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 ABSTRACT
A decision making model was developed for the Pacific Northwest to identify Region wide fire hazards and prioritize fuels treatments. Two sytems are used in tandem; the first model uses quantitative spatial data as input into a hierarchy of fire hazard, fire behavior and ignition risk to identify sub watersheds that have a cumulative risk above determined minimum thresholds. The second model uses value based input of ecological and logistical aspects of conducting a fuels treatment in at risk areas. Data was collected and aggregated from a variety of sources for input. The logic and decision models are executed in EMDS, a decision support system that operates in ArcGIS.the ecological status of each ecosystem can be placed in one or more social values contexts to further inform decision-making. The application can be readily expanded to support strategic planning at national and regional scales, and tactical planning at local scales.
OBJECTIVES - To bring together expert panels of fire managers and decision makers to formulate elementary topics and decision criteria for the Regional logic and decision models.
- To create a fully parameterized decision-support application that evaluates fire danger and recommends priorities for fuels treatment
- To formally present the finished projects to Regional leadership and fire managers
STUDY PLAN Introduction We propose developing a regional-scale decision-support system for evaluating wildland fire danger and prioritizing subwatersheds for vegetation and fuels treatment. The Region of interest will be the Pacific Northwest, which encompasses the States of Oregon and Washington in their entirety. In our descriptions, here and following, we adopt the nomenclature of the National Wildfire Coordinating Group (NWCG 1996, 2005) and Hardy (2005). The decision-support system will consist of a logic model and a decision model. In the logic model, we will evaluate danger as a function of three primary topics: fire hazard, fire behavior, and ignition potential. Each primary topic has secondary topics under which data are evaluated. The logic model shows the evaluated state of each landscape element (subwatershed) with respect to fire danger. In a decision model, we will place the fire danger summary conditions of each evaluated landscape element in the context of a number of key social and economic considerations. The architecture for the logic model will be based on the work of Hessburg et al. 2006, but adapted as needed, as data allows, and as discussions and feedback from Regional fire managers warrant. The decision model will be primarily based on the work of Reynolds et al. 2006, but will also be modified through interactions and feedback from Regional decision-makers. The logic and decision models will be executed in EMDS (Reynolds et al. 2003), a decision-support system selected by the Forest Service as its corporate solution for decision-support modeling. EMDS operates in ArcGIS. NetWeaver Developer software will be used to build the logic model; Criterium DecisionPlus software will be used to develop the decision model. Table1:Logic outline for evaluation of wildfire dangera | Model topic | Primary topicb | Secondary topic | Elementary topic | Propositionc
(stated in the null form) | Data inputsd |
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| Fire danger (unione) | | | | Danger of severe wildfire is low | | | | Fire hazard (union) | | | Fuel conditions do not support severe wildfire | | | | | Surface fuels (union) | | Condition of surface fuels is not conducive to severe wildfire | | | | | | Fire behavior fuel model | Expected fire behavior is not severe | FBFMarea,
FBFMaggregation | | | | | Fuel characterization class | Observed fuel load classes are not conducive to sever wildfire | FCCarea,
FCCaggregation | | | | Canopy fuels (union) | | Condition of canopy fuels is not conducive to severe wildfire | | | | | | Canopy bulk density | Canopy bulk density is not conducive to severe wildfire | CBDarea,
CBDaggregation | | | | | Canopy base height | Canopy base height is not conducive to severe wildfire | CBHarea,
CBHaggregation | | | | Fire regime condition class | | Fire regime condition class is not conducive to sever wildfire | FRCCarea,
FRCCaggregation | | | Fire behavior (union) | | | Expected fire behavior associated with wildfire is relatively benign or low impact | | | | | | Spread rate | Likelihood of high spread rate of surface fire is low | spreadRate | | | | | Flame length | Likelihood of high flame length is low | flameLength | | | | | Fireline intensity | Likelihood of high fire line intensity is low | firelineIntensity | | | | | Crown fire potential | Likelihood of high crown fire spread potential is low | crownfirePotential | | | Ignition risk (union) | | | Likelihood of wildfire ignition is low | | | | | | Palmer drought severity index | Likelihood of long-term drought is low | palmerIndex | | | | | keetch-Byram drought index | Likelihood of short-term drought is low | keetch-byramIndex | | | | | AVHRRNDVI | Relative plant greenness for the subwatershed is high | AVHRR-NDVI | | | | | Lightning strike | Relative lightning strikes in the subwatershed are low | lightningStrike | a The logic outline specifies how data related to wildfire danger (Table 2) are interpreted in NetWeaver®, a logic modeling system.
