Pre-fire Vegetation Research Articles

Using Sentinel-2 Imagery to Measure Spatiotemporal Changes and Recovery across Three Adjacent Grasslands with Different Fire Histories

As a result of the advocacy of Indigenous communities and increasing evidence of the ecological importance of fire, California has invested in the restoration of intentional burning (the practice of deliberately lighting low-severity fires) in an effort to reduce the occurrence and severity of wildfires. Recognizing the growing need to monitor the impacts of these smaller, low-severity fires, we leveraged Sentinel-2 imagery to reveal important inter- and intra-annual variation in grasslands before and after fires. Specifically, we explored three methodological approaches: (1) the complete time series of the normalized burn ratio (NBR), (2) annual summary metrics (mean, fifth percentile, and amplitude of NBR), and (3) maps depicting spatial patterns in these annual NBR metrics before and after fire. We also used a classification of pre-fire vegetation to stratify these analyses by three dominant vegetation cover types (grasses, shrubs, and trees). We applied these methods to a unique study area in which three adjacent grasslands had diverging fire histories and showed how grassland recovery from a low-severity intentional burn and a high-severity wildfire differed both from each other and from a reference site with no recent fire. On the low-severity intentional burn site, our results showed that the annual NBR metrics recovered to pre-fire values within one year, and that regular intentional burning on the site was promoting greater annual growth of both grass and shrub species, even in the third growing season following a burn. In the case of the high-severity wildfire, our metrics indicated that this grassland had not returned to its pre-fire phenological signals in at least three years after the fire, indicating that it may be undergoing a longer recovery or an ecological shift. These proposed methods address a growing need to study the effects of small, intentional burns in low-biomass ecosystems such as grasslands, which are an essential part of mitigating wildfires.

Fire regime attributes shape pre-fire vegetation characteristics controlling extreme fire behavior under different bioregions in Spain

BackgroundDesigning effective land management actions addressed to increase ecosystem resilience requires us to understand how shifting fire regimes are shaping landscapes. In this study, we aim to assess the link between fire regime and pre-fire vegetation biophysical characteristics (type, amount, and structure) in controlling extreme fire behavior across Atlantic-Transition-Mediterranean bioregions in Spain marked by different summer drought conditions and dominant plant regenerative traits. We used remote sensing metrics to estimate fire severity and pre-fire vegetation characteristics in eight study areas recently affected by large and highly severe wildfires under different environmental contexts. Furthermore, to account for fire regime attributes, we retrieved, for each target wildfire, the perimeter of the past wildfires that occurred between 1985 and 2022 and calculated fire recurrence, the time the since last fire (TSLF), and fire severity of previous wildfires (FSPW). The effect of fire regime attributes on pre-fire vegetation was examined using generalized linear mixed models (GLMMs).ResultsDuring the study period, fire recurrence decreased significantly in all bioregions analyzed. Fire severity increased under Atlantic conditions and decreased under Mediterranean environmental context, where the time since the last fire was the highest. Pre-fire fuel type and amount were identified as primary drivers of fire severity, being both strongly modulated by fire regime but following distinct mechanisms depending on the environmental context of each bioregion. In Atlantic sites, more frequent past wildfires of low to moderate fire severity were associated with a greater dominance of fire-prone shrublands with moderate fuel amounts, which increases the risk of severe wildfires. Similar trends occurred in Transition and Mediterranean sites but under the previous occurrence of highly severe wildfires. Specifically, long times after highly severe wildfires (> 30 years) increased fuel amount in conifer-dominated ecosystems in all bioregions analyzed, heightening susceptibility to extreme fire behavior.ConclusionsOur findings highlight that fire-prone ecosystems need adaptative management strategies to mitigate the effects of fire regime changes, but these actions should be specific to the climatic and ecological context.

