Stand-level fuel reduction treatments in the Canadian boreal zone are used predominantly in community protection settings to alter the natural structure of dominant boreal conifer stands such as black spruce (Picea mariana (Mill.) BSP), jack pine (Pinus banksiana Lamb.) and lodgepole pine (Pinus contorta Dougl. ex Loud. var. latifolia). The aim of these fuel treatments is to inhibit the development of fast-spreading, high-intensity crown fires that naturally occur in boreal forest ecosystems. We document fuel treatment design standards used in boreal forests in Canada and review data requirements and methodological approaches for investigating fuel treatment effects on fire behaviour. Through a series of illustrative examples and summaries of empirical observations, we explore the implications of data and modelling assumptions used to estimate fire behaviour in fuel-treated areas and identify insights about fuel treatment effectiveness in boreal conifer stands. Fuel treatments in black spruce, jack pine and lodgepole pine stands were generally effective at reducing modelled and observed fire behaviour and inhibiting crown fire development and spread under low to moderate fire weather conditions. Evidence suggests that fuel treatments in these fuel types will be ineffective when rates of spread and wind speeds are very high or extreme. High surface fuel loads combined with the relatively short stature of boreal conifer trees can further undermine fuel treatment efforts. Priority areas for future study include examining alternatives for managing surface fuel loads in treated stands, exploring the viability of alternative horizontal fuel reduction protocols such as clumped fuel configurations, and integrating suppression and containment strategies within the fuel treatment planning and design process.
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. ,100 m 2 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 (R 2 $0.81 for all metrics except canopy base height which had a R 2 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 m 2 ).
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