Year of Award

2025

Document Type

Thesis

Degree Type

Master of Science (MS)

Degree Name

Environmental Studies

Department or School/College

College of Humanities & Sciences

Committee Chair

Dr. Fernando Sanchez-Trigueros

Commitee Members

Robin Saha, Anna Klene

Keywords

Wildfire Risk Assessment, Spatial Statistics, Multi-Criteria Decision Analysis, Kootenai National Forest, Local Indicators of Spatial Analysis

Subject Categories

Environmental Health and Protection | Environmental Studies

Abstract

Wildfire 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.

Download

Share

COinS
 

© Copyright 2025 Elijah Kordieh Mensah