b The level of each primary, secondary, and elementary topic in the outline is indicated. The overall topic of the model is wildfire danger. Evaluation of overall fire danger depends directly on the evaluation of the primary topics--fire hazard, fire behavior, and ignition risk. Terms in parentheses following a topic indicate the logic operator used to evaluate the propositions under a topic. For example, fire danger is evaluated as the union of hazard, behavior, and ignition risk. Subtopics shown under ‘Elementary topics evaluated’ indicate the elementary topics occurring at lowest level in the logic model where data are evaluated (Table 2).
c Each proposition evaluates a set of premises (see footnote b) or data relative to a specific landscape unit. For this analysis, subwatersheds were the landscape units.
d Definitions of data items are presented in Table 2.
e The union operator treats the premises of a proposition as factors that incrementally contribute to the proposition.Much of the data used to support this modeling effort will come from the National LANDFIRE mapping project (www.landfire.gov). Additional data will be captured from other established sources (e.g., EROS, NCDC, NLDN), or derived using existing, published, and documented models and modeling procedures. The proposed decision-support system is comparable in some respects to the National Fire Danger Rating System (NFDRS, Deeming et al. 1977, Burgan 1988), but there also are important differences and advances. For example, the NFDRS summarizes fire danger information pertaining to fire hazard, fire behavior, and ignition risk, the primary topics of fire danger, at a regional scale using information on annual weather and forest conditions. The fire danger variables computed by FIREHARM, and used in this application, reflect a broader set, are computed at a stand or patch scale and summarized to subwatersheds, and the variables are computed as probabilities of exceeding a severe fire threshold using 18 years rather than a single year of data. Methods Study area We will use data covering 6 map zones (1, 2, 7, 8, 9, 10, 18) to complete our modeling project. Map zones were developed in the US by the Earth Resources Observation and Science (EROS) Data Center (http://www.nationalmap.gov). They are broad biophysical land units represented by similar surface landforms, land-cover conditions, and natural resources; there are 66 in the continental US (Fig. 1). These 6 map zones fully cover the States of Oregon and Washington. Within this spatial domain, we will evaluate the threat of severe wildland fire danger for all subwatersheds that are entirely contained within the 6 map zones. The average size of subwatersheds is about 5,000 to 10,000 ha, but size ranges from 2,000 to 25,000 ha. For reference, a subwatershed represents the 6th level in the watershed hierarchy of the US Geological Survey (Seaber et al. 1987). 
Figure 1. Map zones of the United States from the Earth Surface Resources and Science (EROS) Data Center. There are 66 map zones in the continental United States.
