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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. 

Characterizing pre‐fire fuel load and fuel consumption are critical for assessing fire behavior, fire effects, and smoke emissions. Two approaches for quantifying fuel load are airborne laser scanning (ALS) and the Fuel Characteristic Classification System (FCCS). The implementation of multitemporal ALS (i.e., the use of two or more ALS datasets across time at a given location) in conjunction with empirical models trained with field data can be used to measure fuel and estimate fuel consumption from a fire. FCCS, adapted for use in LANDFIRE (LF), provides 30 m resolution estimates of fuel load across the contiguous United States and can be used to estimate fuel consumption through software programs such as Fuel and Fire Tools (FFT). This study compares the two approaches for two wildfires in the northwestern United States having predominantly sagebrush steppe and ponderosa pine savanna ecosystems. The results showed that the LF FCCS approach yielded higher pre‐fire fuel loads and fuel consumption than the ALS approach and that the coarser scale LF FCCS data did not capture as much heterogeneity as the ALS data. At Tepee, 50.0% of the difference in fuel load and 87.3% of the difference in fuel consumption were associated with distinguishing sparse trees from rangeland. At Keithly, this only accounted for 8.2% and 8.6% of the differences, demonstrating the significance of capturing heterogeneity in rangeland vegetation structure and fire effects. Our results suggest future opportunities to use ALS data to better parametrize fine‐scale fuel load variability that LF FCCS does not capture.

 

Reactive nitrogen (N_r) within smoke plumes plays important roles in the production of ozone, the formation of secondary aerosols, and deposition of fixed N to ecosystems. The Western Wildfire Experiment for Cloud Chemistry, Aerosol Absorption, and Nitrogen (WE‐CAN) field campaign sampled smoke from 23 wildfires throughout the western U.S. during summer 2018 using the NSF/NCAR C‐130 research aircraft. We empirically estimateN_rnormalized excess mixing ratios and emission factors from fires sampled within 80 min of estimated emission and explore variability in the dominant forms ofN_rbetween these fires. We find that reduced N compounds comprise a majority (39%–80%; median = 66%) of total measured reactive nitrogen (ΣN_r) emissions. The smoke plumes sampled during WE‐CAN feature rapid chemical transformations after emission. As a result, within minutes after emission total measured oxidized nitrogen (ΣNO_y) and measured totalΣNH_x(NH₃ + pNH₄) are more robustly correlated with modified combustion efficiency (MCE) than NO_xand NH₃by themselves. The ratio of ΣNH_x/ΣNO_ydisplays a negative relationship with MCE, consistent with previous studies. A positive relationship with total measuredΣN_rsuggests that both burn conditions and fuel N content/volatilization differences contribute to the observed variability in the distribution of reduced and oxidizedN_r. Additionally, we compare our in situ field estimates ofN_rEFs to previous lab and field studies. For similar fuel types, we findΣNH_xEFs are of the same magnitude or larger than lab‐based NH₃EF estimates, andΣNO_yEFs are smaller than lab NO_xEFs.