Ladder fuels rather than canopy volumes consistently predict wildfire severity even in extreme topographic-weather conditions

Drivers of forest wildfire severity include fuels, topography and weather. However, because only fuels can be actively managed, quantifying their effects on severity has become an urgent research priority. Here we employed GEDI spaceborne lidar to consistently assess how pre-fire forest fuel structure affected wildfire severity across 42 California wildfires between 2019-2021. Using a spatial-hierarchical modeling framework, we found a positive concave-down relationship between GEDI-derived fuel structure and wildfire severity, marked by increasing severity with greater fuel loads until a decline in severity in the tallest and most voluminous forest canopies. Critically, indicators of canopy fuel volumes (like biomass and height) became decoupled from severity patterns in extreme topographic and weather conditions (slopes >20°; winds > 9.3 m/s). On the other hand, vertical continuity metrics like layering and ladder fuels more consistently predicted severity in extreme conditions - especially ladder fuels, where sparse understories were uniformly associated with lower severity levels. These results confirm that GEDI-derived fuel estimates can overcome limitations of optical imagery and airborne lidar for quantifying the interactive drivers of wildfire severity. Furthermore, these findings have direct implications for designing treatment interventions that target ladder fuels versus entire canopies and for delineating wildfire risk across topographic and weather conditions.

Model analysis of post-fire management and potential reburn fire behavior

Recent trends in wildfire area burned have been characterized by large patches with high densities of standing dead trees, well outside of historical range of variability in many areas and presenting forest managers with difficult decisions regarding post-fire management. Post-fire tree harvesting, commonly called salvage logging, is a controversial management tactic that is often undertaken to recoup economic loss and, more recently, also to reduce future fuel hazard, especially when coupled with surface fuel reduction. It is unclear, however, whether the reductions in future fuels translate to meaningful changes to reburn fire behavior, particularly in the context of potentially detrimental effects of harvest on other ecosystem services. We used observed post-fire snag structure in four high severity burn scars located in the Western United States that had variable post-fire snag basal area (13.3-63.9 mg ha-2) to initialize a simulation study of future coarse and fine woody fuel hazard and associated reburn fire behavior and effects. We compared untreated controls to intensive and intermediate intensity harvest treatments, both simulated and actual. All treatments showed some number of years of extreme fire behavior during which flame lengths exceeded thresholds associated with wildfire resistance to control, implying that future fuel reductions achieved by the treatments did not translate to conditions conducive for effective reburn fire management. Harvested stands had less severe soil fire effects (soil heating and smoldering duration) than untreated controls, explained by lower predicted peak coarse woody fuels (CWD) in the harvested stands. At higher pre-treatment snag basal area, harvested stands better maintained CWD within the range desired to maintain ecosystem functions such as nutrient cycling and wildlife habitat. These simulation results indicate that, even with reduced fuel hazard, salvage treatments may still be associated with severe fire behavior for some time after wildfire, but achieved reductions in coarse woody fuels may also reduce some soil fire effects. Tradeoffs in the effects of post-fire harvest must be considered carefully in the context of forest regeneration, local conditions that govern salvage methods, snag fall and decomposition, and associated potential reburn fire effects.

Untangling fuel, weather and management effects on fire severity: Insights from large-sample LiDAR remote sensing analysis of conditions preceding the 2019-20 Australian wildfires

Evaluation of fire severity reduction strategies requires the quantification of intervention outcomes and, more broadly, the extent to which fuel characteristics affect fire severity. However, investigations are currently limited by the availability of accurate data on fire severity predictors, particularly relating to fuel. Here, we used airborne LiDAR data collected before the 2019-20 Australian Black Summer fires to investigate the contribution of fuel structure to fire severity under a range of weather conditions. Fire severity was estimated using the Relative Burn Ratio calculated from Sentinel-2 optical remote sensing imagery. We modelled the effects of various fuel structure estimates and other environmental predictors using Random Forest models. In addition to variables estimated at each observation point, we investigated the influence of surrounding landscape characteristics using an innovative method to estimate fireline progression direction. Our models explained 63-76% of fire severity variance using parsimonious predictor sets. Fuel cover in the understorey and canopy, and vertical vegetation heterogeneity, were positively associated with fire severity. Up-fire burnt area and recent planned and unplanned fire reduced fire severity, whereby unplanned fire provided a longer-lasting reduction of fire severity (up to 15 years) than planned fire (up to 10 years). Although fuel structure and land management effects were important predictors, weather and canopy height effects were dominant. By mapping continuous interactions between weather and fuel-related variables, we found strong evidence of diminishing fuel effects below 20-40% relative air humidity. While our findings suggest that land management interventions can provide meaningful fire severity reduction, they also highlight the risk of warmer and drier future climates constraining these advantages.

