Massive Wildfires Research Articles

Post-wildfire boreal forest vegetation cover change mapping via information fusion for secondary disaster risk assessments

Post-wildfire vegetation cover damage and loss can escalate the risks of secondary disasters such as flood, landslide, and water contamination, particularly in a major wildfire affected region where human settlements are situated. In assessments of the secondary disaster risks, the post-wildfire vegetation cover change plays a key role in influencing the distribution and intensity of the risks. In this work, a processing framework for mapping post-wildfire vegetation cover changes through information fusion has been generated and tested using Landsat8 and WorldView imagery data. The test site was the boreal forest region surrounding Fort McMurray, Alberta, Canada, affected by a massive wildfire in May 2016. The derived map results indicate that the fusion process in the framework is effective for generation of post-wildfire vegetation cover change information. The use of WorldView data revealed more variation details in distribution of the vegetation cover burn damages than use of Landsat data. Moreover, the uncertainty in vegetation burn severity using Landsat-based Differenced Normalized Burn Ratio (dNBR) index exists in the areas with low dNBR reading values due to the sub-pixel effect. The forest burn severity measured with dNBR index can be underestimated due to the quick herbaceous cover recovery after wildfire. These uncertainties in the post-wildfire vegetation cover mapping should be taken into consideration when the derived information is being used for risk assessments.

Temporal shifts in sources and composition of particulate metals in an environmental justice community: Impacts of continued emission reductions, COVID-19, and wildfires

In this study, we evaluated trends in the composition and source contributions of ambient particulate metals, many of which are toxic air contaminants, in Paramount, California. Paramount is an environmental justice community disproportionately impacted by air pollution from multiple emission sources near sensitive receptors. Concentrations of ambient metals bound to total suspended particulates (TSP) were measured hourly using a Xact 625 ambient metal monitor between 2017 and 2020. The Positive Matrix Factorization (PMF) model identified traffic, industry, and soil dust as major contributors. Results showed that contributions of traffic and soil dust noticeably declined during the COVID-19 lockdown period (March–April 2020) due to reduced human activities but reverted to normal levels within a few months. Industry-related factors, however, dropped significantly during this period and remained low throughout the year. Contributions from industry-related sources in Paramount began declining even before 2020, despite no similar trend in regional levels, implying local emission reductions. These reductions resulted from tariffs, decreased demand, regulatory actions by the South Coast Air Quality Management District, and voluntary controls by local facilities. Consequently, Paramount is experiencing drastically lower levels of metals (up to 80%, depending on the metal and source) from metal-processing facilities and related sources. Moreover, we observed markedly elevated concentrations of several metals (e.g., Ba, Ca, Mn, K, etc.) during the unprecedented September 2020 wildfires that engulfed much of the western US, adding to the limited body of studies on this phenomenon and emphasizing the considerable impact of massive wildfires on ambient levels of particulate metals.

An Efficient and Lightweight Detection Model for Forest Smoke Recognition

Massive wildfires have become more frequent, seriously threatening the Earth’s ecosystems and human societies. Recognizing smoke from forest fires is critical to extinguishing them at an early stage. However, edge devices have low computational accuracy and suboptimal real-time performance. This limits model inference and deployment. In this paper, we establish a forest smoke database and propose a model for efficient and lightweight forest smoke detection based on YOLOv8. Firstly, to improve the feature fusion capability in forest smoke detection, we fuse a simple yet efficient weighted feature fusion network into the neck of YOLOv8. This also greatly optimizes the number of parameters and computational load of the model. Then, the simple and parametric-free attention mechanism (SimAM) is introduced to address the problem of forest smoke dataset images that may contain complex background and environmental disturbances. The detection accuracy of the model is improved, and no additional parameters are introduced. Finally, we introduce focal modulation to increase the attention to the hard-to-detect smoke and improve the running speed of the model. The experimental results show that the mean average precision of the improved model is 90.1%, which is 3% higher than the original model. The number of parameters and the computational complexity of the model are 7.79 MB and 25.6 GFLOPs (giga floating-point operations per second), respectively, which are 30.07% and 10.49% less than those of the unimproved YOLOv8s. This model is significantly better than other mainstream models in the self-built forest smoke detection dataset, and it also has great potential in practical application scenarios.

