Wildfire Disaster Research Articles

Framing Coherence Across EU Policies Towards Integrated Wildfire Risk Management and Nature-Based Solutions

Wildfire risk has been exacerbated across Europe by climate change favoring more damaging and severe wildfire events. This evolving wildfire risk context interacts with a broad landscape of EU policies including those on nature conservation, forestry, bioeconomy or climate and energy, all of which may increase or reduce fire hazard and the level of exposure and vulnerability of the values at risk. Coherently addressed, policies may support wildfire disaster risk management synergistically while reducing potential dysfunctions. This research conducts a content analysis of EU policies and initiatives under the European Green Deal with respect to integrated wildfire risk management and related nature-based solutions. The results show that a consistent EU policy framework to address wildfire risk reduction in a synergic way exists, with no major conflicts in the policy design. Nevertheless, better guidance on fire-smart land management practices and the conceptualization of wildfire-related nature-based solutions may enhance a more coherent policy implementation. Additional suggestions around the legal status of wildfire protection and ‘whole of government’ governance frameworks are discussed. Notably, within the laws, policies and initiatives analyzed, the beneficial side of fire addressed by integrated fire management is either missing or not explicitly mentioned, although it is considered in policy-related supporting guidelines.

Resilience assessment of power transmission system during wildfire disasters considering spread process

AbstractLarge‐scale wildfires can significantly reduce the air gap insulation resistance of high‐voltage transmission lines and cause chain tripping incidents. To assess the resilience of the power transmission system during wildfire, this paper proposes a resilience assessment framework for transmission system that considers the entire process of wildfire disaster. Firstly, a wildfire spread model, considering multiple influencing factors, is developed based on the cellular automaton. Based on the air gap breakdown mechanism during wildfires, the trip‐out probability of transmission lines is calculated, and various failure scenarios are obtained by using the Monte Carlo sampling. Secondly, considering the geographical location of failures, maintenance personnel schedules and restoration time, a power transmission system restoration model is established. Thus, a resilience assessment method for power transmission system during wildfire disasters is proposed. Finally, IEEE RTS‐79 transmission system is taken as an example to demonstrate the effectiveness of the proposed resilience assessment method. The results show that the proposed method can effectively calculate the wildfire spread tendency and transmission line's trip‐out probability. Furthermore, three typical resilience improvement measures are quantitatively analysed, which provides a quantifiable reference for the power sector to formulate prevention and recovery strategies for extreme wildfire disasters.

Spatial and temporal analysis of vulnerability disparity of minorities to wildfires in California

Wildfires typically have devastating impacts on communities, both in urban and rural areas, resulting in property loss, psychological distress, physical injuries, and loss of life. A notable gap in the literature is the spatial and temporal disproportionate impact of wildfires on underrepresented communities. This lack of attention is concerning, as these underrepresented populations are likely to be more vulnerable to the devastating consequences of wildfire disasters, exacerbating pre-existing social, economic, and environmental disparities. This study aims to address this gap by conducting a comprehensive spatial and temporal analysis of the vulnerability of underrepresented communities such as the Black, Asian, Hispanic, Native American, among others, in the State of California. We deploy methodological tools such as Getis-Ord Gi∗ to identify areas exhibiting heightened vulnerability across diverse minority groups. Additionally, disparity degrees are measured using Location Amplitude Index among these minority populations. Our findings reveal significant spatial and temporal shifts in vulnerability disparities for Native American communities in Los Angeles County, the Black communities in Orange County, and all minority groups in San Diego County. Shedding light on the underrepresented communities' vulnerabilities to wildfires, this research contributes to the development of targeted and equitable strategies for wildfire preparedness, response, and recovery efforts. Furthermore, this study highlights the urgent need for addressing the environmental justice implications of wildfire disasters and considering minorities’ disparity in wildfire risk management and climate change adaptation planning.

Wildfire exposures and in-hospital length of stay following lung cancer surgery.

