Estimating Canopy Fuel Attributes from Low-Density LiDAR

Fire

Aaron Poznanovic¹  Peter Wolter²  PederS. Engelstad³  Michael Falkowski³  Patty Johnson⁴ 

[1] Department of Forest Resources, University of Minnesota, St. Paul, MN 55108, USA;Department of Natural Resource Ecology and Management, Iowa State University, Ames, IA, 50011, USA;Natural Resource Ecology Laboratory, Colorado State University, Fort Collins, CO 80521, USA;USFS Superior National Forest, Grand Marais, MN 55604, USA;

关键词: canopy fuels;    low-density LiDAR;    random forest;    LANDFIRE;    BWCA;    forest structure;    imputation;   

DOI  :  10.3390/fire2030038

来源: DOAJ

【 摘 要 】

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.

【 授权许可】

Unknown