Spatial models for estimating fuel loads in the Black Hills, South Dakota, USA
Robin M. Reich A C , John E. Lundquist B and Vanessa A. Bravo A D
+ Author Affiliations
- Author Affiliations
A Department of Forest, Rangeland, and Watershed Stewardship, Colorado State University, Fort Collins, CO 80523, USA.
B Rocky Mountain Research Station, USDA Forest Service, Fort Collins, CO 80521, USA. Telephone: +1 970 498 1095; fax: +1 970 498 1314; email: jlundquist@fs.fed.us
C Corresponding author. Telephone: +1 970 491 6980; fax: +1 970 491 6754; email: robin@cnr.colostate.edu
D Telephone: +1 970 491 6980; fax: +1 970 491 6754; email: vbravo@cnr.colostate.edu
International Journal of Wildland Fire 13(1) 119-129 https://doi.org/10.1071/WF02049
Submitted: 03 July 2002 Accepted: 10 September 2003 Published: 8 April 2004
Abstract
Fire suppression has increased fuel loadings and fuel continuity in many forested ecosystems, resulting in forest structures that are vulnerable to catastrophic fire. This paper describes the statistical properties of models developed to describe the spatial variability in forest fuels on the Black Hills National Forest, South Dakota. Forest fuel loadings (tonnes/ha) are modeled to a 30 m resolution using a combination of trend surface models to describe the coarse-scale variability in forest fuel, and binary regression trees to describe the fine-scale variability associated with site-specific variability in forest fuels. Independent variables used in the models included various Landsat TM bands, forest class, elevation, slope, and aspect. The models accounted for 55% to 72% of the variability in forest fuels. In spite of having highly skewed distributions, cross-validation showed the models to have nominal prediction bias. This paper also evaluates the feasibility of using the estimation error variance to explain estimation uncertainty. The models are allowing us to study the influence of small-scale disturbances on forest fuel loadings and diversity of resident and migratory birds on the Black Hills National Forest.
Additional keywords: binary regression trees; cross-validation; fuels; fuel loading; fuel variability; Landsat imagery.
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