Rangewide occupancy of a flagship species, the Coastal California Gnatcatcher (Polioptila californica californica) in southern California: Habitat associations and recovery from wildfire.

The Coastal California Gnatcatcher (Polioptila californica californica), a federally threatened species, is a flagship species for regional conservation planning in southern California (USA). An inhabitant of coastal sage scrub vegetation, the gnatcatcher has declined in response to habitat loss and fragmentation, exacerbated by catastrophic wildfires. We documented the status of gnatcatchers throughout their California range and examined post-fire recovery of gnatcatchers and their habitat. We used GIS to develop a habitat suitability model for Coastal California Gnatcatchers using climate and topography covariates and selected over 700 sampling points in a spatially balanced manner. Bird and vegetation data were collected at each point between March and May in 2015 and 2016. Presence/absence of gnatcatchers was determined during three visits to points, using area searches within 150 x 150 m plots. We used an occupancy framework to generate Percent Area Occupied (PAO) by gnatcatchers, and analyzed PAO as a function of time since fire. At the regional scale in 2016, 23% of the points surveyed were occupied by gnatcatchers, reflecting the effect of massive wildfires in the last 15 years. Similarly, PAO in the post-fire subset of points was 24%, with the highest occupancy in unburned (last fire <2002) habitat. Positive predictors of occupancy included percent cover of California sagebrush (Artemisia californica), California buckwheat (Eriogonom fasciculatum), and sunflowers (Encelia spp., Bahiopsis laciniata), while negative predictors included laurel sumac (Malosma laurina) and total herbaceous cover; in particular, non-native grasses. Our findings indicate that recovery from wildfire may take decades, and provide information to speed up recovery through habitat restoration.

Impact of wildfire ash on bacterioplankton abundance and community composition in a coastal embayment (Ría de Vigo, NW Spain)

Wildfire ash can have an impact on coastal prokaryotic plankton. To understand the extent to which community composition and abundance of coastal prokaryotes are affected by ash, two ash addition experiments were performed. Ash from a massive wildfire that took place in the Ría de Vigo watershed in October 2017 was added to natural surface water samples collected in the middle sector of the ría during the summer of 2019 and winter of 2020, and incubated for 72 h, under natural water temperature and irradiance conditions. Plankton responses were assessed through chlorophyll a and bacterial abundance measurements. Prokaryotic DNA was analyzed using 16S rRNA gene partial sequencing. In summer, when nutrient concentrations were low in the ría, the addition of ash led to an increase in phytoplankton and bacterial abundance, increasing the proportions of Alteromonadales, Flavobacteriales, and the potentially pathogenic Vibrio, among other taxa. After the winter runoff events, nutrient concentrations in the Ría de Vigo were high, and only minor changes in bacterial abundance were detected. Our findings suggest that the compounds associated with wildfire ash can alter the composition of bacterioplanktonic communities, which is relevant information for the management of coastal ecosystems in fire-prone areas.

Exposure to wildfires and health outcomes of vulnerable people: Evidence from US data

This paper investigates the causal effect of wildfire exposure on birth outcomes and older people’s health outcomes in United States (US). The study focuses on three sub-questions for each health outcome: (1) the causal effect of each of the five largest wildfires on individual health, (2) the causal impact of multiple large wildfires on individual health outcomes, and (3) the causal influence of wildfires larger than different sizes within different distances of counties on health outcomes at the county level. The analysis exploits data from National Vital Statistics System, Behavioural Risk Factor Surveillance System and FIRESTAT. In terms of birth outcomes, the findings show that the largest wildfire slightly increased the risk of other circulatory or respiratory anomalies. Multiple large wildfires moderately raised the risk of prematurity and led to a small decline in the probability of getting omphalocele and cleft lip. The county-level analysis suggests an increased risk of macrosomia following maternal exposure to wildfires. As for the elderly aged 65 + , the results indicate that exposure to multiple massive wildfires led to frequent occurrence of asthma symptoms, while the largest wildfire led to sleeping difficulty caused by asthma symptoms. The number of days older people experienced psychological problems was increased following exposure to multiple large wildfires.

