Predicting Fuel Characteristics of Black Spruce Stands Using Airborne Laser Scanning (ALS) in the Province of Alberta, Canada

• Author / Creator

  Cameron, Hilary

• 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. 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. Airborne Laser Scanning (ALS), a remote sensing technology that uses LiDAR, has already been used extensively to characterize forest attributes such as stand height, above ground biomass and stem density; however, few studies have used ALS within the boreal forest to describe forest structural attributes such as fuel loading at a fine resolution (i.e., <10 m grid cell resolution), which is particularly relevant to fire behaviour. This study investigates the viability of using ALS to predict forest attributes in dense black spruce (Picea Mariana) stands, located in Alberta, Canada. Five fuel attributes important to wildfire behaviour were investigated: canopy bulk density (CBD), canopy fuel load (CFL), stem density, canopy height and canopy base height (CBH). Predictive models for estimating fuel attributes from ALS data were developed and compared among ALS datasets with three different pulse point cloud densities (i.e., dense, intermediate and thin). Least absolute shrinkage and selection operator (lasso) regression was used to develop linear models with a training dataset (52 field plots) and evaluated on a testing dataset (28 field plots). Statistically significant relationships were found between all ALS datasets and the forestry metrics of interest. Predictive power decreased with decreasing ALS pulse density. Model accuracy was acceptable and consistent with similar prior studies. Results of this study suggest that ALS can be a useful tool for estimating black spruce canopy fuel attributes at a 40 m2 resolution in Alberta, Canada. Maps of model outputs are a cost-effective alternative to field-based sampling to predict potential wildfire behaviour and support with fire-management decisions.

• Subjects / Keywords

  • lidar
  • wildfire
  • black spruce
  • forestry
  • airborne laser scanning

• Graduation date

  Fall 2020

• Type of Item

  Thesis

• Degree

  Master of Science

• DOI

  https://doi.org/10.7939/r3-zc8y-e407

• License

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