Fire

Tropical peat swamp forests, found mainly in Southeast Asia, have been threatened by recurring El Niño fires. Repeated burnings form a complex and heterogeneous landscape comprising a mosaic of burned patches of different fire frequencies, requiring fine-scale assessment to understand their impact. We examined the impact of the El Niño fires of 1998 and 2003 on a tropical peat swamp forest in northern Borneo, with the combined use of high and very high-resolution satellite images. Object-based and pixel-based classifications were compared to classify a QuickBird image. Burned patches of different fire frequencies were derived based on unsupervised classification of the principal components of multitemporal Normalized Difference Water Index (NDWI) data. The results show that the object-based classification was more accurate than the pixel-based classification for generating a detailed land cover map. Fire frequency had a severe impact on the number of burned patches and the residual forest cover. Larger patch area retained more residual forest cover for the burned patches. Forest structure of burned-twice patches was more severely altered compared to burned-once patches. Two burned-once patches had a relatively promising recovery potential by natural regeneration due to higher residual forest cover, a vast number of large trees, and aboveground biomass. Except for the largest patch, rehabilitation seemed inevitable for burned-twice patches. This approach can be applied to assess the impact of multiple fires on other forest types for better post-fire forest management. Full article

Soil changes, including landslides and erosion, are some of the most prominent post-fire effects in Mediterranean ecosystems. Landslide detection and monitoring play an essential role in mitigation measures. We tested two different methodologies in five burned sites with different characteristics in Central Greece. We compared Unmanned Aerial Vehicles (UAV)-derived high-resolution Digital Surface Models and point clouds with terrestrial Light Detection and Ranging (LiDAR)-derived point clouds to reveal new cracks and monitor scarps of pre-existing landslides. New cracks and scarps were revealed at two sites after the wildfire, measuring up to 27 m in length and up to 25 ± 5 cm in depth. Pre-existing scarps in both Kechries sites appeared to be active, with additional vertical displacements ranging from 5–15 ± 5 cm. In addition, the pre-existing landslide in Magoula expanded by 8%. Due to vegetation regrowth, no changes could be detected in the Agios Stefanos pre-existing landslide. This high-spatial-resolution mapping of slope deformations can be used as landslide precursor, assisting prevention measures. Considering the lack of vegetation after wildfires, UAV photogrammetry has great potential for tracing such early landslide indicators and is more efficient for accurately recording soil changes. Full article

The need for sustainable energy has incentivized the use of alternative fuels such as light alcohols. In this work, reduced chemistry mechanisms for the prediction of fires (pool fire, tank fire, and flash fire) for two primary alcohols—methanol and ethanol—were developed, aiming to integrate the detailed kinetic model into the computational fluid dynamics (CFD) model. The model accommodates either the pure reactants and products or other intermediates, including soot precursors (C₂H₂, C₂H₄, and C₃H₃), which were identified via sensitivity and reaction path analyses. The developed reduced mechanism was adopted to predict the burning behavior in a 3D domain and for the estimation of the product distribution. The agreement between the experimental data from the literature and estimations resulting from the analysis performed in this work demonstrates the successful application of this method for the integration of kinetic mechanisms and CFD models, opening to an accurate evaluation of safety scenarios and allowing for the proper design of storage and transportation systems involving light alcohols. Full article

A fire during manned space exploration can cause serious casualties and disrupt the mission if the initial response is delayed. Therefore, measurement technology that can detect fire in the early stage of ignition is important. There have been a number of works that investigate the smoke behaviors in microgravity as the foundation for a reliable method for sensing a fire during spaceflight. For space missions to the outer planets, however, a strategy of detecting smoke as an indicator of fire should be adjusted to cover the fire scenario that can be greatly affected by changes in gravity (microgravity, lunar, Mars, and Earth gravity). Therefore, as a preliminary study on fire detectors of the manned pressurized module, the present study examined the smoke particle behavior and detection characteristics with respect to changes in gravity using numerical analysis. In particular, the effects of the combination of buoyancy and ventilation flow on the smoke particle movement pattern was investigated to further improve the understanding of the fire detection characteristics of the smoke detector, assuming that a fire occurred in different gravity environments inside the pressurized module. To this end, we modeled the internal shape of Destiny and performed a series of numerical analysis using the Fire Dynamics Simulator (FDS). The findings of this study can provide basic data for the design of a fire detection system for manned space exploration modules. Full article