Elucidating factors driving post-fire vegetation recovery in the Mediterranean forests using Landsat spectral metrics

Wildfires represent one of the primary disturbance agents in the Mediterranean, significantly affecting the ecological integrity of forests. Therefore, understanding the spatial patterns of post-fire vegetation recovery is crucial to improving forest restoration planning and assessing the regeneration capacity of different forest stands that have been impacted by wildfires. In this study, we analysed post-fire vegetation recovery rates within the context of fire severity, pre-fire vegetation, and post-fire climate conditions, for different Mediterranean forest classes, namely, Mediterranean pine, holm, deciduous oak forests, sclerophyllous vegetation, and thermophilous shrublands. Basilicata, in Italy, was chosen as a study area, as it represents a wide range of forests.The Relative Recovery Indicator (RRI) was derived from Normalized Burn Ratio (NBR) patterns extracted from 30-meter Landsat time series for different wildfires that occurred during the 2004–2016 within Google Earth Engine (GEE) environment. A Linear Mixed Model (LMM) was used to test the effect of the different variables on the RRI. Results showed a general decrease in recovery rate within five-years post-fire for each forest cover class, which is mainly related to pre- and post-fire conditions. Pre-fire vegetation conditions significantly influenced post-fire vegetation recovery, especially in sclerophyllous and deciduous oak forests. Post-fire climate conditions (e.g., temperature) were also important predictors of vegetation recovery explaining the variation in post-fire RRI patterns. The proposed method could provide new insights into the restoration and management of forest ecosystems in the Mediterranean.

Satellite‐derived prefire vegetation predicts variation in field‐based invasive annual grass cover after fire

AbstractAimsInvasion by annual grasses (IAGs) and concomitant increases in wildfire are impacting many drylands globally, and an understanding of factors that contribute to or detract from community resistance to IAGs is needed to inform postfire restoration interventions. Prefire vegetation condition is often unknown in rangelands but it likely affects variation in postfire invasion resistance across large burned scars. Whether satellite‐derived products like the Rangeland Analysis Platform (RAP) can fulfill prefire information needs and be used to parametrize models of fire recovery to inform postfire management of IAGs is a key question.MethodsWe used random forests to ask how IAG abundances in 669 field plots measured in the 2‐3 years following megafires in sagebrush steppe rangelands of western USA responded to RAP estimates of annual:perennial prefire vegetation cover, the effects of elevation, heat load, postfire treatments, soil moisture–temperature regimes, and land‐agency ratings of ecosystem resistance to invasion and resilience to disturbance.ResultsPostfire IAG cover measured in the field was % and RAP‐estimated prefire annual herbaceous cover was %. The random forest model had an R2 of 0.36 and a root‐mean‐squared error (RMSE) of 4.41. Elevation, postfire herbicide treatment, and prefire estimates from RAP for the ratio of annual:perennial and shrub cover were the most important predictors of postfire IAG cover. Threshold‐like relationships between postfire IAG cover and the predictors indicate that maintaining annual:perennial cover below 0.4 and shrub cover below <10% prior to wildfire would decrease invasion, at low elevations below 1400 m above sea level.ConclusionDespite known differences between RAP and field‐based estimates of vegetation cover, RAP was still a useful predictor of variation in IAG abundances after fire. IAG management is oftentimes reactive, but our findings indicate impactful roles for more inclusively addressing the exotic annual community, and focusing on prefire maintenance of annual:perennial herbaceous and shrub cover at low elevations.

Repeated fires in forested peatlands in sporadic permafrost zone in Western Canada

Wildfires play a crucial role in northern boreal peatland ecosystems, influencing the functioning of these ecosystems by affecting vegetation composition and biomass, peat accumulation patterns, and soil carbon stocks. Northern peatland ecosystems are under pressure due to climate warming and increasing anthropogenic stress. The frequency and severity of wildfires is predicted to increase in the coming years. Therefore, understanding long-term natural fire dynamics and their effect on peatland functionality will provide crucial information for peatland management and preservation policies. To investigate the long-term fire history of Western Canada and its effect on peat accumulation and vegetation succession, we analyzed macroscopic plant remains and charcoal within peat cores taken from five peatlands in the region. Records of the most recent fire events were derived from fire scars and documented fires in the study area. Regional long-term peatland fire patterns were examined by pooling together macroscopic charcoal records and calculating 100 year moving averages. All studied sites, except the northernmost one, demonstrated repeated fires throughout the past 1500 years, suggesting that fires have been an integral part of the peatland ecosystem in Western Canada. Compiled charcoal records indicated a peak in fire activity, with the highest abundance of charcoal for the period from the 1300s to the 1550s and decreasing fire activity during recent centuries. The clear and consistent post-fire increase in the abundance of Sphagnum mosses suggests a relatively rapid recovery of peatland ecosystems after burning. The regeneration pattern, where pre-fire vegetation repeatedly re-establishes, suggests that from a long-term perspective, fires do not necessarily have a negative effect on peatland functioning and peat accumulation. In conclusion, peatlands could remain as effective carbon sinks if their natural state is secured.

