5th Symposium on Fire and Forest Meteorology and the 2nd International Wildland Fire Ecology and Fire Management Congress

Tuesday, 18 November 2003: 9:30 AM
Preliminary height to crown base models for Giant Sequoia Groves
Samantha J. Gill, California Polytechnic State University, San Luis Obispo, CA
Poster PDF (67.1 kB)
Historically, most Sierra Nevada forests experienced frequent, low intensity surface fires that resulted in a patchwork mosaic of sizes and age classes. These light burns would reduce the amount of dead woody materials as well as thin the number of small, shade tolerant trees in the understory, thereby diminishing the continuity of both horizontal and vertical fuels. However, the absence of aboriginal burning as well as decades of wildfire suppression have caused abnormally dense stands of trees to grow in these fire-dependent ecosystems. The absence of fire has led to unusually large accumulations of both surface and aerial fuels to amass there, placing these forests at risk to high intensity, stand-replacing fires. The once-prevalent low intensity surface fires had little damaging effects on giant sequoia as a result of the species' rapid growth, fire resistant bark, elevated canopies and self-pruned lower branches (i.e. higher height to crown base). In the absence of these low intensity fires, enormous loads of surface fuels coupled with the pervasiveness of dense understories of white fir and incense cedar that serve as "ladder fuels" into the overstory canopy could lead to a high intensity crown fires.

Information on ladder fuels and particularly height to crown base of live trees is important for predicting the spread of fires. Models that predict height to crown base may be incorporated into existing simulation programs that predict the spread of fire. This paper presents non-linear regression models of height to crown base for tree species found in the Giant Sequoia National Monument, California. Species modeled include giant sequoia, white fir, sugar pine, incense cedar, and ponderosa pine. Dependent variables considered in the non-linear regression models include DBH, total height, stand basal area, and trees per hectare.

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