The impact of a massive wildfire event on avian species richness and abundance in an arid African savanna ecosystem

Fire shapes ecological diversity, but its influence on bird communities is poorly understood. Fire is often considered crucial to management of savanna systems. Here, we assess effects of fire on bird species richness and density after a wildfire in September 2021, in an arid South African savanna where fire is infrequent. Point counts initially surveyed in 2004 now occurred in burnt and unburnt habitats, and were revisited five times across seasons from December 2021 to January 2023. This enabled identification of interaction effects of change over time and with fire from linear models using site and survey as predictor variables in a Before-After Control-Impact (BACI) framework. Patterns of bird species richness were explored using subsets of repeated points and those of summer to autumn surveys. To explore fire impacts on bird density, we used ‘Distance Sampling’ using additional surveys. Although total species richness during counts did not differ between survey periods and burnt and unburnt areas, densities of all birds were ca. 50% higher in unburnt veld relative to burnt, with this difference driven mostly by granivores. Three of 24 common species were significantly negatively influenced by fire. Fires may profoundly affect this arid zone bird community, a finding that would be overlooked if only species richness had been considered, and densities not accounted for. Fire, although potentially positive for biodiversity in mesic savannas, may negatively impact multiple taxa in arid savannas, which have a very different evolutionary and ecological history to that of more mesic savannas. These differences must be considered in management strategies.

How Wildfire Experience Influences Intentions to Adopt Protective Measures over Time

In 2022, prolonged periods of extreme heat waves fueled huge wildfires across the globe. While research has shown that past experience with wildfire increases homeowners’ intentions to adopt fire mitigation activities, little is known about whether the impact of disaster can be long-lasting and represent a stable behavioral change. Thus, the current work explored whether Chinese homeowners’ intentions to perform fire wise actions would change over time. In Study 1, we compared responses of residents in a fire-stricken area with those of residents in a nondisaster area three days after a massive wildfire. It was found that participants who had just experienced a wildfire reported a stronger intent to take fire mitigation on both self-report and behavioral measures. Study 2 involved a follow-up survey that took place in the same areas one month after the fire had occurred. The results showed that participants in these two areas demonstrated no differences in intentions to implement fire mitigation measures, which suggests that the impact of disasters decayed over time. Combined, these findings suggest that past experience with wildfire will likely give rise to stronger intention to engage in mitigation behavior but this effect may diminish with time ticking away.

Multitask Allocation Framework With Spatial Dislocation Collision Avoidance for Multiple Aerial Robots

Multitask allocation and trajectory planning for multiple unmanned aerial vehicles (UAVs) have been extensively used in various real-world applications. This article presents a framework of multi-UAV multitask allocation and trajectory planning with collision avoidance. The scenario of interest is one where multiple UAVs are launched in order to investigate selected targets in a massive wildfire disaster relief terrain. Initially, one UAV is launched to search wildfire locations and wildfire lines by a developed informative path planning algorithm. An informative exploratory search mechanism is developed that provides the exploration trajectories to precisely locate the wildfire positions in the wildfire environments. Afterward, with the investigated environmental information including GPS coordinates of wildfire positions and distribution as targets, UAVs are deployed to multiple target positions. In order to perform effective collision avoidance, a spatial dislocation scheme is developed by introduction of an additional dimension for UAVs at different altitudes, whereas UAVs avoid collision at the same altitude using a proposed velocity profile paradigm. Concurrent multitask allocation, trajectory planning, and collision avoidance are successfully carried out with unequal numbers of UAVs and targets. The proposed framework has been validated by simulation studies and comparative analyzes.

Spatial distribution and temporal change of vegetation restoration after the eruption of Mount St. Helens: from 1984 to 2019

Approximately ten terrestrial volcanic eruptions occur yearly, significantly impacting the surrounding area. Mount St. Helens has been one of the most popular active volcanoes in recent years. This study analysed the vegetation cover status from 1984 to 2019. Remote Sensing and GIS technology were mainly used in this study to perform detailed research on the rates and trends of vegetation change. The results show that the vegetation in the study area has continued to recover in general, but some areas are consistently devoid of vegetation growth. After analysing the spatial and temporal perspectives, the vegetation recovery rate was relatively slow in the eastern part of the study area. This vegetation recovery status may be related to factors such as volcanic eruption, deep slope, lack of seeds, and massive wildfires. The area in the north-western part of the eruption area gained rapid growth in the first ten years, which may be caused by plant measures.

