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Abstract Managing fuels is a key strategy for mitigating the negative impacts of wildfires on people and the environment. The use of satellite‐based Earth observation data has become an important tool for managers to optimize fuel treatment planning at regional scales. Fortunately, several new sensors have been launched in the last few years, providing novel opportunities to enhance fuel characterization. Herein, we summarize the potential improvements in fuel characterization at large scale (i.e., hundreds to thousands of km²) with high spatial and spectral resolution arising from the use of new spaceborne instruments with near‐global, freely‐available data. We identified sensors at spatial resolutions suitable for fuel treatment planning, featuring: lidar data for characterizing vegetation structure; hyperspectral sensors for retrieving chemical compounds and species composition; and dense time series derived from multispectral and synthetic aperture radar sensors for mapping phenology and moisture dynamics. We also highlight future hyperspectral and radar missions that will deliver valuable and complementary information for a new era of fuel load characterization from space. The data volume that is being generated may still challenge the usability by a diverse group of stakeholders. Seamless cyberinfrastructure and community engagement are paramount to guarantee the use of these cutting‐edge datasets for fuel monitoring and wildland fire management across the world. 

Abstract Navigating uncertainty is a critical challenge in all fields of science, especially when translating knowledge into real-world policies or management decisions. However, the wide variance in concepts and definitions of uncertainty across scientific fields hinders effective communication. As a microcosm of diverse fields within Earth Science, NASA’s Carbon Monitoring System (CMS) provides a useful crucible in which to identify cross-cutting concepts of uncertainty. The CMS convened the Uncertainty Working Group (UWG), a group of specialists across disciplines, to evaluate and synthesize efforts to characterize uncertainty in CMS projects. This paper represents efforts by the UWG to build a heuristic framework designed to evaluate data products and communicate uncertainty to both scientific and non-scientific end users. We consider four pillars of uncertainty: origins, severity, stochasticity versus incomplete knowledge, and spatial and temporal autocorrelation. Using a common vocabulary and a generalized workflow, the framework introduces a graphical heuristic accompanied by a narrative, exemplified through contrasting case studies. Envisioned as a versatile tool, this framework provides clarity in reporting uncertainty, guiding users and tempering expectations. Beyond CMS, it stands as a simple yet powerful means to communicate uncertainty across diverse scientific communities. 

Background There is an ongoing need for improved understanding of wildfire plume dynamics. Aims To improve process-level understanding of wildfire plume dynamics including strong (>10 m s−1) fire-generated winds and pyrocumulus (pyroCu) development. Methods Ka-band Doppler radar and two Doppler lidars were used to quantify plume dynamics during a high-intensity prescribed fire and airborne laser scanning (ALS) to quantify the fuel consumption. Key results We document the development of a strongly rotating (>10 m s−1) pyroCu-topped plume reaching 10 km. Plume rotation develops during merging of discrete plume elements and is characterised by inflow and rotational winds an order of magnitude stronger than the ambient flow. Deep pyroCu is initiated after a sequence of plume-deepening events that push the plume top above its condensation level. The pyroCu exhibits a strong central updraft (~35 m s−1) flanked by mechanically and evaporative forced downdrafts. The downdrafts do not reach the surface and have no impact on fire behaviour. ALS data show plume development is linked to large fuel consumption (~20 kg m−2). Conclusions Interactions between discrete plume elements contributed to plume rotation and large fuel consumption led to strong updrafts triggering deep pyroCu. Implications These results identify conditions conducive to strong plume rotation and deep pyroCu initiation. 

Background The increased interest in why and how trees die from fire has led to several syntheses of the potential mechanisms of fire-induced tree mortality. However, these generally neglect to consider experimental methods used to simulate fire behaviour conditions. Aims To describe, evaluate the appropriateness of and provide a historical timeline of the different approaches that have been used to simulate fire behaviour in fire-induced tree mortality studies. Methods We conducted a historical review of the different actual and fire proxy methods that have been used to further our understanding of fire-induced tree mortality. Key results Most studies that assess the mechanisms of fire-induced tree mortality in laboratory settings make use of fire proxies instead of real fires and use cut branches instead of live plants. Implications Further research should assess mechanisms of fire-induced tree mortality using live plants in paired combustion laboratory and landscape fire experiments. 