Data sources As mentioned, most spatial data used in model development will come from the LANDFIRE mapping effort (Rollins et al. 2006). The LANDFIRE project is creating spatial data layers of topography, biophysical environments, vegetation, and fuels at 30-m resolution for all map zones in the US. All layers are available at the www.landfire.gov web site using the National Map software retrieval system. The fuels layers will include two surface fuel classifications: 1) the 40 recently derived fire behavior fuel models (FBFMs) of Scott and Burgan (2005)mapped using methods described by Keane et al. (1998, 2000, 2007); and 2) the default fuel characterization classes defined in the Fuel Characterization Classification System (FCCS) described by Sandberg et al. (2001) (http://www.fs.fed.us/pnw/fera) and mapped using methods described by Keane et al. (2007). The FBFMs, which do not represent or describe actual surface fuels, provide an indication of the expected surface fire behavior, while the FCCS classes attempt to represent the characteristics of the actual surface fuelbed, and this information is useful for simulating fire effects. The canopy-fuels layers will be taken from the LANDFIRE canopy bulk density and canopy base height layers. Canopy bulk density (CBD) represents the mass of available canopy fuel per unit volume of canopy in a stand (Scott and Reinhardt 2005), and it is defined as the dry weight of available canopy fuel per unit volume of the canopy including spaces between tree crowns (Scott and Reinhardt 2001). Canopy base height (CBH) represents the level above the ground at which there is enough aerial fuel to carry the fire into the canopy, and it is defined as the height from the ground to the bottom of the live canopy (Scott and Reinhardt 2001), but may also include dense, dead crown material that can carry a fire. Fire behavior will be simulated with these surface and canopy fuels layers assuming 90th percentile weather conditions using the FIREHARM (Keane et al. 2004) program to estimate spread rate of surface fire, flame length, and fireline intensity based on the Rothermel (1972) model for fire spread, and crown-fire intensity and spread based on the Rothermel (1991) and the Scott (1999) crown-fire algorithms. In addition, LANDFIRE provides a digital map of fire-regime condition class (FRCC) created by simulating historical landscape conditions and comparing the simulations with current vegetation conditions derived from satellite images. FRCC is an ordinal index with three categories that describe how far the current landscape has departed from presettlement-era conditions (Hann 2004) (see www.frcc.gov for complete details). A map of departure is also available that describes the departure of current from historical landscapes using an index of zero to 100, and this index may be used in place of FRCCs. Several other data layers will be used to derive ignition risk. Relative plant greenness is estimated from an AVHRR image from June 1, 2004 (Burgan and Hartford 1993). These data are obtained from the USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Sciences Laboratory. Effects of long-term drought will estimated from Palmer Drought Severity Index (PDSI) data obtained from the National Climate Data Center. Available PDSI data represent a span of 20 years (1971-1990), and data are derived from a 2.5-degree continental scale grid of PDSI reconstructed by Cook et al. (2004). Lightning strike data are obtained from the National Lightning Detection Network. Data for the 6 map zones became available after the close of calendar year 2006 (http://www.landfire.gov/schedule_map.php). Implementation steps Under the fire hazard topic, we will estimate for each elementary topic (lowest level in the model at which data are evaluated) the percentage area and degree of aggregation of observations exceeding a specified threshold value using spatial data layers provided by the LANDFIRE project and a spatial analysis program (FRAGSTATS, McGarigal et al. 2002, Table 2). For each elementary topic under fire behavior and ignition risk, we will estimate the probability that conditions within a given watershed exceed a specified threshold value based on spatial layers of fire-spread rate and intensity generated by the FIREHARM model using the Rothermel (1972) spread model (Table 2). We will construct a logic model within EMDS to show how all elementary topics contribute to an evaluation of fire danger. We will evaluate evidence for low wildfire danger within watersheds of each map zone to provide an ecological basis for recommending treatment priority. A decision analysis will then be run in a separate but related decision model to incorporate ecological and logistical considerations for planning fuels treatment across the Region. Table 2: Dfenition of data inputs evaluated by elementary topic, data source, and reference conditions fore each datuma. | | | | Reference conditionsb |
