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Wildfire decision support systems combine fuel maps with other fire environment variables to predict fire behaviour and guide management actions. Until recently, financial and technological constraints have limited provincial fuel maps to relatively coarse spatial resolutions. Airborne Laser Scanning (ALS), a remote sensing technology that uses LiDAR (Light Detection and Ranging), is becoming an increasingly affordable and pragmatic tool for mapping fuels across localised and broad areas. Few studies have used ALS in boreal forest regions to describe structural attributes such as fuel load at a fine resolution (i.e. &lt;100 m2 cell resolution). We used ALS to predict five forest attributes relevant to fire behaviour in black spruce (Picea mariana) stands in Alberta, Canada: canopy bulk density, canopy fuel load, stem density, canopy height and canopy base height. Least absolute shrinkage and selection operator (lasso) regression models indicated statistically significant relationships between ALS data and the forest metrics of interest (R2 ≥0.81 for all metrics except canopy base height which had a R2 value of 0.63). Performance of the regression models was acceptable and consistent with prior studies when applied to test datasets; however, regression models presented in this study mapped stand attributes at a much finer resolution (40 m2).

Predicting black spruce fuel characteristics with Airborne Laser Scanning (ALS)