Wildland Fire Effects Research Articles

Model linkage to assess forest disturbance impacts on water quality: A wildfire case study using LANDIS(II)-VELMA

Wildfires in western US forests increased over the last two decades, resulting in elevated solid and nutrient loadings to streams, and occasionally threatening drinking water supplies. We demonstrated that a linked LANDIS (LANDscape DIsturbance and Succession)-VELMA (Visualizing Ecosystem Land Management Assessments) modeling approach can simulate wildland fire effects on water quality using the 2002 Colorado Hayman Fire. Utilizing LANDIS-II's forest landscape model to simulate forest composition and VELMA's eco-hydrologic model to simulate pre- and post-fire water quantity and quality, the best calibration performance yielded a Nash-Sutcliffe Efficiency (NSE) of 0.621 during 2000–2006 (most optimal annual - 0.921) in comparison to North American Land Data Assimilation System (NLDAS) runoff. Pre-fire modeled runoff, nitrate, and surface water temperature (SWT) correlated with observations. Simulated post-fire runoff (229%) and SWT (20.6%) were elevated relative to pre-fire, with nitrate concentrations 34 times greater than the aquatic life threshold (0.01 mg N/L).

Current Status of Research on Wildland Fire Impacts on Soil Environment and Soil Organisms and Hotspots Visualization Analysis

Ecosystems are frequently disturbed by fires that have an important impact on the soil environment and the composition of soil organisms. In order to provide a baseline for the current research and identify trends on the effects of wildland fire on soil environment and biological changes, the available literature was identified from the Web of Science database, covering the period from 1998/1998/1999 (the year of the earliest publication in this field) to 2023. A bibliometric analysis was performed and the data were visually displayed for the number of publications, countries, authors, research institutions, and keywords representing research hotspots. Specifically, the effects of wildland fire on the soil environment, on soil microorganisms and on soil fauna were analyzed. The results show that the annual number of publications describing effects of wildland fire on the soil environment and on soil microorganisms are increasing over time, while those describing effects on soil fauna are fewer and their number remains constant. The largest number of papers originate from the United States, with the United States Department of Agriculture as the research institution with the largest output. The three authors with the largest number of publications are Stefan H. Doerr, Manuel Esteban Lucas-Borja and Jan Jacob Keizer. The research hotspots, as identified by keywords, are highly concentrated on wildfire, fire, organic matter, and biodiversity, amongst others. This study comprehensively analyzes the current situation of the research on the effects of wildland fire on changes in the soil environment and organisms, and provides reference for relevant scientific researchers in this trend and future research hotspots.

Understanding fire regimes in Europe

Wildland fire effects are strongly associated with fire regime characteristics. Here, we developed the first European pyrogeography based on different fire regime components to better understand fire regimes across the continent. We identified four large-scale pyroregions: a non-fire-prone (NFP) pyroregion featuring nominal fire activity across central and northern Europe; a cool-season fire (CSF) pyroregion scattered throughout Europe; a fire-prone (FP) pyroregion extending mostly across southern Europe; and a highly fire-prone (HFP) pyroregion spanning across northern Portugal, Sicily, and western Balkans. Land cover analysis indicates that pyroregions were first shaped by vegetation and then by anthropogenic factors. On interannual timescales the spatial extent of pyroregions was found to vary, with NFP showing more stability. Interannual correlations between climate and burned area, fire frequency, and the length of fire period exhibited distinct patterns, strengthening in fire-prone pyroregions (FP and HFP) and weakening in NFP and CSF. Proportion of cool-season fires and large fires were related to fuel accumulation in fire-prone pyroregions. Overall, our findings indicate that such a pyrogeography should allow a more accurate estimate of the effects of climate on fire regimes while providing an appropriate framework to better understand fire in Europe.

