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Assessing fuel treatment effectiveness using satellite imagery and spatial statistics.
Michael C Wimberly1, Mark A Cochrane, Adam D Baer
1Geographic Information Science Center of Excellence, South Dakota State University, Brookings, South Dakota 57007, USA. michael.wimberly@sdstate.edu
Ecological Applications : a Publication of the Ecological Society of America
|September 23, 2009
Summary
Forest management, particularly prescribed burning, effectively reduces wildfire severity in western US ecosystems. Spatial autoregression models offer improved analysis by accounting for unmeasured factors influencing fire outcomes.
Area of Science:
- Forest Ecology
- Wildfire Science
- Geospatial Analysis
Background:
- Understanding forest management's impact on wildfire severity is crucial for western US fire-prone ecosystems.
- Advancements in geospatial data enable large-scale studies of fuel treatment effectiveness.
Purpose of the Study:
- To analyze the effectiveness of fuel treatment strategies on wildfire burn severity.
- To compare ordinary least-squares (OLS) regression with spatial autoregression (SAR) for analyzing treatment effects.
Main Methods:
- Utilized OLS and SAR regression models to assess fuel treatment impacts on burn severity for three wildfires.
- Employed differenced normalized burn ratio (dNBR) maps for burn severity measurement.
- Incorporated LANDFIRE geospatial data to control for pre-fire vegetation, fuels, and topography.
Main Results:
- Prescribed burning treatments were more effective in reducing wildfire severity than thinning alone across all studied fires.
- SAR models yielded lower treatment effect sizes and higher standard errors compared to OLS models.
- SAR models effectively accounted for unmeasured confounding variables, such as fire weather and landscape effects.
Conclusions:
- Assessing fuel treatment effectiveness is feasible using current geospatial data sets.
- Spatial autoregression is a valuable tool for controlling for unmeasured confounding factors in wildfire research.