The spread of flammable invasive grasses, woody plant encroachment, and enhanced aridity have interacted in many grasslands globally to increase wildfire activity and risk to valued assets. Annual variation in the abundance and distribution of fine-fuel present challenges to land managers implementing prescribed burns and mitigating wildfire, although methods to produce high-resolution fuel estimates are still under development. To further understand how prescribed fire and wildfire influence fine-fuels in a semi-arid grassland invaded by non-native perennial grasses, we combined high-resolution Sentinel-2A imagery with in situ vegetation data and machine learning to estimate yearly fine-fuel loads from 2015 to 2020. The resulting model of fine-fuel corresponded to field-based validation measurements taken in the first (R${}^2$ = 0.52, RMSE = 218 kg/ha) and last year (R${}^2$ = 0.63, RMSE = 196 kg/ha) of this 6-year study. Serial prediction of the fine-fuel model allowed for an assessment of the effect of prescribed fire (average reduction of −80 kg/ha 1-year post fire) and wildfire (−260 kg/ha 1-year post fire) on fuel conditions. Post-fire fine-fuel loads were significantly lower than in unburned control areas sampled just outside fire perimeters from 2015 to 2020 across all fires (t = 1.67, p < 0.0001); however, fine-fuel recovery occurred within 3–5 years, depending upon burn and climate conditions. When coupled with detailed fuels data from field measurements, Sentinel-2A imagery provided a means for evaluating grassland fine-fuels at yearly time steps and shows high potential for extended monitoring of dryland fuels. Our approach provides land managers with a systematic analysis of the effects of fire management treatments on fine-fuel conditions and provides an accurate, updateable, and expandable solution for mapping fine-fuels over yearly time steps across drylands throughout the world. Full article

Biomass burning is a coupled exothermic/endothermic system that transfers carbon in several forms to the atmosphere, ultimately leaving mineral ash. The exothermic phases include flaming and smoldering, which produce the heat that drives the endothermic processes. The endothermic components include pre-heating and pyrolysis, which produce the fuel consumed by flaming and smoldering. These components can be broadly distinguished from each other based on temperature. For several years, we have researched the subpixel analysis of two temperature phases present in fire pixels detected in nighttime VIIRS data. Here, we present the flaming subtractive method, with which we have successfully derived temperatures and source areas for two infrared (IR) emitters and a cooler background. This is developed as an add-on to the existing VIIRS nightfire algorithm version 3 (VNF v.3) which uses Planck curve fitting to calculate temperatures and source areas for a single IR emitter and background. The flaming subtractive method works with data collected in four spectral ranges: near-infrared (NIR), short-wave infrared (SWIR), mid-wave infrared (MWIR) and long-wave infrared (LWIR). With sunlight eliminated, the NIR and SWIR radiances can be fully attributed to the primary IR emitter. The analysis begins with Planck curve modeling for the primary emitter based on the NIR and SWIR radiances, yielding temperature, source area and primary emitter radiances in all spectral bands. The primary emitter radiances are subtracted from each spectral band and then the residual radiance is analyzed for a secondary IR emitter and the background. Spurious results are obtained in pixels lacking a discernable secondary emitter. These misfit pixels revert back to the single IR emitter analysis of VNF v.3. In tests run for two California megafires, we found that the primary emitters straddle the temperature ranges for flaming and smoldering, the exothermic portions of biomass burning, which are apparently commingled on the ground. The secondary emitter temperatures span 350–750 K, corresponding to pre-heating and slow pyrolysis. The natural gas flare test case had few numbers of successful secondary emitter retrievals and a wide range of secondary emitter temperatures. The flaming subtractive analysis is the key addition to VNF version 4, which will commence production later in 2021. In 2022, we will seek validation of the VNF v.4 from nighttime Landsat and other data sources. Full article

During the 2017 wildfire season in Portugal, unprecedented episodes burned 6% of the country’s area and underscored the need for a long-term comprehensive solution to mitigate future wildfire disasters. In this study, we built and calibrated a national-scale fire simulation system including the underlying fuels and weather data and used the system to quantify wildfire exposure to communities and natural areas. We simulated 10,000 fire season replicates under extreme weather to generate 1.6 million large wildfire perimeters and estimate annual burn probability and fire intensity at 100 m pixel resolution. These outputs were used to estimate wildfire exposure to buildings and natural areas. The results showed a fire exposure of 10,394 structures per year and that 30% of communities accounted for 82% of the total. The predicted burned area in natural sites was 18,257 ha yr⁻¹, of which 9.8% was protected land where fuel management is not permitted. The main burn probability hotspots were in central and northern regions. We highlighted vital priorities to safeguard the most vulnerable communities and promote landscape management programs at the national level. The results can be useful to inform Portugal’s new national plan under implementation, where decision-making is based on a probabilistic methodology. The core strategies include protecting people and infrastructure and wildfire management. Finally, we discuss the next steps necessary to improve and operationalize the framework developed here. The wildfire simulation modeling approach presented in this study is extensible to other fire-prone Mediterranean regions where predicting catastrophic fires can help anticipate future disasters. Full article