Wildland Fire Tree Mortality Mapping from Hyperspatial Imagery Using Machine Learning

The use of imagery from small unmanned aircraft systems (sUAS) has enabled the production of more accurate data about the effects of wildland fire, enabling land managers to make more informed decisions. The ability to detect trees in hyperspatial imagery enables the calculation of canopy cover. A comparison of hyperspatial post-fire canopy cover and pre-fire canopy cover from sources such as the LANDFIRE project enables the calculation of tree mortality, which is a major indicator of burn severity. A mask region-based convolutional neural network was trained to classify trees as groups of pixels from a hyperspatial orthomosaic acquired with a small unmanned aircraft system. The tree classification is summarized at 30 m, resulting in a canopy cover raster. A post-fire canopy cover is then compared to LANDFIRE canopy cover preceding the fire, calculating how much the canopy was reduced due to the fire. Canopy reduction allows the mapping of burn severity while also identifying where surface, passive crown, and active crown fire occurred within the burn perimeter. Canopy cover mapped through this effort was lower than the LANDFIRE Canopy Cover product, which literature indicated is typically over reported. Assessment of canopy reduction mapping on a wildland fire reflects observations made both from ground truthing efforts as well as observations made of the associated hyperspatial sUAS orthomosaic.

Mapping Burn Extent of Large Wildland Fires from Satellite Imagery Using Machine Learning Trained from Localized Hyperspatial Imagery

Wildfires burn 4–10 million acres annually across the United States and wildland fire related damages and suppression costs have exceeded $13 billion for a single year. High-intensity wildfires contribute to post-fire erosion, degraded wildlife habitat, and loss of timber resources. Accurate and temporally adequate assessment of the effects of wildland fire on the environment is critical to improving the of wildland fire as a tool for restoring ecosystem resilience. Sensor miniaturization and small unmanned aircraft systems (sUAS) provide affordable, on-demand monitoring of wildland fire effects at a much finer spatial resolution than is possible with satellite imagery. The use of sUAS would allow researchers to obtain data with more detail at a much lower initial cost. Unfortunately, current regulatory and technical constraints prohibit the acquisition of imagery using sUAS for the entire extent of large fires. This research examined the use of sUAS imagery to train and validate burn severity and extent mapping of large wildland fires from various satellite images. Despite the lower resolution of the satellite image, the research utilized the advantages of satellite imagery such as global coverage, low cost, temporal stability, and spectral extent while leveraging the higher resolution of hyperspatial sUAS imagery for training and validating the mapping analytics.

Assessing the Relationship between Litter + Duff Consumption and Post-Fire Soil Temperature Regimes

The immediate effects of wildland fire on soil have been well documented. However, we know much less about the longer-term effects and their implications for plants. Post-fire soil temperature regimes, for example, have received relatively little research attention, despite potential effects on plant phenology and establishment. Using portable temperature datalogger units (iButtons), we conducted an experimental study to assess how fire severity (measured in terms of litter and duff consumption) influences biologically relevant temperature parameters such as diel minimums, maximums, means, and ranges. We also used these data to calculate cumulative soil growing degree days (GDDs). The study was conducted during the early to mid-spring to capture the transition from dormant season to growing season. Results indicate that mean and max soil temperatures increase in the weeks after fire, with the most pronounced effects in the higher severity treatments. By the end of the 40-day study period, soils in the high severity burn treatment had accumulated 72 GDDs, compared to 17.9, 13.6, and 1.4 in moderate, low, and control treatments, respectively. These findings indicate that fire severity has significant and persistent effects on post-fire soil temperature regimes, and this likely has implications for the post-fire vegetation response.

Impacts of wildland fire effects on resources and assets through expert elicitation to support fire response decisions

A modelling framework to spatially score the impacts from wildland fire effects on specific resources and assets was developed for and applied to the province of Ontario, Canada. This impact model represents the potential ‘loss’, which can be used in the different decision-making methods common in fire response operations (e.g. risk assessment, decision analysis and expertise-based). Resources and assets considered include point features such as buildings, linear features such as transmission lines, and areal features such as forest management areas. Three categories of fire impacts were included: social, economic and emergency response. Category-specific scores were determined through expert elicitation and then adjusted to account for fire intensity. Expert elicitation was shown to compare favourably with other methods in terms of the complexity, time, set-up cost and operational use. When compared with historical fire data from Ontario, it was found that impact model scores were associated with the objective to suppress or monitor fires. The model framework provides a consistent pre-fire impact assessment to support individual fire response decisions. The impact assessment can also represent the total impact for areas of Ontario that do not have prescriptive response in a formal fire response plan.

