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CSIRO PUBLISHING | International Journal of Wildland Fire
USDA Forest Service, Rocky Mountain Research Station, PO BoxMissoula MT e-mail: This e-mail address is being protected from spambots. You need JavaScript enabled to view it. Science and Applications Branch, USGS EROS Data Center, Sioux Falls, SD A national 1-km resolution fire danger fuel model map was derived through use of previously mapped land cover classes and ecoregions, and extensive ground sample data, then refined through review by fire managers familiar with various portions of the U.
The fuel model map will be used in the next generation fire danger rating system for the U. The inputs and algorithm of the fire potential index are presented, along with a case study of the correlation between the fire potential index and fire occurrence in California and Nevada.
Application of the fire potential index in the Mediterranean ecosystems of Spain, Chile, and Mexico will be tested. The need for a method to rate wildland fire-danger was recognized at least as far back asin fire control conferences called by the Forest Service, U. Department of Agriculture, in Ogden, Utah. By several fire-danger rating systems were in use across the United States.
In John Keetch, Washington Office, Aviation and Fire Management, headed a team to develop a national system. By most fire control organizations in the United States were using a "spread index" system. In another research effort was established in Fort Collins, Colorado to develop an analytical system based on the physics of moisture exchange, heat transfer and other known aspects of the problem Bradshaw et al.
The resulting fire spread model Rothermel was used in the first truly National Fire Forest service research paper rm-169 Rating System NFDRSintroduced in Deeming et al. This system has since been revised twice, forest service research paper rm-169, in Deeming et al.
Decisions fire managers must make depend on the temporal and spatial scales involved as well as management objectives. Presuppression decisions are often aimed at allocation of firefighting funds, personnel, and equipment. Such decisions usually have a large spatial context, encompassing millions of hectares, and a time scale of 1 to 3 days, forest service research paper rm-169. Once a fire occurs initial attack and suppression decisions are directed at attaining cost-effective management of the fire.
This may include a decision to not suppress the fire if it is burning within predefined constraints. These decisions have a spatial scale of a few thousand hectares and a temporal scale of 24 hours or less.
Once a decision has been made to extinguish a fire, decisions are required on a spatial scale of several hundred hectares or less and a temporal scale of a few minutes to a few hours, forest service research paper rm-169. The attitude toward wildland fire in the United States is changing from that of simply extinguishment to realization that fire must play a role in maintaining forest health, thus the need for prescribed fires is being recognized Mutch Methods to assess fire potential both strategically and tactically must also evolve.
Assessment of fire potential at any scale requires basically the same information about the fuels, topography, and weather conditions that combine to produce the potential fire environment. These factors have traditionally been measured for specific sites, with the resulting fire potential estimates produced as alpha-numeric text, and the results applied to vaguely defined geographic areas and temporal periods, with the knowledge that the further one is displaced in time or space from the point where such measurements have been taken, the less applicable the fire potential estimate is.
This situation is rapidly changing because Geographic Information Systems GIS and space-borne observations are greatly improving the capability to assess fire potential at much finer spatial and temporal resolution. Recent improvements to fire potential assessment technology include both broad scale fire-danger maps and local scale fire behavior simulations. In the context of local scale fire behavior, FARSITE Finney and BEHAVE Burgan and RothermelAndrewsAndrews and Chaseprovide methods to simulate fire behavior for areas up to several thousand hectares.
In the broad area fire danger context, spot measurements of fire danger, forest service research paper rm-169, calculated using the NFDRS at specific weather stations, are being interpolated and mapped on a national basis Figure 1 through the Wildland Fire Assessment System Burgan et al. Figure 1. The U. maps are produced using an inverse distance squared weighting of staffing levels. Staffing level defines the readiness status of the suppression organization.
It is based on comparison of current fire danger index values with historical values. The staffing or readiness level increases as the current index approaches historically high values.
Because fire managers across the United States have not been consistent in their selection of an NFDR index on which to base staffing levels, staffing level itself is the only common parameter with which to map fire danger.
Staffing level normalizes all indexes against their historical values so it does not matter which of the several fire danger indexes a fire manager selected. However this method neither addresses the forest service research paper rm-169 of topography on fire potential, forest service research paper rm-169, nor provides fire potential estimates for specific locations or landscape resolutions.
An operational process that does provide 1 km 2 landscape resolution is the Oklahoma Fire Danger Rating System Carlson et al. htmlalthough it still does not recognize the effect of topography. The Oklahoma Fire Danger Rating System represents the direction of future fire-danger systems research for the United States, but the intensive weather network it relies upon could make this type of system forest service research paper rm-169 for others to apply.
A wildland fuel map, forest service research paper rm-169, terrain data, and a reasonable sampling of weather are inputs to most fire danger systems. This paper discusses development of a national 1 km 2 fuel model map for the United States and describes a Fire Potential Index FPI model that can be used to assess fire hazard at 1 km 2 resolution.
Traditionally 1 to 4 fire danger fuel models Deeming et al. These fuel models represent the most common or most hazardous vegetation types occurring in the vicinity of the weather station. The exact geographic location represented by each fuel model has not been well defined. Progress in assessing fire potential across the landscape obviously requires much better fuels information.
