and indicators of sustainable forestry and for global
change reporting.
A final benefit of Canada’s forest fire management information systems is in the
area of knowledge management. The CWFIS and Fire M3 constitute the fire data
warehouse component of Canada’s National Forest Information System. The fire
data warehouse creates a permanent data archive for the national fire weather and
large fire occurrence databases. Through formal data exchange protocols, the fire
data warehouse provides expanded opportunities for collaborative research and
management between a wide range of knowledge domains including forest policy,
forest management, forest fire science, forest health, forest inventory, global change,
forest economics and remote sensing.
Acknowledgements
The authors wish to thank Kornelia Lewis for her assistance in the preparation of
this article.
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