Classification of forest fuels in selected fire-prone ecosystems of Alberta, Canada—implications for crown fire behaviour prediction and fuel management

Abstract

Key message We used clustering to construct fuel classes from fuel inventory data based on three stand attributes relevant to crown fire behaviour: surface fuel load (SFL), canopy base height (CBH) and canopy bulk density (CBD). Resulting fuel classes explained more of the stand-to-stand variability in predicted crown fire behaviour than fuel types of the Canadian Forest Fire Behaviour Prediction (FBP) System. Context Wildfire behaviour is partly determined by stand structure and composition. Fuel characterization is essential for predicting fire behaviour and managing vegetation. Currently, categorical fuel types based on associations with major forested or open vegetated landcovers are used nationally in Canada for fire research and management applications. Aim To provide an alternative description of selected forest fuels in Alberta, Canada, using direct classification in which fuel categories are constructed from data using analytical methods. Methods Fuel inventory data for 476 stands were used to construct fuel classes with clustering. Potential crown fire behaviour was modelled for resulting fuel class clusters (FCCs) and FCCs were compared with assigned FBP System fuel types. Tree-based modelling was used to identify stand characteristics most influential on FCC membership. Fuel treatment effects on FCC and modelled crown fire behaviour were explored for the FCC most susceptible to crown fire. Results Four FCCs were identified: Red (low SFL, low CBH, low CBD); Green (high SFL, low-moderate CBH, low CBD); Blue (low SFL, high CBH, low-moderate CBD); and Black (low SFL, moderate CBH, high CBD). Stand density of live conifers and FBP System fuel type were the most important variables influencing FCC membership; however, FCCs did not align directly with assigned FBP System fuel types. Fuel reduction treatments in the Black FCC were effective at shifting the stand to a less flammable FCC. Conclusion FCCs explained more of the stand-to-stand variability in predicted crown fire behaviour than assigned FBP System fuel types, which suggests FCCs could be used to improve fire behaviour predictions and aid fire managers in prioritizing areas for fuel treatments. Future technological and remote sensing advances could enable mapping FCCs across large regions.

Characterizing Forest Fuel Properties and Potential Wildfire Dynamics in Xiuwu, Henan, China

“…When the fireline intensity exceeds 4000 kW/m, almost all organisms within the forest will be killed by the fire. Only when the fireline intensity is below 4000 kW/m does it hold ecological significance [49].…”

Fuel types misrepresent forest structure and composition in interior British Columbia: a way forward

“…Despite similarities in fuel attributes, fuel description systems often differ in their application (Sandberg et al 2001 ; Scott and Burgan 2005 ; Keane 2013 , 2015 ). For example, in fuel association approaches (as above), fuel information is assigned to categorical fuel types defined by the dominant vegetation species (Keane 2013 ; Phelps and Beverly 2022 ). In contrast, fuel classification approaches directly or indirectly cluster fuelbeds (the sum of live and dead flammable biomass, often organized by stratum) into unique groups based on fuel attributes (Keane 2013 ).…”

“…The FBP System fuel types emphasize crown fire behavior in subboreal and boreal forests, with descriptions that mirror the forest inventory attributes routinely collected for forest harvesting (Phelps and Beverly 2022 ). However, variations in measured fuel characteristics within a single stand can be greater than variation between fuel types of adjacent stands (Brown and Bevins 1986 ; Miller et al 2003 ).…”

“…However, variations in measured fuel characteristics within a single stand can be greater than variation between fuel types of adjacent stands (Brown and Bevins 1986 ; Miller et al 2003 ). Thus, despite its convenience, this fuel association (Keane 2013 ) approach along with the limited number of FBP System fuel types often results in fuel characteristics and forest inventory attributes that deviate from FBP System fuel type descriptions (Phelps and Beverly 2022 ). As a result, the breadth and diversity of fuelbeds that cover British Columbia (BC), Canada, landscapes are inadequately represented by FBP System fuel types (Hawkes et al 1995 ; Parisien et al 2013 ; Perrakis et al 2018 ).…”