Canopy Fuels Research Articles

Estimating canopy fuels across Europe with satellite data and allometric equations

Estimating canopy fuels across Europe with satellite data and allometric equations

Unoccupied aerial system (UAS) Structure-from-Motion canopy fuel parameters: Multisite area-based modelling across forests in California, USA

There is a pressing need for well-informed management to reduce wildfire hazard and restore fire's beneficial ecological role in the Mediterranean- and temperate-climate forests of California, USA. These efforts rely upon the accessibility of high spatial and temporal resolution data on biomass and canopy fuel parameters such as canopy base height (CBH), mean canopy height, canopy bulk density (CBD), canopy cover, and leaf area index (LAI). Remote sensing using unoccupied aerial system Structure-from-Motion (UAS-SfM) presents a promising technology for this application due to its accessibility, relatively low cost, and possibility for high temporal cadence. However, to date, this method has not been studied in the complex mosaic of forest types found across California. In this study we examined the capacity of structural and multispectral information obtained from UAS-SfM, in conjunction with machine learning methods, to model aboveground biomass and forest canopy fuel structural parameters using an area-based approach across multiple sites representing a diversity of forest types in California.Based on correlations with field measurements, fuel parameters separated into vertical (biomass, CBH, and mean height) and horizontal (LAI, CBD, canopy cover) groups. UAS-SfM random forest models performed well for modelling the vertical structure canopy fuels parameters (R2 0.69–0.75). These models exhibited strong performance in comparison to ALS, as well as when transferred to a novel site. Vertical structure predictors were prominent in these models, and did not improve with the addition of spectral predictors. UAS-SfM random forest models of horizontal structure parameters mainly used raster-based spectral indices (primarily NDVI) and had relatively low performance (R2 0.49–0.59). In addition, these models underperformed ALS and had poor performance when applied to a novel site. When applied to a region with widespread UAS-SfM coverage, models from both groups successfully produced contiguous maps that could be used for modelling fire behavior or in management decision making and monitoring.These findings indicate that UAS-SfM, without the need for multispectral sensors, is well suited for mapping area-based vertical-structure canopy parameters across diverse landscapes supporting a wide range of forest types. In contrast, the identification of spectral mean variables for modelling horizontal structure canopy fuels suggests the potential of multi- or hyperspectral sensors or high-resolution satellite imagery for meeting management information needs.

Estimation of Forest Canopy Fuel Moisture Content in Dali Prefecture by Combining Vegetation Indices and Canopy Radiative Transfer Models from MODIS Data

Forest canopy fuel moisture content (FMC) is a critical factor in assessing the vulnerability of a specific area to forest fires. The conventional FMC estimation method, which relies on look-up tables and loss functions, cannot to elucidate the relationship between FMC and simulated data from look-up tables. This study proposes a novel approach for estimating FMC by combining enhanced vegetation index (EVI) and normalized difference moisture index (NDMI). The method employs the PROSAIL + PROGeoSAIL two-layer coupled radiation transfer model to simulate the vegetation index, the water index, and the FMC value, targeting the prevalent double-layer structure in the study area’s vegetation distribution. Additionally, a look-up table is constructed through numerical analysis to investigate the relationships among vegetation indices, water indices, and FMC. The results reveal that the polynomial equations incorporating vegetation and water indices as independent variables exhibit a strong correlation with FMC. Utilizing the EVI–NDMI joint FMC estimation method enables the direct estimation of FMC. The collected samples from Dali were compared with the estimated values, revealing that the proposed method exhibits superior accuracy (R2 = 0.79) in comparison with conventional FMC estimation methods. In addition, we applied this method to estimate the FMC in the Chongqing region one week before the 2022 forest fire event, revealing a significant decreasing trend in regional FMC leading up to the fire outbreak, highlighting its effectiveness in facilitating pre-disaster warnings.