311 Background: Wildfires pose substantial health and safety threats to patients recovering from lung cancer surgery. Without specific disaster preparedness guidelines, oncologists might resort to improvisational strategies, such as increasing the length of stay (LOS), a federal care quality measure with financial implication for hospitals, to better protect the health and safety of this medically vulnerable population. Methods: Individuals aged ≥18 years who received curative-intent lobectomy or pneumonectomy for stage I-III NSCLC between 2004 and 2021 were selected from the National Cancer Database. Exposure was defined as a Federal Emergency Management Agency (FEMA) Presidential Disaster Declaration in the county of the treatment facility between date of surgery and date of discharge from the hospital. Differences in the cumulative distribution function of LOS, defined as days between date of surgery and date of discharge from the hospital, were evaluated between exposed and propensity score matched unexposed patients, who were treated at the same facility but at a time when no disasters occurred, using the product-limit method and log-rank test. In sensitivity analysis, patients with discharge date equal to date of death (in-hospital mortality) were excluded, and the association was evaluated stratified by stage. Results: Patients exposed to a wildfire disaster declaration in the county of the treating facility had longer LOS (p<0.001) than unexposed patients (9.4days compared to 7.5 days, respectively) overall and for each of the stages (I-III) for which surgery is the recommended treatment modality. In sensitivity analysis, there were no in-hospital mortality difference between exposed and unexposed patients (10.8% and 10.5%, respectively, p=0.76). Excluding patients who died in the hospital did not change the main results. Conclusions: Patients whose facility is impacted by a wildfire disaster during recovery from lung cancer surgery have longer length of stay than similar patients treated at the same facility but at times when no disaster occurred. Future studies should evaluate whether extended hospital stay improves safety and quality of surgical care. Moreover, these findings should be considered for disaster preparedness guidelines tailored to vulnerable patient population.

Reducing the negative impact of wildfire disasters through multi-scenario risk analysis of land investment

Natural meteorological and climatological hazards and vulnerable vegetation are frequent triggers of natural disasters, such as wildfires. These cause loss of life and significant economic damage, likely to increase due to climate and socio-economic changes. The increase in wildfires in recent decades has affected the decision-making of technocrats and policy experts and created the need for more sophisticated risk management. This paper develops a theoretical framework for reducing the economic impact of wildfire risk on forests. Also provides insight into land use decisions regarding losses that this increased risk of forest land management can cause. The impact of wildfire risk on economic land value is evaluated by comparing three scenarios, i.e. the deterministic risk-adjusted land expectation value with its risk-free counterparts calculated assuming the stationary risk and predicted increasing risk as time goes by. Also, for this purpose, we evaluate the economically optimal length rotation period pine stands and the risk premium. Fire risk is described as the probability of pine stands destruction due to their age and meteorological conditions in the experimental area Záhorská lowland (western Slovakia). Our findings highlight the importance of alerting forest policymakers to carefully incorporate assessment of the increasing risk of random natural disasters due to climate change into sustainable forest management. The study's results will also help current or future forest owners estimate and evaluate the economic/investing risk when buying or selling forest land.

Climate Disaster and Cognitive Ability: Evidence From Wildfire.

We investigate the impact of wildfire disasters on cognitive health (i.e., thinking and language skills) in individuals aged 10years and older using 2014 and 2018 wildfire and cognitive ability survey data from China. We distinguished wildfires in each county at different wind directions each day by exogenous wind direction changes, and analyzed the effects of wildfires on cognitive abilities through upwind and non-upwind wildfires. Our analysis shows that for every 10-unit increase in upwind wildfires compared to non-upwind wildfires, respondents' scores on word and math tests decrease by 0.235 and 0.236 standard deviations, respectively. Furthermore, we find that the impact of wildfire on cognitive ability is more pronounced in younger individuals, and those with lower defensive expenditures experience more severe impacts. Additionally, wildfires negatively affect individuals' cognitive abilities by generating air pollution. Wildfires significantly reduce individuals' cognitive abilities. Two recommendations are as follows: 1) governments should follow the principle of green development, introduce relevant regulations, and increase investment in adaptive technologies. 2) Individuals should raise awareness of climate hazards preparedness and strengthen defensive protection.