In this study, we investigate a novel application of the photogrammetric monoplotting technique for assessing wildfires. We demonstrate the use of the software program WSL Monoplotting Tool (MPT) to georeference operational oblique aerial wildfire photographs taken during airtanker response in the early stages of fire growth. We located the position of the fire front in georeferenced pairs of photos from five fires taken 31–118 min apart, and calculated the head fire spread distance and head fire rate of spread (HROS). Our example photos were taken 0.7 to 4.7 km from fire fronts, with camera angles of incidence from −19° to −50° to image centre. Using high quality images with detailed landscape features, it is possible to identify fire front positions with high precision; in our example data, the mean 3D error was 0.533 m and the maximum 3D error for individual fire runs was less than 3 m. This resulted in a maximum HROS error due to monoplotting of only ~0.5%. We then compared HROS estimates with predictions from the Canadian Fire Behavior Prediction System, with differences mainly attributed to model error or uncertainty in weather and fuel inputs. This method can be used to obtain observations to validate fire spread models or create new empirical relationships where databases of such wildfire photos exist. Our initial work suggests that monophotogrammetry can provide reproducible estimates of fire front position, spread distance and rate of spread with high accuracy, and could potentially be used to characterize other fire features such as flame and smoke plume dimensions and spotting. Full article

Over the past century the size and severity of wildfires, as well as post-fire recovery processes (e.g., seedling establishment), have been altered from historical levels due to management policies and changing climate. Tree seedling establishment and growth drive future overstory tree dynamics after wildfire. Post-fire tree regeneration can be highly variable depending on burn severity, pre-fire forest condition, tree regeneration strategies, and climate; however, few studies have examined how different abiotic and biotic factors impact seedling density and growth and the interactions among those factors. We measured seedling density and height growth in the period 2015–2016 on three wildfires that burned in ponderosa pine (Pinus ponderosa) forests in the period 2000–2007 across broad environmental and burn severity gradients. Using a non-parametric multiplicative regression model, we found that downed woody fuel load, duff depth, and fall precipitation best explained variation in seedling density, while the distance to nearest seed tree, a soil productivity index, duff depth, and spring precipitation as snow best explained seedling height growth. Overall, results highlight the importance of burn severity and post-fire climate in tree regeneration, although the primary factors influencing seedling density and height growth vary. Drier conditions and changes to precipitation seasonality have the potential to influence tree establishment, survival, and growth in post-fire environments, which could lead to significant impacts for long-term forest recovery. Full article

Big Pine Key, Florida, is home to one of Earth’s largest swaths of the critically-endangered dry forests. Known as pine rocklands, this fire-adapted ecosystem must experience regular fire to persist and remain healthy. Pine rocklands are composed of a sole canopy species: the South Florida slash pine (Pinus elliottii var. densa), along with a dense understory of various woody and herbaceous species, and minimal surface moisture and soil development. Slash pine record wildfire activity of the surrounding area via fire scars preserved within the annual tree rings formed by the species. Our study used dendrochronology to investigate the fire history of the pine rocklands on Big Pine Key, specifically within and around the National Key Deer Refuge (NKDR) because it is the largest segment of unfragmented pine rockland on the island. We combined the results found within the NKDR with those of a previous study completed in 2011, and incorporated historical documents and reports of prescribed and natural fires through November 2019 into our evaluation of fire history on Big Pine Key. We conclude that prescribed burning practices are vital to truly restore natural fire behavior, and repeated burning on these islands in the future must be prioritized. Full article

