Robert E. Burgan

Mapping wildland fuels for fire management across multiple scales: integrating remote sensing, GIS, and biophysical modeling

Fuel maps are essential for computing spatial fire hazard and risk and simulating fire growth and intensity across a landscape. However, fuel mapping is an extremely difficult and complex process requiring expertise in remotely sensed image classification, fire behavior, fuels modeling, ecology, and geographical information systems (GIS). This paper first presents the challenges of mapping fuels: canopy concealment, fuelbed complexity, fuel type diversity, fuel variability, and fuel model generalization. Then, four approaches to mapping fuels are discussed with examples provided from the literature: (1) field reconnaissance; (2) direct mapping methods; (3) indirect mapping methods; and (4) gradient modeling. A fuel mapping method is proposed that uses current remote sensing and image processing technology. Future fuel mapping needs are also discussed which include better field data and fuel models, accurate GIS reference layers, improved satellite imagery, and comprehensive ecosystem models.

Fuel models and fire potential from satellite and surface observations

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.S. The fuel model map will be used in the next generation fire danger rating system for the U.S., but it also made possible immediate development of a satellite and ground based fire potential index map. 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.

Seasonal fire danger forecasts for the USA

The Scripps Experimental Climate Prediction Center has been making experimental, near-real-time, weekly to seasonal fire danger forecasts for the past 5 years. US fire danger forecasts and validations are based on standard indices from the National Fire Danger Rating System (NFDRS), which include the ignition component (IC), energy release component (ER), burning index (BI), spread component (SC), and the Keetch–Byram drought index (KB). The Fosberg fire weather index, which is a simplified form of the BI, has been previously used not only for the USA but also for other global regions and is thus included for comparison. As will be shown, all of these indices can be predicted well at weekly times scales and there is even skill out to seasonal time scales over many US West locations. The most persistent indices (BI and ER) tend to have the greatest seasonal forecast skill. The NFDRS indices also have a weak relation to observed fire characteristics such as fire counts and acres burned, especially when the validation fire danger indices are used.

Forecasting distributions of large federal-lands fires utilizing satellite and gridded weather information

The current study presents a statistical model for assessing the skill of fire danger indices and for forecasting the distribution of the expected numbers of large fires over a given region and for the upcoming week. The procedure permits development of daily maps that forecast, for the forthcoming week and within federal lands, percentiles of the distributions of (i) number of ignitions; (ii) number of fires above a given size; (iii) conditional probabilities of fires greater than a specified size, given ignition. As an illustration, we used the methods to study the skill of the Fire Potential Index - an index that incorporates satellite and surface observations to map fire potential at a national scale - in forecasting distributions of large fires.

The Oklahoma Fire Danger Model: An operational tool for mesoscale fire danger rating in Oklahoma

This paper describes the Oklahoma Fire Danger Model, an operational fire danger rating system for the state of Oklahoma (USA) developed through joint efforts of Oklahoma State University, the University of Oklahoma, and the Fire Sciences Laboratory of the USDA Forest Service in Missoula, Montana. The model is an adaptation of the National Fire Danger Rating System (NFDRS) to Oklahoma, but more importantly, represents the first time anywhere that NFDRS has been implemented operationally using hourly weather data from a spatially dense automated weather station network (the Oklahoma Mesonet). Weekly AVHRR satellite imagery is also utilized for live fuel moisture and fuel load calculations. The result is a near-real-time mesoscale fire danger rating system to 1-km resolution whose output is readily available on the World Wide Web (http://agweather.mesonet.ou.edu/models/fire). Examples of output from 25 February 1998 are presented. The Oklahoma Fire Danger Model, in conjunction with other fire-related operational tools, has proven useful to the wildland fire management community in Oklahoma, for both wildfire anticipation and suppression and for prescribed fire activities. Instead of once-per-day NFDRS information at two to three sites, the fire manager now has statewide fire danger information available at 1-km resolution at up to hourly intervals, enabling a quicker response to changing fire weather conditions across the entire state.

Assessing forest fire potential in Kalimantan Island, Indonesia, using satellite and surface weather data

An algorithm for assessing forest fire potential is tested for Kalimantan Island, Indonesia. It is based on a fuel model map modified from the US-National Fire Danger Rating System (US-NFDRS), Normalized Difference Vegetation Index (NDVI), and weather data. The Indonesian fuel model map was derived using the global 4-minute land cover data set consisting of 13 classes. The NDVI data were derived from the global 4-minute NOAA-AVHRR data. The output is presented as a monthly Fire Potential Index (FPI) from 1981 to 1993 and compared with trends in fire occurrences over the same time period. A case study illustrates correlation between the FPI and the hot-spot distribution derived from AVHRR data, as well as between the FPI and the Total Ozone Mapping Spectrometer (TOMS) Aerosol Index.

Chapter 9. A Database for Spatial Assessments of Fire Characteristics, Fuel Profiles, and PM10 Emissions

Summary This paper describes the procedures and data used to develop a database of 28 fire, fuels, and smoke attributes for the broad-scale scientific assessment of the Interior Columbia River Basin. These attributes relate to three general areas: (1) fire weather, fuel moisture, and fire characteristics; (2) fuel loading and fuel consumption; and (3) PM10 smoke emissions. The process flow and development protocols for creation of the database are fully described and illustrated, with examples provided where appropriate. This database was developed for application to a certain geographic area with parameters specific to both the biophysical environment and the management issues of that area. However, the methods and protocols used to develop this comprehensive suite of fire-related data are applicable to any ecosystem for which predictions are needed for wildfire hazard, fire potential, biomass consumption, and smoke emissions.