Wildfire Management Research Articles

Optimized stratification approach enhances the weight-of-evidence method: Transparently uncovering wildfire probability and drivers-wildfire relationships in the southwest mountains of China

The mountainous region of southwest China serves as a significant treasure for resources and ecological security. Due to its topographic heterogeneity and cultural diversity, the wildfire regime in this area exhibits unique features. To uncover the distinct mechanisms that drive wildfire incidents and assess their occurrence probability, an enhanced weight-of-evidence (WofE) method was proposed by improving the stratification of evidence layers. This approach seeks to heighten effectiveness while maintaining procedural transparency and robustness. In our case study focusing on Yunnan Province, we extracted wildfire data from medium-resolution remote sensing imagery from 2006 to 2020. Various wildfire drivers were considered including wildfire environment, fuel conditions, and ignition sources. We utilized the WofE method grounded on Bayesian principles to clearly calculate the spatial association strength between these drivers and wildfires, thereby developing a reliable wildfire occurrence probability map. To enhance the explanatory power of the drivers within the WofE method, a discretization method based on the theory of spatial stratified heterogeneity was incorporated. Our results suggested that the WofE method can be optimized using the spatial stratified heterogeneity measured via GeoDetector, leading to an improved solution. Implementing optimized discrete drivers amplified the spatial explanatory power of wildfires by an average of 7.55%, supporting their inclusion as evidence layers within the WofE method. The optimized discrete WofE method yielded a wildfire occurrence probability map with an Area Under the Curve (AUC) value of 0.91, indicating high predictive accuracy. This map revealed notable spatial clustering and regional variations in wildfire occurrences across Yunnan Province. Additionally, we observed variable spatial correlation between each driver and wildfire occurrence, and the wildfire drivers indicative of ignition sources characteristics were relatively more dominant locally. This research contributes valuable insights towards enhancing the WofE method and provides a helpful reference for local wildfire management practices and resource allocation strategies.

Bridging scales for landscape-level wildfire adaptation: A case study of the Kittitas Fire Adapted Communities Coalition

Federal-level strategies or guidance for addressing wildfire risk encourage adaptation activities that span progressively larger scales, often focusing on landscape-level action that necessitates coordination between decision-makers and socially diverse communities. Collaborative organizations are increasingly explored as one approach for coordinating local efforts that address wildfire risk and adaptation, offering a platform for scaling and adjusting federal and state guidance that align with the needs of local landscapes. We conducted semi-structured interviews with members and supporters of the Kittitas Fire Adapted Communities Coalition (KFACC) and later facilitated two workshops at the behest of the organization. The goal of our interviews and workshops were to better understand how organizations such as KFACC emerge, function, and evolve in complex social and ecological landscapes, with a focus on their role in addressing landscape-level wildfire adaptation. We use an existing theoretical analogy of fire adaptation that crosses institutional and physical scales to help conceive of lessons from in-depth analysis of KFACC functioning. We found that KFACC originated from a need to establish a shared local mission for fire adaptation and a recognition that federal and state initiatives surrounding wildfire management needed further contextualization to be effective among diverse local social conditions. Later organizational foci included identifying key audiences for targeted adaptation efforts, including the identification of key messages and communities where specific mitigation actions might be needed. KFACC members were effective in strategically advocating for fire adaptation resources and policies at broader scales that might increase adaptation within Kittitas County, including caveats to local planning efforts designed for wildfire risk reduction. Likewise, the organization had begun to focus on tailoring mitigation efforts to different communities in the landscape as an effective means of catalyzing sustained, realistic fire adaptation actions. We suggest that organizations like KFACC are well-positioned to act as “board hoppers” who can integrate community-based needs into wildfire management, but caution that the functioning and ‘niche’ of such organizations may require strategic development or regular reflection on organizational goals.

How Can Participating in a Forest Community and Citizen Science Program Support Elementary School Students’ Understanding of Socio-Ecological Systems?

