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    Author(s): Lisa HolsingerRobert E. KeaneRussell ParsonsEva Karau
    Date: 2006
    Source: In: Rollins, Matthew G.; Frame, Christine K., tech. eds. 2006. The LANDFIRE Prototype Project: nationally consistent and locally relevant geospatial data for wildland fire management Gen. Tech. Rep. RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 99-122
    Publication Series: General Technical Report (GTR)
    Station: Rocky Mountain Research Station
    PDF: View PDF  (1.5 MB)

    Description

    Distributions of plant species are generally continuous, gradually changing across landscapes and blending into each other due to the influence of, and interactions between, a complex array of biophysical gradients (Whittaker 1967; 1975). Key biophysical gradients for understanding vegetation distributions include moisture, temperature, evaporative demand, nutrient availability, and solar radiation. Models to predict plant community distributions across landscapes can be developed by identifying the unique set of biophysical gradients that drive the physiological responses of plant species across landscapes (Guissan and Zimmerman 2000). This method of incorporating information about ecological characteristics into analyses of vegetation distribution, termed gradient modeling, is a standard technique for describing ecosystem composition, structure, and function (Gosz 1992; Kessell 1976; Kessell 1979; Whittaker 1973) and has been applied extensively at varying scales, from local to regional (see Keane and others 2002 for a review of gradient modeling applications). The modeling process essentially involves developing empirical relationships between vegetation distributions and geospatial data describing biophysical gradients to enable extrapolation over space. Modeling accuracy becomes substantially improved by incorporating those biophysical gradients that directly affect vegetation dynamics such as temperature, light, and water (Austin 1980, 1985; Austin and Smith 1989; Franklin 1995). Recent efforts have further demonstrated that the accuracy of mapping vegetation and ecological characteristics using remote sensing techniques is greatly improved through the inclusion of biophysical gradient data as predictive variables (Franklin 1995; Keane and others 2002; Ohmann and Gregory 2002; Rollins and others 2004).

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    Citation

    Holsinger, Lisa; Keane, Robert E.; Parsons, Russell; Karau, Eva. 2006. Chapter 5 - Development of biophysical gradient layers for the LANDFIRE Prototype Project. In: Rollins, Matthew G.; Frame, Christine K., tech. eds. 2006. The LANDFIRE Prototype Project: nationally consistent and locally relevant geospatial data for wildland fire management Gen. Tech. Rep. RMRS-GTR-175. Fort Collins: U.S. Department of Agriculture, Forest Service, Rocky Mountain Research Station. p. 99-122

    Keywords

    mapping wildland fuel, mapping fire regimes, Geographic Information Systems, GIS, remote sensing, fire ecology, fire behavior, wildland fire management

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