The goal of this project is to advance the understanding of 3D fuel characterization and provide evaluation datasets that advance physics-based fire behavior, smoke and other fire effects models for operational use at scales relevant to Department of Defense (DoD) land managers. The study will be guided by the following research questions:
- What are the appropriate sampling resolutions of wildland fuels to model fire behavior and consumption, ranging from full physics-based modeling applications to operational models of fire behavior and consumption?
- What are the critical fuelbeds and physical fuel properties required to advance physics-based fire behavior, smoke, and fire effects models for operational use on DoD installations? Specifically, is fine-scale heterogeneity in surface fuels critical to mapping and quantifying fuel consumption in sites commonly burned on DoD lands?
- How can gridded, 3D maps of surface fuel properties most efficiently and accurately be created based on the integration of remotely sensed imagery and fuel properties informed from field-based and laboratory measurements?
- What are the tradeoffs between input precision, model fidelity, and time to collect and integrate 3D datasets?
This project will advance wildland fire science by investigating how to most effectively and efficiently measure and represent, in a virtual 3D environment, the nuances of the spatial variation of fuel composition and loading in a way that is useful for both land managers and as inputs for various types of wildland fire models. The project will utilize a hierarchical sampling design for mapping 3D surface and canopy fuels that relies on intensive, highly-resolved, ground-based measures of physical fuel properties and fuel sampling in 3D voxels to inform the quantitative assignment of physical fuel properties. The primary approach will be to oversample the observed spatial variation of fuel loading and composition using novel destructive harvesting and remote sensing technologies including airborne and terrestrial light detection and ranging (LiDAR), and multispectral imagery. These data will be used to produce 3D maps of surface and canopy fuels across a range of spatial scales and vegetation types that function as inputs for physics based, coupled fire-atmosphere models and other developing models reliant on spatially explicit fuels. Additionally, advanced mathematical modeling techniques will be used to develop quantitative models that assign measured properties of fuels, and in the case of highly intermixed fuels (e.g., complex arrangements of surface fuels including shrubs, herbaceous fuels and intermixed live and dead fuel), develop 3D models of functional plant groups to inform mapping assignments.
The study will significantly contribute to wildland fuels research by integrating state-of-the-art fuel sampling techniques and quantitative fuels modeling with model sensitivity analyses to provide foundational methods and tools for both scientists and managers. At present, limitations in representation of 3D fuels and incomplete understanding of how to scale this information to operational fires currently limits wildland fire science and application of the latest fire-atmosphere models. New methods and metrics for 3D fuels will be developed that will provide useful interpretation of remotely sensed datasets and insights to fire and fuels managers. In particular, this research will provide guidance as to how to integrate 3D vegetative data sources that are of inconsistent scale, resolution, and quality.