In the face of the climate crisis, sustainability education must include a focus on understandings of socio-ecological systems (SES) and applying systems thinking (ST) skills. This study investigates a Community and Citizen Science (CCS) program designed for elementary school students to collect forest data to better understand their SES and gain insights into wildfire management in a California region recently ravaged by wildfires. We examine the development of fourth-grade students’ systems thinking skills and understandings of SES, seeing these as crucial components toward fostering agency with science to support climate resilience. We found that students were capable of recognizing the intricate impacts of wildfires on interconnected human and ecological systems and demonstrated varying levels of proficiency in systems thinking skills. This place-based CCS program cultivated knowledge and skills in young learners that may contribute to better socio-ecological resilience and proactive sustainability efforts.

TREE-LEVEL FUEL CONNECTIVITY TO ASSESS CROWN WILDFIRE POTENTIAL BY UAS-BASED PHOTOGRAMMETRY

Abstract. Evaluating the potential for crown fires remains a pivotal concern in wildfire management because it affects fire behavior, causing them to spread further. In this work, we propose a methodology to assess crown fire potential based on the tree connectivity at crown level from UAS (Unmanned Aircraft Systems)-based Structure-from-Motion Photogrammetry. The approach is usable in a large landscape with the aim of reducing crown fire potential by considering the spatial variability of fuels within a stand. The utilization of UAS for photogrammetry holds immense promise in transforming the approach to assessing and managing forest fires. This cutting-edge technology offers the potential to deliver highly precise and comprehensive data concerning forest structure and connectivity, thereby presenting a groundbreaking opportunity for enhanced forest fire analysis and control.

Resource objective wildfire leveraged to restore old growth forest structure while stabilizing carbon stocks in the southwestern United States

Wildfire futures and aboveground carbon (C) dynamics associated with forest restoration programs that integrate resource objective wildfire as part of a larger treatment strategy are not well understood. Using simulation modeling, we examined alternative forest and fuel management strategies on a 237,218-ha study area within a 778,000-ha landscape that is a high priority target for federal restoration programs. We simulated two wildfire management scenarios combined with three levels of conventional forest restoration treatments over 64 years using a detailed landscape disturbance and succession model developed in prior work. We found accelerated forest restoration used in concert with resource objective wildfire was the most effective at returning old growth forest structure, while stabilizing aboveground C stocks and restoring the fire return interval to its historic range of variation. In scenarios without forest restoration, the continued practice of resource objective wildfires during shoulder fire seasons reduced summer emissions in a negative feedback loop. In the short term, scenarios without forest restoration increased live tree C, but also increased the likelihood of C loss during wildfire activity driven by extreme fire weather. We found scenarios most effective at restoring fire-excluded pine forests to their historical old growth conditions came at a short-term cost of lost C, but with the long-term benefit of substantially increasing fire-resistant live tree C. Our results inform how local decision making can best balance competing goals of sequestering C, and stabilizing C stocks in frequent-fire pine forests using the principles of local fire ecology to restore and maintain old growth forest structure.

Improving the prediction of wildfire susceptibility on Hawaiʻi Island, Hawaiʻi, using explainable hybrid machine learning models

This study presents a comparative analysis of four Machine Learning (ML) models used to map wildfire susceptibility on Hawaiʻi Island, Hawaiʻi. Extreme Gradient Boosting (XGBoost) combined with three meta-heuristic algorithms – Whale Optimization (WOA), Black Widow Optimization (BWO), and Butterfly Optimization (BOA) – were employed to map areas susceptible to wildfire. To generate a wildfire inventory, 1408 wildfire points were identified within the study area from 2004 to 2022. The four ML models (XGBoost, WOA-XGBoost, BWO-XGBoost, and BOA-XGBoost) were run using 14 wildfire-conditioning factors categorized into four main groups: topographical, meteorological, vegetation, and anthropogenic. Six performance metrics – sensitivity, specificity, positive predictive values, negative predictive values, the Area Under the receiver operating characteristic Curve (AUC), and the average precision (AP) of Precision–Recall Curves (PRCs) – were used to compare the predictive performance of the ML models. The SHapley Additive exPlanations (SHAP) framework was also used to interpret the importance values of the 14 influential variables for the modeling of wildfire on Hawaiʻi Island using the four models. The results of the wildfire modeling indicated that all four models performed well, with the BWO-XGBoost model exhibiting a slightly higher prediction performance (AUC = 0.9269), followed by WOA-XGBoost (AUC = 0.9253), BOA-XGBoost (AUC = 0.9232), and XGBoost (AUC = 0.9164). SHAP analysis revealed that the distance from a road, annual temperature, and elevation were the most influential factors. The wildfire susceptibility maps generated in this study can be used by local authorities for wildfire management and fire suppression activity.