Inthe U. Geological Survey's Earth Resources Observation Systems EROS Data Center, Sioux Falls, South Dakota, prepared a class, forest service research paper rm-169, 1 km 2 resolution, land cover characteristics database Loveland et al. The initial vegetation map was produced by unsupervised clustering of eight monthly composites of Normalized Difference Vegetation Index NDVI Goward et al. A postclassification refinement was accomplished through use of several ancillary data layers, however ground truth data was not used.
It was obvious this map could provide the basis for a national fire danger fuel model map for the next generation National Fire Danger Rating System. However, because the vegetation map was designed to satisfy a wide range of applications, it was necessary to obtain ground sample data specifically for the purpose of developing an NFDRS fuel model map. The first author and Colin Hardy of the Intermountain Fire Sciences Laboratory collaborated with the EROS Data Center to collect ground sample data for numerous locations across the U, forest service research paper rm-169.
Help was enlisted from numerous federal and state land management agencies to collect the ground data. Burgan et al. A total of 1 km 2 ground sample plots were located on seven hundred 7½ minute USGS quadrangle maps Figure 2. Figure 2. Ground sample data was collected from plots on these 7.
There were up to 5 plots per quadrangle map. Data was obtained from of these plots. Percent cover, height, forest service research paper rm-169, and diameter data were recorded on the four major tree and shrub species, and percent cover and depth were recorded for subshrubs, forbs, mosses and grass.
Shrub and grass morphology and density classes were also recorded. Up to four 35 mm slides were taken for many of the plots. All data were entered into a database for analysis, and the slides and graphical analysis summaries were recorded on a CDROM and are available for viewing with a standard browser Burgan et al. Because a major objective of the ground sampling was to relate fire danger fuel models to the EROS Land Cover Classes, a fuel model assignment was required for each plot.
The fuel model assignments were not made in the field however, because it was felt the diversity of people involved would produce large inconsistencies in making these assignments. Instead, one knowledgeable person was asked to review the data sheets and plot photographs to make the fuel model assignments, which were then added to the database.
The Land Cover Characteristics Database also contained a map of Omernick Ecoregions Figure 3 of the conterminous U. Omernickso the ecoregion for each plot was also recorded.
With this data, a frequency count of fuel model by Omernick Ecoregion and Land Cover Class was obtained through a contract with Statistical Sciences Incorporated, Westlake Ave. The purpose of including ecoregion data was to permit regionalizing fuel model assignments. The program built the NFDR fuel model map by using the ecoregion and landcover class values read from separate binary data files.
With these inputs a table lookup method was used to determine the fuel model assignment for each 1 km square pixel. This became the "first draft" NFDR fuel model map. Omernick ecoregions were used forest service research paper rm-169 localize refinements to the NFDRS fuel model map.
was necessary. This was accomplished by having individual fire managers come to the Intermountain Fire Sciences Laboratory to use the GRASS U. Army Construction Engineering Research Laboratory GIS software for detailed review of the fuel model map within their area of knowledge.
This process permitted alteration of fuel models by Land Cover Class within individual ecoregions by modifying the lookup table based on ecoregions and landcover class. Although there were changes, they were surprisingly limited considering the sparseness of the ground sample data. Fire danger fuel models E, I, J, and K Deeming et al. Satellite observation of seasonal changes in vegetation greenness eliminates the need for using model E as a winter season subsititue for model R, and the slash models I, J, and K don't cover sufficient area to be considered.
The EROS Data Center has completed a 1-km resolution land cover database for the world Belward Loveland et al. In press. These data will provide the key to development of fuel model maps for many countries. The Fire Potential Index FPI model was developed to incorporate both satellite and surface observations in an index that correlates well with fire occurrence and can be used to map fire potential from national to local scales through use of a GIS.
The primary reasons for developing the model were: 1 forest service research paper rm-169 produce a method to forest service research paper rm-169 fire potential at continental scale and at 1 km resolution, 2 provide a method of estimating fire potential that was simpler to operate than the current U.
National Fire Danger Rating System. The assumptions of the FPI model are: 1 fire potential can be assessed if the proportion of live vegetation is defined, and it is known how close the dead fine fuel moisture is to the moisture of extinction, 2 vegetation greenness provides a useful parameterization of the quantity of high moisture content live vegetation, 3 ten hour timelag fuel moisture should be used to represent the dead vegetation because the moisture content of small dead fuels is critical to determination of fire spread, and 4 wind should not be included because it is so transitory.
Thus the inputs to the FPI model are a 1-km resolution fuel model map, a Relative Greenness RG map Burgan and Hartford that indicates current vegetation greenness compared to historical maximum and minimum values, a maximum vegetation greenness map, and 10 hour timelag dead fuel moisture Fosberg and Deeming Ten hour timelag fuels are defined as dead woody vegetation in the size range of 0.
These inputs must be in raster format and provided as byte data representing 1-km pixels. The output is forest service research paper rm-169 national scale,1-km resolution map that presents FPI values ranging from 1 to In the traditional sense, fuel models are a set of numbers that forest service research paper rm-169 vegetation in terms that are required by the Rothermel fire model.
Thus fuel models used in the U. National Fire Danger Rating System have numerous parameters that define live and dead fuel loads by size class, surface area to volume ratios of the various size classes, heat content, forest service research paper rm-169, dead fuel moisture of extinction, wind reduction factors, and mineral and moisture damping coefficients.
Lindsey Rusted - Research Ecologist with USDA Forest Service
, time: 40:39US Forest Service Research & Development
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