Current forest monitoring technologies including satellite remote sensing, manned/piloted aircraft, and observation towers leave uncertainties about a wildfire’s extent, behavior, and conditions in the fire’s near environment, particularly during its early growth. Rapid mapping and real-time fire monitoring can inform in-time intervention or management solutions to maximize beneficial fire outcomes. Drone systems’ unique features of 3D mobility, low flight altitude, and fast and easy deployment make them a valuable tool for early detection and assessment of wildland fires, especially in remote forests that are not easily accessible by ground vehicles. In addition, the lack of abundant, well-annotated aerial datasets – in part due to unmanned aerial vehicles’ (UAVs’) flight restrictions during prescribed burns and wildfires – has limited research advances in reliable data-driven fire detection and modeling techniques. While existing wildland fire datasets often include either color or thermal fire images, here we present (1) a multi-modal UAV-collected dataset of dual-feed side-by-side videos including both RGB and thermal images of a prescribed fire in an open canopy pine forest in Northern Arizona and (2) a deep learning-based methodology for detecting fire and smoke pixels at accuracy much higher than the usual single-channel video feeds. The collected images are labeled to "fire" or "no-fire" frames by two human experts using side-by-side RGB and thermal images to determine the label. To provide context to the main dataset’s aerial imagery, the included supplementary dataset provides a georeferenced pre-burn point cloud, an RGB orthomosaic, weather information, a burn plan, and other burn information. By using and expanding on this guide dataset, research can develop new data-driven fire detection, fire segmentation, and fire modeling techniques.

Background: Prescribed fire is vital for fuel reduction and ecological restoration, but the effectiveness and fine-scale interactions are poorly understood. Aims: We developed methods for processing uncrewed aircraft systems (UAS) imagery into spatially explicit pyrometrics, including measurements of fuel consumption, rate of spread, and residence time to quantitatively measure three prescribed fires. Methods: We collected infrared (IR) imagery continuously (0.2 Hz) over prescribed burns and one experimental calibration burn, capturing fire progression and combustion for multiple hours. Key results: Pyrometrics were successfully extracted from UAS-IR imagery with sufficient spatiotemporal resolution to effectively measure and differentiate between fires. UAS-IR fuel consumption correlated with weight-based measurements of 10 1-m2 experimental burn plots, validating our approach to estimating consumption with a cost-effective UAS-IR sensor (R2 = 0.99; RMSE = 0.38 kg m−2). Conclusions: Our findings demonstrate UAS-IR pyrometrics are an accurate approach to monitoring fire behaviour and effects, such as measurements of consumption. Prescribed fire is a fine-scale process; a ground sampling distance of <2.3 m2 is recommended. Additional research is needed to validate other derived measurements. Implications: Refined fire monitoring coupled with refined objectives will be pivotal in informing fire management of best practices, justifying the use of prescribed fire and providing quantitative feedback in an uncertain environment.

Pandora moths (Coloradia pandora subsp. davisi Barnes and Benjamin) have been observed to reach epidemic populations on the Kaibab Plateau, resulting in relatively small, localized defoliation events of ponderosa (Pinus ponderosa Lawson). We reconstructed the historical pandora moth outbreak regime using tree rings and forest health records to explore how exogenous factors, climate, and fire, are related to outbreak dynamics close to the driest range of ponderosa pine. We collected eight tree-ring chronologies dating back 400 years, geographically dispersed around the plateau, and inferred past outbreaks by comparison with non-host tree-ring chronologies, weather records, and historical observations. Eleven outbreaks were detected between 1744 and the present, many of them occurring at all the sites. Outbreaks were found to be synchronous, typically lasting 10 years at 25-year intervals. Interruption of the frequent fire regime that prevailed prior to 1880 was associated with a shift to shorter, less frequent outbreaks. Dry to wet oscillations in climate were correlated with outbreak initiations. Pandora moth outbreaks appear to have been an intrinsic part of the Kaibab Plateau’s forest ecosystems, though more research is needed to understand outbreak effects on the ecosystem and future directions of the moth–host relationship under climate warming.