Retrospective Demonstrations of an Integrated Team Approach to Fire Warnings for Western United States Wildfire Disasters

Retrospective Demonstrations of an Integrated Team Approach to Fire Warnings for Western United States Wildfire Disasters

Multistage stochastic program for mitigating power system risks under wildfire disruptions

The frequency of wildfire disasters has surged fivefold in the past 50 years due to climate change. Preemptive de-energization is a potent strategy to mitigate wildfire risks but substantially impacts customers. We propose a multistage stochastic programming model for proactive de-energization planning, aiming to minimize economic loss while accomplishing a fair load delivery. We model wildfire disruptions as stochastic disruptions with varying timing and intensity, introduce a cutting-plane decomposition algorithm, and test our approach on the RTS-GLMC test case. Our model consistently offers a robust and fair de-energization plan that mitigates wildfire damage costs and minimizes load-shedding losses, particularly when pre-disruption restoration is considered.

Multi-resolution monitoring of the 2023 maui wildfires, implications and needs for satellite-based wildfire disaster monitoring

The August 2023 wildfires over the island of Maui, Hawaii were one of the deadliest U.S. wildfire incidents on record with 100 deaths and an estimated U.S. $5.5 billion cost. This study documents the incidence, extent, and characteristics of the 2023 Maui wildfires using multi-resolution global satellite fire products, and in so doing demonstrates their utility and limitations for detailed fire monitoring, and highlights outstanding satellite fire observation needs for wildfire monitoring. The NASA 500 m Moderate Resolution Imaging Spectroradiometer (MODIS) burned area product is compared with PlanetScope 3 m burned areas that were mapped using a published deep learning algorithm. In addition, all the August 2023 active fire detections provided by MODIS on the Terra and Aqua satellites and by the Visible Infrared Imaging Radiometer Suite (VIIRS) on the S-NPP and NOAA-20 satellites are used to investigate the geographic and temporal occurrence of the fires and their incidence relative to the 3 m mapped burned areas. The geographic and diurnal variation on the fire radiative power (FRP), available with the active fire detections, is presented to examine how energetically the fires were burning. The analysis is undertaken for all of Maui and for the town of Lahaina that was the major population center that burned. Satellite active fires were first detected August 8th, 2023 in the early morning (1:45 onwards) on the western slopes of Mt. Haleakalā and were last detected August 10th in the early morning (at 2:46) over Lahaina and on the western slopes of Mt. Haleakalā. The FRP available with the VIIRS satellite active fire detections indicate that the fires burned less intensely from the beginning to the end of this three day period, the nighttime fires generally burned more intensely than the daytime fires, and the most intensely burning fires occurred over Lahaina likely due to the high fuel load in the buildings compared to the vegetation that burned elsewhere. The MODIS 500 m burned area product was too coarse to map most of the 18 burned areas that were mapped unambiguously at 3 m resolution with PlanetScope and covered 29.60 km2, equivalent to about 1.6% of Maui. This study highlights the limitations of systematically derived satellite fire products for assessment before, during and after wildfire disaster events such as those experienced over Maui. The needs for future fire monitoring of wildfire disaster events, and the recommendation for a fire monitoring satellite constellation, are discussed.