Forest thinning and prescribed burning treatments reduce wildfire severity and buffer the impacts of severe fire weather

BackgroundThe capacity of forest fuel treatments to moderate the behavior and severity of subsequent wildfires depends on weather and fuel conditions at the time of burning. However, in-depth evaluations of how treatments perform are limited because encounters between wildfires and areas with extensive pre-fire data are rare. Here, we took advantage of a 1200-ha randomized and replicated experiment that burned almost entirely in a subsequent wildfire under a wide range of weather conditions. We compared the impacts of four fuel treatments on fire severity, including two thin-only, a thin-burn, a burn-only, and an untreated control. We evaluated four fire severity metrics—tree mortality, average bole char height, percent crown volume consumed (PCVC), and percent crown volume affected (PCVA)—and leveraged data from pre-fire surface and canopy fuels to better understand the mechanisms driving differences in wildfire severity among treatments and how they changed with fire weather.ResultsWe found strong mitigating effects of treatments on fire behavior and tree mortality, despite 20 years having elapsed since mechanical thinning and 10 years since the second entry of prescribed fire. The thin-burn treatment resulted in the lowest fire severity across all four metrics and the untreated control the highest. All four fire severity metrics were positively associated with pre-fire canopy and surface fuel loads, with the exception that PCVC (a fire severity metric related to crown fire behavior) was not associated with surface fuel load. The fire weather conditions under which fuel treatment was most effective varied among fire severity metrics. Fuel treatment benefit was maximized at intermediate burning index values for tree mortality, intermediate to high burning index values for PCVA, and high burning index for bole char height and PCVC.ConclusionsWe conclude that reducing canopy bulk density via mechanical thinning treatments can help to limit crown fire behavior for 20 years or more. However, reducing surface fuels is necessary to limit scorching and the total crown impacts associated with tree mortality. Further, while fuel treatment effectiveness may decline under the most severe fire weather conditions for fire severity metrics associated with tree mortality, it is maximized under severe fire weather conditions for fire severity metrics associated with crown fire behavior (bole charring and torching). Our results provide strong evidence for the use of fuel treatments to mitigate fire behavior and resulting fire severity even under extreme fire weather conditions.

Forest Canopy Fuel Loads Mapping Using Unmanned Aerial Vehicle High-Resolution Red, Green, Blue and Multispectral Imagery

Canopy fuels determine the characteristics of the entire complex of forest fuels due to their constant changes triggered by the environment; therefore, the development of appropriate strategies for fire management and fire risk reduction requires an accurate description of canopy forest fuels. This paper presents a method for mapping the spatial distribution of canopy fuel loads (CFLs) in alignment with their natural variability and three-dimensional spatial distribution. The approach leverages an object-based machine learning framework with UAV multispectral data and photogrammetric point clouds. The proposed method was developed in the mixed forest of the natural protected area of “Sierra de Quila”, Jalisco, Mexico. Structural variables derived from photogrammetric point clouds, along with spectral information, were used in an object-based Random Forest model to accurately estimate CFLs, yielding R2 = 0.75, RMSE = 1.78 Mg, and an average Biasrel = 18.62%. Canopy volume was the most significant explanatory variable, achieving a mean decrease in impurity values greater than 80%, while the combination of texture and vegetation indices presented importance values close to 20%. Our modelling approach enables the accurate estimation of CFLs, accounting for the ecological context that governs their dynamics and spatial variability. The high precision achieved, at a relatively low cost, encourages constant updating of forest fuels maps to enable researchers and forest managers to streamline decision making on fuel and forest fire management.

