Studying the effects of fuel treatment based on burn probability on a boreal forest landscape

Fuel treatment is assumed to be a primary tactic to mitigate intense and damaging wildfires. However, how to place treatment units across a landscape and assess its effectiveness is difficult for landscape-scale fuel management planning. In this study, we used a spatially explicit simulation model (LANDIS) to conduct wildfire risk assessments and optimize the placement of fuel treatments at the landscape scale. We first calculated a baseline burn probability map from empirical data (fuel, topography, weather, and fire ignition and size data) to assess fire risk. We then prioritized landscape-scale fuel treatment based on maps of burn probability and fuel loads (calculated from the interactions among tree composition, stand age, and disturbance history), and compared their effects on reducing fire risk. The burn probability map described the likelihood of burning on a given location; the fuel load map described the probability that a high fuel load will accumulate on a given location. Fuel treatment based on the burn probability map specified that stands with high burn probability be treated first, while fuel treatment based on the fuel load map specified that stands with high fuel loads be treated first. Our results indicated that fuel treatment based on burn probability greatly reduced the burned area and number of fires of different intensities. Fuel treatment based on burn probability also produced more dispersed and smaller high-risk fire patches and therefore can improve efficiency of subsequent fire suppression. The strength of our approach is that more model components (e.g., succession, fuel, and harvest) can be linked into LANDIS to map the spatially explicit wildfire risk and its dynamics to fuel management, vegetation dynamics, and harvesting.