Research on the Quantitative Assessment Method of HVDC Transmission Line Failure Risk during Wildfire Disaster

It is increasingly important to effectively predict the failure of HVDC transmission lines caused by wildfire disasters. On the basis of comprehensively considering the distribution of fire points, the characteristics of wildfire propagation, and the failure factors of the transmission line, a method for calculating the probability of failure in HVDC transmission lines during wildfire disasters is proposed to quantify the risk of HVDC transmission line failures caused by wildfire disasters. Using the ArcGIS 10.7. platform, the study examined the quantity of fire points within the buffer zone of each HVDC transmission line from 2001 to 2022. The results indicate significant variations in the number of fire incidents in the buffer zones of various transmission lines. Notably, there has been a noticeable increase in the number of fire incidents along several HVDC transmission lines, including Xizhe, Baihetan-Jiangsu, Baihetan-Zhejiang, and Fufeng, in recent years. Based on the number of fire points in the buffer zone obtained through ArcGIS processing and the proposed failure probability calculation model, six HVDC hydropower transmission channels in the Sichuan Province were analyzed. At the same time, the proposed probability calculation model was simplified, and a corresponding linear evaluation index was introduced. The regression analysis results indicate that the proposed index can effectively assess the failure risk of HVDC transmission lines during wildfire disasters.

Wildfires and social media discourse: exploring mental health and emotional wellbeing through Twitter.

The rise in global temperatures due to climate change has escalated the frequency and intensity of wildfires worldwide. Beyond their direct impact on physical health, these wildfires can significantly impact mental health. Conventional mental health studies predominantly rely on surveys, often constrained by limited sample sizes, high costs, and time constraints. As a result, there is an increasing interest in accessing social media data to study the effects of wildfires on mental health. In this study, we focused on Twitter users affected by the California Tubbs Fire in 2017 to extract data signals related to emotional well-being and mental health. Our analysis aimed to investigate tweets posted during the Tubbs Fire disaster to gain deeper insights into their impact on individuals. Data were collected from October 8 to October 31, 2017, encompassing the peak activity period. Various analytical methods were employed to explore word usage, sentiment, temporal patterns of word occurrence, and emerging topics associated with the unfolding crisis. The findings show increased user engagement on wildfire-related Tweets, particularly during nighttime and early morning, especially at the onset of wildfire incidents. Subsequent exploration of emotional categories using Linguistic Inquiry and Word Count (LIWC) revealed a substantial presence of negative emotions at 43.0%, juxtaposed with simultaneous positivity in 23.1% of tweets. This dual emotional expression suggests a nuanced and complex landscape, unveiling concerns and community support within conversations. Stress concerns were notably expressed in 36.3% of the tweets. The main discussion topics were air quality, emotional exhaustion, and criticism of the president's response to the wildfire emergency. Social media data, particularly the data collected from Twitter during wildfires, provides an opportunity to evaluate the psychological impact on affected communities immediately. This data can be used by public health authorities to launch targeted media campaigns in areas and hours where users are more active. Such campaigns can raise awareness about mental health during disasters and connect individuals with relevant resources. The effectiveness of these campaigns can be enhanced by tailoring outreach efforts based on prevalent issues highlighted by users. This ensures that individuals receive prompt support and mitigates the psychological impacts of wildfire disasters.

Research progress and hotspot evolution analysis of wildfire disasters in national park communities

In recent years, wildfire disasters have had an essential impact on national park communities' social, economic, and ecological structure, which has gradually become the forefront of academic attention. Therefore, to review the research history of wildfire disasters in national park communities, this paper used CiteSpace and Bibliometric R analysis tools to conduct a detailed analysis of 328 kinds of literature in the Web of Science database from 2002 to 2023. The data showed that the number of papers published was rising, and the subjects involved included ecology, botany and zoology. However, there is still a need for increased collaboration among the collaborating groups of authors, institutions, and countries. From the perspective of the research content, it mainly focused on the mathematical model and established a comprehensive database to simulate and predict wildfire disasters. Finally, we predict a future research direction using keyword clustering and burst detection -the management framework for wildfire disaster risk prevention. The key points include improving the governance mode of the country, community and enterprise and improving the simulation and prediction ability of mathematical models. This study provided a reference for promoting wildfire hazard risk identification and management in national park communities.