Low-intensity fires mitigate the risk of high-intensity wildfires in California's forests

The increasing frequency of severe wildfires demands a shift in landscape management to mitigate their consequences. The role of managed, low-intensity fire as a driver of beneficial fuel treatment in fire-adapted ecosystems has drawn interest in both scientific and policy venues. Using a synthetic control approach to analyze 20 years of satellite-based fire activity data across 124,186 square kilometers of forests in California, we provide evidence that low-intensity fires substantially reduce the risk of future high-intensity fires. In conifer forests, the risk of high-intensity fire is reduced by 64.0% [95% confidence interval (CI): 41.2 to 77.9%] in areas recently burned at low intensity relative to comparable unburned areas, and protective effects last for at least 6 years (lower bound of one-sided 95% CI: 6 years). These findings support a policy transition from fire suppression to restoration, through increased use of prescribed fire, cultural burning, and managed wildfire, of a presuppression and precolonial fire regime in California.

Human and natural impacts on the U.S. freshwater salinization and alkalinization: A machine learning approach

Ongoing salinization and alkalinization in U.S. rivers have been attributed to inputs of road salt and effects of human-accelerated weathering in previous studies. Salinization poses a severe threat to human and ecosystem health, while human derived alkalinization implies increasing uncertainty in the dynamics of terrestrial sequestration of atmospheric carbon dioxide. A mechanistic understanding of whether and how human activities accelerate weathering and contribute to the geochemical changes in U.S. rivers is lacking. To address this uncertainty, we compiled dissolved sodium (salinity proxy) and alkalinity values along with 32 watershed properties ranging from hydrology, climate, geomorphology, geology, soil chemistry, land use, and land cover for 226 river monitoring sites across the coterminous U.S. Using these data, we built two machine-learning models to predict monthly-aggregated sodium and alkalinity fluxes at these sites. The sodium-prediction model detected human activities (represented by population density and impervious surface area) as major contributors to the salinity of U.S. rivers. In contrast, the alkalinity-prediction model identified natural processes as predominantly contributing to variation in riverine alkalinity flux, including runoff, carbonate sediment or siliciclastic sediment, soil pH and soil moisture. Unlike prior studies, our analysis suggests that the alkalinization in U.S. rivers is largely governed by local climatic and hydrogeological conditions.

Characterization of biophysical contexts leading to severe wildfires in Portugal and their environmental controls

Characterizing the fire regime in regions prone to extreme wildfire behavior is essential for providing comprehensive insights on potential ecosystem response to fire disturbance in the context of global change. We aimed to disentangle the linkage between contemporary damage-related attributes of wildfires as shaped by the environmental controls of fire behavior across mainland Portugal. We selected large wildfires (≥100 ha, n = 292) that occurred during the 2015-2018 period, covering the full spectrum of large fire-size variation. Ward's hierarchical clustering on principal components was used to identify homogeneous wildfire contexts at landscape scale on the basis of fire size, proportion of high fire severity, and fire severity variability, and their bottom-up (pre-fire fuel type fraction, topography) and top-down (fire weather) controls. Piecewise Structural Equation Modeling was used to disentangle the direct and indirect relationships between fire characteristics and fire behavior drivers. Cluster analysis evidenced severe and large wildfires in the central region of Portugal displaying consistent fire severity patterns. Thus, we found a positive relationship between fire size and proportion of high fire severity, which was mediated by distinct fire behavior drivers involving direct and indirect pathways. A high fraction of conifer forest within wildfire perimeters and extreme fire weather were primarily responsible for those interactions. In the context of global change, our results suggest that pre-fire fuel management should be targeted at expanding the fire weather settings in which fire control is feasible and promote less flammable and more resilient forest types.