Evaluation of Texture as an Input of Spatial Context for Machine Learning Mapping of Wildland Fire Effects

Evaluation of Texture as an Input of Spatial Context for Machine Learning Mapping of Wildland Fire Effects

Spectroscopic Analysis for Mapping Wildland Fire Effects from Remotely Sensed Imagery.

1.5 to 4 million hectares of land burns in wildfire across the United States each year, contributing to post-fire erosion, ecosystem degradation and loss of wildlife habitat. Unmanned Aircraft Systems (UAS) and sensor miniaturization offer a new paradigm, providing an affordable, safe, and responsive on-demand tool for monitoring fire effects at a much finer spatial resolution than is possible with current technology. Using spectroscopic analysis of a variety of live as well as combusted vegetation samples to identify the spectral separability of vegetation classes, an optimal set of spectra was selected to be utilized by machine learning classifiers. This approach allows high resolution mapping of wildland fire severity and extent.

Effects of wildland fire on the tropical alpine moorlands of Mount Kenya

Tropical alpine ecosystems serve vital roles in the areas of hydrology, biodiversity and carbon storage, due to their unique set of climatic and topographic conditions. These ecosystems are particularly vulnerable to climate change and anthropogenic influence as they exist in a fragile balance. Mount Kenya is an example of such a tropical alpine environment, and in 2012 a large fire burned through the upper southwestern slopes of the mountain. The fire burned at the transition zone between the moorland and the forest at an elevation of 3300–4000m, and covered almost 100km2. The impacts of this fire on soil properties are investigated three years after the fire. The severity from the time of the fire was determined using a satellite derived index known as dNBR (difference Normalized Burn Ratio). This was used to stratify the fire scar into 3 classes: low, moderate, and high severity areas. Soil samples were collected from the top 10cm layer (O-horizon) in each of these 3 severity classes and from unburnt areas outside the fire scar, and analyzed in the laboratory for physical and chemical properties. The results mostly showed no difference in soil properties between the three severity classes and unburnt areas. Only cation exchange capacity (CEC) and bulk density were found to be significantly different between the severity classes (p<0.001 and p=0.003 respectively), with high severity areas having lower bulk density and higher CEC as compared to unburnt areas. When other explanatory variables were taken into consideration, only CEC remained significantly different according to severity rating. CEC increased from 40.13cmolckg−1 outside the fire to 65.91cmolckg−1 in the high severity areas. The increase in CEC can be explained either by an increase in organic matter, as partially burnt material gradually incorporated into the soil, or by a change in the quality of the organic matter. Thus the impact of fire on soil properties in the tropical alpine moorland of Mount Kenya appears to be small, at least after 3years.

Toward a more ecologically informed view of severe forest fires

AbstractWe use the historical presence of high‐severity fire patches in mixed‐conifer forests of the western United States to make several points that we hope will encourage development of a more ecologically informed view of severe wildland fire effects. First, many plant and animal species use, and have sometimes evolved to depend on, severely burned forest conditions for their persistence. Second, evidence from fire history studies also suggests that a complex mosaic of severely burned conifer patches was common historically in the West. Third, to maintain ecological integrity in forests born of mixed‐severity fire, land managers will have to accept some severe fire and maintain the integrity of its aftermath. Lastly, public education messages surrounding fire could be modified so that people better understand and support management designed to maintain ecologically appropriate sizes and distributions of severe fire and the complex early‐seral forest conditions it creates.