Deterioration of air quality associated with the 2020 US wildfires

The wildfires of August and September 2020 in the western part of the United States were characterized by an unparalleled duration and wide geographical coverage. A particular consequence of massive wildfires includes serious health effects due to short and long-term exposure to poor air quality. Using a variety of data sources including aerosol optical depth (AOD) and ultraviolet aerosol index (UVAI), obtained with the Moderate-Resolution Imaging Spectroradiometer (MODIS), Multi-Angle Implementation of Atmospheric Correction (MAIAC) and Tropospheric Monitoring Instrument (TROPOMI), combined with meteorological information from the European Center for Medium-Range Weather Forecasts (ECMWF) and other supporting data, the impact of wildfires on air quality is examined in the three western US states, California, Oregon, and Washington, and areas to the east. The results show that smoke aerosols not only led to a significant deterioration in air quality in these states but also affected all other states, Canada, and surrounding ocean areas. The wildfires increased the average daily surface concentration of PM2.5 posing significant health risks, especially for vulnerable populations. Large amounts of black carbon (BC) aerosols were emitted into the atmosphere. AOD and UVAI exceeded 1 and 2 over most of the country. In parts of the three western states, those values reached 3.7 and 6.6, respectively. Moreover, a reanalysis based on MERRA-2 (Modern-Era Retrospective Analysis for Research and Applications, version 2) showed that the maximum values of BC surface mass concentration during the wildfires were about 370 μg/m3. These various indicators provide a better understanding of the extent of environmental and atmospheric degradation associated with these forest fires.

Megafires in a Warming World: What Wildfire Risk Factors Led to California’s Largest Recorded Wildfire

Massive wildfires and extreme fire behavior are becoming more frequent across the western United States, creating a need to better understand how megafire behavior will evolve in our warming world. Here, the fire spread model Prometheus is used to simulate the initial explosive growth of the 2020 August Complex, which occurred in northern California (CA) mixed conifer forests. High temperatures, low relative humidity, and daytime southerly winds were all highly correlated with extreme rates of modeled spread. Fine fuels reached very dry levels, which accelerated simulation growth and heightened fire heat release (HR). Model sensitivity tests indicate that fire growth and HR are most sensitive to aridity and fuel moisture content. Despite the impressive early observed growth of the fire, shifting the simulation ignition to a very dry September 2020 heatwave predicted a &gt;50% increase in growth and HR, as well as increased nighttime fire activity. Detailed model analyses of how extreme fire behavior develops can help fire personnel prepare for problematic ignitions.

Discovery of underground shelters occupied by the Chacoan Marsh Rat after massive wildfires in Pantanal, Brazil

Las sequías e incendios forestales que afectaron el Pantanal, la mayor llanura inundada de Sudamérica, en 2020 impactaron fuertemente la biota, cuya resiliencia a estos tipos de eventos extremos todavía es desconocida. Durante muestreos posteriores a los incendios en sitios impactados, encontramos tres cuevas habitadas por la rata colorada chaqueña, Holochilus chacarius. Además, encontramos a un bagre calíctido (Hoplosternum sp.) y a un cangrejo tricodactílido (Dilocarcinus pagei) cohabitando una de las cuevas con H. chacarius. Reportamos, por primera vez, el uso de una cueva inundada por la rata colorada. Discutimos la importancia de dichas estructuras para la supervivencia posterior al incendio y si pueden servir como refugio para H. chacarius y otras especies a eventos extremos de incendios y sequía, a la luz de estudios previos con otros taxones.

Unfinished extinction and the velocities of capitalist sacrifices in the woodlands of central Chile

ABSTRACT Through an exploration of the reconstruction process of the town of Santa Olga, which acquired nationwide relevance in Chile when it was destroyed by massive wildfires that occurred in 2017, in this paper we attempt to explain a paradox: that which totally destroys Santa Olga is precisely what ends up saving it. To do this, we first unravel the birth of the town to try to understand why, for its inhabitants, Santa Olga can be sacrificed. Then we describe the visibility that Santa Olga reached, thanks to its tragedy. Finally, we reflect on the way the velocity of disasters enables or limits the visibility of the sacrifices they convey, an interplay that ends with an unfinished extinction.