Wildfire management and evacuation in indigenous communities in Canada and the United States: A scoping review

Indigenous people and communities are disproportionately affected by wildfire and the accompanying smoke, motivating calls for revitalization, integration, and greater engagement with Indigenous people regarding fire knowledges and practices. We conducted a scoping review of literature on wildfire management and evacuation in Canada and the United States to understand impacts on Indigenous communities, and to inform ways that Indigenous communities can be more involved in decision-making in these areas. The literature reviewed suggests that, despite progress, there are still a range of barriers that leave little scope for Indigenous participation in shaping wildfire management on their territories. The literature also illustrates that the evacuation process can be traumatic and, at times, more harmful than the risk posed by the wildfire itself. However, the challenge is deeper than government agencies and external support organizations needing to improve their acceptance and utilization of Indigenous experiences and knowledges, identify opportunities for effective partnerships between government and Indigenous communities and organizations, and provide adequate funding and resources. More so, the challenge relates to the need to make space for Indigenous people's self-determination, with more agency and control over key aspects of wildfire management and evacuation. Some of the most promising examples from the literature were situations in which Indigenous communities took initiative on their own, had more control over decisions, or were otherwise enabled to express their collective agency in both wildfire management and evacuation. The pursuit of more integrative and holistic approaches in wildfire management generally will strengthen Indigenous community sovereignty, capacity, and community resilience.

Improving societal resilience through a GIS-based approach to manage road transport networks under wildfire hazards

Climate change is causing an increase in the frequency and intensity of wildfires, demonstrating that our capacity to respond to them is insufficient. Therefore, it is necessary to reconsider wildfire management policies, practices, and decision-support tools, extending beyond emergency measures. This study presents the extension of a GIS-based methodology for fire analysis, providing decision-making support for the implementation of new fire-related policies for road transportation infrastructure. It represents a novel contribution that facilitates the transition towards proactive wildfire policies. The framework is demonstrated to support informed decision-making, addressing both reactive actions, i.e., emergency response, and the evaluation of proactive adaptation measures at a system level. The results suggest that landscape management policies can play an important role in improving the resilience of road networks to wildfires.

Analysis of Wildfire Danger Level Using Logistic Regression Model in Sichuan Province, China

Sichuan Province preserves numerous rare and ancient species of plants and animals, making it an important bio-genetic repository in China and even the world. However, this region is also vulnerable to fire disturbance due to the rich forest resources, complex topography, and dry climate, and thus has become one of main regions in China needing wildfire prevention. Analyzing the main driving factors influencing wildfire incidence can provide data and policy guidance for wildfire management in Sichuan Province. Here we analyzed the spatial and temporal distribution characteristics of wildfires in Sichuan Province based on the wildfire spot data during 2010–2019. Based on 14 input variables, including climate, vegetation, human factors, and topography, we applied the Pearson correlation analysis and Random Forest methods to investigate the most important factors in driving wildfire occurrence. Then, the Logistic model was further applied to predict wildfire occurrences. The results showed that: (1) The southwestern Sichuan Province is a high-incidence area for wildfires, and most fires occurred from January to June. (2) The most important factor affecting wildfire occurrence is monthly average temperature, followed by elevation, monthly precipitation, population density, Normalized Difference Vegetation Index (NDVI), NDVI in the previous month, and Road kernel density. (3) The Logistic wildfire prediction model yielded good performance, with the area under curve (AUC) values higher than 0.94, overall accuracy (OA) higher than 86%, true positive rate (TPR) values higher than 0.82, and threat score (TS) values higher than 0.71. The final selected prediction model has an AUC of 0.944, an OA of 87.28%, a TPR of 0.829, and a TS of 0.723. (4) The results of the prediction indicate that extremely high danger of wildfires (probability of fire occurrence higher than 0.8) is concentrated in the southwest, which accounted for about 1% of the area of the study region, specifically in Panzhihua and Liangshan. These findings demonstrated the effectiveness of the Logistic model in predicting forest fires in Sichuan Province, providing valuable insights regarding forest fire management and prevention efforts in this region.

