Assessing fuel treatment effectiveness using satellite imagery and spatial statistics

Understanding the influences of forest management practices on wildfire severity is critical in fire-prone ecosystems of the western United States. Newly available geospatial data sets characterizing vegetation, fuels, topography, and burn severity offer new opportunities for studying fuel treatment...

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Bibliographic Details
Published in:Ecological applications 2009-09, Vol.19 (6), p.1377-1384
Main Authors: Wimberly, Michael C, Cochrane, Mark A, Baer, Adam D, Pabst, Kari
Format: Article
Language:English
Subjects:
burn severity
Burns
Communications
Datasets
differenced normalized burn ratio (dNBR)
Ecosystem
Fire behavior
fire ecology
Fire severity
fire weather
Fires
forest fires
Forestry
fuel treatments
fuels (fire ecology)
Geographic Information Systems
geospatial data processing
geospatial technology
LANDFIRE project
Modeling
ordinary least squares regression
Polygons
Prescribed burning
regression analysis
remote sensing
satellites
sequential autoregression
spatial autoregression
Spatial models
United States
Weather
Wildfires
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Summary:Understanding the influences of forest management practices on wildfire severity is critical in fire-prone ecosystems of the western United States. Newly available geospatial data sets characterizing vegetation, fuels, topography, and burn severity offer new opportunities for studying fuel treatment effectiveness at regional to national scales. In this study, we used ordinary least-squares (OLS) regression and sequential autoregression (SAR) to analyze fuel treatment effects on burn severity for three recent wildfires: the Camp 32 fire in western Montana, the School fire in southeastern Washington, and the Warm fire in northern Arizona. Burn severity was measured using differenced normalized burn ratio (dNBR) maps developed by the Monitoring Trends in Burn Severity project. Geospatial data sets from the LANDFIRE project were used to control for prefire variability in canopy cover, fuels, and topography. Across all three fires, treatments that incorporated prescribed burning were more effective than thinning alone. Treatment effect sizes were lower, and standard errors were higher in the SAR models than in the OLS models. Spatial error terms in the SAR models indirectly controlled for confounding variables not captured in the LANDFIRE data, including spatiotemporal variability in fire weather and landscape-level effects of reduced fire severity outside the treated areas. This research demonstrates the feasibility of carrying out assessments of fuel treatment effectiveness using geospatial data sets and highlights the potential for using spatial autoregression to control for unmeasured confounding factors
ISSN:1051-0761
1939-5582
DOI:10.1890/08-1685.1