The Impact of Severe Wildfires on Mental Health: Prevalence of Major Depressive Disorder and Related Factors among Residents in Alberta and Nova Scotia, Canada.

IntroductionHundreds of fires have been burning from coast to coast across the country since March 2023, putting Canada on track to experience the worst wildfire season ever. From East to West, provinces such as Quebec, Ontario, Nova Scotia, Alberta, and British Columbia have been particularly affected by large and uncontrollable wildfires.Objectives This study aimed to determine the prevalence and predictors of depression symptoms among residents of Alberta and Nova Scotia during the Canadian wildfires of 2023.Methods This study conducted a cross-sectional quantitative survey for data collection. In the period between 14th May and 23rd June 2023, an online survey was administered using REDCap. Through the Text4Hope program, participants subscribe to receive supportive SMS messages daily. After the first message, participants were invited to complete an online questionnaire, containing demographic information, wildfire-related information, and responses to the Patient Health Questionnaire-9 (PHQ-9) for depression assessment. SPSS version 25 was used to analyze the data. Descriptive, univariate, and multivariate regression analyses were employed.Results A total of 298 respondents completed the online survey out of 1802 who accessed it, resulting in a response rate of 16.54 %. Most of the respondents were females (85.2%, 253), below 40 years of age (28.3%, 84), employed (63.6%, 189), and in a relationship (56.4%, 167). A historical depression diagnosis (OR = 3.15; 95% CI: 1.39–7.14) was a significant predictor of moderate to severe MDD in our study. While employment status did not significantly predict MDD, unemployed individuals were two times more likely to report moderate-to-severe symptoms of MDD than employed individuals (OR = 2.46; 95% CI: 1.06–5.67). Among the total sample population, the moderate to severe MDD prevalence was 50.4%, whereas it was 56.1% among those living in wildfire-affected areas.Image:Image 2:ConclusionsAs a result of our study, the development of moderate to severe MDD symptoms during wildfire disasters was significantly associated with a history of depression diagnosis. Although employment status did not significantly predict MDD, unemployed individuals had a greater likelihood of experiencing moderate-to-severe symptoms than employed individuals. Further research is necessary to ascertain reliable predictors of mental health issues among those who have experienced disasters, as well as to offer appropriate interventions and treatment options to the communities and individuals who are most vulnerable.Disclosure of InterestNone Declared

Development of a Decision Support Model for Establishing Response Strategies to Large-scale Wildfires Using GIS

Every year, wildfire disasters of various scales occur in Korea, causing damage to forests, facilities, and human lives owing to the difficulties in early suppression. Effective wildfire response requires collaboration between multiple agencies, necessitating a decision support system for strategy formulation. However, while there is active research on technology development related to situation prediction and suppression equipment, discussions on response technologies and strategies are insufficient. In this study, we propose a GIS-based decision-making methodology for establishing initial response strategies through prioritization of suppression zones during wildfires. A case study on past wildfires was conducted to evaluate the applicability of the proposed methodology. The methodology suggested in this study integrates wildfire suppression elements using the equal weight method to derive suppression priority scores by combining these elements with the distance to wildfire occurrence points. In this study, we aim to provide a decision-making foundation that considers spatial factors in initial wildfire responses, which can serve as a crucial basis for effective allocation of firefighting resources and strategy formulation in the field.

Devastating Wildfires and Mental Health: Major Depressive Disorder Prevalence and Associated Factors among Residents in Alberta and Nova Scotia, Canada.

