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Summary The dead foliage of scorched crowns is one of the most conspicuous signatures of wildland fires. Globally, crown scorch from fires in savannas, woodlands and forests causes tree stress and death across diverse taxa. The term crown scorch, however, is inconsistently and ambiguously defined in the literature, causing confusion and conflicting interpretation of results. Furthermore, the underlying mechanisms causing foliage death from fire are poorly understood. The consequences of crown scorch – alterations in physiological, biogeochemical and ecological processes and ecosystem recovery pathways – remain largely unexamined. Most research on the topic assumes the mechanism of leaf and bud death is exposure to lethal air temperatures, with few direct measurements of lethal heating thresholds. Notable information gaps include how energy transfer injures and kills leaves and buds, how nutrients, carbohydrates, and hormones respond, and what physiological consequences lead to mortality. We clarify definitions to encourage use of unified terminology for foliage and bud necrosis resulting from fire. We review the current understanding of the physical mechanisms driving foliar injury, discuss the physiological responses, and explore novel ecological consequences of crown injury from fire. From these elements, we propose research needs for the increasingly interdisciplinary study of fire effects.

Wildland fuels, defined as the combustible biomass of live and dead vegetation, are foundational to fire behavior, ecological effects, and smoke modeling. Along with weather and topography, the composition, structure and condition of wildland fuels drive fire spread, consumption, heat release, plume production and smoke dispersion. To refine inputs to existing and next-generation smoke modeling tools, improved characterization of the spatial and temporal dynamics of wildland fuels is necessary. Computational fluid dynamics (CFD) models that resolve fire–atmosphere interactions offer a promising new approach to smoke prediction. CFD models rely on three-dimensional (3D) characterization of wildland fuelbeds (trees, shrubs, herbs, downed wood and forest floor fuels). Advances in remote sensing technologies are leading to novel ways to measure wildland fuels and map them at sub-meter to multi-kilometer scales as inputs to next-generation fire and smoke models. In this chapter, we review traditional methods to characterize fuel, describe recent advances in the fields of fuel and consumption science to inform smoke science, and discuss emerging issues and challenges.

Methods to accurately estimate spatially explicit fuel consumption are needed because consumption relates directly to fire behavior, effects, and smoke emissions. Our objective was to quantify sparkleberry (Vaccinium arboretum Marshall) shrub fuels before and after six experimental prescribed fires at Fort Jackson in South Carolina. We used a novel approach to characterize shrubs non-destructively from three-dimensional (3D) point cloud data collected with a terrestrial laser scanner. The point cloud data were reduced to 0.001 m–3 voxels that were either occupied to indicate fuel presence or empty to indicate fuel absence. The density of occupied voxels was related significantly by a logarithmic function to 3D fuel bulk density samples that were destructively harvested (adjusted R2 = .32, P < .0001). Based on our findings, a survey-grade Global Navigation Satellite System may be necessary to accurately associate 3D point cloud data to 3D fuel bulk density measurements destructively collected in small (submeter) shrub plots. A recommendation for future research is to accurately geolocate and quantify the occupied volume of entire shrubs as 3D objects that can be used to train models to map shrub fuel bulk density from point cloud data binned to occupied 3D voxels.

The realm of wildland fire science encompasses both wild and prescribed fires. Most of the research in the broader field has focused on wildfires, however, despite the prevalence of prescribed fires and demonstrated need for science to guide its application. We argue that prescribed fire science requires a fundamentally different approach to connecting related disciplines of physical, natural, and social sciences. We also posit that research aimed at questions relevant to prescribed fire will improve overall wildland fire science and stimulate the development of useful knowledge about managed wildfires. Because prescribed fires are increasingly promoted and applied for wildfire management and are intentionally ignited to meet policy and land manager objectives, a broader research agenda incorporating the unique features of prescribed fire is needed. We highlight the primary differences between prescribed fire science and wildfire science in the study of fuels, fire behavior, fire weather, fire effects, and fire social science. Wildfires managed for resource benefits (“managed wildfires”) offer a bridge for linking these science frameworks. A recognition of the unique science needs related to prescribed fire will be key to addressing the global challenge of managing wildland fire for long-term sustainability of natural resources.