System-level feedbacks of active fire regimes in large landscapes

BackgroundClimate is a main driver of fire regimes, but recurrent fires provide stabilizing feedbacks at several spatial scales that can limit fire spread and severity—potentially contributing to a form of self-regulation. Evaluating the strength of these feedbacks in wildland systems is difficult given the spatial and temporal scales of observation required. Here, we used the REBURN model to directly examine the relative strengths of top-down and bottom-up drivers of fire over a 3000-year simulation period, within a 275,000-ha conifer-dominated landscape in north central Washington State, USA.ResultsWe found strong support for top-down and bottom-up spatial and temporal controls on fire patterns. Fire weather was a main driver of large fire occurrence, but area burned was moderated by ignition frequencies and by areas of limited fuels and fuel contagion (i.e., fire fences). Landscapes comprised of >40% area in fire fences rarely experienced large fire years. When large fires did occur during the simulation period, a recovery time of 100–300 years or more was generally required to recover pre-fire vegetation patterns.ConclusionsSimulations showed that interactions between fire weather, fuel contagion, topography, and ignitions manifest variability in fire size and severity patch size distributions. Burned and recovering vegetation mosaics provided functional stabilizing feedbacks, a kind of metastability, which limited future fire size and severity, even under extreme weather conditions. REBURN can be applied to new geographic and physiographic landscapes to simulate these interactions and to represent natural and culturally influenced fire regimes in historical, current, or future climatic settings.

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.

Wildland-urban interface typologies prone to high severity fires in Spain

Due to complex interactions between climate and land use changes, large forest fires have increased in frequency and severity over the last decades, impacting dramatically on biodiversity and society. In southern European countries affected by demographic challenges, fire risk and danger play special relevance at the wildland-urban interfaces (WUIs), where decision-making and land management have strong socio-ecological implications. WUIs have been historically typified according to both fire occurrence probability and settlement vulnerability, but those classifications lack generality regarding fire regime components. We aim to develop an integrated and comprehensive scheme for identifying the WUI typologies most at risk to fire severity across large territories. We selected fourteen large wildfires (over than 500 ha) occurred in Spain (2016–2021) containing different WUI scenarios. First, based on a building cartography and a multi-temporal series of Sentinel-2 imagery, each WUI was delimited and spatially characterized according to building density and pre-fire fuel characteristics (type, amount, and structure). Afterwards, a decision tree regression model was applied to identify the most relevant pre-fire vegetation parameters driving burn severity. The combined effect of the selected pre-fire vegetation drivers and the building density patterns on fire severity was evaluated using linear mixed models. Finally, the WUI typologies most prone to high burn severity were recognized using Tukey post-hoc tests. Results indicated that building density, land cover class and vegetation cover fraction determined fire severity in areas close to human settlements. Specifically, isolated, scattered and sparsely clustered buildings enclosed in a high-cover shrub matrix were the WUI typologies most susceptible to high-severity fires. These findings contribute to the development of appropriate strategies to minimize the risk of severe fires in WUIs and avoid potential losses of multiple ecosystem services valuable for society.

Implications of fire-induced evapotranspiration shifts for recharge-runoff generation and vegetation conversion in the western United States