Exploratory Analysis of Driving Force of Wildfires in Australia: An Application of Machine Learning within Google Earth Engine

Recent studies have suggested that due to climate change, the number of wildfires across the globe have been increasing and continue to grow even more. The recent massive wildfires, which hit Australia during the 2019–2020 summer season, raised questions to what extent the risk of wildfires can be linked to various climate, environmental, topographical, and social factors and how to predict fire occurrences to take preventive measures. Hence, the main objective of this study was to develop an automatized and cloud-based workflow for generating a training dataset of fire events at a continental level using freely available remote sensing data with a reasonable computational expense for injecting into machine learning models. As a result, a data-driven model was set up in Google Earth Engine platform, which is publicly accessible and open for further adjustments. The training dataset was applied to different machine learning algorithms, i.e., Random Forest, Naïve Bayes, and Classification and Regression Tree. The findings show that Random Forest outperformed other algorithms and hence it was used further to explore the driving factors using variable importance analysis. The study indicates the probability of fire occurrences across Australia as well as identifies the potential driving factors of Australian wildfires for the 2019–2020 summer season. The methodical approach and achieved results and drawn conclusions can be of great importance to policymakers, environmentalists, and climate change researchers, among others.

On the Use of Artificial Intelligence for High Impedance Fault Detection and Electrical Safety

Accidents caused by faults on overhead power lines have been more frequently reported under extreme weather conditions and may strongly threaten the safety and stability of the power grids, e.g., massive wildfires caused by the electrical arcs or lines getting in touch with vegetation, relay miss-operations, etc. It has been widely recognized that the electric safety concerns engendered by overhead line faults have to be timely and properly addressed to minimize the subsequent risks and damages. The existing monitoring devices and protective relays can barely detect high impedance faults (HIFs) and are unable to warn the system operators until serious abnormalities or damages are observed. Aiming at avoiding the damaging consequences of HIFs, an online monitoring system embedded with machine learning analytics is proposed that ensures a fast and accurate detection of HIFs in power systems. The performance of the proposed artificial intelligence engine is tested under a variety of simulated conditions and the numerical results demonstrate its efficacy and superiority over the state-of-the-art advancements.

Post-wildfire slope stability effects and mitigation: a case study from hilly terrains with unmanaged forest

The combined effects of wildfires and storms strongly induce slopes stability phenomena, thus requiring mitigation measures to manage long-lasting wildfire-induced risks. In Portugal, thousands of increasingly destructive wildfires occur during the hottest months. In the last two decades, almost 3,000,000 ha of forest, bush and agricultural land burned in Portugal, 20% of which caused by massive wildfires that occurred in 2017. Portuguese central mountain range was one of the most affected areas, namely the study area of Serra do Acor and Serra da Estrela, where extreme rainfall events in 2018 and 2019 caused extensive slope instability phenomena that created a unique opportunity for assessing long-term field performance of treated and untreated burnt areas. The registered phenomena as well as the treatment solutions applied to mitigate instability effects on slopes were studied in two sites. Over 3 years, several field research trips were carried out to scientifically assess the situation over time. By comparing areas with distinct response to the extreme rainfall events and based on the data collected, namely prior to 2017, it was been possible to link the registered instability events and the October 2017 wildfires and qualitatively evaluate the effectiveness of the adopted mitigation solutions. This study not only confirms that the study area is prone to slope instability wildfire-related phenomena, with a 13-year return period between similar events in the study area, but also validates the use of protective barriers as a mitigation measure, contrary to previous studies carried out in other countries.