Graphical abstract, Surface fuels are the critical link between structure and function in frequently burned pine ecosystems, which are found globally (Williamson and Black, 1981; Rebertus et al., 1989; Glitzenstein et al., 1995) [[1], [2], [3]]. We bring fuels to the forefront of fire ecology through the concept of the Ecology of Fuels (Hiers et al. 2009) [4]. This concept describes a cyclic process between fuels, fire behavior, and fire effects, which ultimately affect future fuel distribution (Mitchell et al. 2009) [5]. Low-intensity surface fires are driven by the variability in fine-scale (sub-m level) fuels (Loudermilk et al. 2012) [6]. Traditional fuel measurement approaches do not capture this variability because they are over-generalized, and do not consider the fine-scale architecture of interwoven fuel types. Here, we introduce a new approach, the “3D fuels sampling protocol” that measures fuel biomass at the scale and dimensions useful for characterizing heterogeneous fuels found in low-intensity surface fire regimes. • Traditional fuel measurements are oversimplified, prone to sampling bias, and unrealistic for relating to fire behavior (Van Wagner, 1968; Hardy et al., 2008) [7,8]. • We developed a novel field sampling approach to measuring 3D fuels using an adjustable rectangular prism sampling frame. This voxel sampling protocol records fuel biomass, occupied volume, and fuel types at multiple scales. • This method is scalable and versatile across ecosystems, and reduces sampling bias by eliminating the need for ocular estimations.

The spatial pattern of surface fuelbeds in fire-dependent ecosystems are rarely captured using long-standing fuel sampling methods. New techniques, both field sampling and remote sensing, that capture vegetation fuel type, biomass, and volume at super fine-scales (cm to dm) in three-dimensions (3D) are critical to advancing forest fuel and wildland fire science. Such scales are particularly important for some computational fluid dynamics fire behavior models that operate in 3D and have implications for wildland fire operations and fire effects research. This study describes the coupling of new 3D field sampling data with terrestrial laser scanning (TLS) data to infer fine-scale fuel mass in 3D. We found that there are strong relationships between fine-scale mass and TLS occupied volume, porosity, and surface area, which were used to develop fine-scale prediction equations using TLS across vegetative fuel types, namely grasses and shrubs. The application of this novel 3D sampling technique to high resolution TLS data in this study represents an advancement in producing inputs for computational fluid dynamics fire behavior models that will improve understanding fire-vegetation feedbacks in highly managed fire-dependent ecosystems.

Requirements for describing coniferous forests are changing in response to wildfire concerns, bio-energy needs, and climate change interests. At the same time, technology advancements are transforming how forest properties can be measured. Terrestrial Laser Scanning (TLS) is yielding promising results for measuring tree biomass parameters that, historically, have required costly destructive sampling and resulted in small sample sizes. Here we investigate whether TLS intensity data can be used to distinguish foliage and small branches (≤0.635 cm diameter, coincident with the one-hour timelag fuel size class) from larger branchwood (>0.635 cm) in Douglas-fir (Pseudotsuga menziesii) branch specimens. We also consider the use of laser density for predicting biomass by size class. Measurements are addressed across multiple ranges and scan angles. Results show TLS capable of distinguishing fine fuels from branches at a threshold of one standard deviation above mean intensity. Additionally, the relationship between return density and biomass is linear by fuel type for fine fuels (r2 = 0.898, SE 22.7%) and branchwood (r2 = 0.937, SE 28.9%), as well as for total mass (r2 = 0.940, SE 25.5%). Intensity decays predictably as scan distances increase, however, the range-intensity relationship is best described by an exponential model rather than 1/d2. Scan angle appears to have no systematic effect on fine fuel discrimination, while some differences are observed in density-mass relationships with changing angles due to shadowing.

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.

The success of a local maximum (LM) tree detection algorithm for detecting individual trees from lidar data depends on stand conditions that are often highly variable. A laser height variance and percent canopy cover (PCC) classification is used to segment the landscape by stand condition prior to stem detection. We test the performance of the LM algorithm using canopy height model (CHM) smoothing decisions and crown width estimation for each stand condition ranging from open savannah to multi-strata stands. Results show that CHM smoothing improves stem predictions for low density stands and no CHM smoothing better detects stems in dense even-aged stands, specifically dominant and co-dominant trees (R 2 � 0.61, RMSE � 20.91 stems with smoothing; R 2 � 0.85, RMSE � 46.02 stems with no-smoothing; combined smoothed CHM for low density and unsmoothed CHM for high density stands R 2 � 0.88, RMSE � 28.59 stems). At a threshold of approximately 2,200 stems ha � 1 , stem detection accuracy is no longer obtainable in any