Predicting Post-Fire Severity Effects in Coast Redwood Forests Using FARSITE

Abstract

Assessing post-fire impacts in coast redwood (Sequoia sempervirens) forests can be difficult due to rough terrain, limited roads, and dense canopies. Remote sensing techniques can identify overstory damage, locating high intensity damage areas, although this can underestimate the effects on the understory vegetation and soils. To accurately assess understory impacts requires field assessment techniques, which can be expensive for larger burn areas. Where geospatial data for fuels and topography can be combined with weather data using FARSITE, a fire behavior simulation model, landscape fire behavior predictions can be made. Fire behavior outputs can be generated to produce a post-fire predicted landscape map of fire severity. The 2003 Canoe fire burned 4,000 hectares, primarily in old-growth redwood forests in Humboldt County, California. Post-fire sampling of burn impact was assessed using the Composite Burn Index methodology and found to be unrelated to FARSITE produced fire behavior variables using regression analysis. This finding is understandable because basic FARSITE landscape data available for this fire lacked fuel load information for post-combustion analysis. The Canoe Fire had a slow rate of spread, and with the deep fuel beds present; long duration burning was observed. Fire severity, as described by the Composite Burn Index, was greatest in the forest understory. FARSITE was a useful projection tool for perimeter advance and flame lengths associated with the fire front.

Parent Publication

• Fuels Management-How to Measure Success: Conference Proceedings

Citation

Scanlon, Hugh; Valachovic, Yana. 2006. Predicting Post-Fire Severity Effects in Coast Redwood Forests Using FARSITE. In: Andrews, Patricia L.; Butler, Bret W., comps. 2006. Fuels Management-How to Measure Success: Conference Proceedings. 28-30 March 2006; Portland, OR. Proceedings RMRS-P-41. Fort Collins, CO: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 587-591