Social acceptability of fuel management in the Australian Capital Territory and surrounding region

Managing fuel to reduce wildland fire risk often creates substantial public debate. Although the acceptability of various fuel management strategies has been explored in some regions, particularly North America, the social acceptability of fuel management is less well understood in other countries. This paper begins to address this knowledge gap by exploring acceptability by residents living in and near the Australian Capital Territory, Australia of three fuel management strategies (prescribed burning, livestock grazing and mechanical thinning) used to reduce wildland fire risk to life and property. All three were considered acceptable by most survey respondents. Acceptability did not vary substantially between strategies or by the location in which the strategy was undertaken. Acceptability of fuel management was associated with trust in fire management agencies, having knowledge of fuel management, feeling vulnerable to wildland fire and respondent characteristics such as previous effects of wildland fires, location of residence, gender, age, income and employment status.

FireStem2D – A Two-Dimensional Heat Transfer Model for Simulating Tree Stem Injury in Fires

FireStem2D, a software tool for predicting tree stem heating and injury in forest fires, is a physically-based, two-dimensional model of stem thermodynamics that results from heating at the bark surface. It builds on an earlier one-dimensional model (FireStem) and provides improved capabilities for predicting fire-induced mortality and injury before a fire occurs by resolving stem moisture loss, temperatures through the stem, degree of bark charring, and necrotic depth around the stem. We present the results of numerical parameterization and model evaluation experiments for FireStem2D that simulate laboratory stem-heating experiments of 52 tree sections from 25 trees. We also conducted a set of virtual sensitivity analysis experiments to test the effects of unevenness of heating around the stem and with aboveground height using data from two studies: a low-intensity surface fire and a more intense crown fire. The model allows for improved understanding and prediction of the effects of wildland fire on injury and mortality of trees of different species and sizes.

Simulating smoke transport from wildland fires with a regional‐scale air quality model: Sensitivity to uncertain wind fields

AbstractUncertainties associated with meteorological inputs which are propagated through atmospheric chemical transport models may constrain their ability to replicate the effects of wildland fires on air quality. Here, we investigate the sensitivity of predicted fine particulate matter (PM2.5) levels to uncertain wind fields by simulating the air quality impacts of two fires on an urban area with the Community Multiscale Air Quality modeling system (CMAQ). Brute‐force sensitivity analyses show that modeled concentrations at receptors downwind from the fires are highly sensitive to variations in wind speed and direction. Additionally, uncertainty in wind fields produced with the Weather Research and Forecasting model was assessed by evaluating meteorological predictions against surface and upper air observations. Significant differences between predicted and observed wind fields were identified. Simulated PM2.5 concentrations at urban sites displayed large sensitivities to wind perturbations within the error range of meteorological inputs. The analyses demonstrate that normalized errors in CMAQ predictions attempting to model the regional impacts of fires on PM2.5 levels could be as high as 100% due to inaccuracies in wind data. Meteorological drivers may largely account for the considerable discrepancies between monitoring site observations and predicted concentrations. The results of this study demonstrate that limitations in fire‐related air quality simulations cannot be overcome by solely improving emission rates.

Charcoal and Total Carbon in Soils from Foothills Shrublands to Subalpine Forests in the Colorado Front Range

Temperate conifer forests in the Colorado Front Range are fire-adapted ecosystems where wildland fires leave a legacy in the form of char and charcoal. Long-term soil charcoal C (CC) pools result from the combined effects of wildland fires, aboveground biomass characteristics and soil transfer mechanisms. We measured CC pools in surface soils (0–10 cm) at mid-slope positions on east facing aspects in five continuous foothills shrubland and conifer forest types. We found a significant statistical effect of vegetation type on CC pools along this ecological gradient, but not a linear pattern increasing with elevation gain. There is a weak bimodal pattern of CC gain with elevation between foothills shrublands (1.2 mg CC ha−1) and the lower montane, ponderosa pine (1.5 mg CC ha−1) and Douglas-fir (1.5 mg CC ha−1) forest types prior to a mid-elevation decline in upper montane lodgepole pine forests (1.2 mg CC ha−1) before increasing again in the spruce/subalpine fir forests (1.5 mg CC ha−1). We propose that CC forms and accumulates via unique ecological conditions such as fire regime. The range of soil CC amounts and ratios of CC to total SOC are comparable to but lower than other regional estimates.

