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.

While operational fire severity products inform fire management decisions in Grand Canyon National Park (GRCA), managers have expressed the need for better quantification of the consequences of severity, specifically forest structure. In this study we computed metrics related to the forest structure from airborne laser scanning (ALS) data and investigated the influence that fires that burned in the decade previous had on forest structure on the North Rim of the Grand Canyon in Arizona. We found that fire severity best explains the occurrence of structure classes that include canopy cover, vertical fuel distribution, and surface roughness. In general we found that high fire severity resulted in structure types that exhibit lower canopy cover and higher surface roughness. Areas that burned more frequently with lower fire severity in general had a more closed canopy and a lower surface roughness, with less brush and less conifer regeneration. In a random forests modeling exercise to examine the relationship between severity and structure we found mean canopy height to be a powerful explanatory variable, but still proved less informative than the three-component structure classification. We show that fire severity not only impacts forest structure but also brings heterogeneity to vegetation types along the elevation gradient on the Kaibab plateau. This work provides managers with a unique dataset, usable in conjunction with vegetation, fuels and fire history data, to support management decisions at GRCA.

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.

Unmanned aerial vehicles (UAVs) equipped with RGB, multispectral, or thermal cameras have demonstrated their potential to provide high-resolution data before, during, and after wildfires and prescribed burns. Pre-burn point clouds generated through the photogrammetric processing of UAV images contain geometrical and spectral information of vegetation, while active fire imagery allows for deriving fire behavior metrics. This paper focuses on characterizing the relationship between the fire rate of spread (RoS) in prescribed burns and a set of independent geometrical, spectral, and neighborhood variables extracted from UAV-derived point clouds. For this purpose, different flights were performed before and during the prescribed burning in seven grasslands and open forest plots. Variables extracted from the point cloud were interpolated to a grid, which was sized according to the RoS semivariogram. Random Forest regressions were applied, obtaining up to 0.56 of R² in the different plots studied. Geometric variables from the point clouds, such as planarity and the spectral normalized blue–red difference index (NBRDI), are related to fire RoS. In analyzing the results, the minimum value of the eigenentropy (Eigenentropy_MIN), the mean value of the planarity (Planarity_MEAN), and percentile 75 of the NBRDI (NBRDI_P75) obtained the highest feature importance. Plot-specific analyses unveiled distinct combinations of geometric and spectral features, although certain features, such as Planarity_MEAN and the mean value of the grid obtained from the standard deviation of the distance between points (Dist_std_MEAN), consistently held high importance across all plots. The relationships between pre-burning UAV data and fire RoS can complement meteorological and topographic variables, enhancing wildfire and prescribed burn models.