Wildfire has been shown to increase, decrease, or have no detectable effect on actual evapotranspiration (ETa) fluxes in the western United States. Where disturbance-induced shifts are significant, source-water hydrology may be impacted as ETa constitutes the largest outgoing water flux in much of the arid West. We conducted pixel-scale analysis of 30-m ETa data and various meteorologic and landscape variables at 13 burn scars to understand how wildfire disturbance impacted hillslope-to-burn scar-scale hydrology and vegetation conversion. Significant fire-induced ETa reductions (between approximately −15 to −50%) were detected at nine burn scars through the tenth post-fire year, while ETa recovery rate varied substantially by ecoregion and pre-fire vegetation type. Along elevation gradients, both climate and land disturbance influenced the location of runoff/recharge generation zones, and more net water was generated from a snow-dominated burn scar in dry post-fire years than in wet pre-fire years. However, especially in arid locations where ETa is water-limited, compensatory ETa pathways may be more likely to dampen fire effects on total basin water yield where intact vegetation is located between the disturbance footprint and the basin outlet. Relationships between post-fire ETa shifts and early-successional vegetation conversion were also tracked. The majority of burn scars with significant fire-induced ETa reductions experienced conversion patterns typical of the western United States following stand-replacing disturbance, with forests converting to shrub/scrub and/or grassland/herbaceous cover through at least the end of the study period (eight to 15 years depending on date of the fire event). This could have important implications for high-elevation, snow-dominated watersheds – some of the most critical source water areas - as previous research indicates that wildfire activity is moving upslope and into vegetation communities that have not evolved to withstand fire. Finally, we show that much of the Colorado River Basin’s high-yield source water areas are vulnerable to the fire-induced ETa reductions and vegetation conversion observed herein. As such, water managers in the Colorado River Basin can anticipate changes in burn scar hydrology and snowpack mechanics following fire disturbance.

Characterizing spatial burn severity patterns of 2016 Chimney Tops 2 fire using multi-temporal Landsat and NEON LiDAR data

The Chimney Tops 2 wildfire (CT2) in 2016 at Great Smoky Mountains National Park (GSMNP) was recorded as the largest fire in GSMNP history. Understanding spatial patterns of burn severity and its underlying controlling factors is essential for managing the forests affected and reducing future fire risks; however, this has not been well understood. Here, we formulated two research questions: 1) What were the most important factors characterizing the patterns of burn severity in the CT2 fire? 2) Were burn severity measures from passive and active optical remote sensing sensors providing consistent views of fire damage? To address these questions, we used multitemporal Landsat- and lidar-based burn severity measures, i.e., relativized differenced Normalized Burn Ratio (RdNBR) and relativized differenced Mean Tree Height (RdMTH). A random forest approach was used to identify key drivers in characterizing spatial variability of burn severity, and the partial dependence of each explanatory variable was further evaluated. We found that pre-fire vegetation structure and topography both play significant roles in characterizing heterogeneous mixed burn severity patterns in the CT2 fire. Mean tree height, elevation, and topographic position emerged as key factors in explaining burn severity variation. We observed generally consistent spatial patterns from Landsat- and lidar-based burn severity measures. However, vegetation type and structure-dependent relations between RdNBR and RdMTH caused locally inconsistent burn severity patterns, particularly in high RdNBR regions. Our study highlights the important roles of pre-fire vegetation structure and topography in understanding burn severity patterns and urges to integrate both spectral and structural changes to fully map and understand fire impacts on forest ecosystems.

Effects of Pre-Fire Vegetation on the Post-Fire Plant Community Response to Wildfire along a Successional Gradient in Western Juniper Woodlands

Western juniper was often historically restricted to fire refugia such as rocky outcrops but has since Euro-American settlement expanded into areas previously dominated by sagebrush steppe. Wildfires in developed woodlands have been rare. In 2007, the Tongue-Crutcher Wildland Fire burned 18,890 ha in southwestern Idaho along a woodland development gradient, providing unique research opportunities. To assess fire effects on vascular plants, field data were collected in 2012/2013 and 2019/2020. Species richness was uniform along the sere, while species diversity declined in late woodland stages attributed to juniper dominance. The greatest changes in species composition following fire occurred in later woodland development phases. Herbaceous vegetation increased following fire, but sagebrush cover was still lower in burned plots 12–13 years post-fire. Many stands dominated by juniper pre-fire became dominated by snowbrush ceanothus post-fire. Juniper seedlings were observed post-fire, indicating that juniper will reoccupy the area. Our research demonstrates resilience to fire and resistance to annual grasses particularly in early successional stages, which provides opportunities for fire use as a management tool on cool and moist ecological sites. Loss of old-growth juniper to wildfire underlines the importance of maintaining and provisioning for future development of some old growth on the landscape given century-long recovery times.

Towards a whole-system framework for wildfire monitoring using Earth observations.