Introduction: Strengthening the Foundation of Wildland Fire Effects Prediction for Research and Management

As prescribed fire use increases and the options for responding to wildfires continue to expand beyond suppression, the need for improving fire effects prediction capabilities becomes increasingly apparent. The papers in this Fire Ecology special issue describe recent advances in fire effects prediction for key classes of direct (first-order) fire effects. Important gaps in predictive capabilities exist in pre-, active-, and post-fire measurement technology; in our ability to predict heat deposition to soil and plant surfaces from knowledge of fuels and models that simulate smoldering combustion, flame spread, and plume dynamics; in our ability to predict above and below ground plant heating and injury; in our understanding of the physiological causes of plant mortality; and in our knowledge of direct effects of fire on fauna and their habitats. Fire effects on shrub and grassland systems are particularly poorly studied. Recent advances in software systems, in which multiple models and databases are included in a single application tailored to address fire management questions, give impetus to foundational fire effects research that would improve fire effects prediction. In this introduction, we describe the range of approaches to predicting fire effects, from statistical to process; we define terminology used throughout the issue; and we highlight research and development needs. We offer the following goal as a challenge to the research community: the development of a comprehensive, first-order fire effects model employing a diversity of approaches (from statistical to process) and built to serve a range of applications (from research to land management).

Calibration and validation of the relative differenced Normalized Burn Ratio (RdNBR) to three measures of fire severity in the Sierra Nevada and Klamath Mountains, California, USA

Abstract Multispectral satellite data have become a common tool used in the mapping of wildland fire effects. Fire severity, defined as the degree to which a site has been altered, is often the variable mapped. The Normalized Burn Ratio (NBR) used in an absolute difference change detection protocol (dNBR), has become the remote sensing method of choice for US Federal land management agencies to map fire severity due to wildland fire. However, absolute differenced vegetation indices are correlated to the pre-fire chlorophyll content of the vegetation occurring within the fire perimeter. Normalizing dNBR to produce a relativized dNBR (RdNBR) removes the biasing effect of the pre-fire condition. Employing RdNBR hypothetically allows creating categorical classifications using the same thresholds for fires occurring in similar vegetation types without acquiring additional calibration field data on each fire. In this paper we tested this hypothesis by developing thresholds on random training datasets, and then comparing accuracies for (1) fires that occurred within the same geographic region as the training dataset and in similar vegetation, and (2) fires from a different geographic region that is climatically and floristically similar to the training dataset region but supports more complex vegetation structure. We additionally compared map accuracies for three measures of fire severity: the composite burn index (CBI), percent change in tree canopy cover, and percent change in tree basal area. User's and producer's accuracies were highest for the most severe categories, ranging from 70.7% to 89.1%. Accuracies of the moderate fire severity category for measures describing effects only to trees (percent change in canopy cover and basal area) indicated that the classifications were generally not much better than random. Accuracies of the moderate category for the CBI classifications were somewhat better, averaging in the 50%–60% range. These results underscore the difficulty in isolating fire effects to individual vegetation strata when fire effects are mixed. We conclude that the models presented here and in Miller and Thode ([Miller, J.D. & Thode, A.E., (2007). Quantifying burn severity in a heterogeneous landscape with a relative version of the delta Normalized Burn Ratio (dNBR). Remote Sensing of Environment, 109, 66–80.]) can produce fire severity classifications (using either CBI, or percent change in canopy cover or basal area) that are of similar accuracy in fires not used in the original calibration process, at least in conifer dominated vegetation types in Mediterranean–climate California.