Spatial-temporal characteristics and influencing factors of wildfire occurrence and correlation with WUI presence in Beijing-Tianjin-Hebei region, China

Wildfires not only directly affect ecology but also have a huge impact on social and economic stability. Enhancing the understanding of wildfire influences can guide the design of targeted strategies in mitigation plans and adaptation strategies. Here, we explored the spatial-temporal characteristics of wildfires in the Beijing-Tianjin-Hebei region of China and the mechanisms of the effect of potential influencing factors on wildfire occurrence based on 2014–2020 wildfire data. The results show that wildfires mainly occur in spring and winter and decrease yearly. Among all the considered influencing factors, the occurrence of wildfire is most significantly affected by the temperature vegetation dry index (TVDI) and is also greatly affected by population and economy. Through interaction detection, it was found that the normalized difference vegetation index (NDVI) greatly interfered with wildfires. The high occurrence risk of wildfire was distributed in a blocky pattern in the middle and east of the study area. In addition, we also found a positive spatial correlation between the high wildland-urban interface (WUI) presence and high wildfire occurrence. These findings have improved our understanding of the impact of multiple influencing factors on wildfires and are supposed to facilitate the development of wildfire management strategies.

Wildfire univariate and bivariate characteristics simulation based on multiple machine learning models and applicability analysis of wildfire models

Wildfires can significantly impact regional and global climate, human health, and ecosystems, making it necessary to model their behavior and predict their outcomes. With increasing global temperatures and changing precipitation patterns due to climate change, the frequency and intensity of wildfires are expected to increase, heightening the requirement for accurate wildfire simulation models to support wildfire management and mitigation efforts. However, the interactions between the causative variables of wildfires and the wildfire bivariate characteristics have not been explored in wildfire modeling. Therefore, the copula function was applied to solve the complicated and nonlinear relationship of the dependence structure in wildfire statistics and the relationship between wildfire causative variables. Subsequently, we modeled wildfire characteristics globally using six machine learning models and compared the performances of the models. Specifically, the main conclusions were obtained as follows: (1) among the six machine learning models, long short-term memory had the best applicability in simulating wildfire characteristics; (2) when there were 4 predictors, the accuracy of wildfire characteristic simulation reached the average level; and (3) long short-term memory achieved excellent model performance within 56% of the global climate sub-regions. Overall, this analysis provides a reference to better understand wildfire and contributes to wildfire management.

How Does Fire Suppression Alter the Wildfire Regime? A Systematic Review

Fire suppression has become a fundamental approach for shaping contemporary wildfire regimes. However, a growing body of research suggests that aggressive fire suppression can increase high-intensity wildfires, creating the wildfire paradox. Whether the strategy always triggers the paradox remains a topic of ongoing debate. The role of fire suppression in altering wildfire regimes in diverse socio-ecological systems and associated research designs demands a deeper understanding. To reconcile these controversies and synthesize the existing knowledge, a systematic review has been conducted to screen 974 studies on the relationship between fire suppression and wildfire regimes. The rigorous screening process led to the selection of 37 studies that met our stringent criteria for inclusion. The selected literature was quantitatively analyzed in terms of study areas, study design and methods, and the impact of fire suppression on wildfire regimes. Several critical findings were revealed: 1. Numerous studies have focused on northern mid- and high-latitude biomes, neglecting tropical savannas where wildfires are frequent and intense. Further exploration in these regions is imperative. 2. Existing studies have predominantly employed methods such as difference analysis, regression analysis, and scenario simulations. Appropriate methods could be selected based on the study area, data availability, and understanding of fire regimes. 3. Despite the consensus that fire suppression reduces the total burned area, the emergence of the wildfire paradox remains controversial, with approximately equal amounts of the literature supporting and contradicting the wildfire paradox. A noteworthy pattern was observed: the wildfire paradox is more likely to occur in fuel-limited systems, specific vegetation types, and smaller scale and longer term studies. This systematic review highlights that the occurrence of the wildfire paradox is intricately tied to ecosystem feedback mechanisms for suppression and the research scale adopted. It is necessary to incorporate a comprehensive and multi-scale assessment of how local wildlands respond to suppression into wildfire management policy-making processes. This assessment will ensure a more informed and effective wildfire management strategy adapted to local conditions.

