Comprehensive spatial coverage of forest canopy fuels is relied upon by fire management in the US to predict fire behavior, assess risk, and plan forest treatments. Here, a collection of light detection and ranging (LiDAR) datasets from the western US are fused with Landsat-derived spectral indices to map the canopy fuel attributes needed for wildfire predictions: canopy cover (CC), canopy height (CH), canopy base height (CBH), and canopy bulk density (CBD). A single, gradient boosting machine (GBM) model using data from all landscapes is able to characterize these relationships with only small reductions in model performance (mean 0.04 reduction in R²) compared to local GBM models trained on individual landscapes. Model evaluations on independent LiDAR datasets show the single global model outperforming local models (mean 0.24 increase in R²), indicating improved model generality. The global GBM model significantly improves performance over existing LANDFIRE canopy fuels data products (R² ranging from 0.15 to 0.61 vs. −3.94 to −0.374). The ability to automatically update canopy fuels following wildfire disturbance is also evaluated, and results show intuitive reductions in canopy fuels for high and moderate fire severity classes and little to no change for unburned to low fire severity classes. Improved canopy fuel mapping and the ability to apply the same predictive model on an annual basis enhances forest, fuel, and fire management.

The emergence of affordable unmanned aerial systems (UAS) creates new opportunities to study fire behavior and ecosystem pattern—process relationships. A rotor-wing UAS hovering above a fire provides a static, scalable sensing platform that can characterize terrain, vegetation, and fire coincidently. Here, we present methods for collecting consistent time-series of fire rate of spread (RoS) and direction in complex fire behavior using UAS-borne NIR and Thermal IR cameras. We also develop a technique to determine appropriate analytical units to improve statistical analysis of fire-environment interactions. Using a hybrid temperature-gradient threshold approach with data from two prescribed fires in dry conifer forests, the methods characterize complex interactions of observed heading, flanking, and backing fires accurately. RoS ranged from 0–2.7 m/s. RoS distributions were all heavy-tailed and positively-skewed with area-weighted mean spread rates of 0.013–0.404 m/s. Predictably, the RoS was highest along the primary vectors of fire travel (heading fire) and lower along the flanks. Mean spread direction did not necessarily follow the predominant head fire direction. Spatial aggregation of RoS produced analytical units that averaged 3.1–35.4% of the original pixel count, highlighting the large amount of replicated data and the strong influence of spread rate on unit size.

Remotely piloted aircraft systems (RPAS) are providing fresh perspectives for the remote sensing of fire. One opportunity is mapping tree crown scorch following fires, which can support science and management. This proof-of-concept shows that crown scorch is distinguishable from uninjured canopy in point clouds derived from low-cost RGB and calibrated RGB-NIR cameras at fine resolutions (centimeter level). The Normalized Difference Vegetation Index (NDVI) provided the most discriminatory spectral data, but a low-cost RGB camera provided useful data as well. Scorch heights from the point cloud closely matched field measurements with a mean absolute error of 0.52 m (n = 29). Voxelization of the point cloud, using a simple threshold NDVI classification as an example, provides a suitable dataset worthy of application and further research. Field-measured scorch heights also showed a relationship to RPAS-thermal-camera-derived fire radiative energy density (FRED) estimates with a Spearman rank correlation of 0.43, but there are many issues still to resolve before robust inference is possible. Mapping fine-scale scorch in 3D with RPAS and SfM photogrammetry is a viable, low-cost option that can support related science and management.

Extreme spread events (ESEs), often characterized by high intensity and rapid rates of spread, can overwhelm fire suppression and emergency response capacity, threaten responder and public safety, damage landscapes and communities, and result in high socioeconomic costs and losses. Advances in remote sensing and geospatial analysis provide an improved understanding of observed ESEs and their contributing factors; however, there is a need to improve anticipatory and predictive capabilities to better prepare, mitigate, and respond. Here, leveraging individual-fire day-of-arrival raster outputs from the FSim fire modeling system, we prototype and evaluate methods for the simulation and categorization of ESEs. We describe the analysis of simulation outputs on a case study landscape in Colorado, USA, summarize daily spread event characteristics, threshold and probabilistically benchmark ESEs, spatially depict ESE potential, and describe limitations, extensions, and potential applications of this work. Simulation results generally showed strong alignment with historical patterns of daily growth and the proportion of cumulative area burned in the western US and identified hotspots of high ESE potential. Continued analysis and simulation of ESEs will likely expand the horizon of uses and grow in salience as ESEs become more common.