Wildland fire effects on understory plant communities in two fire-prone forests

Our understanding of wildland fire effects on understory plant communities is limited because of a lack of repeated measurements before and after lightning-ignited fires. We examined vegetation responses to a surface fire in a ponderosa pine forest and a mixed-severity fire in a spruce–fir–aspen forest using before–after, control–impact (BACI) study designs. We hypothesized that the surface fire would stimulate plant species richness and minimally alter community composition, but that the mixed-severity fire would decrease richness and significantly alter composition. In ponderosa pine forests, total species richness and plant cover increased slightly because of annual and biennial forb and grass establishment in soils where duff layers were reduced by the surface fire. In spruce–fir–aspen forests, total species richness and plant cover were similar in burned and unburned forests after 2 years, although annual and biennial forbs and graminoids increased significantly in the burned area. Plant community composition was altered by both fires. Wildfires may indirectly influence the understory plant community through the mediating effects of overstory basal area and litter depth. Fire effects on plant species richness and cover were weaker than effects due to environmental factors. Managers should anticipate increases in both native and non-native ruderal species following landscape-scale fires.

Wildland fire effects on forest structure over an altitudinal gradient, Grand Canyon National Park, USA

Summary Restoration of wildland fire to forests is a challenge when historical fire regimes have been altered. We studied four fires that burned over approximately 7865 ha on an altitudinal gradient in Grand Canyon National Park, USA, in 2003. The fires met criteria for the current USA policy allowing the restoration of fire's ecological role in forest landscapes: Wildland Fire Use for Resource Benefits. After the fires burned out, we remeasured 82 permanent pre‐established monitoring plots burned by the fires plus 43 additional plots on unburned companion sites. The maximum height of charring of tree boles and basal area mortality increased in mean value and variability with altitude. At a low‐altitude Pinus–Quercus site, tree density declined significantly but basal area was unchanged. At a mid‐altitude mixed‐conifer site and a high‐altitude Picea/Abies/Populus site, both density and basal area declined. The thinning effect of fire was concentrated on smaller, shorter, fire‐susceptible trees. Small‐diameter trees (&lt; 20 cm diameter) made up 79–95% of all tree mortality. Shade‐tolerant conifers, particularly true firs and spruce, experienced disproportionate mortality (31–82% basal area decline), while fire‐resistant ponderosa pine and Douglas fir tended to survive (2–8% basal area decline). Delayed mortality between the first and second years following the fires accounted for only 4·2% of trees dying at the low‐altitude site but 15·6% and 11·2% at the mid‐ and high‐altitude sites, respectively. Regeneration density was highly variable but forest floor and woody debris declined in burned areas. Synthesis and applications. This study shows that, even after an unusually long fire‐free period (1880–2003), at the mid‐ and high‐altitude burned sites fire effects were consistent with restoration of historical patterns, moving the ecosystems closer to historical reference conditions. Fires simultaneously reduced the living, dead and ladder fuels that made the forest vulnerable to uncharacteristically severe fire. These effects make the forests more resistant to the expected increases in fire size and severity under future climate conditions. Even at longer‐than‐historical fire intervals, the wildland fire use policy can benefit Grand Canyon forests.

Fire and aquatic ecosystems of the western USA: current knowledge and key questions

Understanding of the effects of wildland fire and fire management on aquatic and riparian ecosystems is an evolving field, with many questions still to be resolved. Limitations of current knowledge, and the certainty that fire management will continue, underscore the need to summarize available information. Integrating fire and fuels management with aquatic ecosystem conservation begins with recognizing that terrestrial and aquatic ecosystems are linked and dynamic, and that fire can play a critical role in maintaining aquatic ecological diversity. To protect aquatic ecosystems we argue that it will be important to: (1) accommodate fire-related and other ecological processes that maintain aquatic habitats and biodiversity, and not simply control fires or fuels; (2) prioritize projects according to risks and opportunities for fire control and the protection of aquatic ecosystems; and (3) develop new consistency in the management and regulatory process. Ultimately, all natural resource management is uncertain; the role of science is to apply experimental design and hypothesis testing to management applications that affect fire and aquatic ecosystems. Policy-makers and the public will benefit from an expanded appreciation of fire ecology that enables them to implement watershed management projects as experiments with hypothesized outcomes, adequate controls, and replication.