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INTEGRATING SPATIAL STATISTICS AND DECISION ANALYSIS FOR WILDFIRE RISK MAPPING: A CASE STUDY OF THE KOOTENAI NATIONAL FOREST
No full textWildfire risk in the western United States has intensified in recent decades due to intersecting forces of climate change, systematic fire suppression, and expanding human settlement in fire-prone regions. This thesis presents a comprehensive spatial assessment of wildfire risk in the Kootenai National Forest (KNF) and its surrounding landscape in northwestern Montana, integrating wildfire likelihood, suppression difficulty, evacuation vulnerability, and building exposure into a unified spatial statistical framework. The research addresses a critical gap in spatial wildfire risk modeling by focusing on the need to assess fire threats in relation to local operational constraints and community vulnerabilities.Drawing on geospatial datasets from the U.S. Forest Service’s Risk Management Assistance (RMA) Dashboard, this study operationalizes risk through a bivariate spatial analysis and a composite risk model. Burn probability serves as the core measure of wildfire threat, while suppression difficulty index and ground evacuation time represent vulnerabilities of the landscape to prevent fire from harm on settlements and people. Structures data captures the geographical exposure of the human communities to wildfire and informs about a part of the values at risk.Spatial data were processed using a 1 km² fishnet grid, and Local Indicators of Spatial Association (LISA) were applied to detect statistically significant clusters of intersecting threats and vulnerabilities. LISA scatter plots were enhanced through two-dimensional kernel density for improved visualization of correlations between risk factors.To inform prioritization of mitigation measures, a Multi-Criteria Decision Analysis (MCDA) approach was employed, normalizing and aggregating the risk factors into a composite risk index. The resulting risk surface delineates zones of converging fire hazard and socio-technical constraints. A spatial overlay analysis with fuel treatment data revealed a significant implementation gap: only 8.83% of the high-risk areas identified had received any form of fuel treatment. This disconnect underscores the need for more spatially responsive mitigation strategies under initiatives like the Wildfire Crisis Strategy.By integrating hazard modeling with localized vulnerability indicators, this thesis provides a replicable, data-driven framework for wildfire risk assessment. The findings support spatially targeted planning efforts in the Kootenai National Forest and offer a methodological basis for refining mitigation priorities in wildfire-prone landscapes
RG94-144: Foresters Ball Dance Card
No full textDance card for the 1935 Foresters ball with 6 pages. The cover is grey paper with a horse shoe and tree stump design. The inner pages are on gold paper. There is a charm on the card that is a metal stone hammer and chisel. The cover reads, Foresters Ball University of Montana Forest School, February 1, 1935. The first page has slots for 4 dances. The second page has slots for dances 5-8. The third page has slots for dances 9-12 and This Charm of Paul\u27s tramps down all cares tonight. Page 4 has slots for dances 13-16 and reads, Guests of Honor - His excellency, The Governor and Mrs. Frank H. Cooney; Dr. and Mrs. H.H. Swain; Dr. and Mrs. Charles H. Clapp; Major and Mrs. Evan W. Kelley; Mr. and Mrs. W.C. Lubrecht; Mr. and Mrs. Rutledge Parker; Dean and Mrs. T.C. Spaulding Page 5 has slots for dances 17-20 and reads, Chaperones - Dean and Mrs. R.H. Jesse; Dean and Mrs. Burly Miller; Dean Harriet Rankin Sedman; Dean A.L. Stone; Dr. and Mrs. J.W. Severy; Dr. and Mrs. W.E. Schreiber; Professor and Mrs. J. B. Speer; Professor and Mrs. I.W. Cook; Professor and Mrs. J.H. Ramskill; Professor F.G. Clark; Mr. and Mrs. E.W. Nelson; Mr. and Mrs. T.G. Swearingen . Page 6 reads, There\u27s nothing so steadfast as Dobin\u27s foot on the ground. We hope that lost worries and new love is twice and sound.https://scholarworks.umt.edu/universityofmontana_artifacts/1146/thumbnail.jp
ADVANCING WILDFIRE SCIENCE THROUGH THE APPLICATION AND PARAMETRIZATION OF PROCESS BASED FIRE MODELS
No full textComputational models of wildfires are an important tool for fire managers and scientists. However, representing fuel inputs with sufficient detail for process-based fire models to accurately reflect observed fire phenomena while remaining computationally tractable is a major challenge. This dissertation advances wildland fire science by applying and parametrizing such models to investigate complex fire-fuel interactions across multiple scales.
A primary contribution addresses fine-scale fuel characterization through a novel voxel-based technique converting Terrestrial Laser Scan (TLS) point clouds into three-dimensional (3D) fuel representations. This method was evaluated by comparing simulated fire behavior in the Fire Dynamics Simulator (FDS), optimized using the DAKOTA toolkit, with observed mass loss data from laboratory experiments conducted at the Missoula Fire Sciences Laboratory. Key findings reveal that while point cloud-derived fuels can accurately describe observed fire behavior, exceptionally high-resolution 3D fuel models do not necessarily improve predictive parity, suggesting an optimal balance between fidelity and computational demand. This research offers guidelines for translating LiDAR data into 3D fire models and determining appropriate fuel cell resolutions for capturing accurate fire behavior.
To overcome data limitations at broader scales, this work introduces FastFuels, a novel system generating landscape-scale 3D fuel data suitable for next-generation fire models. FastFuels integrates disparate data sources, including forest inventory data, plot imputation maps, and incorporates flexible LiDAR data assimilation to produce detailed and mutable fuel landscapes. Its utility is demonstrated through applications in evaluating fuel treatment effectiveness with FDS and simulating prescribed fire operations with QUIC-Fire, thereby enhancing decision support capabilities.
Furthermore, this dissertation contributes to a mechanistic understanding of dynamic fire behaviors by investigating convective mechanisms and wind thresholds governing junction fire formation in heterogeneous fuel treatments. Utilizing FDS simulations informed by field observations, this research reveals how fuel structure and alterations in convective heat transfer, modulated by critical wind speeds, initiate transitions between fireline slowing and acceleration regimes within fuel treatments.
Collectively, these studies enhance the fundamental understanding of fire-fuel interactions, introduce innovative methods for fuel parameterization, and deliver advanced modeling tools. The findings and developed systems contribute significantly to improving the practical utility, accessibility, and predictive power of process-based models, thereby advancing wildland fire science and supporting more effective, ecologically informed fire management strategies