Fire seasons have become increasingly variable and extreme due to changing climatological, ecological, and social conditions. Earth observation data are critical for monitoring fires and their impacts. Herein, we present a whole-system framework for identifying and synthesizing fire monitoring objectives and data needs throughout the life cycle of a fire event. The four stages of fire monitoring using Earth observation data include the following: (1) pre-fire vegetation inventories, (2) active-fire monitoring, (3) post-fire assessment, and (4) multi-scale synthesis. We identify the challenges and opportunities associated with current approaches to fire monitoring, highlighting four case studies from North American boreal, montane, and grassland ecosystems. While the case studies are localized to these ecosystems and regional contexts, they provide insights for others experiencing similar monitoring challenges worldwide. The field of remote sensing is experiencing a rapid proliferation of new data sources, providing observations that can inform all aspects of our fire monitoring framework; however, significant challenges for meeting fire monitoring objectives remain. We identify future opportunities for data sharing and rapid co-development of information products using cloud computing that benefits from open-access Earth observation and other geospatial data layers.

Post-Fire Forest Vegetation State Monitoring through Satellite Remote Sensing and In Situ Data

Wildfires have significant environmental and socio-economic impacts, affecting ecosystems and people worldwide. Over the coming decades, it is expected that the intensity and impact of wildfires will grow depending on the variability of climate parameters. Although Bulgaria is not situated within the geographical borders of the Mediterranean region, which is one of the most vulnerable regions to the impacts of temperature extremes, the climate is strongly influenced by it. Forests are amongst the most vulnerable ecosystems affected by wildfires. They are insufficiently adapted to fire, and the monitoring of fire impacts and post-fire recovery processes is of utmost importance for suggesting actions to mitigate the risk and impact of that catastrophic event. This paper investigated the forest vegetation recovery process after a wildfire in the Ardino region, southeast Bulgaria from the period between 2016 and 2021. The study aimed to present a monitoring approach for the estimation of the post-fire vegetation state with an emphasis on fire-affected territory mapping, evaluation of vegetation damage, fire and burn severity estimation, and assessment of their influence on vegetation recovery. The study used satellite remotely sensed imagery and respective indices of greenness, moisture, and fire severity from Sentinel-2. It utilized the potential of the landscape approach in monitoring processes occurring in fire-affected forest ecosystems. Ancillary data about pre-fire vegetation state and slope inclinations were used to supplement our analysis for a better understanding of the fire regime and post-fire vegetation damages. Slope aspects were used to estimate and compare their impact on the ecosystems’ post-fire recovery capacity. Soil data were involved in the interpretation of the results.

Fire severity and the legacy of mountain pine beetle outbreak: high-severity fire peaks with mixed live and dead vegetation

Bark beetle outbreaks and wildfires are two of the most prevalent disturbances that influence tree mortality, regeneration, and successional trajectories in western North American forests. Subboreal forests have experienced broad overlaps in these disturbances, and recent wildfires have burned through landscapes with substantial tree mortality from prior outbreaks. This study investigated how fuel conditions associated with mountain pine beetle outbreaks influence the probability of high burn severity (i.e. stand-replacing fire) across a range of fire weather conditions in subboreal forests of central interior British Columbia, Canada. We focused on three large fires that occurred in 2012, 2013, and 2014. We characterized outbreak severity, outbreak-influenced prefire vegetation, and subsequent burn severity using Landsat spectral vegetation indices, high-resolution imagery, and field observations. Substantial portions of the prefire landscape contained mixtures of live and dead vegetation created by variable beetle damage and vegetation response—spatial patterns that are related to, but distinct from, peak outbreak severity. We evaluated drivers—fuels, weather, and topography—of high-severity fire under ‘extreme,’ ‘moderate,’ and ‘benign’ fire weather conditions (i.e. burning conditions) using Boosted Regression Trees. While fire weather was a primary driver in most cases, prefire vegetation was an influential predictor variable across all burning conditions, and the probability of high-severity fire was highest when prefire vegetation was a mixture of tree mortality from bark beetles and live vegetation. Thus, while weather and drought are important drivers of wildfires in subboreal forests, bottom-up drivers of elevation and vegetation, including the fuel legacies of bark beetle outbreaks, are crucial factors influencing high-severity burning. The legacy of recent bark beetle outbreaks will continue for decades on these landscapes, affecting fuel structures, future wildfires, forest dynamics, and the broader social-ecological systems of the region.