Fuel treatments in shrublands experiencing pinyon and juniper expansion result in trade-offs between desired vegetation and increased fire behavior

BackgroundNative pinyon (Pinus spp.) and juniper (Juniperus spp.) trees are expanding into shrubland communities across the Western United States. These trees often outcompete with native sagebrush (Artemisia spp.) associated species, resulting in increased canopy fuels and reduced surface fuels. Woodland expansion often results in longer fire return intervals with potential for high severity crown fire. Fuel treatments are commonly used to prevent continued tree infilling and growth and reduce fire risk, increase ecological resilience, improve forage quality and quantity, and/or improve wildlife habitat. Treatments may present a trade-off; they restore shrub and herbaceous cover and decrease risk of canopy fire but may increase surface fuel load and surface fire potential. We measured the accumulation of surface and canopy fuels over 10 years from ten sites across the Intermountain West in the Sagebrush Steppe Treatment Evaluation Project woodland network (www.SageSTEP.org), which received prescribed fire or mechanical (cut and drop) tree reduction treatments. We used the field data and the Fuel Characteristic Classification System (FCCS) in the Fuel and Fire Tools (FFT) application to estimate surface and canopy fire behavior in treated and control plots in tree expansion phases I, II, and III.ResultsIncreased herbaceous surface fuel following prescribed fire treatments increased the modeled rate of surface fire spread (ROS) 21-fold and nearly tripled flame length (FL) by year ten post-treatment across all expansion phases. In mechanical treatments, modeled ROS increased 15-fold, FL increased 3.8-fold, and reaction intensity roughly doubled in year ten post-treatment compared to pretreatment and untreated controls. Treatment effects were most pronounced at 97th percentile windspeeds, with modeled ROS up to 82 m min−1 in mechanical and 106 m min−1 in prescribed fire treatments by 10 years post-treatment compared to 5 m min−1 in untreated controls. Crown fire transmissivity risk was eliminated by both fuel treatments.ConclusionsWhile prescribed fire and mechanical treatments in shrublands experiencing tree expansion restored understory vegetation and prevented continued juniper and pinyon infilling and growth, these fuel treatments also increased modeled surface fire behavior. Thus, management tradeoffs occur between desired future vegetation and wildfire risk after fuel treatments.

Rapid fuel recovery after stand-replacing fire in closed-cone pine forests and implications for short-interval severe reburns

Accelerating disturbance activity under a warming climate increases the potential for multiple disturbances to overlap and produce compound effects that erode ecosystem resilience — the capacity to experience disturbance without transitioning to an alternative state. A key concern is the potential for amplifying or attenuating feedbacks via interactions among successive, linked disturbance events. Following severe wildfires, fuel limitation is a negative feedback that may reduce the likelihood of subsequent fire. However, the duration of, and pre-fire vegetation effects on fuel limitation remain uncertain. To address this knowledge gap, we characterized fuel profiles over a 35-year post-fire chronosequence in California closed-cone pine forests, a system with stand dynamics comparable to stand-replacing fire regimes in other temperate and boreal conifer forests with much longer fire return intervals. We used this system to ask: 1) How do fuel profiles change with time since recent stand-replacing fire? 2) How do trajectories of fuel profiles differ between forests established from different pre-fire vegetation legacies — specifically areas that were forest vs. non-forest pre-fire? We also assessed fuel profiles that supported stand-replacing fire during a large (>90,000 ha) wildfire that burned through our study area shortly after data collection and compared with fuel profiles across the chronosequence. Overall, live and dead surface fuels accumulated quickly, reaching levels comparable to those capable of supporting severe fire after 10–15 years. Canopy fuels peaked at 20 years post-fire but were at levels comparable to those capable of supporting severe fire from 15 years onwards. Pre-fire vegetation legacy drove divergence in post-fire dead surface fuel trajectories, but effects on live surface and canopy fuels were minor. Fuel profiles in California closed-cone pine forests can support subsequent severe fire within 10 years, a fraction of the historical minimum interval between stand-replacing fires (≥30 years) that supports robust post-fire regeneration, but potential for severe fire remains lower for an extended period in areas where pre-fire vegetation was non-forest. Our findings show that the negative feedback of fuel limitation disappears quickly following fire, suggesting that these systems are vulnerable to short-interval severe reburns that could erode forest resilience.

