Background: The structure and function of fire-prone ecosystems are influenced by many interacting processes that develop over varying time scales. Fire creates both instantaneous and long-term changes in vegetation (defined as live, dead, and decomposing plant material) through combustion, heat transfer to living tissues, and subsequent patterns of recovery. While fuel available for combustion may be relative to the amount of vegetation, it is equally instructive to evaluate how the physical structure and other characteristics of vegetation influence fire dynamics, and how these interactions change between fire events. This paper presents a conceptual framework for how vegetation not only embodies the legacy of previous fires but creates the physical environment that drives fire behavior beyond its combustion as a fuel source. Results: While many environmental factors affect both the post-fire vegetation trajectory and fire dynamics themselves, we present a conceptual framework describing how vegetation’s structural characteristics control the local microclimate and fluid dynamics of fire-induced flows, and how that is influenced by ecosystem and atmospheric processes. Shifting our focus from fuels to vegetation allows us to integrate spatial and temporal feedbacks between fire, vegetation, soil, and the atmosphere across scales. This approach synthesizes the combustion and flammability science, the physical influence on fire behavior, and the ecosystem dynamics and processes that occur between fires and within a fire regime. Conclusions: We conclude that fire behavior, including its prediction and ecological effects, should be broadened to include the dynamic processes that interact with vegetation, beyond its role as fuel. Our conceptual framework illustrates the crucial feedbacks across scales that link the finer details of vegetation and fire behavior processes that occur within a fire and have additive effects that feedback into the coarser scale processes and functions within an ecosystem. Shifting the fuels paradigm to integrate the combustion, physical, and ecological roles of vegetation as complex drivers of fire behavior and outcomes will broaden discovery within wildland fire science and ecology.

Summary: Wildfires are a global crisis, but current fire models fail to capture vegetation response to changing climate. With drought and elevated temperature increasing the importance of vegetation dynamics to fire behavior, and the advent of next generation models capable of capturing increasingly complex physical processes, we provide a renewed focus on representation of woody vegetation in fire models. Currently, the most advanced representations of fire behavior and biophysical fire effects are found in distinct classes of fine‐scale models and do not capture variation in live fuel (i.e. living plant) properties. We demonstrate that plant water and carbon dynamics, which influence combustion and heat transfer into the plant and often dictate plant survival, provide the mechanistic linkage between fire behavior and effects. Our conceptual framework linking remotely sensed estimates of plant water and carbon to fine‐scale models of fire behavior and effects could be a critical first step toward improving the fidelity of the coarse scale models that are now relied upon for global fire forecasting. This process‐based approach will be essential to capturing the influence of physiological responses to drought and warming on live fuel conditions, strengthening the science needed to guide fire managers in an uncertain future.

Fire ecology is a complex discipline that can only be understood by integrating biological, physical, and social sciences. The science of fire ecology explores wildland fire’s mechanisms and effects across all scales of time and space. However, the lack of defined, organizing concepts in fire ecology dilutes its collective impact on knowledge and management decision-making and makes the discipline vulnerable to misunderstanding and misappropriation. Fire ecology has matured as a discipline and deserves an enunciation of its unique emergent principles of organization. Most scientific disciplines have established theories, laws, and principles that have been tested, debated, and adopted by the discipline’s practitioners. Such principles reflect the consensus of current knowledge, guide methodology and interpretation, and expose knowledge gaps in a coherent and structured way. In this manuscript, we introduce five comprehensive principles to define the knowledge fire ecology has produced and provide a framework to support the continued development and impact of the fire ecology discipline.

Terrestrial laser scanning of forest structure is used increasingly in place of traditional technologies; however, deriving physical parameters from point clouds remains challenging because LiDAR returns do not have defined areas or volumes. While voxelization methods overcome this challenge, estimation of canopy gaps and other structural attributes are often performed by reducing the point cloud to two-dimensions, thus decreasing the fidelity of the data. Furthermore, relatively few studies have evaluated voxel-size effects on estimation accuracy. Here, we show that voxelized laser-scanning data can be used for canopy-gap estimation without performing dimensionality reduction to the point cloud. Both airborne and terrestrial LiDAR were used to estimate canopy gaps along six vertical transects and four height intervals. Voxel-based estimates were evaluated against hemispherical photography and a sensitivity analysis was performed to identify an optimal voxel size. While the results indicate that our approach can be used with both airborne and terrestrial LiDAR, voxel size has a considerable influence on canopy-gap estimation. Results from our sensitivity analysis indicate that TLS estimation performs best when using 10 cm voxels, yielding canopy gaps ranging from 32–78%. The optimal voxel size for ALS estimation was obtained with 25 cm voxels, yielding estimates ranging from 25–68%.

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.

Monitoring wildland fire burn severity is important for assessing ecological outcomes of fire and their spatial patterning as well as guiding efforts to mitigate or restore areas where ecological outcomes are negative. Burn severity mapping products are typically created using satellite reflectance data but must be calibrated to field data to derive meaning. The composite burn index (CBI) is the most widely used field-based method used to calibrate satellite-based burn severity data but important limitations of this approach have yet to be resolved. The objective of this study was focused on predicting CBI from point cloud and visible-spectrum camera (RGB) metrics derived from single-scan terrestrial laser scanning (TLS) datasets to determine the viability of TLS data as an alternative approach to estimating burn severity in the field. In our approach, we considered the predictive potential of post-scan-only metrics, differenced pre- and post-scan metrics, RGB metrics, and all three together to predict CBI and evaluated these with candidate algorithms (i.e., linear model, random forest (RF), and support vector machines (SVM) and two evaluation criteria (R-squared and root mean square error (RMSE)). In congruence with the strata-based observations used to calculate CBI, we evaluated the potential approaches at the strata level and at the plot level using 70 TLS and 10 RGB independent variables that we generated from the field data. Machine learning algorithms successfully predicted total plot CBI and strata-specific CBI; however, the accuracy of predictions varied among strata by algorithm. RGB variables improved predictions when used in conjunction with TLS variables, but alone proved a poor predictor of burn severity below the canopy. Although our study was to predict CBI, our results highlight that TLS-based methods for quantifying burn severity can be an improvement over CBI in many ways because TLS is repeatable, quantitative, faster, requires less field-expertise, and is more flexible to phenological variation and biomass change in the understory where prescribed fire effects are most pronounced. We also point out that TLS data can also be leveraged to inform other monitoring needs beyond those specific to wildland fire, representing additional efficiency in using this approach.