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Datum | Defenition | Data Source | No evidence | Full evidence | | AVHRR-NDVI | AVHRR-NDVId relative greenness value on June 1, 2004 | Missoula Fire Labe | 0.00 | 1.00 | | CBDaggregation | Aggregation index for canopy bulk density >0.15 kg/m3 | LANDFIREg (derived) | 93.02 | 75.97 | | CBDarea | Likelihoodf of canopy bulk density >0.15 kg/m3 | LANDFIRE | 0.79 | 0.29 | | CBHaggregation | Aggregation of index for canopy base height <3.1 m | LANDFIRE (derived) | 72.99 | 36.92 | | CBHarea | Likelihood of canopy base height <3.1m | LANDFIRE | 0.38 | 0.04 | | crownfirePotential | Likelihood of index for crown fire potential >7 | FIREHARMe (derived) | 1.00 | 0.02 | | FBFMaggregation | Aggregation index for fire behavior fuel model>9h | LANDFIRE (derived) | 35.83 | 3.05 | | FBFMarea | Likelihood of value for fire behavior fuel model>9 | LANDFIRE | 1.00 | 0.02 | | firelineIntensity | Likelihood of fireline intensity >400kW/m | FIREHARM | 0.97 | 0.59 | | flameLength | Likelihood of flame lenght>1.2m | FIREHARM | 0.92 | 0.09 | | FCCaggregation | Aggregation index for fuel loading>56Mg/hai | FCCSi (derived) | 89.73 | 33.00 | | FCCarea | Likelihood of fuel loading>56 Mg/ha | FCCS | 0.80 | 0.03 | | FRCCaggregation | Aggregation index for fire regime condition classj | FIREHARM (derived) | 99.50 | 97.76 | | FRCCarea | Likelihood of fire regime condition class>2 | FIREHARM | 0.28 | 0.01 | | keetch-byramIndex | Likelihood of a Keetc-Byram drought index valuek >400 | FIREHARM | 0.84 | 0.46 | | lightningStrike | Probability of cloud-to-ground lightning strike indexed by the maximum valuel | NLDNm | 1.00 | 0.00 | | palmerIndex | Likelihood of summer Palmer drought severity indexn value<-2 | NCDCo | 37.00 | 0.00 | | spreadRate | Likelihood of a wildfire spread reate >8.0 kph | FIREHARM | 1.00 | 0.89 | a Data items in this table correspond to data listed for elementary topics evaluated in Table 1. Each datum represents on observation for a subwatershed, the unit of analysis in this model.
b Reference conditions for no evidence and full evidence define critical values for which the fuzzy membership function of the associated elementary topic (Table 1) indicates no support and full support, respectively, for the proposition. The range of the reference conditions is the median 80-percent range of data for the variable of interest. An observed value for the associated datum that falls in the open interval defined by the two reference conditions maps to partial support for the proposition based on linear interpolation. Data with the suffix, Area, are not evaluated with respect to reference conditions; however, they are compared to minimum and maximum conditions within conditional tests to determine the logic for evaluation of elementary topics.
c Each likelihood is estimated as the proportion of raster grid cells in the subwatershed area that exceed the specified threshold for the attribute. All likelihoods are estimated from 30-m resolution data, except those for lightningStrike, and palmerIndex, which are estimated from available 1-km resolution data.
d The normalized difference vegetation index (NDVI), obtained from NOAA-11, AVHRR satellite image, represents relative greenness, and in this usage, the effect of apparent moisture level on vegetation drying or curing. For further details see Burgan and Hartford 1993, White et al. 1997, and http://www.fs.fed.us/land/wfas/wfas11.html.
e Obtained from the USDA Forest Service, Rocky Mountain Research Station, Missoula Fire Laboratory, Missoula, MT.
f An aggregation index is computed with FRAGSTATS (McGarigal and Marks 1995) for each attribute of hazard (see also Table 1) by reclassifying data in the 30-m resolution raster grid for the attribute to 0 (attribute ≤ threshold) or 1 (attribute > threshold).
g LANDFIRE (http://www.landfire.gov/) is a multi-partner wildland fire, ecosystem, and fuel mapping project, one of whose partners is the Missoula Fire Sciences Laboratory of the USDA Forest Service, Rocky Mountain Research Station, Missoula, MT, who provided the data. Data sources labeled “LANDFIRE” indicate base data layers provided by the LANDFIRE project. Data sources labeled “FIREHARM” indicate data derived from base LANDFIRE layers by the FIREHARM model (Keane et al. 2004) of the LANDFIRE project. With the exception of the data source for crownfirePotential, data sources labeled “(derived)” indicate an aggregation statistic that we derive from the LANDFIRE base layers with the FRAGSTATS (McGarigal and Marks 1995) spatial analysis package. In the case of crownfirePotential, “(derived)” indicates a composite index that we develop from FIREHARM crown fire ignition and crown fire spread outputs.
h Fire behavior fuel models represent 40 distinct distributions of fuel loadings found among surface fuel components (live and dead), fuel size classes, and fuel types. The fuel models are described by the most common fire carrying fuel type (grass, brush, timber litter, or slash), fuel loading and surface area-to-volume ratio by size class and component, fuelbed depth, and moisture of extinction. Further detail about the original fire behavior fuel models can be found in Albini 1976, Anderson 1982, Rothermel 1972 and 1983, and Scott and Burgan 2005.