Less fuel for the next fire? Short-interval fire delays forest recovery and interacting drivers amplify effects.

As 21st-century climate and disturbance dynamics depart from historic baselines, ecosystem resilience is uncertain. Multiple drivers are changing simultaneously, and interactions among drivers could amplify ecosystem vulnerability to change. Subalpine forests in Greater Yellowstone (Northern Rocky Mountains, USA) were historically resilient to infrequent (100-300 year), severe fire. We sampled paired short-interval (<30-year) and long-interval (>125-year) post-fire plots most recently burned between 1988 and 2018 to address two questions: (1) How do short-interval fire, climate, topography, and distance to unburned live forest edge interact to affect post-fire forest regeneration? (2) How do forest biomass and fuels vary following short-interval versus long-interval severe fires? Mean post-fire live tree stem density was an order of magnitude lower following short-interval versus long-interval fires (3240 vs. 28,741 stems ha-1 , respectively). Differences between paired plots were amplified at longer distances to live forest edge. Surprisingly, warmer-drier climate was associated with higher seedling densities even after short-interval fire, likely relating to regional variation in serotiny of lodgepole pine (Pinus contorta var. latifolia). Unlike conifers, density of aspen (Populus tremuloides), a deciduous resprouter, increased with short-interval versus long-interval fires (mean 384 vs. 62 stems ha-1 , respectively). Live biomass and canopy fuels remained low nearly 30 years after short-interval fire, in contrast with rapid recovery after long-interval fire, suggesting that future burn severity may be reduced for several decades following reburns. Short-interval plots also had half as much dead woody biomass compared with long-interval plots (60 vs. 121 Mg ha-1 ), primarily due to the absence of large snags. Our results suggest differences in tree regeneration following short-interval versus long-interval fires will be especially pronounced where serotiny was high historically. Propagule limitation will also interact with short-interval fires to diminish tree regeneration but lessen subsequent burn severity. Amplifying driver interactions are likely to threaten forest resilience under expected trajectories of a future fire.

Classification and mapping of European fuels using a hierarchical, multipurpose fuel classification system

Abstract. Accurate and spatially explicit information on forest fuels becomes essential to designing an integrated fire risk management strategy, as fuel characteristics are critical for fire danger estimation, fire propagation, and emissions modelling, among other aspects. This paper proposes a new European fuel classification system that can be used for different spatial scales and purposes (propagation, behaviour, and emissions). The proposed classification system is hierarchical and encompasses a total of 85 fuel types, grouped into six main fuel categories (forest, shrubland, grassland, cropland, wet and peat/semi-peat land, and urban), plus a nonfuel category. For the forest cover, fuel types include two vertical strata, overstorey and understorey, to account for both surface and canopy fuels. In addition, this paper presents the methods to map fuel types at the European scale, including the first-level of the classification system. Land cover, biogeographic datasets, and bioclimatic modelling were used. The final map, which is publicly available (https://doi.org/10.21950/YABYCN; Aragoneses et al., 2022a), included 20 fuel categories at 1 km spatial resolution. A first assessment of this map was performed using field information obtained from LUCAS (Land Use and Coverage Area frame Survey), complemented with high-resolution data. This validation exercise provided an overall accuracy of 88 % for the main fuel types and 81 % for all mapped fuel types. To facilitate the use of this fuel dataset in fire behaviour modelling, an assignment of fuel parameters to each fuel type was performed by developing a crosswalk to the standard fuel models defined by Scott and Burgan (2005; FBFMs – Fire Behavior Fuel Models), considering European climate diversity. This work has been developed within the framework of the FirEUrisk project, which aims to create a European-integrated strategy for fire danger assessment, reduction, and adaptation.