Background: Since March 2023, hundreds of fires have burned from coast to coast throughout the country, placing Canada on track to have the worst wildfire season ever recorded. From East to West, provinces such as Quebec, Ontario, Nova Scotia, Alberta, and British Columbia have been particularly affected by large and uncontrollable wildfires. Objectives: The objective of this study was to determine the prevalence of depression symptoms and predictors among residents living in extreme climate conditions during the Canadian wildfires of 2023 in Alberta and Nova Scotia and to update the literature with data related to those wildfires. Methods: A cross-sectional quantitative survey was conducted in this study. REDCap was used to administer an online survey between 14 May and 23 June 2023. Through the Text4Hope program, participants subscribe to receive supportive SMS messages daily. As part of the initial welcome message, participants were invited to complete an online questionnaire, containing demographic information, wildfire-related information, and responses to the Patient Health Questionnaire-9 (PHQ-9) for depression assessment. SPSS version 25 was used to analyze the data. Descriptive, univariate, and multivariate regression analyses were employed. Results: A total of 298 respondents completed the survey out of 1802 who self-subscribed to the Text4Hope program in Alberta and Nova Scotia and received a link to the online survey, producing a response rate of 16.54%. Most of the respondents were females (85.2%, 253), below 40 years of age (28.3%, 84), employed (63.6%, 189), and in a relationship (56.4%, 167). A historical depression diagnosis (OR = 3.15; 95% CI: 1.39-7.14) was a significant predictor of moderate to severe MDD in our study. The unemployed individuals were two times more likely to report moderate to severe symptoms of MDD than employed individuals (OR = 2.46; 95% CI: 1.06-5.67). Among the total sample population, the moderate to severe MDD prevalence was 50.4%, whereas it was 56.1% among those living in areas affected by wildfires. Conclusion: Based on our study findings, unemployment and a history of depression diagnosis were independently significant risk factors associated with the developing moderate to severe MDD symptoms during wildfire disasters. Further research is required to identify robust predictors of mental health disorders in disaster survivors and provide appropriate interventions to the most vulnerable communities and individuals.

Advancements in Wildfire Detection and Prediction: An In-Depth Review

Wildfires pose a significant hazard, endangering lives, causing extensive damage to both rural and urban areas, causing severe harm for forest ecosystems, and further worsening the atmospheric conditions and the global warming crisis. Electronic bibliographic databased were searched in accordance with PRISMA guidelines. Detected items were screened on abstract and title level, then on full-text level against inclusion criteria. Data and information were then abstracted into a matrix and analyzed and synthesized narratively. Information was classified into 2 main categories- GIS-based applications, GIS-based machine learning (ML) applications. Thirty articles published between 2004 and 2023 were reviewed, summarizing the technologies utilized in forest fire prediction along with comprehensive analysis (surveys) of their techniques employed for this application. Triangulation was performed with experts in GIS and disaster risk management to further analyze the findings. Discussion includes assessing the strengths and limitations of fire prediction systems based on different methods, intended to contribute to future research projects targeted at enhancing the development of early warning fire systems. With advancements made in technologies, the methods with which wildfire disasters are detected have become more efficient by integrating ML Techniques with GIS.

Climate grief, resilience and Brian Fies’s A fire story

ABSTRACT Climate change has profoundly impacted ecosystems, communities, and individuals worldwide. Climate change comics have emerged as a powerful medium for raising environmental awareness, particularly by highlighting the emotional toll it takes on humans. This paper examines the graphic narrative, A Fire Story: A Memoir by Brian Fies, which depicts the devastating wildfire disaster that struck Northern California in 2017. The narrative effectively conveys the ecological and human habitat destruction caused by climate change-induced natural disasters through autobiographical and witnesses accounts. It offers reflections on the past, present, and future while challenging climate denial and exploring themes of climate grief and resilience. The narrative explores how the traumatic event disrupts personal chronology and exposes the emotional vulnerability of survivors in their post-disaster lives. This article analyzes climate grief and resilience in climate change comics, emphasising the significance of A Fire Story as a valuable artefact that documents the testimonies of witnesses and survivors. To challenge climate denial, graphic narratives go beyond generalities and present highly detailed and emotionally charged narratives, including social fragmentation, forced evacuation, and economic devastation.

Climate Change, Landscape Fires, and Human Health: A Global Perspective.