i Fuel Characteristic Class System (Sandberg et al. 2001, http://www.fs.fed.us/pnw/fera/nfp/haze/FCCS-lower48.zip)
j Fire regime condition class is a qualitative measure of departure from historic vegetation and fire regime conditions (Schmidt et al. 2002).
k In contrast to the Palmer drought severity index, the Keetch-Byram drought index represents the short-term effects of precipitation and temperature on duff, litter, and soil drying in the top 20 cm. An index value of 400 corresponds to a deficit of 10 cm of water in the top 20 cm.
l The lightning strike probability is based on actual strikes triangulated and recorded over 15 years (1990 to 2004, Schmidt et al. 2002).
m Data were obtained from the National Lightning Detection Network (NLDN, http://ghrc.msfc.nasa.gov/).
n The Palmer drought severity index is used to characterize effects of long-term drought. An index value of −2 corresponds to moderate drought conditions. Continuous maps of PDSI for the continental US were interpolated by Cook et al. (2004) based on their reconstructions of drought at grid points on a 2.5 degree grid of the continent.
o Website for the National Climate Data Center (NCDC), NOAA. (http://www.ncdc.noaa.gov/paleo/newpdsi.html).Logic model design We present the proposed formal logic specification as a dendrogram for readability and compactness (Figure 2). Each topic in a NetWeaver model represents a topic for which a premise or proposition is evaluated. For example, the overall fire-danger topic, representing the top level in the model, evaluates the proposition that wildland fire danger is low. All other propositions in the model similarly take the null form; i.e., the test for all topics is always for a low condition. 
Figure 2. Dendrogram showing how the overall fire danger topic is organized and evaluated. The complete evaluation of fire danger is made up of three parts—evaluation of fire hazard, fire behavior, and ignition risk, which are primary topics. Under each of these 3 primary topics are secondary and elementary topics. Under hazard are the topics surface fuels, canopy fuels, and fire regime. Under behavior are the elementary topics spread rate, flame length, fireline intensity, and crown fire potential. Under ignition risk are the secondary topics fire weather and ignition potential. The complete evaluation of fire danger will depend on three primary topics--fire hazard, fire behavior, and ignition risk--each of which incrementally contribute to the evaluation of fire danger. Notice that if the fire-danger topic is thought of as testing a conclusion, then the three topics on which it depends can be thought of as its logical premises. Similarly, each of the three topics under fire danger has its own logic specification that includes a set of secondary topics or premises. Note also that this logic model represents one of many possible logical configurations, and the current configuration is readily adapted. Any of the primary and secondary topics may be modified, and topics may be added or removed with relative ease. Decision model design A decision model for determining priorities of subwatersheds for fuels treatment will be designed with Criterium DecisionPlus (InfoHarvest, Inc., Seattle, WA), which uses both the Analytic Hierarchy Process (AHP, Saaty 1992) and the Simple Multi-Attribute Rating Technique (SMART, Kamenetsky 1982) to support planning activities such as priority setting, alternative selection, and resource allocation. We’ll start by using a decision model structure that is nearly identical to that used by Reynolds et al. (2006). In the context of decision models based on the AHP, the concept of topics is replaced by criteria. Thus, in the decision model for fuels treatment, the first level of the model will contain the three primary fire danger criteria, fire hazard, wildfire behavior, and ignition risk. However, for purposes of recommending treatment priorities for subwatersheds, we will also add numerous other decision criteria as a starting point from the Reynolds et al. (2006) model. Criteria will be added, deleted, and modified through discussions with Regional decision makers. Initial decision criteria and data sources are shown in Table 3. Note that numerous other criteria and subcriteria could be included to account for other logistical considerations that might influence decisions about treatment priorities. Table3: Decision criteria and data sources for a decision model to set Regional fuels treatment priorities. | Datum | Source |