Fuels change quickly after California drought and bark beetle outbreaks with implications for potential fire behavior and emissions

BackgroundAn extreme drought from 2012–2016 and concurrent bark beetle outbreaks in California, USA resulted in widespread tree mortality. We followed changes in tree mortality, stand structure, and surface and canopy fuels over four years after the peak of mortality in Sierra mixed conifer and pinyon pine (Pinus monophylla) forests to examine patterns of mortality, needle retention after death, and snag fall across tree species. We then investigated how the tree mortality event affected surface and canopy fuel loading and potential impacts on fire hazard and emissions.ResultsDrought and beetle-related tree mortality shifted mortality patterns to be more evenly distributed across size classes and concentrated in pines. Substantial changes to surface fuel loading, stand density, canopy fuel loads, and potential wildfire emissions occurred within four years following peak levels of tree mortality, with the largest changes related to increases in coarse woody debris. Nearly complete needle fall occurred within four years of mortality for all species except red fir (Abies magnifica). Pine species and incense cedar (Calocedrus decurrens) snags fell more quickly than fir species. Potential fire behavior modelling suggested that crowning and torching hazard decreased as trees dropped dead needles and fell, but as canopy fuels were transferred to surface fuels, potential for smoldering combustion increased, causing greater emissions.ConclusionsOur study increases understanding of how extreme tree mortality events caused by concurrent disturbances alter canopy and surface fuel loading and have the potential to affect fire behavior and emissions in two compositionally different seasonally dry forest types. After a major tree mortality event, high canopy fuel flammability may only last a few years, but surface fuels can increase considerably over the same time period in these forest types. The accumulation of coarse woody surface fuels resulting from multi-year drought and concurrent bark beetle outbreaks combined with the increasing frequency of drought in the western U.S. have the potential to lead to heavy and dry fuel loads that under certain weather conditions may result in more extreme fire behavior and severe effects, particularly in forest types where decades of successful fire suppression has caused forest densification.

Using Pre-Fire High Point Cloud Density LiDAR Data to Predict Fire Severity in Central Portugal

The wall-to-wall prediction of fuel structural characteristics conducive to high fire severity is essential to provide integrated insights for implementing pre-fire management strategies designed to mitigate the most harmful ecological effects of fire in fire-prone plant communities. Here, we evaluate the potential of high point cloud density LiDAR data from the Portuguese áGiLTerFoRus project to characterize pre-fire surface and canopy fuel structure and predict wildfire severity. The study area corresponds to a pilot LiDAR flight area of around 21,000 ha in central Portugal intersected by a mixed-severity wildfire that occurred one month after the LiDAR survey. Fire severity was assessed through the differenced Normalized Burn Ratio (dNBR) index computed from pre- and post-fire Sentinel-2A Level 2A scenes. In addition to continuous data, fire severity was also categorized (low or high) using appropriate dNBR thresholds for the plant communities in the study area. We computed several metrics related to the pre-fire distribution of surface and canopy fuels strata with a point cloud mean density of 10.9 m−2. The Random Forest (RF) algorithm was used to evaluate the capacity of the set of pre-fire LiDAR metrics to predict continuous and categorized fire severity. The accuracy of RF regression and classification model for continuous and categorized fire severity data, respectively, was remarkably high (pseudo-R2 = 0.57 and overall accuracy = 81%) considering that we only focused on variables related to fuel structure and loading. The pre-fire fuel metrics with the highest contribution to RF models were proxies for horizontal fuel continuity (fractional cover metric) and the distribution of fuel loads and canopy openness up to a 10 m height (density metrics), indicating increased fire severity with higher surface fuel load and higher horizontal and vertical fuel continuity. Results evidence that the technical specifications of LiDAR acquisitions framed within the áGiLTerFoRus project enable accurate fire severity predictions through point cloud data with high density.