Landscape fires are an integral component of the Earth system and a feature of prehistoric, subsistence, and industrial economies. Specific spatiotemporal patterns of landscape fire occur in different locations around the world, shaped by the interactions between environmental and human drivers of fire activity. Seven distinct types of landscape fire emerge from these interactions: remote area fires, wildfire disasters, savanna fires, Indigenous burning, prescribed burning, agricultural burning, and deforestation fires. All can have substantial impacts on human health and well-being directly and indirectly through (a) exposure to heat flux (e.g., injuries and destructive impacts), (b) emissions (e.g., smoke-related health impacts), and (c) altered ecosystem functioning (e.g., biodiversity, amenity, water quality, and climate impacts). Minimizing the adverse effects of landscape fires on population health requires understanding how human and environmental influences on fire impacts can be modified through interventions targeted at individual, community, and regional levels.

Global Health Impacts of Wildfire Disasters From 2000 to 2023: A Comprehensive Analysis of Mortality and Injuries.

Wildfires have escalated into a global threat with profound impacts on health, society, and the environment. The increasing frequency and intensity of these disasters, influenced by climate change and urban expansion, necessitate a comprehensive understanding of their direct health consequences. This study conducted a retrospective analysis of global wildfire disasters from January 2000 to December 2023, utilizing data from the Emergency Events Database (EM-DAT). The analysis focused on the direct health outcomes-mortalities and injuries-excluding indirect effects such as smoke inhalation. Data were meticulously cleaned, categorized, and analyzed using quantitative methods, with statistical tests employed to validate the findings. The study identified 309 significant wildfire disasters, with forest fires accounting for 80% of these events. These incidents resulted in 1890 fatalities and 14 360 injuries, with the highest tolls observed in Southern Europe, Northern America, and the Australia-New Zealand region. A notable rise in wildfire incidents was observed over the study period, underscoring the critical intersections between climate change, urban expansion, and wildfire risks. The analysis highlighted significant geographical and temporal patterns, emphasizing the regions and factors contributing to heightened wildfire vulnerability. The findings underscore the urgent need for robust disaster preparedness and effective mitigation strategies. Integrating advanced early warning systems and Traditional Ecological Knowledge into wildfire management practices is essential. The study calls for proactive public health measures and interdisciplinary approaches to address the multifaceted challenges posed by wildfires. Continuous research and policy formulation are crucial to protect vulnerable communities and mitigate the increasing threat of wildfires globally.

Rapid wildfire damage estimation using integrated object-based classification with auto-generated training samples from Sentinel-2 imagery on Google Earth Engine

Satellite data are essential during wildfires for understanding its adverse effects and improving the effectiveness of rapid disaster management. However, existing techniques used for damage assessments are inaccurate and lack automation. In this study, we propose an integrated machine learning approach with auto-generated training samples for a rapid wildfire disaster response framework using Sentinel-2 imagery at 10 m resolution from Google Earth Engine (GEE). First, training samples of burned areas were obtained by utilizing textural data based on features that had changed because of the wildfire, and samples of unburned areas were obtained using the normalized difference vegetation index (NDVI). The images were categorized as burned and unburned images using the object-based image analysis (OBIA) classification method. Finally, using the classified maps, burn severity maps and estimated pixel counts for each severity class were generated and compared. The proposed method was implemented to put out a wildfire that broke out in Uljin, Gyeongsangbuk-do, South Korea in March 2022 and the transferability of the model was evaluated in Gangneung, Gangwon-do, South Korea. The study findings indicate that the random forest (RF) classifier acquired the greatest overall accuracy (OA) of 97.6 % in Uljin; additionally, the model transferability performed well in Gangneung with an OA of 93.8 %. The RF also generated the fewest pixels of the unchanged class when the burn severity map was evaluated. Overall, our study proposes a quick and automated approach for estimating wildfire damage that could be used for immediate mitigation actions.