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| Associated benefits of fuels treatment | WOa | | Biomass opportunity with treatment | RSACb | | Relative ecosystem health status before treatment | FSLc | | Potential wildfire emissions under a severe scenario | WO | | Potential wildfire influence on fish/wildlife habitat objectives | WO | | Influence on extant insect and disease conditions | FHPd | | Invasive species concerns under a severe fire scenario | RPAe | | Availability of legislative tools to enact treatments | WO | | Life cycle cost of treatments | WO | | TES species considerations under a severe fire/treatment secenario | TNC | | Potential timber values protected | WO | | Potential treatment influence on vegetation maintenance | FSL | | Potential treatment influence on vegetation restoration | FSL | | Potential wildfire impacts on freshwater supply | EPAf | | Potential wildfire impacts watershed condition | WO | | Amount of wildlan-urban interface present | UWg | aUSDA Forest Service, Washington Office
bRemote Sensing Applications Center (USFS)
cMissoula Fire Sciences Laboratory
dForest Health and Protection (USFS)
eResources Planning Act Assessment (USFS)
fEnvironmental Protection Agency
gUniversity of Wisconsin, Silvis Lab. Weights for each criterion at the first level of the decision model will be derived from the standard pair-wise comparison procedure of the AHP (Saaty 1992), in which a decision maker (or team of decision makers) is asked to judge the relative importance of one criterion versus each of the others. For purposes of subsequent discussion, criteria at the lowest level of an AHP model are commonly referred to as attributes of a decision alternative, and these attributes correspond to the elementary topics of the logic model. A SMART utility function will be specified for each attribute of a subwatershed on the SMART utility scale of [0, 1]. Analysis Evaluation of fire danger for all subwatersheds in the Region will be performed with the NetWeaver logic engine (Miller and Saunders 2002) in EMDS (Reynolds et al. 2003). Priority setting for fuels treatments among subwatersheds will be performed with Priority Analyst, an engine for running Criterium DecisionPlus models in EMDS. REFERENCES Albini, F.A. 1976. Estimating wildfire behavior and effects. General Technical Report INT-30, United States< Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT. 92 p. Anderson, H.E. 1982. Aids to determining fuel models for estimating fire behavior. General Technical Report INT-122, United States Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT. Burgan, R.E. 1988. 1988 Revisions to the 1978 National Fire-Danger Rating System. Research Paper RP-SE-28 273. Asheville, NC: U.S. Department of Agriculture, Forest Service, Southeastern Forest Experiment Station. Burgan, R.E., Hartford, R.A. 1993. Monitoring vegetation greenness with satellite data. General Technical Report INT-297. United States Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, UT. Cook, E.R., Woodhouse, C.A., Eakin, C.M., Meko, D.M., Stahle, D.W. 2004. Long-Term Aridity Changes in the Western United States. Science, Vol. 306, No. 5698, pp. 1015-1018, 5 November 2004. Deeming, J.E., Burgan, R.E., Cohen, J.D. 1977. The National Fire-Danger Rating System--1978. General Technical Report INT-39. Ogden, UT: U.S. Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station. 63 p. Hardy, C.C. 2005. Wildland fire hazard and risk: Problems, definitions, and context. Forest Ecology and Management 211:73-82. Hessburg, P.F.; Reynolds, K.M.; Keane, R.E.; Salter, R.B. 2006. A multi-scale decision support system for evaluating wildland fire hazard and prioritizing treatments. In: Viegas, D.X. (Ed.); Proceedings of the 5th International Conference on Forest Fire Research. CD Rom (3 pp.). ADAI, Figueira da Foz. Keane, R.E., Garner, J.L., Schmidt, K.M., Long, D.G., Menakis, J.P., Finney, M.A. 1998. Development of input data layers for the FARSITE fire growth model for the Selway-Bitterroot Wilderness Complex, USA. General Technical Report RMRS-GTR-3, USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO. Keane, R.E., Mincemoyer, S.A., Schmidt, K.M., Long, D.G., Garner, J.L. 