How Vegetation Recovery and Fuel Conditions in Past Fires Influences Fuels and Future Fire Management in Five Western U.S. Ecosystems
Principal Investigator(s):
  • Andrew T. Hudak
    US Forest Service, Moscow Forestry Sciences Laboratory
Co-Principal Investigator(s):
  • Penelope Morgan
    University of Idaho, College of Natural Resources, Department of Forest Resources
  • Beth A. Newingham
  • Eva K. Strand
    University of Idaho, College of Natural Resources
  • Jan U. H. Eitel
  • Robert E. Kennedy
  • Alistair M. S. Smith
    University of Idaho, College of Natural Resources, Department of Forest, Rangeland, and Fire Sciences
Completion Date: August 15, 2018

Cataloging Information

  • fuel conditions
  • fuel treatments
  • Landsat
  • LandTrendr
  • MTBS - Monitoring Trends in Burn Severity
  • plant species diversity
  • tree regeneration
  • vegetation cover
  • vegetation recovery
  • WFDSS - Wildland Fire Decision Support System
JFSP Project Number(s):
Record Maintained By:
Record Last Modified: November 20, 2019
FRAMES Record Number: 56554


Mixed severity wildfires burn large areas in western North America forest ecosystems in most years and this is expected to continue or increase with climate change. Little is understood about vegetation recovery and changing fuel conditions 7-15 years post-fire because it exceeds the duration of most studies of fire effects. We propose to re-measure field sites established at 15 wildfires 7-15 years ago in five western U.S. ecosystems distributed across eight states including Alaska. We propose to supplement these existing field sites with additional ones in order to populate landscape stratifications conditioned on burn severity classifications by the Monitoring Trends in Burn Severity (MTBS) Project. We can then link field measures of vegetation cover, plant species diversity, tree regeneration, and fuel conditions to remotely sensed trajectories of vegetation recovery extracted from Landsat satellite image time series using the Landsat Trends in Disturbance and Recovery (LandTrendr) change analysis tool. Although Landsat imagery is insensitive to surface fuel attributes, it is sensitive to vegetation canopy attributes, the latter of which can be used to drive the predictive model. This is an advantage of multivariate imputation modeling. Multiple fuel (and other) attributes of interest can be linked to the vegetation attributes predicted by the model, by virtue of their associations in the field plot observations; thus, fuel attributes can be predicted as ancillary variables. Field measures of vegetation and fuel response variables will be imputed into landscape-wide maps as generated from LandTrendr-derived predictor variables. The vegetation and fuel responses can also be summarized within MTBS burn severity polygons in FVS-ready format, for ingestion into ArcFuels and Climate-FVS. A workshop will be offered at the University of Idahos McCall Outdoor Science School (MOSS), to demonstrate to managers how to use these tools for planning fuel and fire management strategies under alternative climate scenarios. This will be useful for management planning. For instance, information collected in the field regarding accelerating surface fuel accumulation rates 7-15 post-fire, and linked to LandTrendr-derived rates of vegetation recovery estimated as a function of burn severity and forest type, will inform managers regarding how long past wildfires may continue to act as an effective fuel treatment, before returning to the pre-fire condition with its associated risk of reburning. The anticipated utility of this approach for fuel and fire managers will be publicized via webinars offered through the Northern Rockies Fire Science Network and integrated into six graduate or undergraduate courses taught at the University of Idaho, including online. Our data collections, both from the field and remote sensing, will produce more accurate estimates of post-fire vegetation recovery and fuel accumulation, will contribute substantively to the bi-annual updates of the LANDFIRE database, models, and spatial layers. Such improved datasets also will inform fire behavior predictions and decision making by incident management via the Wildland Fire Decision Support System (WFDSS). Fire and fuel managers will benefit from having better knowledge for estimating the effectiveness of past wildfires as fuel treatments, 7-15 years post-fire, in the five forest ecosystems we will sample. Besides the tech transfer deliverables that directly target managers as just described, this research will fund two graduate students and lead to a minimum of three peer-reviewed journal articles that will benefit both managers and researchers. Finally, all data collections will be archived in the Forest Service Research & Development (FS R&D) permanent data archive to promote their greater use.