Wildfire Detection Using Convolutional Neural Networks and PRISMA Hyperspectral Imagery: A Spatial-Spectral Analysis

The exacerbation of wildfires, attributed to the effects of climate change, presents substantial risks to ecological systems, infrastructure, and human well-being. In the context of the Sustainable Development Goals (SDGs), particularly those related to climate action, prioritizing the assessment and management of the occurrence and intensity of extensive wildfires is of utmost importance. In recent times, there has been a significant increase in the frequency and severity of widespread wildfires worldwide, affecting several locations, including Australia, Italy, and the United States of America. The presence of complex phenomena marked by limited predictability leads to significant negative impacts on biodiversity and human lives. The utilization of satellite-derived data with neural networks, such as convolutional neural networks (CNNs), is a potentially advantageous approach for augmenting the monitoring capabilities of wildfires. This research examines the generalization capability of four neural network models, namely the fully connected (FC), one-dimensional (1D) CNN, two-dimensional (2D) CNN, and three-dimensional (3D) CNN model. Each model’s performance, as measured by accuracy, recall, and F1 scores, is assessed through K-fold cross-validation. Subsequently, T-statistics and p-values are computed based on these metrics to conduct a statistical comparison among the different models, allowing us to quantify the degree of similarity or dissimilarity between them. By using training data from Australia and Sicily, the performances of the trained model are evaluated on the test dataset from Oregon. The results are promising, with cross-validation on the training dataset producing mean precision, recall, and F1 scores ranging between approximately 0.97 and 0.98. Especially, the fully connected model has superior generalization capabilities, whilst the 3D CNN offers more refined and less distorted classifications. However, certain issues, such as false fire detection and confusion between smoke and shadows, persist. The aforementioned methodologies offer significant perspectives on the capabilities of neural network technologies in supporting the detection and management of wildfires. These approaches address the crucial matter of domain transferability and the associated dependability of predictions in new regions. This study makes a valuable contribution to the ongoing efforts in climate change by assisting in monitoring and managing wildfires.

Estimating the economic value of carbon losses from wildfires using publicly available data sources: Eagle Creek Fire, Oregon 2017

BackgroundWildfires are increasingly frequent in the Western US and impose a number of costs including from the instantaneous release of carbon when vegetation burns. Carbon released into the atmosphere aggravates climate change while carbon stored in vegetation helps to mitigate climate change. The need for climate change mitigation is becoming more and more urgent as achieving the Paris climate agreement target of limiting global warming to 1.5 °C seems ever more challenging. A clear understanding of the role of different carbon sources is required for understanding the degree of progress toward meeting mitigation objectives and assessing the cost and benefits of mitigation policies.ResultsWe present an easily replicable approach to calculate the economic cost from carbon released instantaneously from wildfires at state and county level (US). Our approach is straightforward and relies exclusively on publicly available data that can be easily obtained for locations throughout the USA. We also describe how to apply social cost of carbon estimates to the carbon loss estimates to find the economic value of carbon released from wildfires. We demonstrate our approach using a case study of the 2017 Eagle Creek Fire in Oregon. Our estimated value of carbon lost for this medium-sized (19,400 ha) fire is $187.2 million (2020 dollars), which highlights the significant role that wildfires can have in terms of carbon emissions and their associated cost. The emissions from this fire were equivalent to as much as 2.3% of non-fire emissions for the state of Oregon in 2020.ConclusionsOur results demonstrate an easily replicable method for estimating the economic cost of instantaneous carbon dioxide emissions for individual wildfires. Estimates of the potential economic costs associated with carbon dioxide emissions help to provide a more complete picture of the true economic costs of wildfires, thus facilitating a more complete picture of the potential benefits of wildfire management efforts.

