Year of Award
Master of Science (MS)
Department or School/College
Jesse Johnson, Carl Seielstad, Doug Brinkerhoff
Wildfire, CFD, LIDAR, fuel modeling, fire modeling
University of Montana
Numerical Analysis and Scientific Computing
Computational models of wildfires are an important tool for fire managers and scientists. However, fuel inputs to wildfire models can be difficult to represent with sufficient detail to be both computationally efficient and representative of observations. Recent advances in fuel mapping with airborne and terrestrial laser scanning (LIDAR) techniques present new opportunities to capture variation in fuels within a tree canopy and on a landscape. In this paper, we develop a technique for building 3D representations of vegetation from point clouds created by Terrestrial Laser Scans (TLS). Our voxel based approach can be extended to represent heterogeneous crown fuels as collections of fuel cells in modern 3D Computational Fluid Dynamics wildfire models such as FDS, QUIC-Fire, or FIRETEC. We evaluated the effectiveness of our technique at different fuel cell resolutions by using the DAKOTA optimization toolkit to compare simulated fire behavior in FDS with observed burn data collected during a series of experiments at the Missoula Fire Sciences Laboratory. The primary finding was that within the search space of point cloud derived fuel cells, we find accurate descriptions of observed fire behavior with the FDS model. We also find that within our search space, regions of global minima are consistent across fuel cells at different resolutions. This finding suggests that while new techniques are capable of characterizing fuel models with a high degree of fidelity, high resolution 3D fuel models do not improve parity with observed fire behavior in the FDS fire model. The results of this paper offer fire modelers guidelines for translating LIDAR data to 3D fire models, and what fuel cell resolution can best capture accurate fire behavior.
Marcozzi, Anthony Albert, "SENSITIVITY OF LIDAR DERIVED FUEL CELLS TO FIRE MODELING AT LABORATORY SCALE" (2022). Graduate Student Theses, Dissertations, & Professional Papers. 11934.
© Copyright 2022 Anthony Albert Marcozzi