Transferability of Airborne LiDAR Data for Canopy Fuel Mapping: Effect of Pulse Density and Model Formulation

Canopy fuel characterization is critical to assess fire hazard and potential severity in forest stands. Simulation tools provide useful information for fire prevention planning to reduce wildfire impacts, provided that reliable fuel maps exist at adequate spatial resolution. Free airborne LiDAR data are becoming available in many countries providing an opportunity to improve fuel monitoring at large scales. In this study, models were fitted to estimate canopy base height (CBH), fuel load (CFL) and bulk density (CBD) from airborne LiDAR in a pine stand area where four point-cloud datasets were acquired at different pulse densities. Best models for CBH, CFL and CBD fitted with LiDAR metrics from the 1 p/m2 dataset resulted in an adjusted R2 of 0.88, 0.68 and 0.58, respectively, with RMSE (MAPE) of 1.85 m (18%), 0.16 kg/m2 (14%) and 0.03 kg/m3 (20%). Transferability assessment of fitted models indicated different level of accuracy depending on LiDAR pulse density (both higher and lower than the calibration dataset) and model formulation (linear, power and exponential). Best results were found for exponential models and similar pulse density (1.7 p/m2) compared to lower (0.5 p/m2) or higher return density (4 p/m2). Differences were also observed regarding the canopy fuel attributes.

The Alberta Wildland Fuels Inventory Program (AWFIP): data description and reference tables

Key messageThis document describes a dataset obtained from a field sampling program conducted in Alberta, Canada. Field data were used to describe the structure and composition of forest stands, including several fuel loads (e.g., surface, understory, canopy fuels). The dataset can be downloaded from https://doi.org/10.17605/OSF.IO/FZ8E4 and metadata is available at https://metadata-afs.nancy.inra.fr/geonetwork/srv/fre/catalog.search#/metadata/527efb49-43b4-43eb-88b2-70535ff99fc5We present a quality-checked and curated dataset obtained from a field sampling program conducted in the province of Alberta, Canada. Field data were used to describe the structure and composition of forest stands documented in 476 sampling events. Each sampling event record consists of 42 different variables, including several fuel loads (e.g., surface, understory, canopy fuels). The dataset has been created for operational and research applications including but not limited to fuel classification, estimation of fuel attributes from remote sensing technologies, fuel treatment planning, fire behavior prediction, and use in high resolution fire growth models.

Young forests and fire: Using lidar–imagery fusion to explore fuels and burn severity in a subalpine forest reburn

AbstractAnticipating fire behavior as climate change and fire activity accelerate is an increasingly pressing management challenge in fire‐prone landscapes. In subalpine forests adapted to infrequent, stand‐replacing fire, self‐limitation of burn severity in short‐interval fire is incompletely understood. Spatially explicit fuels data can support assessments of landscape‐scale fire risk and fuel feedbacks on burn severity. For a ~1450‐km2 largely forested landscape in the US Northern Rocky Mountains, we used airborne lidar and imagery to predict and map canopy and surface fuels. In a fire that burned mature (&gt;125‐year‐old) and also reburned young (~30‐year‐old) subalpine forest, we then asked: (1) How do prefire fuels and burn severity compare between young and mature forests that burned under similar fire weather conditions? (2) How well do prefire fuels and forest structure predict burn severity under extreme versus moderate fire weather? Lidar–imagery fusion predicted fuel characteristics with high accuracy across forest and shrubland vegetation. Young postfire forests had abundant, densely packed canopy fuels, and both young and mature forests had similar canopy fuel loads and coarse wood biomass. Under similar weather conditions, young and mature forests burned at similar severity. Overall, fuels were weak predictors of burn severity and, surprisingly, better predicted severity under extreme rather than moderate fire weather. Our findings are relevant for subalpine landscapes increasingly dominated by young lodgepole pine (Pinus contorta var. latifolia) forests vulnerable to short‐interval fire and provide a benchmark to assess how fuels influence burn severity in future fires. Fire managers should continually reassess fuels and update expectations about fire behavior as landscapes change. Although recovering postfire forests can limit fire spread and severity for a period of time, our results suggest that young subalpine forests in the Northern Rocky Mountains have sufficient fuel loads to burn at high severity and should not be considered effective fire breaks.