Using heat plumes to simulate post-fire effects on cambial viability and hydraulic performance in Sequoia sempervirens stems

Injury to the xylem and vascular cambium is proposed to explain mortality following low severity fires. These tissues have been assessed independently, but the relative significance of the xylem and cambium is still uncertain. The goal of this study is to evaluate the xylem dysfunction hypothesis and cambium necrosis hypothesis simultaneously. The hot dry conditions of a low severity fire were simulated in a drying oven, exposing Sequoia sempervirens (Lamb. ex D. Don) shoots to 70 and 100 °C for 6-60 min. Cambial viability was measured with Neutral Red stain and water transport capacity was assessed by calculating the loss of hydraulic conductivity. Vulnerability curves were also constructed to determine susceptibility to drought-induced embolism following heat exposure. The vascular cambium died completely at 100 °C after only 6 min of heat exposure, while cells remained viable at 70 °C temperatures for up to 15 min. Sixty minutes of exposure to 70 °C reduced stem hydraulic conductivity by 40%, while 45 min at 100 °C caused complete loss of conductivity. The heat treatments dropped hydraulic conductivity irrecoverably but did not significantly impact post-fire vulnerability to embolism. Overall, the damaging effects of high temperature occurred more rapidly in the vascular cambium than xylem following heat exposure. Importantly, the xylem remained functional until the most extreme treatments, long after the vascular cambium had died. Our results suggest that the viability of the vascular cambium may be more critical to post-fire survival than xylem function in S. sempervirens. Given the complexity of fire, we recommend ground-truthing the cambial and xylem post-fire response on a diverse range of species.

Influence of fuel structure derived from terrestrial laser scanning (TLS) on wildfire severity in logged forests

CONTEXT

Logging and wildfire can reduce the height of the forest canopy and the distance to the understorey vegetation below. These conditions may increase the likelihood of high severity wildfire (canopy scorch or consumption), which may explain the greater prevalence of high severity wildfire in some recently logged or burnt forests. However, the effects of these structural characteristics on wildfire severity have not clearly been demonstrated.

OBJECTIVES

We aimed to assess how the structure of forests affected by logging and wildfire influence the probability of high severity wildfire.

METHODS

We used terrestrial laser scanning to measure the connectivity of canopy and understorey vegetation in forests at various stages of recovery after logging and wildfire (approximately 0-80 years since disturbance). These sites were subsequently burnt by mixed severity wildfire during the 2019-20 'Black Summer' fire season in south-eastern Australia. We assessed how these forest structure metrics affected the probability of high severity wildfire.

RESULTS

The probability of high severity fire decreased as the canopy base height increased, and the distance between the canopy base and understorey increased. High severity wildfire was less likely in forests with taller understoreys and greater canopy or understorey cover, but these effects were not considered causal. Fire weather was the strongest driver of wildfire severity, which was also affected by topography.

CONCLUSIONS

These findings demonstrate a link between forest structure characteristics, that are strongly shaped by antecedent logging and fire, and fire severity. They also indicate that vertical fuel structure should be incorporated into assessments of fire risk.

Where and why do conifer forests persist in refugia through multiple fire events?

Changing wildfire regimes are causing rapid shifts in forests worldwide. In particular, forested landscapes that burn repeatedly in relatively quick succession may be at risk of conversion when pre-fire vegetation cannot recover between fires. Fire refugia (areas that burn less frequently or severely than the surrounding landscape) support post-fire ecosystem recovery and the persistence of vulnerable species in fire-prone landscapes. Observed and projected fire-induced forest losses highlight the need to understand where and why forests persist in refugia through multiple fires. This research need is particularly acute in the Klamath-Siskiyou ecoregion of southwest Oregon and northwest California, USA, where expected increases in fire activity and climate warming may result in the loss of up to one-third of the region's conifer forests, which are the most diverse in western North America. Here, we leverage recent advances in fire progression mapping and weather interpolation, in conjunction with a novel application of satellite smoke imagery, to model the key controls on fire refugia occurrence and persistence through one, two, and three fire events over a 32-year period. Hotter-than-average fire weather was associated with lower refugia probability and higher fire severity. Refugia that persisted through three fire events appeared to be partially entrained by landscape features that offered protection from fire, suggesting that topographic variability may be an important stabilizing factor as forests pass through successive fire filters. In addition, smoke density strongly influenced fire effects, with fire refugia more likely to occur when smoke was moderate or dense in the morning, a relationship attributable to reduced incoming solar radiation resulting from smoke shading. Results from this study could inform management strategies designed to protect fire-resistant portions of biologically and topographically diverse landscapes.