Community Forests advance local wildfire governance and proactive management in British Columbia, Canada

As wildfires are increasingly causing negative impacts to communities and their livelihoods, many communities are demanding more proactive and locally driven approaches to address wildfire risk. This marks a shift away from centralized governance models where decision-making is concentrated in government agencies that prioritize reactive wildfire suppression. In British Columbia (BC), Canada, Community Forests—a long-term, area-based tenure granted to Indigenous and/or local communities—are emerging as local leaders facilitating proactive wildfire management. To explore the factors that are enabling local governance approaches to managing wildfire risk, we conducted semi-structured interviews with 26 Community Forest managers across BC. Managers highlighted financial and social capacity, especially trust and relationships with both community members and government agencies, as crucial factors influencing their ability to undertake proactive management. These factors enable Community Forests to address wildfire risk not only within their own tenure area, but also at household, community, and landscape scales, while balancing diverse community values, objectives for forest management, and legal and policy obligations. Despite ongoing challenges, Community Forests emphasized the importance of scaling up their efforts to address wildfire risk and are a critical form of local wildfire governance that can help advance proactive wildfire management across BC.

Indigenous Fire Futures

Abstract Dominant causal explanations of the wildfire threat in California include anthropogenic climate change, fire suppression, industrial logging, and the expansion of residential settlements, which are all products of settler colonial property regimes and structures of resource extraction. Settler colonialism is grounded in Indigenous erasure and dispossession through militarism and incarceration, which are prominent tools in California's fire industrial complex. To challenge settler colonial frameworks within fire management, Indigenous peoples are organizing to expand Indigenous cultural controlled burning, fire stewardship, and sovereignty. These initiatives emphasize reciprocal human-fire relations and uphold Indigenous knowledge systems and livelihoods. Concurrently, Indigenous fire sovereignty is threatened by knowledge appropriation and superficial collaborations. In this article, we review contemporary research on Indigenous burning in order to highlight the strategies that Indigenous communities and scholars employ to subvert colonial power relations within wildfire management and actualize regenerative Indigenous futures.

Wildfires Risk Assessment Using Hotspot Analysis and Results Application to Wildfires Strategic Response in the Region of Tangier-Tetouan-Al Hoceima, Morocco

In recent years, changes in climate, land cover, and sociodemographic dynamics have created new challenges in wildfire management. As a result, advanced and integrated approaches in wildfire science have emerged. The objective of our study is to use geospatial analysis to identify strategic responses to wildfires in the Tangier-Tetouan-Al Hoceima (TTA) region, widely reputed to exhibit the most significant incidences of wildfires in Morocco. We adopted a combined approach, using burned area products (Fire_CCI51: 2002–2020) from the Moderate Resolution Imaging Spectroradiometer (MODIS) and active fires from the Fire Information for Resource Management System (FIRMS: 2001–2022) and processing them with spatiotemporal statistical methods: optimized hotspot analysis (OHA) and emerging hotspot analysis (EHA). The main findings indicate that the TTA region recorded an average of 39.78 km2/year of burned areas, mostly located in forests (74%), mainly cork oak and matorral stands (50%). The OHA detected hotspots covering 2081 km2, with 63% concentrated in the provinces of Chefchaouen and Larache. Meanwhile, clusters of EHA extended over 740 km2 and were composed of the oscillating coldspot (OCS) and oscillating hotspot (OHS) patterns at 50% and 30%, respectively. Additionally, an average of 149 fires/year occurred, located mostly in forests (75%), mainly cork oak and matorral stands (61%). The OHA detected active fire hotspots covering 3904 km2, with 60% located in the provinces of Chefchaouen and Larache. Clusters of EHA over 941 km2 were composed of the oscillating hotspot (OHS) and new hotspot (NHS) patterns at 57% and 25%, respectively. The prevalence of the oscillating and new models mirrors, respectively, the substantial fluctuations in wildfires within the region alternating between periods of high and low wildfire activities and the marked increase in fires in recent times, which has occasioned the emergence of novel hotspots. Additionally, we identified six homogeneous wildfire zones to which we assigned three strategic responses: “maintain” (73% of the territory), “monitor and raise awareness” (14% of the territory), and “reinforce” (13% of the territory). These strategies address current wildfire management measures, which include prevention, risk analysis, preparation, intervention, and rehabilitation. To better allocate firefighting resources, strategic responses were classified into four priorities (very high, high, medium, and low). Last, the wildfire zoning and strategic responses were validated using burned areas from 2021 to 2023, and a global scheme was suggested to assess the effectiveness of future wildfire measures.