Predicting Wildfire Fuels and Hazard in a Central European Temperate Forest Using Active and Passive Remote Sensing

Climate change causes more extreme droughts and heat waves in Central Europe, affecting vegetative fuels and altering the local fire regime. Wildfire is projected to expand into the temperate zone, a region traditionally not concerned by fire. To mitigate this new threat, local forest management will require spatial fire hazard information. We present a holistic and comprehensible workflow for quantifying fuels and wildfire hazard through fire spread simulations. Surface and canopy fuels characteristics were sampled in a small managed temperate forest in Northern Germany. Custom fuel models were created for each dominant species (Pinus sylvestris, Fagus sylvatica, and Quercus rubra). Canopy cover, canopy height, and crown base height were directly derived from airborne LiDAR point clouds. Surface fuel types and crown bulk density (CBD) were predicted using random forest and ridge regression, respectively. Modeling was supported by 119 predictors extracted from LiDAR, Sentinel-1, and Sentinel-2 data. We simulated fire spread from random ignitions, considering eight environmental scenarios to calculate fire behavior and hazard. Fuel type classification scored an overall accuracy of 0.971 (Kappa = 0.967), whereas CBD regression performed notably weaker (RMSE = 0.069; R2 = 0.73). Higher fire hazard was identified for strong winds, low fuel moisture, and on slopes. Fires burned fastest and most frequently on slopes in large homogeneous pine stands. These should be the focus of preventive management actions.

Centuries of Repeated Mountain Pine Beetle Outbreaks Increase Ground and Surface Fuels, But Have Negligible Effects on Canopy Fuels after Two Decades of Recovery

Centuries of Repeated Mountain Pine Beetle Outbreaks Increase Ground and Surface Fuels, But Have Negligible Effects on Canopy Fuels after Two Decades of Recovery

Repeated Mountain Pine Beetle Outbreaks Have Negligible Effects on Canopy Fuels While Enhancing Ecological Diversity

Repeated Mountain Pine Beetle Outbreaks Have Negligible Effects on Canopy Fuels While Enhancing Ecological Diversity

Changes in fire behavior caused by fire exclusion and fuel build-up vary with topography in California montane forests, USA

Wildfire sizes and proportions burned with high severity effects are increasing in seasonally dry forests, especially in the western USA. A critical need in efforts to restore or maintain these forest ecosystems is to determine where fuel build-up caused by fire exclusion reaches thresholds that compromise resilience to fire. Empirical studies identifying drivers of fire severity patterns in actual wildfires can be confounded by co-variation of vegetation and topography and the stochastic effects of weather and rarely consider long-term changes in fuel caused by fire exclusion.To overcome these limitations, we used a spatially explicit fire model (FlamMap) to compare potential fire behavior by topographic position in Lassen Volcanic National Park (LAVO), California, a large (43,000 ha), mountainous, unlogged landscape with extensive historical and contemporary fuels data. Fuel loads were uniformly distributed and incrementally increased across the landscape, meaning variation in fire behavior within each simulation was due to topography and among simulations, to fuels. We analyzed changes in fire line intensity (FLI) and crown fire potential as surface and canopy fuels increased from historical to contemporary levels and with percentile and actual wildfire weather conditions.Sensitivity to the influence of fuel build-up on fire behavior varied by topographic position. Steep slopes and ridges were most sensitive. At lower surface fuel loads, under pre-exclusion and contemporary canopy conditions, fire behavior was comparable and remained surface-type. As fuels increased, FLI and passive crown fire increased on steep slopes and ridgetops but remained largely unchanged on gentle slopes. Topographic variability in fire behavior was greatest with intermediate fuels. At higher surface fuel loads, under contemporary canopy fuels, passive crown fire dominated all topographic positions. With LAVO's current surface fuels, the area with potential for passive crown fire during actual fire weather increased from 6% pre-exclusion to 34% due to canopy fuel build-up. For topographically diverse landscapes, the results highlight where contemporary fire characteristics are most likely to deviate from historical patterns and may help managers prioritize locations for prescribed burning and managed wildfire to increase fire resilience in fuel rich landscapes.

