Forest Fire Risk Research Articles

Monchique’s Innovation Laboratory—A Space for Dialogue and Knowledge Sharing to Foster Community-Based Disaster Risk Reduction

BRIDGE (bridging science and local communities for wildfire risk reduction) is an action–research project developing a community-based disaster risk reduction (CBDRR) process which is being fostered through a collaborative innovation laboratory (InnoLab). BRIDGE integrates different forms of knowledge and action, linking science and local communities to reduce their vulnerability and to enhance strategies for forest fire risk reduction. Applied in Monchique, a forest-fire-prone municipality in Portugal, the InnoLab creates a space for dialogue and knowledge sharing between multiple actors that, directly or indirectly, manage forest territories. BRIDGE attempts to facilitate social learning about forest fire risks, strengthen collaborative networks and enhance adaptive capacities (socially and institutionally) for forest fire prevention. This paper shares results of the InnoLab activities, which include (i) school community activities on wildfire risk perception; (ii) participatory mapping of Monchique’s vulnerabilities and risks; (iii) participatory sessions to create the community visioning for Monchique’s territory, to define priorities and capacities to be improved, and to identify functions and responsibilities to act upon; and (iv) workshop on CBDRR strategies for action. Lessons are shared on how InnoLab can represent an innovative participatory approach to promote CBDRR processes in forest-fire-prone territories by both contributing to the recognition of local knowledge systems and encouraging the active role of forest communities through strengthened local networks for a more lasting commitment to forest fire risk reduction policy.

Količine goriva u šumskoj prostirci sastojina hrasta crnike (Quercus ilex L.) i hrasta medunca (Quercus pubescens Willd.)

The application of newer remote sensing methods, such as aerial and terrestrial lidar scanning and the use of "Structure-from-motion" (SfM) photogrammetry, complemented field data collection and enabled 3D mapping of forest fuel layers, greatly simplifying and improving their characterization. However, these methods are not suitable for quantifying forest floor characteristics. For this purpose, it is still necessary to collect data using classical field methods, determining the presence of subhorizons and their depth, while the characteristics of the forest floor: bulk density, load, carbon concentration and carbon stock are determined in the laboratory. Therefore, it is still common practice to create regression equations that allow operatives to determine the amount of available forest floor fuel and the carbon stock it contains based on the depth of the forest floor, which is an easily measurable variable, or to determine forest floor loading by subhorizon and overall. Forest floor information is used in models for predicting forest fire behavior and spread, in fire effects models, in planning and monitoring mechanical fuel reduction, in quantifying fuel consumption and smoke emissions, in quantifying carbon stocks, in describing habitat and its productivity, and in planning for preparedness. As stands of holm oak (Quercus ilex L.) and pubescent oak (Quercus pubescens Willd.) are located in the Mediterranean part of Croatia, where the risk of forest fires is the highest, and the previously published data on the forest floor are not suitable for the models, the main objectives of the research were to determine the depth, bulk density and load of individual subhorizons of the forest floor and to create regression equations that allow estimating the amount of available fuel in the forest floor based on its depth. Sampling of forest floor in holm oak stands (60, 90 and 100 years old) was conducted on the islands of Lastovo and Mljet, while sampling in pubescent oak stands (53 and 90 years old) was conducted in Dugopolje and Biograd na Moru. The amount of forest floor and OC stock in old stands of holm oak and pubescent oak is twice as small as in old stands of Aleppo pine and Dalmatian black pine. This suggests that pine stands have a significantly greater amount of potentially available fuel in the forest floor and are therefore potentially more at risk from fire, but also that they store twice as much carbon in the forest floor. In this study, for the first time in Croatia, bulk densities of individual subhorizons of the forest floor were determined for holm oak and pubescent oak and regression equations were created that allow estimating the amount of available fuel in the forest floor based on its depth per subhorizon and overall. The results of this research have practical value in simpler quantification of forest floor fuel loading, which is important for operational use of models to predict forest fire behavior and spread, but they can also be used in other models mentioned earlier.

