Journal
ECOLOGICAL APPLICATIONS
Volume 14, Issue 1, Pages 75-95Publisher
WILEY
DOI: 10.1890/02-5145
Keywords
ecosystem simulation; fire ecology; fire regimes; fuels; Geographic Information Systems; gradient modeling; predictive mapping; remote sensing
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Maps of fuels and fire regimes are essential for understanding ecological relationships between wildland fire and landscape structure, composition, and function, and for managing wildland fire hazard and risk with an ecosystem perspective. While critical for successful wildland fire management, there are no standard methods for creating these maps, and spatial data representing these important characteristics of wildland fire are lacking in many areas. We present an integrated approach for mapping fuels and fire regimes using extensive field sampling, remote sensing, ecosystem simulation, and biophysical gradient modeling to create predictive landscape maps of fuels and fire regimes. A main objective was to develop a standardized, repeatable system for creating these maps using spatial data describing important landscape gradients along with straightforward statistical methods. We developed a hierarchical approach to stratifying field sampling to ensure that samples represented variability in a wide variety of ecosystem processes. We used existing and derived spatial layers to develop a modeling database within a Geographic Information System that included 38 mapped variables describing gradients of physiography, spectral characteristics, weather, and biogeochemical cycles for a 5830-km(2) study area in northwestern Montana. Using general linear models, discriminant analysis, classification and regression trees, and logistic regression, we created maps of fuel load, fuel model, fire interval, and fire severity based on spatial predictive variables and response variables measured in the field. Independently evaluated accuracies ranged from 51 to 80%. Direct gradient modeling improved map accuracy significantly compared to maps based solely on indirect gradients. By focusing efforts on direct as opposed to indirect gradient modeling, our approach is easily adaptable to mapping potential future conditions under a range of possible management actions or climate scenarios. Our methods are an example of a standard yet flexible approach for mapping fuels and fire regimes over broad areas and at multiple scales. The resulting maps provide fine-grained, broad-scale information to spatially assess both ecosystem integrity and the hazards and risks of wildland fire when making decisions about how best to restore forests of the western United States to within historical ranges and variability.
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