M.Sc student Hilary Cameron (supervised by University of Alberta’s Dr. Jen Beverly)
Fuel is a key determinant of wildfire behaviour and is therefore a required input to any fire behaviour model. Maps that describe the characteristics of live and dead biomass across large areas (i.e., fuel maps) are a critical input to a wide range of research models and decision support systems that aim to describe potential fire behaviour and inform fire management actions. Directly measuring fuel attributes to create fuel maps is time consuming, expensive and results in a limited inventory of stand attributes across a landscape. As remote sensing technologies become more affordable, the ability to utilize these technologies to create comprehensive fuel maps on small and large scales is becoming increasingly pragmatic.
M.Sc student Hilary Cameron (supervised by University of Alberta’s Dr. Jen Beverly) investigated the viability of using Airborne Laser Scanning (ALS), a form of remote sensing that uses LiDAR, to predict forest attributes that are important to wildfire behaviour in black spruce stands located in Alberta, Canada. Results suggest that detailed fuel maps can be made with ALS and can be a cost-effective alternative to field-based sampling to predict potential wildfire behaviour and support with fire-management decisions.