Multiple-Scale Relationships between Vegetation, the Wildland–Urban Interface, and Structure Loss to Wildfire in California

Abstract

Recent increases in destructive wildfires are driving a need for empirical research documenting factors that contribute to structure loss. Existing studies show that fire risk is complex and varies geographically, and the role of vegetation has been especially difficult to quantify. Here, we evaluated the relative importance of vegetation cover at local (measured through the Normalized Difference Vegetation Index) and landscape (as measured through the Wildland–Urban Interface) scales in explaining structure loss from 2013 to 2018 in California—statewide and divided across three regions. Generally, the pattern of housing relative to vegetation better explained structure loss than local-scale vegetation amount, but the results varied regionally. This is likely because exposure to fire is a necessary first condition for structure survival, and sensitivity is only relevant once the fire reaches there. The relative importance of other factors such as long-term climatic variability, distance to powerlines, and elevation also varied among regions. These suggest that effective fire risk reduction strategies may need to account for multiple factors at multiple scales. The geographical variability in results also reinforces the notion that “one size does not fit all”. Local-scale empirical research on specific vegetation characteristics relative to structure loss is needed to inform the most effective customized plan.

Parcel-Level Risk Affects Wildfire Outcomes: Insights from Pre-Fire Rapid Assessment Data for Homes Destroyed in 2020 East Troublesome Fire

“…Driveway clearance (8) and address visibility (9), two out of three attributes related to access, are positive and significant, as are both siding (11) and attachments (12) for the structure category. In addition, all five summary measures (13)(14)(15)(16)(17), including the overall risk score (variable 17), are also found to have a positive and significant total impact. The mean estimated impact of 0.0512 from the (scaled) overall risk score variable suggests an approximate 5% increase in the likelihood of a structure being lost for every 100-point increase in risk, with the result coming from the combined direct and indirect impacts.…”

“…Although embedded within the general framework of risk described above, PLR is motivated by the recognition that large scale wildfire risk assessments tend to have limited coverage of conditions that vary at the scale of individual properties and treat entire classes of assets, such as residential property, as responding uniformly to a hazard. In contrast, PLR offers an explicit focus on the heterogeneity of risk among the residential parcels that comprise a geographic community, consistent with fire science establishing differences in wildfire vulnerability [14] and recent studies emphasizing the influence of factors at multiple scales on the effectiveness of fire risk reduction strategies [15]. By design, PLR focuses on property conditions that can be influenced by residents or property owners.…”

Optimized Hot Spot and Directional Distribution Analyses Characterize the Spatiotemporal Variation of Large Wildfires in Washington, USA, 1970−2020

“…Because we only focused on incident count per grid cell, other variables shown to have a relationship with wildfire occurrence, such as climate regime (Westerling et al 2003), and variables shown to be associated with wildfire risk to human communities, such as housing patterns (Syphard et al 2021) were not considered in this study. Additionally, these missing variables can change dramatically over a 47 km distance in some parts of our study area, such as the Cascade Range.…”

Estimating WUI exposure probability to a nearby wildfire

“…Spatial metrics like building density, vegetation cover, and proximity to large areas of wildland fuels are used to identify buildings or neighborhoods that are vulnerable to wild re (Gibbons et al 2012, Bar-Massada et al 2013, Radeloff et al 2018. A growing body of research on the factors in uencing home loss uses GIS focal analyses (within a neighborhood instead of at a single pixel) to evaluate which landscape factors and at what scales are most predictive of home loss given exposure to wild re (Price and Bradstock 2013, Knapp et al 2021, Syphard et al 2021. Such models complement WUI classi cation methods with quantitative estimates of building loss risk, but they do not address the likelihood of WUI exposure to wild re.…”