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BackgroundPrescribed fire is vital for fuel reduction and ecological restoration, but the effectiveness and fine-scale interactions are poorly understood. AimsWe 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. MethodsWe 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 resultsPyrometrics 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). ConclusionsOur 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. ImplicationsRefined 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. 

Abstract Smoke from wildland fires contains more diverse, viable microbes than typical ambient air, yet little is known about the sources and sinks of smoke‐borne microorganisms. Data from molecular‐based surveys suggest that smoke‐borne microorganisms originate from material associated with the vegetation and underlying soils that becomes aerosolized during combustion, however, the sources of microbes in smoke have not yet been experimentally assessed. To elucidate this link, we studied high‐intensity forest fires in the Fishlake National Forest, Utah, USA and applied source‐sink modeling to assemblages of 16S ribosomal RNA (rRNA) gene sequences recovered from samples of smoke, vegetation, and soil. Our results suggest that 70% of the bacterial taxa in smoke originated from the local aspen (Populus tremuloides) (33%) and soil (37%) communities. In comparison, 42% of bacteria in air sampled prior to the fires could be attributed to these terrestrial sources. When the bacterial assemblages in smoke were modeled as sources to the local communities, they contributed an average of 25% to the terrestrial sinks versus an estimated contribution of <4% from ambient air. Our results provide support for the role of wildland fire in bacterial dispersal and the working hypothesis that smoke is an environmental reservoir of microbes for receiving ecosystems. 

Wildfires are an essential part of a healthy ecosystem, yet the expansion of the wildland-urban interface, combined with climatic changes and other anthropogenic activities, have led to the rise of wildfire hazards in the past few decades. Managing future wildfires and their multi-dimensional impacts requires moving from traditional reactive response to deploying proactive policies, strategies, and interventional programs to reduce wildfire risk to wildland-urban interface communities. Existing risk assessment frameworks lack a unified analytical method that properly captures uncertainties and the impact of decisions across social, ecological, and technical systems, hindering effective decision-making related to risk reduction investments. In this paper, a conceptual probabilistic wildfire risk assessment framework that propagates modeling uncertainties is presented. The framework characterizes the dynamic risk through spatial probability density functions of loss, where loss can include different decision variables, such as physical, social, economic, environmental, and health impacts, depending on the stakeholder needs and jurisdiction. The proposed approach consists of a computational framework to propagate and integrate uncertainties in the fire scenarios, propagation of fire in the wildland and urban areas, damage, and loss analyses. Elements of this framework that require further research are identified, and the complexity in characterizing wildfire losses and the need for an analytical-deliberative process to include the perspectives of the spectrum of stakeholders are discussed. 

Drone based wildfire detection and modeling methods enable high-precision, real-time fire monitoring that is not provided by traditional remote fire monitoring systems, such as satellite imaging. Precise, real-time information enables rapid, effective wildfire intervention and management strategies. Drone systems’ ease of deployment, omnidirectional maneuverability, and robust sensing capabilities make them effective tools for early wildfire detection and evaluation, particularly so in environments that are inconvenient for humans and/or terrestrial vehicles. Development of emerging drone-based fire monitoring systems has been inhibited by a lack of well-annotated, high quality aerial wildfire datasets, largely as a result of UAV flight regulations for prescribed burns and wildfires. The included dataset provides a collection of side-by-side infrared and visible spectrum video pairs taken by drones during an open canopy prescribed fire in Northern Arizona in 2021. The frames have been classified by two independent classifiers with two binary classifications. The Fire label is applied when the classifiers visually observe indications of fire in either RGB or IR frame for each frame pair. The Smoke label is applied when the classifiers visually estimate that at least 50% of the RGB frame is filled with smoke. To provide additional context to the main dataset’s aerial imagery, the provided supplementary dataset includes weather information, the prescribed burn plan, a geo-referenced RGB point cloud of the preburn area, an RGB orthomosaic of the preburn area, and links to further information. 

ABSTRACT We present SAMI-H i, a survey of the atomic hydrogen content of 296 galaxies with integral field spectroscopy available from the SAMI Galaxy Survey. The sample spans nearly 4 dex in stellar mass ($$M_\star = 10^{7.4}-10^{11.1}~ \rm M_\odot$$), redshift z < 0.06, and includes new Arecibo observations of 153 galaxies, for which we release catalogues and H i spectra. We use these data to compare the rotational velocities obtained from optical and radio observations and to show how systematic differences affect the slope and scatter of the stellar-mass and baryonic Tully–Fisher relations. Specifically, we show that $$\rm H\alpha$$ rotational velocities measured in the inner parts of galaxies (1.3 effective radii in this work) systematically underestimate H i global measurements, with H i/$$\rm H\alpha$$ velocity ratios that increase at low stellar masses, where rotation curves are typically still rising and $$\rm H\alpha$$ measurements do not reach their plateau. As a result, the $$\rm H\alpha$$ stellar mass Tully–Fisher relation is steeper (when M⋆ is the independent variable) and has larger scatter than its H i counterpart. Interestingly, we confirm the presence of a small fraction of low-mass outliers of the $$\rm H\alpha$$ relation that are not present when H i velocity widths are used and are not explained by ‘aperture effects’. These appear to be highly disturbed systems for which $$\rm H\alpha$$ widths do not provide a reliable estimate of the rotational velocity. Our analysis reaffirms the importance of taking into account differences in velocity definitions as well as tracers used when interpreting offsets from the Tully–Fisher relation, at both low and high redshifts and when comparing with simulations.