Fire is a powerful natural disturbance influencing vegetation patterns across landscapes. Recent transitions from mixed-species forests to post-fire shrublands after severe wildfire is an increasingly prevalent phenomenon in pine-oak and conifer forest ecosystems in southwestern North America. However, we know little about how variation in fire severity influences other common forest types in the region. In this study, we evaluated fire-induced changes in woody plant community composition and forest structure in Chiricahua Mountains in southeastern Arizona in the United States that hosts a diverse set of vegetation types. Cluster analysis of the pre-fire vegetation data identified three dominant pre-fire vegetation types including juniper woodland, piñon forest, and pine-oak forest. All vegetation types experienced significant tree mortality across a wide range of size classes and species, from forests to shrublands. The magnitude of change within sample plots varied with fire severity, which was mediated by topography. Significant shifts in dominance away from coniferous obligate seeder trees to resprouting hardwoods and other shrubs occurred across all vegetation types in response to the fire. Regeneration from seed can be episodic, but projected increases in aridity and fire frequency may promote continued dominance by hardwoods and fire- and drought-resistant shrub communities, which is a regional forest management concern as wildfire size and severity continue to increase throughout the southwestern USA. Full article

Increased prescribed burning is needed to provide a diversity of public benefits, including wildfire hazard reduction, improved forest resilience, and biodiversity conservation. Though rare, escaped burns or significant smoke impacts may result in harm to individuals and property. Liability for potential damages reduces the willingness of fire managers to expand the practice, particularly where the wildland–urban interface creates the greatest risk. Across the United States of America, efforts have been made to reduce prescribed fire-related risks through statutory reform, training and certification requirements, and private insurance. An increasing number of states have adopted the liability standard of gross negligence to protect prescribed fire practitioners. When liability relief is tied to best practices or burn manager certification, risk to the public from potential prescribed fire impacts is reduced. Under this model, however, those harmed by prescribed fire may have little legal recourse for compensation from losses. Here, we explore the pairing of a mechanism to compensate losses while limiting liability for practitioners who use best management practices. Specifically, we assess the suitability of using a catastrophe fund in conjunction with adoption of gross negligence standards, modeled after other natural hazards examples. This model could ensure public support and sustain and expand prescribed fire in many fire-prone landscapes. Full article

Vegetation recovery after the large wildfires that occurred in central Portugal in 2017 is assessed in the present study. These wildfires had catastrophic consequences, among which were human losses and a vast extent of forest devastation. Landsat 8 OLI images were used to obtain the land use and cover (LUC) classification and to determine the Normalized Burned Ratio index (NBR) for different times. NBR results were used to determine the difference between the NBR (dNBR) before the fire (pre-fire) and after the fire (post-fire), and the results obtained were cross-checked with the LUC. The dNBR results were cross-referenced with biophysical data to identify the characteristics of the most important burned areas in need of vegetative recovery. The results showed the spatial differentiation in vegetation recovery, highlighting different factors in this process, in particular the type of vegetation (the predominant species and bank of seeds available), the biophysical characteristics of burned areas (for example, the soil type in burned areas), the continentality gradient, and the climate conditions. The vegetation recovery was differentiated by time according to the species present in the burned areas pre-fire. In general, shrubland recovery was faster than that of tree species, and the recovery was more marked for species that were regenerated by the rhizomes after fire. The recovery process was also influenced by the season in the study area. It was more efficient in the spring and at the beginning of the summer, highlighting the importance of optimal conditions needed for vegetation regeneration, such as the temperature and precipitation (soil humidity and water availability for growing plants). The results of this research are important to forest planning: the definition of the strategies for the ecosystems’ recovery, the adoption of preventive measures to avoid the occurrence of large wildfires, the modification of anthropogenic practices, etc. Full article

Forest fires are still a large concern in several countries due to the social, environmental and economic damages caused. This paper aims to show the design and validation of a proposed system for the classification of smoke columns with object detection and a deep learning-based approach. This approach is able to detect smoke columns visible below or above the horizon. During the dataset labelling, the smoke object was divided into three different classes, depending on its distance to the horizon, a cloud object was also added, along with images without annotations. A comparison between the use of RetinaNet and Faster R-CNN was also performed. Using an independent test set, an F1-score around 80%, a G-mean around 80% and a detection rate around 90% were achieved by the two best models: both were trained with the dataset labelled with three different smoke classes and with augmentation; Faster R-CNNN was the model architecture, re-trained during the same iterations but following different learning rate schedules. Finally, these models were tested in 24 smoke sequences of the public HPWREN dataset, with 6.3 min as the average time elapsed from the start of the fire compared to the first detection of a smoke column. Full article

