Comparing selected fire regime condition class (FRCC) and LANDFIRE vegetation model results with tree-ring data

Fire Regime Condition Class (FRCC) has been developed as a nationally consistent interagency method in the US to assess degree of departure between historical and current fire regimes and vegetation structural conditions across differing vegetation types. Historical and existing vegetation map data also are being developed for the nationwide LANDFIRE project to aid in FRCC assessments. Here, we compare selected FRCC and LANDFIRE vegetation characteristics derived from simulation modeling with similar characteristics reconstructed from tree-ring data collected from 11 forested sites in Utah. Reconstructed reference conditions based on trees present in 1880 compared with reference conditions modeled by the Vegetation Dynamics Development Tool for individual Biophysical Settings (BpS) used in FRCC and LANDFIRE assessments showed significance relationships for ponderosa pine, aspen, and mixed-conifer BpS but not for spruce–fir, piñon–juniper, or lodgepole pine BpS. LANDFIRE map data were found to be ~58% accurate for BpS and ~60% accurate for existing vegetation types. Results suggest that limited sampling of age-to-size relationships by different species may be needed to help refine reference condition definitions used in FRCC assessments, and that more empirical data are needed to better parameterize FRCC vegetation models in especially low-frequency fire types.

Implementation of mid-scale fire regime condition class mapping

We used mid-scale Fire Regime Condition Class (FRCC) mapping to provide Hawthorne Army Depot in the Mount Grant area of Nevada, USA, with data layers to plan fuels restoration projects to meet resource management goals. FRCC mapping computes an index of the departure of existing conditions from the natural range of variability, and consists of five primary steps: (1) mapping the Potential Natural Vegetation Types (PNVT) based on interpretation of a soil survey; (2) refining PNVTs based on additional information; (3) modelling the natural range of variability (NRV) per PNVT; (4) using field verification, calculation and mapping of departure of current distribution of structural vegetation classes interpreted by remote sensing (IKONOS 4-m resolution satellite imagery) from the NRV; and (5) mapping structural vegetation classes that differ from reference conditions. Pinyon–juniper and mountain mahogany woodlands were found within the NRV, whereas departure increased from moderate for low and big sagebrush PNVTs and mixed desert shrub to high for riparian mountain meadow. Several PNVTs showed departures that were close to FRCC class limits. The common recommendation to reach the NRV was to decrease the percentage of late-development closed and cheatgrass-dominant classes, thus increasing the percentage of early and mid-development classes.