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LANDFIRE - Fuel Characteristic Classification System Fuelbeds (LANDFIRE.US_110FCCS)

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Citation

Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey, 2010, LANDFIRE.US_110FCCS: Wildland Fire Science, Earth Resources Observation and Science Center, U.S. Geological Survey: Sioux Falls, SD, http://www.landfire.gov, http://landfire.cr.usgs.gov/viewer/.

Summary

The LANDFIRE fuel data describe the composition and characteristics of both surface fuel and canopy fuel. Specific products include fire behavior fuel models, canopy bulk density (CBD), canopy base height (CBH), canopy cover (CC), canopy height (CH), fuel characterization classification system (FCCS), and fuel loading models (FLMs). These data may be implemented within models to predict the behavior and effects of wildland fire. These data are useful for strategic fuel treatment prioritization and tactical assessment of fire behavior and effects. Data Summary: The Fuel Characteristic Classification System (FCCS) defines a fuelbed as the inherent physical characteristics of fuel that contribute to fire behavior and effects (Riccardi [...]

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Purpose

LANDFIRE is a multi-partner mapping project which generates consistent, comprehensive maps and data for the entire United States. The data describe existing vegetation composition and structure, wildland fuel (crown and surface), simulated historical fire regimes, and current departure from simulated historical vegetation conditions. The consistent and comprehensive nature of LANDFIRE methods ensures data are nationally relevant, while the 30-meter grid resolution assures that data has local application. Data is based on peer-reviewed science and relies on methodologies from remote sensing, ecosystem simulation, vegetation and disturbance ecology, predictive landscape mapping, landscape simulation, and fire behavior and effects modeling. The initial data, referred to as LANDFIRE National (lf_1.0.0), was completed winter 2010. Subsequent versions include LANDFIRE Rapid Refresh (lf_1.0.1), LANDFIRE Improvements (lf_1.0.2), and LANDFIRE Refresh 2001 (lf_1.0.5) and 2008 (lf_1.1.0). Biennial and decadal remaps are also scheduled.LANDFIRE Rapid Refresh (lf_1.0.1) increased Canopy Bulk Density and reduced Canopy Base Height. Fires greater than 1000 acres through 2007 were incorporated in the data. Rapid Refresh was completed in 2008.LANDFIRE Improvements (lf_1.0.2), based on user feedback, include refinement of the international borders, riparian and wetlands filter, assignment of certain agricultural and urban classes as burnable, and an increase in the occurrence of barren and water. Improvements completion is scheduled for spring 2010.LANDFIRE Refresh 2001 (lf_1.0.5) incorporates all Improvement enhancements as well as remodeled cover and height layers. Imagery used in LANDFIRE National covered a span of years, and because of this, several large wildland fires are not represented in the data. Refresh 2001 ensures wildland fires through 2001 are represented. Refresh 2001 completion is scheduled for spring 2011.LANDFIRE Refresh 2008 (lf_1.1.0) uses 2001 (lf_1.0.5) data as a launching point to incorporate disturbance that occurred on the landscape after 2001. Disturbances include not only wildland fire but other activities, both managed and naturally occurring. Vegetation growth was modeled where both disturbance and non-disturbance occurs. Refresh 2008 completion is scheduled for spring 2011.Biennial remaps will be a refinement of prior process and methods and the first cycle is scheduled for 2012 through 2013. Decadal remaps are planned with specific dates to be determined.LANDFIRE is supported by the US Department of Agriculture (USDA) Forest Service Office of Fire and Aviation Management, the US Department of Interior (US DOI) Office of Wildland Fire Coordination, and The Nature Conservancy. LANDFIRE meets agency, partner, and stakeholder needs for data to support landscape fire management planning, prioritization of fuel treatments, collaboration, community and firefighter protection, and effective resource allocation.

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