Vegetation structure parameters determine high burn severity likelihood in different ecosystem types: A case study in a burned Mediterranean landscape

The design and implementation of pre-fire management strategies in heterogeneous landscapes requires the identification of the ecological conditions contributing to the most adverse effects of wildfires. This study evaluates which features of pre-fire vegetation structure, estimated through broadband land surface albedo and Light Detection and Ranging (LiDAR) data fusion, promote high wildfire damage across several fire-prone ecosystems dominated by either shrub (gorse, heath and broom) or tree species (Pyrenean oak and Scots pine). Topography features were also considered since they can assist in the identification of priority areas where vegetation structure needs to be managed. The case study was conducted within the scar of a mixed-severity wildfire that occurred in the Western Mediterranean Basin. Burn severity was estimated using the differenced Normalized Burn Ratio index computed from Sentinel-2 multispectral instrument (MSI) Level 2 A at 10 m of spatial resolution and validated in the field using the Composite Burn Index (CBI). Ordinal regression models were implemented to evaluate high burn severity outcome based on three groups of predictors: topography, pre-fire broadband land surface albedo computed from Sentinel-2 and pre-fire LiDAR metrics. Models were validated both by 10-fold cross-validation and external validation. High burn severity was largely ecosystem-dependent. In oak and pine forest ecosystems, severe damage was promoted by a high canopy volume (model accuracy = 79%) and a low canopy base height (accuracy = 82%), respectively. Land surface albedo, which is directly related to aboveground biomass and vegetation cover, outperformed LiDAR metrics to predict high burn severity in ecosystems with sparse vegetation. This is the case of gorse and broom shrub ecosystems (accuracy of 80% and 77%, respectively). The effect of topography was overwhelmed by that of the vegetation structure portion of the fire triangle behavior, except for heathlands, in which warm and steep slopes played a key role in high burn severity outcome together with horizontal and vertical fuel continuity (accuracy = 71%). The findings of this study support the fusion of LiDAR and satellite albedo data to assist forest managers in the development of ecosystem-specific management actions aimed at reducing wildfire damage and promote ecosystem resilience.

Survey of Transverse Range Fire Scars in 10 Years of UAVSAR Polarimetry

Because cross-polarized radar returns are highly associated with volume scatter, radar polarimetry returns tend to show strong evidence of wildfire scars and recovery in forest and chaparral. We focus on the polarimetry images from UAVSAR (PolSAR) line SanAnd_08525, which covers a roughly 20 km wide swath over the Transverse Range including parts of the Santa Monica, San Gabriel and San Bernardino Mountains. We select images from four acquisition dates from October 2009 to September 2020, very roughly 4 years apart. These are compared to fire perimeters from the national Geospatial Multi-Agency Coordination and NIFC databases for years 2003-2020, which shows the areas affected by the major fires (west to east) Springs2013, Woolsey2018, Topanga2005, LaTuna2017, Station2009, BlueCut2016, Pilot2016, Slide2007, Butler2007, and many smaller fires. PolSAR images are shown to be helpful in identifying types and boundaries of fire, 50-meter scale details of vegetation loss, and variability of vegetation recovery in post-fire years.

Evaluation of fire severity in fire prone-ecosystems of Spain under two different environmental conditions

Severe fires associated to climate change and land cover changes are becoming more frequent in Mediterranean Europe. The influence of environmental drivers on fire severity, especially under different environmental conditions is still not fully understood. In this study we aim to determine the main environmental variables that control fire severity in large fires (>500 ha) occurring in fire-prone ecosystems under two different environmental conditions following a transition (Mediterranean-Oceanic)-Mediterranean climatic gradient within the Iberian Peninsula, and to provide management recommendations to mitigate fire damage. We estimated fire severity as the differenced Normalized Burn Ratio, through images obtained from Landsat 8 OLI. We also examined the relative influence of pre-fire vegetation structure (vegetation composition and configuration), pre-fire weather conditions, fire history and topography on fire severity using Random Forest machine learning algorithms. The results indicated that the severity of fires occurring along the transition (Mediterranean-Oceanic)-Mediterranean climatic gradient was primarily controlled by pre-fire vegetation composition. Nevertheless, the effect of vegetation composition was strongly dependent on interactions with fire recurrence and pre-fire vegetation structural configuration. The relationship between fire severity, weather and topographic predictors was not consistent among fires occurring in the Mediterranean-Oceanic transition and Mediterranean sites. In the Mediterranean-Oceanic transition site, fire severity was determined by weather conditions (i.e., summer cumulative rainfall), rather than being associated to topography, suggesting that the control exerted by topography may be overwhelmed by weather controls. Conversely, results showed that topography only had a major effect on fire severity in the Mediterranean site. The results of this study highlight the need to prioritise fuel treatments aiming at breaking fuel continuity and reducing fuel loads as an effective management strategy to mitigate fire damage in areas of high fire recurrence.

