Estimating canopy fuel attributes from low-density LiDAR
Document Type: Journal Article
Author(s): Peder S. Engelstad; Michael J. Falkowski; Peter T. Wolter; Aaron Poznanovic; Patty Johnson
Publication Year: 2019

Cataloging Information

  • BWCAW - Boundary Waters Canoe Area Wilderness
  • canopy fuels
  • forest structure
  • imputation
  • low-density LIDAR
  • Minnesota
  • Random Forests
  • remote sensing
Record Maintained By:
Record Last Modified: July 15, 2019
FRAMES Record Number: 58224


Simulations of wildland fire risk are dependent on the accuracy and relevance of spatial data inputs describing drivers of wildland fire, including canopy fuels. Spatial data are freely available at national and regional levels. However, the spatial resolution and accuracy of these types of products often are insufficient for modeling local conditions. Fortunately, active remote sensing techniques can produce accurate, high-resolution estimates of forest structure. Here, low-density LiDAR and field-based data were combined using randomForest k-nearest neighbor imputation (RF-kNN) to estimate canopy bulk density, canopy base height, and stand age across the Boundary Waters Canoe Area in Minnesota, USA. RF-kNN models produced strong relationships between estimated canopy fuel attributes and field-based data for stand age (Adj. R2 = 0.81, RMSE = 10.12 years), crown fuel base height (Adj. R2 = 0.78, RMSE = 1.10 m), live crown base height (Adj. R2 = 0.7, RMSE = 1.60 m), and canopy bulk density (Adj. R2 = 0.48, RMSE = 0.09kg/m3). These results suggest that low-density LiDAR can help estimate canopy fuel attributes in mixed forests, with robust model accuracies and high spatial resolutions compared to currently utilized fire behavior model inputs. Model map outputs provide a cost-efficient alternative for data required to simulate fire behavior and support local management.

Online Link(s):
Engelstad, Peder S.; Falkowski, Michael J.; Wolter, Peter T.; Poznanovic, Aaron; Johnson, Patty. 2019. Estimating canopy fuel attributes from low-density LiDAR. Fire 2(3):38.