|
|
LANDFIRE is Evolving
Based on user feedback, the LANDFIRE Program is exploring options to provide annual product updates. To meet that need, LANDFIRE will be in a transition phase so users should be aware that there will be product changes over the next few update cycles as processes evolve. Providing your landscape disturbances (natural and human-caused) to LANDFIRE will be even more important in the future than the past, to be submitted by November 30th of each year. If you will submit your landscape changes directly to the LANDFIRE Program or data bases/sources we regularly utilize (FACTS, NFPORS, etc.), our commitment will be to track and incorporate those changes into LANDFIRE products more quickly than the past, and when update processes are fully tested and implemented, within a year of submission. We will provide details on what to expect in the next update cycle as they become available.
Picture: © Wolfe Repass | TNC Photo Contest 2019. Mule Deer at Sunrise, Rocky Mountain Arsenal National Wildlife Refuge, Colorado.
Check out Good reading below to see how LANDFIRE data informed a recent publication about ungulate forage distribution models in Idaho.
|
|
The Fall MoD-FIS for the Great Basin and Southwest has been released and is available for use.  | In roughly 50 percent of New Mexico, Arizona, and west Texas, wildfires that burn across hard-baked desert landscapes need annual/invasive herbaceous and other fine fuels to drive them. For fire managers and those who do modeling, understanding when and where those fuels are present, how abundant they are and even how tall they are is vital to preparing for a fire season and addressing wildfires as they’re happening. Those people need to know how seasonal variation changes over time and thus affects fire behavior. Download the data from the Data Distribution Site | Read more.
|
|
|
|
LANDFIRE Release: Fire Regime, Vegetation & Fuel Products for CONUS
In this release of LF Remap 2016 products there are six products from Fire Regime, Vegetation, and Fuel now available for download. The Vegetation products are a Biophysical Settings (BpS) spatial layer with an updated attribute table and a revised suite of Biophysical Settings models and descriptions; from Fire Regime are Succession Class (SClass), Vegetation Departure (VDep), and Vegetation Condition Class (VCC); and from Fuel is the Fuel Characteristic Classification System (FCCS). A little bit about these products:
• BPS Spatial Data represents the vegetation that may have been dominant on the landscape prior to European colonization of North America based on both the current biophysical environment and an approximation of the historical disturbance regime. To ensure the linkage to BPS is maintained, LF Remap 2016 BPS includes the following products as attributes of BPS: Mean Fire Return Interval (MFRI), Percent of Low-severity Fire (PLS), Percent of Mixed-severity Fire (PMS), Percent of Replacement-severity Fire (PRS). and Fire Regime Groups (FRG).
• BpS Models and Descriptions include a quantitative state and transition model and full text description for every BpS mapped by LANDFIRE.
• Succession Class (SClass) characterizes current vegetation conditions with respect to the vegetation species composition (leaf-form and life-form primarily), cover, and height ranges of the successional states that occur within each BPS.
• Vegetation Departure (VDep) indicates how different current vegetation on a landscape is from estimated historical conditions. Based on changes to vegetation composition, structural stage, and canopy closure, VDep is based only on departure of current vegetation conditions from reference vegetation conditions.
• Vegetation Condition Class (VCC), a derivative of the VDep layer, represents a simple categorization of the associated Vegetation Departure (VDep) layer and indicates the general level to which current vegetation is different from the estimated historical vegetation reference conditions.
• Fuel Characteristic Classification system (FCCS) summarizes and classifies wildland fuel characteristics and can also be used to predict surface fire behavior, crown fire potential and available fuel for estimating consumption, fire effects, and emissions. In disturbed areas FCCS has been revised to expected 2020 vegetation conditions for disturbances that have occurred between 2010 and 2016, making LF Remap 2016 FCCS, 2020 capable. |
|
SAVE THE DATE:
Where and when are high severity fires more likely to occur?
Predicting severe fire potential across the United States with the FIRESEV project
Date: December 9, 2020 | 11 am (PT)
Speaker: Greg Dillon, Spatial Fire Analyst, U.S. Forest Service
Details: Coming soon
WATCH RECENT WEBINARS:
|
|
T. Ryan McCarley, Ball, Tara M., Aycrigg, Jocelyn L., Strand, Eva K., Svancara, Leona K., Horne, Jon S., Johnsond, Tracey N., Lonneker, Meghan K., Hurley, Mark. 2020. Predicting fine-scale forage distribution to inform ungulate nutrition. Ecological Informatics, Vol. 60.
LANDFIRE program product applications go far beyond wildland fire. In this study, the authors tested a new approach that combined existing vegetation surveys, maps, and remotely sensed data to develop 20 fine scale forage species distribution models suitable for mule deer and Rocky Mountain elk in Idaho. A key predictor variable in the models was percent shrub cover from LANDFIRE. Cross-validation curves indicated that all models had predictive accuracies higher than chance. The authors also found that the prediction models revealed important species-environment connections supported by ecological theory. These models do have limitations, but they can be used to inform habitat management and restoration activities that improve nutritional quality of ungulate habitat.
|
|
|
|
|
The LANDFIRE Program is a cooperative agreement between the USDA Forest Service, agencies of the Department of the Interior, and The Nature Conservancy. In accordance with Federal law and U.S. Department of Agriculture policy, the Program is prohibited from discriminating on the basis of race, color, national origin, sex, age, or disability.
|
|
|
|
|
