Modeling Neighborhoods as Fuel for Wildfire

Author

Bryce Alan Young, The University Of MontanaFollow

Year of Award

2025

Document Type

Thesis

Degree Type

Master of Science (MS)

Degree Name

Forestry

Department or School/College

W.A. Franke College of Forestry

Committee Chair

Dr. Carl Seielstad

Commitee Members

Dr. Carl Seielstad, Dr. Joseph St. Peter, Dr. Anna Klene

Keywords

wildfire, vulnerability, structure risk, mitigation, lidar, wildfire risk

Subject Categories

Applied Statistics | Natural Resources Management and Policy | Other Forestry and Forest Sciences | Other Physical Sciences and Mathematics | Structural Engineering | Urban Studies and Planning

Abstract

Wildfire models drive billions of dollars in risk mitigation efforts. However, the modeling community currently lacks a representative fuelscape on which to base simulations of fire spread in the built environment and the wildland-urban interface (WUI) where vegetation and structures act together as fuel for wildfire. This thesis advances wildfire risk modeling by addressing the underdeveloped representation of the built environment in existing frameworks. By identifying inconsistencies in how structure and defensible space features are defined and used across empirical studies, predictive indices, and fire spread models, this research lays the groundwork for standardized modeling approaches and feature selection (Chapter 2). Through remote assessment methods using LiDAR and tax assessor data, it demonstrates the feasibility of scalable structure-level susceptibility modeling (Chapter 3). These methods offer a practical pathway for extending risk assessments beyond field-surveyed areas and across larger geographic regions. Finally, this research introduces an approach to defining structure archetypes based on unsupervised clustering (Chapter 4). These archetypes represent a critical step toward developing fire behavior fuel models for the built environment — models that better capture the diversity of WUI structure configurations and their role in fire dynamics. Together, these contributions help bridge the gap between theory, data availability, and risk modeling, supporting more accurate and actionable wildfire mitigation planning at both the community and regional scales.

Recommended Citation

Young, Bryce Alan, "Modeling Neighborhoods as Fuel for Wildfire" (2025). Graduate Student Theses, Dissertations, & Professional Papers. 12495.
https://scholarworks.umt.edu/etd/12495