Wildfires are sudden and destructive natural hazards that pose significant challenges in response and relief efforts. Wildfires occur annually across the globe, influenced by factors such as climate, combustible materials, and ignition sources. In recent years, researchers have shown increasing interest in studying wildfires, resulting in a large number of related studies. These studies cover a variety of topics including wildfire forecasting and forecasting, spatial and temporal pattern analysis, ecological impact assessment, simulation of wildfire behavior, identification of contributing factors, development of risk assessment models, management techniques for combustible materials, firefighting decision-making techniques, and fire protection. burning method. Understanding the factors that influence wildfire spread behavior, employing modeling methods, and conducting risk assessments are critical for effective wildfire prevention, mitigation, and emergency response.野火是一种突发且极具破坏性的自然灾害,在响应和救援工作方面带来了重大挑战。受气候、可燃材料和点火源等因素的影响,野火在全球范围内每年都在发生。近年来,研究人员对研究野火表现出越来越大的兴趣,导致了大量的相关研究。这些研究涵盖了各种主题,包括野火预测和预报,空间和时间模式分析,生态影响评估,野火行为模拟,影响因素的识别,风险评估模型的开发,可燃材料的管理技术,消防决策技术和阻燃方法。了解影响野火蔓延行为的因素、采用模拟方法和进行风险评估对于有效的野火预防、减灾和应急响应至关重要。
From Figure 1, it can be observed that the overall keywords related to forest fires/wildfires (referred to as “wildfires” collectively) are divided into four clusters. “Wildfires” are the most central and core term, located in the center of the network graph. In the red cluster on the left side, “severity”, “vegetation”, “fire regime”, and “diversity” are some of the typical keywords. In the literature, this cluster mainly represents research in the field of regional ecology, such as studies related to vegetation after wildfires [6][7][23,24]. In the yellow cluster below, “soil”, “rainfall”, “erosion”, and “organic-matter” are some representative keywords. The research direction of this cluster primarily focuses on the chemical changes in the environment (especially soil) after wildfires [8][9][10][25,26,27]. It also includes some concurrent disasters, such as rainfall and debris flows [11][1[28,2]9]. The blue cluster on the right side of the map includes representative keywords such as “impact”, “emission”, “smoke”, and “air pollution.” The research related to these keywords is mostly focused on the atmospheric environment field, such as studies on gas emissions and air pollution caused by fires [1[3][0,314][15][16],32,33].
proposed a new fire spread modeling approach, LSSVM-CA, which combines the least squares support vector machine (LSSVM) with a three-dimensional wildfire CA framework and considers the effect of adjacent winds on fire spread patterns for analysis. To summarize the cases, the combination of wildfire spread visualization and the application system developed by meta-automata has become a popular and viable choice in simulating wildfire spread behavior.
The wildfire risk assessment method based on wildfire prediction has high accuracy and is greatly affected by weather forecast factors. They are suitable for medium size ranges. On the other hand, the method of establishing a risk index for wildfire risk assessment takes into account a variety of factors and is more suitable for macro-level applications. Methods based on information diffusion theory are more suitable for analyzing small samples due to their inherent characteristics. In recent years, with the rapid development of scientific and technological capabilities, comprehensive wildfire risk assessment using "3S" technology and wildfire risk assessment based on deep learning have been widely used. However, they require strict data accuracy. The accuracy of wildfire risk assessment method and wildfire spread behavior simulation based on wildfire behavior influencing factors is high.
In recent years, there has been significant progress in the research of wildfire spread behavior. This paper aims to systematically construct a wildfire spread behavior and risk assessment system based on a dataset of 20,000 papers from the Web of Science (WOS) core database. Through an analysis using VOSviewer (V_1.6.19), it is evident that the research on wildfire spread behavior and risk assessment is closely related and represents important components of the wildfire research field. Based on this foundation, the paper summarizes the impacts of meteorological, topographical, combustible, and human factors on wildfire spread behavior. Additionally, it provides a brief description of several commonly used software applications for simulating fire spread processes and discusses the selection of data for simulation using this software. Moreover, the paper provides a comprehensive overview of various methods for wildfire spread behavior risk assessment, with a particular focus on two risk assessment methods based on wildfire spread behavior influencing factors and simulation.
This study serves as a valuable reference for wildfire prevention and emergency decision-making. However, it acknowledges the need for further in-depth research in certain areas. The paper suggests that future research should concentrate on the dynamic monitoring of wildfire spread behavior, quantitative analysis of wildfire spread behavior and driving factors, development of wildfire spread behavior simulation algorithms and software, as well as wildfire spread behavior risk assessment.
(1) Enhancing research on dynamic monitoring of wildfire spread behavior is crucial. The key to firefighting is early detection and early resolution. Dynamic monitoring serves as an important foundation for emergency management when wildfires are approaching or just starting, as it can minimize disaster losses and enhance disaster prevention and mitigation capabilities. The data required for wildfire monitoring encompass various aspects, such as fire front position, fire extent, fire tracking, and other monitoring measures. The accuracy of these data directly impacts the effectiveness of monitoring efforts and the ability to reduce losses. In recent years, China has launched multiple domestically developed high-resolution remote sensing satellites and Beidou Navigation Satellites. However, each satellite has its own strengths and limitations. Therefore, it is imperative to explore methods for integrating and utilizing multi-source satellite data in a complementary and coordinated manner to maximize practical applications. Furthermore, it is essential to address the timely, efficient, and accurate communication of monitoring information, particularly during abnormal weather conditions. Developing effective communication channels and systems to disseminate monitoring information to relevant stakeholders is crucial for facilitating prompt decision-making and response actions.
(2) The study of driving factors of wildfires has gained significant attention, and it is crucial to identify and quantify the relationship between wildfires and these driving factors for global wildfire research. Wildfire spread behavior exhibits various characteristics, such as the extent and intensity of fire spread. It is also worth researching and discussing which specific driving factors have a greater impact on these characteristics. Additionally, it is worth exploring whether the driving factors at regional and local scales during wildfire spread exhibit uniformity or significant differences, which is another topic worthy of discussion.
(3) In the current context, due to spatial heterogeneity, most wildfire spread modeling software focuses on specific scenarios and environments, making them less applicable to a wide range of environments and unsuitable for broad-scale implementation. Therefore, in future research, it is important to prioritize the comprehensiveness of the models. For example, incorporating wildfire visualization into wildfire spread models can be explored. On the other hand, since wildfire spread models originated from overseas, many of the parameters in these models are not applicable to the forest conditions in China. In recent years, thanks to the continuous efforts of domestic experts and scholars, wildfire spread models in China have been improving and maturing. In future research, it is necessary to gradually establish accurate and mature wildfire spread models specific to China and develop application software that simulates wildfire spread behavior suitable for China's context.
(4) For wildfire risk assessment, each assessment method has its own applicable environment. The introduction of scientific remote sensing technology and the concepts and techniques of geographic information systems (GIS) has led to significant progress in wildfire spatial data processing and analysis of wildfire spread. These advancements have further facilitated the assessment of wildfire risk at the landscape scale. In future research, the primary focus should be on utilizing 3S technology (Remote Sensing, GIS, and GPS) and deep learning techniques, supplemented by other methods, to achieve a comprehensive and accurate assessment of wildfire spread. This approach will result in more intuitive and scientifically sound outcomes. In summary, conducting precise and scientific wildfire risk assessment can effectively guide relevant departments in implementing sound fire prevention measures, thereby reducing the occurrence of large-scale wildfires and protecting various resources and ecosystems more effectively.