102 Wildfire Burns: Implications for Burn Care

Abstract Introduction Climate change, the encroachment of populations into wilderness, and carelessness have combined to increase the incidence of wildfire injuries. With the increased incidence has come an increase in the number of burn injuries. Prolonged extrication, delays in resuscitation, and the extreme fire and toxic air environment in a wildfire has the potential to cause more severe burn injury. The purpose of this study is to examine the demographics and outcomes of wildfire injuries and compare those outcomes to non-wildfire injuries. Methods Charts of patients admitted to a regional burn center during a massive wildfire in 2018 were reviewed for demographic, treatment, and outcome. We then obtained age, gender, and burn size matched controls from within 2 years of the incident, analyzed the same measures, and compared treatment and outcomes between the two groups. Results A total of 20 patients, 10 wildfire (WF) burns and 10 non-wildfire (NWF) burns, were included in the study. Age (59.6±7.8 WF vs. 59.4±7.4 years), total body surface area burn (TBSA) (14.9±4.7 WF vs. 17.2±0.9 NWF) and inhalation injury incidence (2 WF and 2 NWF) were similar between groups. Days on mechanical ventilation (24.3±19.4 WF vs. 9.4±9.8 NWF), length of stay (49.9±21.8 WF vs. 28.2±11.7 days) and ICU length of stay (43.0±25.6 WF vs 24.4±11.2 NWF) were higher in the WF group. WF patients required twice the number of operations. Mortality was similar in both groups (1 death/group). Conclusions Wildfire burn injuries, when compared to age, inhalation injury, and burn size matched controls, require more ventilatory support and have more operations. As a result, they have longer lengths of stay and have a prolonged ICU course. Burn centers should be prepared for the increased resource utilization that accompanies wildfire injuries. Applicability of Research to Practice All burn centers must be prepared for the possibility of wildfires and the increased resource utilzation that accompanies mass casualty events.

INTELLIGENT MANAGEMENT OCCURRENCE AND SPREAD OF FRONT FIRE IN GIS BY USING CELLULAR AUTOMATA. CASE STUDY: GOLESTAN FOREST

Abstract. Jungles are a country’s treasures, and the survival of humans and the earth depends on the oxygen that they produce (trees). Three million hectares of jungle are destroyed each year as a result of natural or human-caused wildfires. The massive wildfires occurring in the world confirms that this destructive phenomenon leaves irreconcilable effects on regional ecosystems, vegetation and wildlife; presenting more effective approaches for preventing and managing this complex phenomenon has therefore always been the focus of environmental managers and planners. In other words, it is necessary to model the spread of wildfires, which can be used for utilizing forces and equipment most effectively. Cellular automaton is considered a very simple yet highly effective method that is very capable of modeling dynamic events, like urban development, spread of wildfires, disease outbreaks and others. This study used layers such as height, slope, aspect, vegetation density, roads, rivers and climate as its basis. This was a case study of the Golestan region’s jungles, which calculated the general accuracy of the wildfire spread model to be 65 percent, and the Cohen’s kappa coefficient to be 59 percent.

Optimal decisions for salvage logging after wildfires

Strategic, tactical, and operational harvesting plans for the forestry and logging industry have been widely studied for more than 60 years. Many different settings and specific constraints due to legal, environmental, and operational requirements have been modeled, improving the performance of the harvesting process significantly. During the summer of 2017, Chile suffered from the most massive wildfires in its history, affecting almost half a million hectares, of which nearly half were forests owned by medium and small forestry companies. Some of the stands were burned by intense crown fires, which always spread fast, that burned the foliage and outer layer of the bark but left standing dead trees that could be salvage harvested before insect and decay processes rendered them unusable for commercial purposes. Unlike the typical operational programming models studied in the past, in this setting, companies can make insurance claims on part or all of the burnt forest, whereas the rest of the forest needs to be harvested before it loses its value. This problem is known as the salvage logging problem. The issue also has an important social component when considering medium and small forestry and logging companies: most of their personnel come from local communities, which have already been affected by the fires. Harvesting part of the remaining forest can allow them to keep their jobs longer and, hopefully, leave the company in a better financial situation if the harvesting areas are correctly selected. In this work, we present a novel mixed-integer optimization-based approach to support salvage logging decisions, which helps in the configuration of an operational-level harvesting and workforce assignment plan. Our model takes into account the payment from an insurance claim as well as future income from harvesting the remaining trees. The model also computes an optimal assignment of personnel to the different activities required. The objective is to improve the cash position of the company by the end of the harvest and ensure that the company is paying all its liabilities and maintaining personnel. We show how our model performs compared to the current decisions made by medium and small-sized forestry companies, and we study the specific case of a small forestry company located in Cauquenes, Chile, which used our model to decide its course of action.