Wildfire Risk Assessment Considering Seasonal Differences: A Case Study of Nanning, China

Wildfire disasters pose a significant threat to the stability and sustainability of ecosystems. The assessment of wildfire risk based on a seasonal dimension has contributed to improving the spatiotemporal targeting of fire prevention efforts. In this study, Nanning, China, was selected as the research area. The wildfire driving factors were chosen from both seasonal and nonseasonal aspects, and the datasets were divided into five periods: all seasons, spring, summer, autumn, and winter. The light gradient boosting machine (LGBM) was employed to construct wildfire danger models for different periods, evaluating the spatial distribution of high-wildfire-danger areas during these periods and the predictive performance differences. The SHapley Additive exPlanations (SHAP) method was utilized to analyze the differential contributions of various factors to wildfire occurrence in different seasons. Subsequently, the remote sensing ecological index (RSEI) was calculated using four indicators, greenness, heat, wetness, and dryness, to assess the ecological vulnerability in different seasons. Finally, by integrating danger and vulnerability information, wildfire risk models were developed to systematically assess the risk of wildfire disasters causing losses to the ecological environment in different seasons. The results indicate that: (1) The evaluation of wildfire danger based on individual seasons effectively compensates for the shortcomings of analyzing danger across all seasons, exhibiting higher predictive performance and richer details. (2) Wildfires in Nanning primarily occur in spring and winter, while the likelihood of wildfires in summer and autumn is relatively lower. In different seasons, NDVI is the most critical factor influencing wildfire occurrence, while slope is the most important nonseasonal factor. The influence of factors varies among different seasons, with seasonal factors having a more significant impact on wildfire danger. (3) The ecological vulnerability in Nanning exhibits significant differences between different seasons. Compared to spring and winter, the ecological environment is more vulnerable to wildfire disasters during summer and autumn. (4) The highest wildfire risk occurs in spring, posing the greatest threat to the ecological environment, while the lowest wildfire risk is observed in winter. Taking into account information on danger and vulnerability in different seasons enables a more comprehensive assessment of the risk differences in wildfire disasters causing ecological losses. The research findings provide a scientific theoretical basis for relevant departments regarding the prevention, control, and management of seasonal wildfires.

The Influence of Socioeconomic Factors on Human Wildfire Ignitions in the Pacific Northwest, USA

Historical land and fire management practices coupled with climate change and modern human development pressures are contributing to larger, more frequent, and more severe wildfires across Western U.S. forests. Human ignitions are the predominant cause of wildfire throughout the United States, necessitating wildfire management strategies that consider both the causes of human ignitions and the factors that influence them. Using a dataset of over 104,000 ignitions from 1992 to 2018 for Oregon and Washington (U.S), we examine the major causes of wildfire ignitions and build regression models to evaluate the potential influence of both biophysical and socioeconomic factors on human and natural ignitions across distinct fire regimes west and east of the Cascade Range. Our results corroborate prior findings that socioeconomic factors such as income, employment, population density, and age demographics are significantly correlated with human ignitions. In the Pacific Northwest, we found that the importance of socioeconomic factors on human ignitions differs significantly between the west and east sides of the Cascade Range. We also found that most human ignitions are linked to escaped fires from recreation or debris and open burning activities, highlighting opportunities to tailor wildfire prevention efforts to better control higher risk activities and reduce accidental ignitions.

Local community participation and volunteerism in wildfire area management: A systematic review

Wildfires have caused considerable economic, ecological, and social damage in recent years. For this reason, decision-makers in wildfire management (WFM) have face increasingly difficult challenges. While decision support systems help decision-makers, it is clear that some deficiencies still persist. In this context, it is thought that the active role of local people involved in wildfire management can eliminate these deficiencies because they have experience and direct knowledge about fighting wildfires in Wildland Urban Interface (WUI) areas. Hence, their expertise can be utilized in future wildfire containment efforts. In this study, the author presents some general information about WUI, public participation, and the decision-making process and its relation to wildfires. In addition, the author will also examine previous studies on the participation of local people in wildfire management and investigate the decision-making processes regarding this issue by using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) technique. The results of this study suggest that there should be a widespread adoption of the community-based integrated fire management (CBIFM) approach, instead of the top-down management approach commonly employed in wildfire management. The results strongly suggest that decision-makers should reach a consensus as to how the principle of participation in fire management should work by reviewing the socio-economic interests of the local community.