Forest Fire Risk Mapping Using GIS Based Analytical Hierarchy Process Approach

Turkiye is located in a region sensitive to forest fires due to the Mediterranean climate, vegetation and topography of the south and west coasts. Every year, forest fires for various reasons cause thousands of hectares of forest area to be burned. Fires damage the ecosystem as well as have economic consequences. While the Mediterranean, Aegean and Marmara coasts, where the Mediterranean climate and fire-sensitive tree species are dominant, are at primary risk against forest fires, the forests located on the Black Sea coasts where the Black Sea climate is dominant are less risky in terms of fire. For an effective fight against forest fires, it is crucial to identify zones with fire risk based on various parameters such as forest structures (tree species, crown closure, stand age), topographic features (slope, aspect), climate, and proximity to certain points (such as roads, settlements, agricultural areas). Fire risk data will shed light on the measures that can be taken against fire. In this study, GIS (Geographic Information Systems) based Analytical Hierarchy Process (AHP) method, one of the well know MCDA (Multi-criteria Decision Making Analysis) methods, was used to develop the fire risk map of Mersin Forestry Regional Directorate (FRD) within the Mediterranean region of Turkiye. Then, the accuracy of the fire risk map was evaluated by taking into account the previous fires in the regional directorate. As a result, it was found that 13.87% of the study area was classified as very high, 25.87% high, 24.68% medium, 22.44% low and 13.14% very low risk areas. The results showed that tree species are the most effective risk factor in forest fires, and followed by stand age factor. The accuracy of the fire risk map was evaluated by using the location information of a total of 562 fires in Mersin FRD between 2003-2022 that damaged forest areas. In order to determine the accuracy of the fire risk map, ROC (Receiver Operating Characteristic) curve method was used in ArcGIS environment. As a result, the AUC (Area Under Curve) value was found to be approximately 74%, and these values showed that the fire risk map developed for Mersin FRD was moderately reliable. With this study, it has been demonstrated that it is possible to produce reliable fire risk maps in a short time using the GIS-based AHP method.

Spatio-Temporal Dynamics of Forest Fires in Poland and Consequences for Fire Protection Systems: Seeking a Balance between Efficiency and Costs

An important issue from the forest fire protection system perspective is forecasting fires and maintaining a high readiness of firefighting units at a low cost. In Poland, the level of fire protection is defined by the degree of forest fire risk, based on forecasts made for 1 March to 30 September, i.e., the risk period (the so-called fire season). In this paper, based on classical statistical and spatial analysis, we positively verify the research hypothesis that the fire season in Poland changes in terms of start and end dates depending on the region. We therefore propose a modification to the forecasting period, adapted to local conditions and calculated annually on the basis of the previous period. Then, using network analysis, we negatively verify the hypothesis that the size of the burned area is related to the distance from fire brigades, which proves that the reasons for such a differentiation in the number of fires should be sought elsewhere than in the system used for allocating fire units. On the basis of cluster analysis, attention is drawn to areas where large fires break out more often.

DEVELOPING A MULTI-VARIABLE FOREST FIRE RISK MODEL AND FIRE RISK ZONE MAPPING

Abstract. Forest steppe and taiga regions with a dry climate, small fires occur frequently between large fires, and therefore groups of trees of various ages and structures are formed in these forests, which has the effect of increasing fire exposure and "fuel reserves". The main goal of this research is to develop a multi-variable forest fire risk model and to mapping forest fire risk. The study area is the forest community in Bulgan province. Forest cover maps are essential for current research of community. We used in this research Landsat OLI and Digital Elevation Model (DEM) data. Soil moisture index and land surface temperature data from Landsat satellite data and elevation data (DEM) were used to determine slope and aspect. A multi-variable forest fire risk model was developed in this research. The final output delineated fire risk zones in the study area in four categories that include high-risk, moderate-risk, low-risk, and non-fire risk zones. Accuracy assessments on the two Landsat scenes indicates that forest/non-forest maps derived using the forest index (FI) have high accuracy.

Forest fires in a changing climate: risk assessment and management in Leiria national forest, Portugal

Forest ecosystems are vital for sustainable development and human well-being globally and in Europe. Sustainably managed forests are fundamental in combating natural disasters and providing multiple important goods and services for humans and the environment. However, with increasing climate change and its associated effects, forests have become severely and regularly prone to fires. This is seriously threatening forest protection, human safety, the economy, and biodiversity. In this context, understanding future forest fire risks, susceptibility, hazards, and fire prevention is essentially needed. This study thus examines the forest fire risks and hazards in Leiria National Forest (Mata Nacional de Leiria), Portugal using the 2017 forest fire as a benchmark. With the adoption of GIS and remote sensing techniques and data, vegetation type (NDMI), human factors (roads and settlement proximity), and terrain characteristics (slope and aspect) were assessed to map fire risk. Through multi-criteria analysis, these data were integrated to generate a forest fire risk index. Results demonstrate that about 46% of the study area is within high-risk and risky zones, 50% is considered moderate-risk fire zones and 3% is classified as low and risk-free zones. Sensitivity analysis indicated that high-risk areas are mostly low moisture coniferous fuel types while risk-free areas are high moisture deciduous fuel types. Further, it was established that the observed high-risk and risky zones are attributed mostly to proximity to settlements and roads and little topographical influence. The study thus suggests an increased future forest fire risk under the prevailing conditions and a hiking potential of increased burnt areas. We thus proposed effective proactive measures and adaptive management approaches to prevent and mitigate the devastating impacts of forest fires within the study location.