We present an analysis of the spatio-temporal trends derived from long-term burned area (BA) data series. Two global BA products were included in our analysis, the FireCCI51 (2001–2019) and the FireCCILT11 (1982–2018) datasets. The former was generated from Moderate Resolution Imaging Spectroradiometer (MODIS) 250 m reflectance data, guided by 1 km active fires. The FireCCILT11 dataset was generated from Land Long-Term Data Record data (0.05°), which provides a consistent time series for Advanced Very High Resolution Radiometer images, acquired from the NOAA satellite series. FireCCILT11 is the longest time series of a BA product currently available, making it possible to carry out temporal analysis of long-term trends. Both products were developed under the FireCCI project of the European Space Agency. The two datasets were pre-processed to correct for temporal autocorrelation. Unburnable areas were removed and the lack of the FireCCILT11 data in 1994 was examined to evaluate the impact of this gap on the BA trends. An analysis and comparison between the two BA products was performed using a contextual approach. Results of the contextual Mann-Kendall analysis identified significant trends in both datasets, with very different regional values. The long-term series presented larger clusters than the short-term ones. Africa displayed significant decreasing trends in the short-term, and increasing trends in the long-term data series, except in the east. In the long-term series, Eastern Africa, boreal regions, Central Asia and South Australia showed large BA decrease clusters, and Western and Central Africa, South America, USA and North Australia presented BA increase clusters. Full article

The probability of extreme events such as an earthquake, fire or blast occurring during the lifetime of a structure is relatively low but these events can cause serious damage to the structure as well as to human life. Due to the significant consequences for occupant and structural safety, an accurate analysis of the response of structures exposed to these events is required for their design. Some extreme events may occur as a consequence of another hazard, for example, a fire may occur due to the failure of the electrical system of a structure following an earthquake. In such circumstances, the structure is subjected to a multi-hazard loading scenario. A post-earthquake fire (PEF) is one of the major multi-hazard events that is reasonably likely to occur but has been the subject of relatively little research in the available literature. In most international design codes, structures exposed to multi-hazards scenarios such as earthquakes, which are then followed by fires are only analysed and designed for as separate events, even though structures subjected to an earthquake may experience partial damage resulting in a more severe response to a subsequent fire. Most available analysis procedures and design codes do not address the association of the two hazards. Thus, the design of structures based on existing standards may contribute to a significant risk of structural failure. Indeed, a suitable method of analysis is required to investigate the behaviour of structures when exposed to sequential hazards. In this paper, a multi-hazard analysis approach is developed, which considers the damage caused to structures during and after an earthquake through a subsequent thermal analysis. A methodology is developed and employed to study the nonlinear behaviour of a steel framed structure under post-earthquake fire conditions. A three-dimensional nonlinear finite element model of an unprotected steel frame is developed and outlined. Full article

New physics-based fire behavior models are poised to revolutionize wildland fire planning and training; however, model testing against field conditions remains limited. We tested the ability of QUIC-Fire, a fast-running and computationally inexpensive physics-based fire behavior model to numerically reconstruct a large wildfire that burned in a fire-excluded area within the New York–Philadelphia metropolitan area in 2019. We then used QUIC-Fire as a tool to explore how alternate hypothetical management scenarios, such as prescribed burning, could have affected fire behavior. The results of our reconstruction provide a strong demonstration of how QUIC-Fire can be used to simulate actual wildfire scenarios with the integration of local weather and fuel information, as well as to efficiently explore how fire management can influence fire behavior in specific burn units. Our results illustrate how both reductions of fuel load and specific modification of fuel structure associated with frequent prescribed fire are critical to reducing fire intensity and size. We discuss how simulations such as this can be important in planning and training tools for wildland firefighters, and for avenues of future research and fuel monitoring that can accelerate the incorporation of models like QUIC-Fire into fire management strategies. Full article

Failures during the drilling and exploitation of hydrocarbons that result in catastrophic offshore oil and gas accidents are relatively rare but if they occur the consequences can be catastrophic in terms of loss of life and environmental damage. Therefore, to gain insight into their prevention, the largest major offshore oil and gas accidents, those with more than 10 fatalities or with a large environmental impact, are analyzed in this article. Special attention is placed on fire as a cause and a consequence. Relevant technological and legislative changes and updates regarding safety that have followed such accidents and that can prevent potential future similar misfortunes are evaluated. Two main approaches to safety are compared: (1) the American prescriptive vs. (2) the European goal-oriented approach. The main causes of accidents are tested statistically in respect of failure probability, where the exact confidence limits for the estimated probabilities are computed. The results of the statistical test based on exact confidence intervals show that there is no significant difference between the analysed factors, which describe the main causes of offshore oil and gas accidents. Based on the small but carefully chosen group of 24 of the largest accidents, it can be concluded that there is no evidence of a difference between the categories of the main causes of accidents. Full article