A Machine Learning-Based Approach for Wildfire Susceptibility Mapping. The Case Study of the Liguria Region in Italy

Wildfire susceptibility maps display the spatial probability of an area to burn in the future, based solely on the intrinsic local proprieties of a site. Current studies in this field often rely on statistical models, often improved by expert knowledge for data retrieving and processing. In the last few years, machine learning algorithms have proven to be successful in this domain, thanks to their capability of learning from data through the modeling of hidden relationships. In the present study, authors introduce an approach based on random forests, allowing elaborating a wildfire susceptibility map for the Liguria region in Italy. This region is highly affected by wildfires due to the dense and heterogeneous vegetation, with more than 70% of its surface covered by forests, and due to the favorable climatic conditions. Susceptibility was assessed by considering the dataset of the mapped fire perimeters, spanning a 21-year period (1997–2017) and different geo-environmental predisposing factors (i.e., land cover, vegetation type, road network, altitude, and derivatives). One main objective was to compare different models in order to evaluate the effect of: (i) including or excluding the neighboring vegetation type as additional predisposing factors and (ii) using an increasing number of folds in the spatial-cross validation procedure. Susceptibility maps for the two fire seasons were finally elaborated and validated. Results highlighted the capacity of the proposed approach to identify areas that could be affected by wildfires in the near future, as well as its goodness in assessing the efficiency of fire-fighting activities.

Vegetation-fire feedback reduces projected area burned under climate change

Climate influences vegetation directly and through climate-mediated disturbance processes, such as wildfire. Temperature and area burned are positively associated, conditional on availability of vegetation to burn. Fire is a self-limiting process that is influenced by productivity. Yet, many fire projections assume sufficient vegetation to support fire, with substantial implications for carbon (C) dynamics and emissions. We simulated forest dynamics under projected climate and wildfire for the Sierra Nevada, accounting for climate effects on fuel flammability (static) and climate and prior fire effects on fuel availability and flammability (dynamic). We show that compared to climate effects on flammability alone, accounting for the interaction of prior fires and climate on fuel availability and flammability moderates the projected increase in area burned by 14.3%. This reduces predicted increases in area-weighted median cumulative emissions by 38.3 Tg carbon dioxide (CO2) and 0.6 Tg particulate matter (PM1), or 12.9% and 11.5%, respectively. Our results demonstrate that after correcting for potential over-estimates of the effects of climate-driven increases in area burned, California is likely to continue facing significant wildfire and air quality challenges with on-going climate change.

Deconstructing the King megafire

Hypotheses that megafires, very large, high-impact fires, are caused by either climate effects such as drought or fuel accumulation due to fire exclusion with accompanying changes to forest structure have long been alleged and guided policy, but their physical basis remains untested. Here, unique airborne observations and microscale simulations using a coupled weather-wildland-fire-behavior model allowed a recent megafire, the King Fire, to be deconstructed and the relative impacts of forest structure, fuel load, weather, and drought on fire size, behavior, and duration to be separated. Simulations reproduced observed details including the arrival at an inclined canyon, a 25-km run, and later slower growth and features. Analysis revealed that fire-induced winds that equaled or exceeded ambient winds and fine-scale airflow undetected by surface weather networks were primarily responsible for the fire's rapid growth and size. Sensitivity tests varied fuel moisture and amount across wide ranges and showed that both drought and fuel accumulation effects were secondary, limited to sloped terrain where they compounded each other, and, in this case, unable to significantly impact the final extent. Compared to standard data, fuel models derived solely from remote sensing of vegetation type and forest structure improved simulated fire progression, notably in disturbed areas, and the distribution of burn severity. These results point to self-reinforcing internal dynamics rather than external forces as a means of generating this and possibly other outlier fire events. Hence, extreme fires need not arise from extreme fire environment conditions. Kinematic models used in operations do not capture fire-induced winds and dynamic feedbacks so can underestimate megafire events. The outcomes provided a nuanced view of weather, forest structure, fuel accumulation, and drought impacts on landscape-scale fire behavior-roles that can be misconstrued using correlational analyses between area burned and macroscale climate data or other exogenous factors. A practical outcome is that fuel treatments should be focused on sloped terrain, where factors multiply, for highest impact.