Rules for the construction of firebreaks along public roads in selected European countries

Abstract Firebreaks are one of the protective measures used to safeguard forests in case of fire. Their purpose is to limit the possibility of fire spreading. This article describes how these protective measures are implemented along public roads in selected European countries. Based on the information gathered, it was found that there is a wide variety of approaches to firebreaks’ construction, mostly due to different climatic and geomorphological conditions that influence the overall risk of forest fires. The forms of firebreaks used in Europe along public roads are either a mineralised belt (without vegetation) or a cleared belt of materials that are particularly susceptible to ignition and fire spread. Methods combining both are also used. In Poland, there is a Type A belt, that is, a 30-m-wide belt at the border of a roadway or a facility that is free of dead trees, lying branches and felled or broken trees. In the absence of pan-European rules on preventive measures to protect forests from road fires, it might be helpful to establish a set of best practices that could assist in the revision of guidelines in individual countries.

Modeling the forest fire risk by incorporating a new human activity factor from nighttime light data

Fire was a serious natural disturbance to forest ecosystems. In this study, a national-scale forest fire risk model (RF_NTL) was proposed in mainland China by incorporating a new human activity factor (NTL) from nighttime light data. For objective verification of model’s performance, the logistic regression model (LR) and random forest model (RF) were also built in this paper. Results showed that the RF_NTL model fitted with an AUC value up to 0.95, which was superior to the conventional LR model and RF model. The RF_NTL model also had good generalization ability and stability on annual and monthly scales, helping to accurately forecast forest fire risk. Moreover, the spatial distribution of forest fire risk based on RF_NTL model was detailed and reasonable on a national scale. Forest fire high-risk areas in China were mainly concentrated in the northeast and southwest regions. This study could provide references for future relevant research.

Disaster Vulnerability Assessment of Low-Cost Houses in Java Island

Among all of the Indonesian islands, Java poses the highest risk for all types of disasters, making it a significant threat to low-cost houses due to its high population vulnerability. The objective is to enhance comprehension of it by conducting a building typology analysis concerning various natural hazards/ risks. The methodology consists of 10 sample houses field observation, AutoCAD drawing, literature analysis, and built environment expert interviews. The results developed four categories of houses emphasising brick or timber walls with a combination of clay tile or zinc roofs. Related to disaster vulnerability, an improper building structure is the main problem. Smooth roof material is significant in a volcano eruption, a complete interconnection of structure is a must for the tornado, light materials are preferable in an earthquake zone, natural material is not recommended for high risk of forest fire, and elevating floor is mandatory in flooding. Focus on designing the four categories’ houses and quality control of the construction process are two strategies recommended. Also, a minimum of two types of disasters in the area should be considered in the design. Resilient low-cost houses will have an impact on reducing casualties, environmental damage, and economic losses. Covering more samples and areas in Java in future studies will provide a comprehensive understanding of low-cost houses.

Accounting for forest fire risks: global insights for climate change mitigation

Fire is an important risk in global forest loss and contributed 20% to 25% of the global anthropogenic greenhouse gas emissions between 1997 and 2016. Forest fire risks will increase with climate change in some locations, but existing estimates of the costs of using forests for climate mitigation do not yet fully account for these risks or how these risks change inter-temporally. To quantify the importance of forest fire risks, we undertook a global study of individual country fire risks, combining economic datasets and global remote sensing data from 2001 to 2020. Our estimates of forest fire risk premia better account for the risk of forest burning that would be additional to the risk-free and break-even price of credits or offsets to promote carbon sequestration and storage in forests. Our results show the following: (1) forest fire risk premia can be much larger than the historical forest area burned; (2) for some countries, forest fire risk premia have a large impact on the relative country-level break-even price of carbon credits or offsets; (3) a large spatial and inter-temporal heterogeneity of forest fires across countries between 2001 and 2020; and (4) the importance of properly incorporating forest fire risk premia into carbon credits/offset programs. As part of our analysis, and to emphasise the possible sub-national scale differences, our results highlight the heterogeneity in fire risk premia across 10 Canadian provinces.

A Comparative Study of Genetic Algorithm-Based Ensemble Models and Knowledge-Based Models for Wildfire Susceptibility Mapping

