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Abstracts

“Improving Surface Fuel Density Estimation at Large Scales: Coupling Surface Fuel Bed Development to Remotely Sensed Tree Crown Fuels and Fire History Information” by Dr. Andrew Hudak (RC20- 1346) 

This project supports SERDP’s efforts to improve management of fire-maintained forests on DoD lands. Trees are the foundational objects that determine patterns in forest ecosystem structure, and fire is the principal process driving ecosystem function and condition in fire-managed forests. In forests, trees provide most of the material that contributes to surface fuel loads, namely down woody debris (DWD), litter, and, in the event of eventual decomposition in the absence of fire, duff. However, these fuel attributes are all difficult to measure, not only in the field due to their high heterogeneity, but also via remote sensing, especially through the overstory canopy. LiDAR is the best available technology to penetrate the canopy, but it has only limited sensitivity to surface fuel depth. 

The project approach is to use LiDAR to accurately estimate foliage and branch biomass at the scale of tree crowns, and then model deposition rates of these materials to the ground, decomposition rates following deposition, and consumption from past fires. Improved surface fuel estimates are thus obtained indirectly, then voxelized at high resolution to maintain heterogeneity along with the crown fuel estimates, since they are coupled to heterogeneous tree crown distributions. These improved surface (and canopy) fuel maps provide more realistic inputs to fire behavior models as needed by fire and fuel managers. 

“Validation of QUIC-Fire Smoke Plume Dispersion Modeling for Complex Wildland Fires” by Dr. Sara Brambilla (RC23-7656) 

To manage wildland fire risks due to operations and meet conservation objectives, DoD conducts prescribed (Rx) fires. However, smoke impacts on the public and impacts on DoD missions are significant constraints on prescribed fires. Hence, the objective of this SERDP project is to provide DoD fire managers with a new computationally efficient tool so support fire and smoke management planning at DoD installations. This project will address the demonstration and validation of the Quick Urban & Industrial Complex (QUIC)-Fire/QUIC SMOKE tool. 

QUIC-Fire was designed to capture the influences of complex fire geometries, typical of Rx fires, on fire behavior and smoke development. By extending QUIC-Fire using the QUIC-SMOKE model, downwind smoke dispersal can now be modeled far downstream of the burn plot. In particular, QUIC-SMOKE takes the fire-aware winds and fire emissions and transports them downwind to assess air quality, health, and visibility impacts. The QUIC-Fire/QUIC-SMOKE tool will allow fire managers to understand how their chosen ignition patterns impact smoke. Fire managers will have access to an improved and validated wildland fire/smoke modeling system developed to address their requirements. In practice, the new modeling tool will reduce impacts from smoke on test and training as well as nearby communities.

Speaker Biographies

Dr. Andrew Hudak has worked for the United States Forest Service since 1999, first as a postdoctoral Research Ecologist with the Pacific Northwest Research Station and then as a Research Forester with the Rocky Mountain Research Station. He currently studies biophysical relationships between field and remotely sensed data, including estimating aboveground biomass carbon across the western U.S. from airborne LiDAR, Landsat time series, and other environmental data. His work also focuses on predicting fuel/carbon loads from 3D point cloud metrics at multiple scales, and relating fuel consumption to energy flux and fire effects. Andrew received his associate degree from Itasca Community College in Grand Rapids, Minnesota, his bachelor’s degree in ecology, evolution, and behavior from the University of Minnesota, and his doctoral degree in environmental, population, and organismic biology from the University of Colorado.

Dr. Sara Brambilla is a scientist at the Los Alamos National Laboratory in New Mexico where she currently leads the countering weapons of mass destruction team, doing research on topics related to urban transport and dispersion of toxic chemicals and biological agents, dirty bomb modeling, and particle resuspension and deposition. Sara’s work in wildland fire modeling has focused on fast-running microscale wind modeling in complex terrain, fire and atmosphere interaction, smoke transport and dispersion, and smoke impacts on road visibility. She is a developer for the QUIC-Fire and QUIC-SMOKE models, and has participated in field campaigns to gather data for model validation. Sara received her bachelor’s, master’s, and a doctoral degrees in chemical engineering from the Politecnico di Milano in Italy.