2000. Mapping Vegetation and Fuels for Fire Management on the Gila National Forest Complex, New Mexico. General Technical Report RMRS-GTR-46-CD, USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO. Keane, R.E., Frescino, T.L., Reeves, M.C., Long, J. 2007. Mapping wildland fuels across large regions. In:Rollins, M.G., Frame, C. (eds). The LANDFIRE prototype project: nationally consistent and locally relevant geospatial data for wildland fire management. General Technical Report RMRS-GTR-175.USDA Forest Service, Rocky Mountain Research Station, Fort Collins, CO. Keane, R.E., Rollins, M., and Parsons, R. 2004. Developing the spatial programs and models needed for the implementation of the LANDFIRE Project. In: Proceedings of the 5th Symposium on Fire and Forest Meteorology and the 2nd International Wildland Fire Ecology and Fire Management Congress McGarigal, K., Cushman, S.A., Neel, M.C., Ene, E. 2002. FRAGSTATS 3.0: spatial pattern analysis program for categorical maps. University of Massachusetts, Amherst. Miller, B.J., Saunders, M.C., 2002. The NetWeaver reference manual. Pennsylvania State University, College Park, PA. 61 p. NWCG. 1996. Glossary of wildland fire terminology. PMS 205 NFES 1832, National Interagency Fire Center, Boise, ID. NWCG. 2005. Glossary of wildland fire terminology. PMS 205 NFES 1832, National Interagency Fire Center, Boise, ID. Reynolds, K.M., Rodriguez, S., Bevans, K. 2003. User guide for the Ecosystem Management Decision Support System, version 3.0. Environmental Systems Research Institute, Redlands, CA. Rollins, M.G., Keane, R.E., Zhu, Z. 2006. An overview of the LANDFIRE Prototype Project. General Technical Report RMRS-GTR-175, USDA Forest Service Rocky Mountain Research Station, Fort Collins, CO USA. 840 p. Rothermel, R.C. 1972. A mathematical model for predicting fire spread in wildland fuel. Research Paper INT-115, United States Department of Agriculture, Forest Service, Intermountain Forest and Range Experiment Station, Ogden, Utah. Rothermel R.C. 1983. How to predict the spread and intensity of forest and range fires. General Technical Report INT-143, United States Department of Agriculture, Forest Service, Intermountain Reserach Station, Ogden, Utah. Rothermel, R.C. 1991. Predicting behavior and size of crown fires in the Northern Rocky M 1 ountains. Research Paper INT-438, United States Department of Agriculture, Forest Service Intermountain Forest and Range Experiment Station, Ogden, Utah USA. Saaty, T.L. 1992. Multicriteria Decision Making: The Analytical Hierarchy Process. RWS Publications, Pittsburgh, PA. Sandberg, D.V., Ottmar, R.D., Cushon, G.H.. 2001. Characterizing fuels in the 21st century. International Journal of Wildland Fire 10: 381-387. Schmidt, K.M., Menakis, J.P., Hardy, C.C., Hann, W.J., Bunnell, D.L. 2002. Development of coarse-scale spatial data for wildland fire and fuel management. Gen. Tech. Rep. RMRS-GTR-87. U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station, Fort Collins, CO. 41 p. Scott, J.H. 1999. NEXUS: a system for assessing crown fire hazard. Fire Management Notes. 59(2): 20–24. Scott, J.H., Burgan, R.E. 2005. Standard fire behavior fuel models: a comprehensive set for use with Rothermel's surface fire spread model. General Technical Report RMRS-GTR-153, USDA Forest Service Rocky Mountain Research Station, Fort Collins, CO. 72 p. Scott, J.H., Reinhardt, E.D. 2001. Assessing crown fire potential by linking models of surface and crown fire behavior. Research Paper RMRS-RP-29, USDA Forest Service Rocky Mountain Research Station, Fort Collins, CO. Seaber, P.R., Kapinos, P.F., Knapp, G.L. 1987. Hydrologic unit maps. United States Geological Survey, Washington, D.C., United States. Water-Supply Paper 2294. STATUS Time is needed to work with Regional fire managers and decision makers to adapt both the logic and decision models to Regional preferences. The Fire Lab continues to provide FIREHARM firehazard information for this project. PRODUCTS - A fully parameterized and operational decision-support application that evaluates fire danger for all subwatersheds and recommends priorities for fuels treatment.
- Technology transfer of the final application.
- At least 1 refereed publication on the final model, to be completed after the FY08 delivery.
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