Improved decision support for allocating landscape scale targeted grazing for reducing wildfire risk

Each year, most of the burned area in the western U.S. is on non-forest lands. The spread of invasive annual grasses is causing double trouble: 1) they add to fire risk on a landscape because they ignite more easily and spread more quickly, and 2) after a burn, they thrive, often replacing at-risk grasses such as low resistance and resilience sagebrush. This expansion of invasive grass degrades biodiversity and leaves the landscape more prone to catastrophic wildfire.

Sagebrush on fire.

Targeted livestock grazing is one way to help control the spread and abundance of invasive grasses that act as act fuel during wildfire events. However, existing constraints to widespread adoption must be overcome for this practice to feasibly make a dent in fuels management at landscape scale. 

Photo Credit

USDA photo by Alexis Neukirch

The project team will develop a decision support system that overcomes these barriers and enables rangeland managers to more easily target livestock grazing in areas where invasive grasses amplify fire risk and degrade ecosystem health. It will indicate in a spatially explicit manner where targeted grazing is permitted, where there is administrative authority to implement virtual fencing and targeted grazing to reduce invasive annual grasses, and where land characteristics are amenable (suitable topography). Non-forest landscapes vary much more on an interannual basis than forests, and as a result, risk assessments become quickly out of date in the span of just a few months. Therefore, this decision support system will also incorporate and automatically link near-term projections of fine fuels to estimates of treatment levels that are effective and durable.

Project Significance

Applying this system against the backdrop of national risk assessments will greatly increase situational awareness and provide new opportunities to improve fire behavior estimates and therefore risk. In addition, at a regional scale, targeted grazing and decision support tools to guide its application are lacking. Reducing the cover and prevalence of invasive annual grasses at the right time of the year can reduce seed production, which, in turn, should benefit rangeland health significantly.

Photo Credit

Jennifer Strickland

Close-up of cheatgrass, a non-native weed that fuels wildland fires in the west, with its characteristic purple hues.

Objectives

• Identify areas that are most suitable for fuel treatments and reduce the most risk per unit invested on non-forested landscapes in the western US.
• Create an automated fuel forecasting system describing expected yield of herbaceous fuels with special emphasis on invasive annual grasses

Project Implementation

Photo Credit

USDA photo by Alexis Neukirch

1. Identify values most at risk across the western U.S.
2. Answer the following questions:
   • Where is targeted grazing with virtual fencing authorized?
   • Where is targeted grazing of invasive annual grasses an ecologically acceptable fuel treatment?
   • What is the potential spatial coverage by virtual fencing on federal lands where it meets the above criteria?
3. Develop predictive models for red brome and cheatgrass, quantifying the probability that a given area will exhibit growth of these species more than 800 pounds per acre (a critical fuel amount threshold to sustain large wildfires).
4. Create a decision support system displayed in a viewing and data query platform. 

Development of the Decision Support System

This decision support system will use cutting-edge remote sensing tools and virtual fencing to determine where to target grazing for managing fine fuels, or invasive grasses. Moreover, the data and technology needed to incorporate forecasting and assessing fuel loads relative to existing values at risk and in relation to PODS already exist.

We will intersect our estimates of fine fuel increase with treatment opportunities to identify the size, configuration, and extent of targeted grazing needed to reduce risk to values at risk. Since we are projecting fuel conditions, we will also monitor vegetation phenology and therefore estimate whether targeted grazing makes sense at a given time in given area. For example, to achieve multiple management objectives, it may be more beneficial to implement targeted grazing at certain growth stages. Therefore, we will also model and map growth using thermal time, remotely sensed imagery, and site characteristics.   

Expected Project Results

Expected Outputs

• Spatially explicit information describing opportunities for targeted grazing as a fuel management tool for reducing wildfire risk and improving ecosystem structure and function.
• Automated projections of fuel bed yield from invasive annual grasses. These data will have numerous benefits beyond fire and fuel management. This research will also provide associated estimates of phenological development to guide treatment options in a spatially explicit manner.
• We will provide all these data in a system such as ArcGIS Experience Builder with the ability to query, download and inspect data. Bringing the opportunities for targeted grazing together with the expected fuel (forage) opportunities against the backdrop of national values at risk yields unprecedented situational awareness for fire and fuel managers.
• A series of publications describing our findings with special emphasis on invasive annual grasses.

Expected Outcomes

• Advanced ability to strategically think about landscape scale treatments to reduce fire risk through reduced cover, vigor, and seed production by invasive annual grasses with managed herbivory.
• A broader agency desire for adaptive management. This project provides heretofore unavailable analysis to achieve that aim.
• This early warning system will create new programs and usher in more sophisticated and place-based analyses.
• Greater ability to foster ecosystem resilience through analysis of new fuel treatment options in tandem with other resource management concerns.
• Improved national decision support systems, as they may adopt the new data describing fuel bed parameters relating to invasive annual grasses.