Key Factors Affecting the Initial Regeneration Following Forest Fires

The independent and relative importance of ten variables related to fire, site, and prefire vegetation affecting the initial regeneration after fires was analyzed using the Random Forest statistical technique. The technique supplemented with the decision tree method was developed. This study was performed in prefire Pinus densiflora-dominated forests affected by large-scale fires in Korea. Among the ten variables, the basal area of prefire resprouter trees showed an overwhelmingly high contribution on the postfire regeneration (model improvement ratio (MIR) = 1.00). Consequently, stands with a high abundance of the resprouters showed rapid regeneration. Site factors including elevation, aspect, and slope had an MIR of 0.71, 0.63, and 0.57, respectively, while those for flame ratio and burn severity were 0.39 and 0.20. We revealed that the legacy of prefire vegetation had the most significant effect on the initial regeneration of stands, while site-variables played a lesser role, and fire-variables such as burn severity had a relatively minor effect. This technique was an important tool for explaining ecological phenomena involving the simultaneous action of multiple factors. Understanding the priorities of factors affecting the fast regeneration would be useful in establishing a restoration plan in forests where soil erosion is an issue following a fire.

Prefire Vegetation Structure of High Severity Wildfires in Nonherbaceous‐Dominated Rangelands in the Western United States

AbstractHigh severity rangeland wildfires have enormous negative impacts on human safety and infrastructure and may have long‐term consequences on ecosystem structure, function, and services, across the vast rangelands in the western United States. We explored prefire vegetation structure of all high severity wildfires, as well as associated areas with low and moderate burn severity, in nonherbaceous‐dominated rangelands in western United States from 1985 to 2018, based on fractional cover of annual forbs and grasses, perennial forbs and grasses, shrubs and trees from the Rangeland Analysis Platform. These wildfires were grouped into nine clusters using Expectation Maximization algorithms and patterns of prefire vegetation structure and the major vegetation types were assessed. High woody cover is a common characteristic of the prefire vegetation structure. Four of the clusters, which accounted for more than 70% of the areas of high severity wildfires, were characterized by high (∼50%) woody cover in prefire vegetation structure. The areas with high severity burns also had significantly higher woody cover than the areas with low or moderate severity burns in eight of the nine clusters. Some of these ecosystems can have abundant fine fuel, structural connectivity such as fuel laddering, and/or highly flammable materials, which can facilitate the ignition of the woody crowns, leading to high severity wildfires. Management strategies (such as prescribed burning) that can decrease woody cover and reduce standing dead vegetation and fuel laddering could be effective in reducing the risk and extent of high severity wildfires on rangelands in the western United States.

Modeling of fire spread in sagebrush steppe using FARSITE: an approach to improving input data and simulation accuracy

BackgroundModel simulations of wildfire spread and assessments of their accuracy are needed for understanding and managing altered fire regimes in semiarid regions. The accuracy of wildfire spread simulations can be evaluated from post hoc comparisons of simulated and actual wildfire perimeters, but this requires information on pre-fire vegetation fuels that is typically not available. We assessed the accuracy of the Fire-Area Simulator (FARSITE) model parameterized with maps of fire behavior fuel models (FBFMs) obtained from the widely used LANDFIRE, as well as alternative means which utilized the classification of Rangeland Analysis Platform (RAP) satellite-derived vegetation cover maps to create FBFM maps. We focused on the 2015 Soda wildfire, which burned 113,000 ha of sagebrush steppe in the western USA, and then assessed the transferability of our RAP-to-FBFM selection process, which produced the most accurate reconstruction of the Soda wildfire, on the nearby 2016 Cherry Road wildfire.ResultsParameterizing FARSITE with maps of FBFMs from LANDFIRE resulted in low levels of agreement between simulated and observed area burned, with maximum Sorensen’s coefficient (SC) and Cohen’s kappa (K) values of 0.38 and 0.36, respectively. In contrast, maps of FBFMs derived from unsupervised classification of RAP vegetation cover maps led to much greater simulated-to-observed burned area agreement (SC = 0.70, K = 0.68). The FBFM map that generated the greatest simulated-to-observed burned area agreement for the Soda wildfire was then used to crosswalk FBFMs to another nearby wildfire (2016 Cherry Road), and this FBFM selection led to high FARSITE simulated-to-observed burned area agreement (SC = 0.80, K = 0.79).ConclusionsUsing RAP to inform pre-fire FBFM selection increased the accuracy of FARSITE simulations compared to parameterization with the standard LANDFIRE FBFM maps, in sagebrush steppe. Additionally, the crosswalk method appeared to have regional generalizability. Flanking and backfires were the primary source of disagreements between simulated and observed fire spread in FARSITE, which are sources of error that may require modeling of lateral heterogeneity in fuels and fire processes at finer scales than used here.