Estimation of Vertical Fuel Layers in Tree Crowns Using High Density LiDAR Data

The accurate prediction and mitigation of wildfire behaviour relies on accurate estimations of forest canopy fuels. New techniques to collect LiDAR point clouds from remotely piloted aerial systems (RPAS) allow for the prediction of forest fuels at extremely fine scales. This study uses a new method to examine the ability of such point clouds to characterize the vertical arrangement and volume of crown fuels from within individual trees. This method uses the density and vertical arrangement of LiDAR points to automatically extract and measure the dimensions of each cluster of vertical fuel. The amount and dimensions of these extracted clusters were compared against manually measured clusters that were collected through the manual measurement of over 100 trees. This validation dataset was composed of manual point cloud measurements for all portions of living crown fuel for each tree. The point clouds used for this were ground-based LiDAR point clouds that were ~80 times denser than the RPAS LiDAR point clouds. Over 96% of the extracted clusters were successfully matched to a manually measured cluster, representing ~97% of the extracted volume. A smaller percentage of the manually measured clusters (~79%) were matched to an extracted cluster, although these represented ~99% of the total measured volume. The vertical arrangement and dimensions of the matched clusters corresponded strongly to one another, although the automated method generally overpredicted each cluster’s lower boundary. Tree-level volumes and crown width were, respectively, predicted with R-squared values of 0.9111 and 0.7984 and RMSE values of 44.36 m2 and 0.53 m. Weaker relationships were observed for tree-level metrics that relied on the extraction of lower crown features (live crown length, live crown base height, lowest live branch height). These metrics were predicted with R-squared values of 0.5568, 0.3120, and 0.2011 and RMSE values of 3.53 m, 3.55 m, and 3.66 m. Overall, this study highlights strengths and weaknesses of the developed method and the utility of RPAS LiDAR point clouds relative to ground-based point clouds.

Evidence for widespread changes in the structure, composition, and fire regimes of western North American forests.

Implementation of wildfire‐ and climate‐adaptation strategies in seasonally dry forests of western North America is impeded by numerous constraints and uncertainties. After more than a century of resource and land use change, some question the need for proactive management, particularly given novel social, ecological, and climatic conditions. To address this question, we first provide a framework for assessing changes in landscape conditions and fire regimes. Using this framework, we then evaluate evidence of change in contemporary conditions relative to those maintained by active fire regimes, i.e., those uninterrupted by a century or more of human‐induced fire exclusion. The cumulative results of more than a century of research document a persistent and substantial fire deficit and widespread alterations to ecological structures and functions. These changes are not necessarily apparent at all spatial scales or in all dimensions of fire regimes and forest and nonforest conditions. Nonetheless, loss of the once abundant influence of low‐ and moderate‐severity fires suggests that even the least fire‐prone ecosystems may be affected by alteration of the surrounding landscape and, consequently, ecosystem functions. Vegetation spatial patterns in fire‐excluded forested landscapes no longer reflect the heterogeneity maintained by interacting fires of active fire regimes. Live and dead vegetation (surface and canopy fuels) is generally more abundant and continuous than before European colonization. As a result, current conditions are more vulnerable to the direct and indirect effects of seasonal and episodic increases in drought and fire, especially under a rapidly warming climate. Long‐term fire exclusion and contemporaneous social‐ecological influences continue to extensively modify seasonally dry forested landscapes. Management that realigns or adapts fire‐excluded conditions to seasonal and episodic increases in drought and fire can moderate ecosystem transitions as forests and human communities adapt to changing climatic and disturbance regimes. As adaptation strategies are developed, evaluated, and implemented, objective scientific evaluation of ongoing research and monitoring can aid differentiation of warranted and unwarranted uncertainties.