Wildland fire is a major global driver in the exchange of aerosols among terrestrial environments and the atmosphere. This exchange is commonly quantified using emission factors, or the mass of a pollutant emitted per mass of fuel burned. However, emission factors for microbes aerosolized by fire have yet to be determined. Using bacterial cell concentrations collected on unmanned aircraft systems over forest fires in Utah, USA, we determine bacterial emission factors (BEFs) for the first time. We estimate that 1.39 x 10^10 and 7.68 × 10^11 microbes are emitted for each Mg of biomass consumed in fires burning thinning residues and intact forests, respectively. These emissions exceed estimates of background bacterial emissions in other studies by three to four orders of magnitude. For the ~2,631 ha of similar forests in the Fishlake National Forest that burn each year on average, 1.35 × 10^17 cells or 8.1 kg of bacterial biomass were emitted. BEFs were then used to parameterize a computationally scalable particle transport model that predicted 99% of the emitted cells were transported beyond the 17.25 km^2 model domain. BEFs can be used to expand understanding of global wildfire microbial emissions. and their consequences to ecosystems, the atmosphere, and humans. 

Fire-prone landscapes found throughout the world are increasingly managed with prescribed fire for a variety of objectives. These frequent low-intensity fires directly impact lower forest strata, and thus estimating surface fuels or understory vegetation is essential for planning, evaluating, and monitoring management strategies and studying fire behavior and effects. Traditional fuel estimation methods can be applied to stand-level and canopy fuel loading; however, local-scale understory biomass remains challenging because of complex within-stand heterogeneity and fast recovery post-fire. Previous studies have demonstrated how single location terrestrial laser scanning (TLS) can be used to estimate plot-level vegetation characteristics and the impacts of prescribed fire. To build upon this methodology, co-located single TLS scans and physical biomass measurements were used to generate linear models for predicting understory vegetation and fuel biomass, as well as consumption by fire in a southeastern U.S. pineland. A variable selection method was used to select the six most important TLS-derived structural metrics for each linear model, where the model fit ranged in R2 from 0.61 to 0.74. This study highlights prospects for efficiently estimating vegetation and fuel characteristics that are relevant to prescribed burning via the integration of a single-scan TLS method that is adaptable by managers and relevant for coupled fire–atmosphere models. 

Protecting and enhancing forest carbon sinks is considered a natural solution for mitigating climate change. However, the increasing frequency, intensity, and duration of droughts due to climate change can threaten the stability and growth of existing forest carbon sinks. Extreme droughts weaken plant hydraulic systems, can lead to tree mortality events, and may reduce forest diversity, making forests more vulnerable to subsequent forest disturbances, such as forest fires or pest infestations. Although early warning metrics (EWMs) derived using satellite remote sensing data are now being tested for predicting post-drought plant physiological stress and mortality, applications of unmanned aerial vehicles (UAVs) are yet to be explored extensively. Herein, we provide twenty-four prospective approaches classified into five categories: (i) physiological complexities, (ii) site-specific and confounding (abiotic) factors, (iii) interactions with biotic agents, (iv) forest carbon monitoring and optimization, and (v) technological and infrastructural developments, for adoption, future operationalization, and upscaling of UAV-based frameworks for EWM applications. These UAV considerations are paramount as they hold the potential to bridge the gap between field inventory and satellite remote sensing for assessing forest characteristics and their responses to drought conditions, identifying and prioritizing conservation needs of vulnerable and/or high-carbon-efficient tree species for efficient allocation of resources, and optimizing forest carbon management with climate change adaptation and mitigation practices in a timely and cost-effective manner.