Analyzing Resilience in the Greater Yellowstone Ecosystem after the 1988 Wildfire in the Western U.S. Using Remote Sensing and Soil Database

The 1988 Yellowstone fire altered the structure of the local forest ecosystem and left large non-recovery areas. This study assessed the pre-fire drivers and post-fire characteristics of the recovery and non-recovery areas and examined possible reasons driving non-recovery of the areas post-fire disturbance. Non-recovery and recovery areas were sampled with 44,629 points and 77,501 points, from which attribute values related to topography, climate, and subsequent soil conditions were extracted. We calculated the 1988 Yellowstone fire burn thresholds using the differenced Normalized Burn Ratio (dNBR) and official fire maps. We used a burn severity map from the US Forest Service to calculate the burn severity values. Spatial regressions and Chi-Square tests were applied to determine the statistically significant characteristics of a lack of recovery. The non-recovery areas were found to cover 1005.25 km2. Among 11 variables considered as potential factors driving recovery areas and 13 variables driving non-recovery areas, elevation and maximum temperature were found to have high Variance Inflation Factors (4.73 and 4.72). The results showed that non-recovery areas all experienced severe burns and were located at areas with steeper slopes (13.99°), more precipitation (871.73 mm), higher pre-fire vegetation density (NDVI = 0.38), higher bulk density (750.03 kg/m3), lower soil organic matter (165.61 g/kg), and lower total nitrogen (60.97 mg/L). Chi-square analyses revealed statistically different pre-fire forest species (p < 0.01) and soil order (p < 0.01) in the recovery and non-recovery areas. Although Inceptisols dominated in both recovery and non-recovery areas, however, the composition of Mollisols was higher in the non-recovery areas (14%) compared to the recovery areas (11%). This indicated the ecological memory of the non-recovery site reverting to grassland post-disturbance. Unlike conventional studies only focusing on recovery areas, this study analyzed the non-recovery areas and found the key characteristics that make a landscape not resilient to the 1988 Yellowstone fire. The significant effects of elevation, precipitation, and soil pH on recovery may be significant to the forest management and forest resilience in the post-fire period.

Machine learning to understand patterns of burn severity from the SCU Lightning Complex Fires of August 2020

The SCU Lightning Complex Fire started on 16 August 2020 and burned more than 395,000 acres of woodlands and grasslands in six California counties. Satellite images of pre-fire green vegetation biomass from both 2020 springtime (moist) and summertime (drier) periods, along with slope and aspect were used as predictors of burn severity patterns on the SCU Complex landscape using machine learning algorithms. The main finding from this analysis was that the overall burn severity patterns of the SCU Complex fires could be predicted from pre-fire vegetation biomass, slope, and aspect model input variables with high accuracies of between 50% and 80% using Random Forest machine learning techniques. The August and April biomass cover variables had the highest feature importance values. It can be concluded that the amount of dry biomass present at a given location was essential to predict how severely and completely the 2020 fires burned the vegetation cover and surface soils across this landscape.

How Much of a Pixel Needs to Burn to Be Detected by Satellites? A Spectral Modeling Experiment Based on Ecosystem Data from Yellowstone National Park, USA

We present a simple modeling technique based on linear spectral mixture analysis to assess satellite detectability of sub-pixel burned area. Pixel observations are modeled using a linear combination of pure land covers, called endmembers. We executed an experiment using spectral data from Yellowstone National Park, USA. Using endmember samples from spectral libraries, pixel samples were assessed on burn detectability using the widely used differenced Normalized Burn Ratio (dNBR). While individual samples yielded differing results for Landsat 8, Sentinel-2, and the Moderate Resolution Imaging Spectroradiometer (MODIS), the average park-wide detectability of burned area was consistent across satellites. For the commonly used dNBR threshold of 0.15, the results indicated that detectability is reached when around a quarter of a pixel’s area is burned. However, a significant percentage of the modeled burned pixels remained undetectable, especially those with low pre-fire vegetation cover. This has consequences for burned area estimates, as smaller fires in sparsely vegetated terrain may remain undetected in moderate resolution burned area products.