Wildfire susceptibility mapping (WSM) plays a crucial role in identifying areas with heightened vulnerability to forest fires, allowing for proactive measures in fire prevention, management, and resource allocation, ultimately leading to more effective fire control and mitigation strategies. This paper describes our undertaking to develop and compare the performance of two knowledge-based models, namely the analytic hierarchy process (AHP) and the technique for order performance by similarity to ideal solution (TOPSIS), as well as two novel genetic algorithm (GA)-based ensemble data-driven models: boosting and random subspace. The objective was to map susceptibility to forest fires in the Northern Mazar District in Jordan. The ensemble models were constructed using four well-known classifiers: decision tree (DT), support vector machine (SVM), k-nearest neighbors (kNN), and naive Bayes (NB) algorithms. This study utilized seventy forest fire locations and twelve influential factors to build and evaluate the models. To identify the optimal features for constructing the data-driven models, a GA-based wrapper method and four machine learning models were applied. During the validation phase, the area under the receiver operating characteristic curve (AUROCC) values for the single SVM, single NB, single DT, single kNN, GA-based boosting, GA-based random subspace, FR-AHP, and AHP-TOPSIS models were found to be 85.3%, 85.9%, 73.8%, 88.7%, 95.0%, 95.0%, 74.0%, and 65.4% respectively. The results indicated that the GA-based ensemble models outperformed both the single machine learning models and the knowledge-based techniques in terms of performance. The developed models in this study can be effectively utilized in various management and decision-making processes aimed at mitigating forest fire risks and enhancing fire control strategies.

Wildfire risk weighting and behaviour prediction using open geospatial data and ontologies

This article presents a novel approach to wildfire risk assessment and behaviour prediction by leveraging open geospatial data and ontologies. The proposed methodology includes a spatially weighted index model and multicriteria analysis to represent the risk of forest fires in the affected area. It bridges gaps in theory and practice, offering a comprehensive solution for evaluating potential forest fire risk in near real time, predicting fire behaviour and elucidating the semantics of fire management. During dry and hot conditions, forest fires tend to escalate. Hence, we propose an algorithm that combines experts’ empirical criteria and open-source data to identify dangerous fires in near real time, aiding authorities in directing attention to the riskiest areas. The objective is to predict forest fire behaviour, a complex and nonlinear system influenced by dynamic factors such as weather conditions, topography and land use. Our methodology enables real-time assessment of potential forest fire risks, complemented by predictive fire behaviour scenarios and a descriptive ontology of fire management semantics. We examine existing fire-related ontologies and propose a comprehensive one encompassing incident descriptions, firefighting resources, actor interrelations and knowledge for effective action. By classifying fire sources, our algorithm enables strategic decision-making to prevent uncontrolled fires. This solution significantly enhances data using semantic and spatial relationships among wildfire resources. Furthermore, we demonstrate how ontologies improve data integration and interoperability among diverse systems and organisations involved in forest fire risk management, fostering better coordination and faster responses to critical situations. To facilitate decision-making, we create decision-making scenarios linked to analysed hot spots, drawing from open hot spot data such as National Aeronautics and Space Administration (NASA) Fire Information for Resource Management System (FIRMS), OpenStreetMap (OSM), OpenWeatherMap (OWM) and OpenTopoData (OTD). We propose an ordinal and linguistic classification system (F1–F5) denoting risk levels as low, moderate, high, very high and extreme. These values are obtained through factor aggregation and fuzzy logic. A publicly accessible, interactive web map displays the results derived from this model. Overall, our contributions to wildfire risk management provide authorities with a valuable tool to make informed decisions and mitigate the damaging effects of wildfires.

FOOD SELF-SUFFICIENCY AT THE LOCAL LEVEL: A CASE STUDY IN PEATLANDS OF SOUTH SUMATRA

The COVID-19 pandemic has brought attention to the need for Indonesia to prepare for future food challenges. The government is focusing on developing underdeveloped areas, including peatlands, which have great potential for integrated farming systems. Bangsal is a village with peat swamp lands that serve as a source of food in OKI Regency, South Sumatera. During the dry season, the land is used for agriculture, while during the rainy season, it is used for fisheries. This paper analyzes (a) the village-based farming patterns in peatland areas, (b) the institutional framework of peatland farmer groups in Bangsal, and (c) the strategy for sustainable peatland utilization for food security. The study used both qualitative and quantitative data analysis methods. The research showed that the role of the Ulak Kuto Mandiri effectively and efficiently increased community income and food self-sufficiency. Integrated farming systems, including swamp buffalo farming, swamp fish farming, and swamp agriculture, optimally and sustainably increased the potential of peatlands. Sustainable peatland utilization through integrated farming systems technically preserves the peatland ecosystem from land conversion, is economically feasible, increases the income of farmers, livestock breeders, and local cultural traditions, and empowers women in farming activities. It also reduces forest fire risks, chemical fertilizer and pesticide use, and increases peatland productivity.

Development of an Index for Forest Fire Risk Assessment Considering Hazard Factors and the Hazard-Formative Environment

Forest fires are characterized by a rapid and devastating nature, underscoring the practical significance of forest fire risk monitoring. Currently, forest fire risk assessments inadequately account for non-meteorological hazard factors, lack the hazard-formative environment and contextual disaster knowledge for fire occurrence mechanisms. In response, based on MODIS products, we augmented the FFDI (forest fire danger index) with the RDST (regional disaster system theory) and selected various fire risk indicators, including lightning. MOD14 was used for the correlation analysis of fire and its indicators. Through the amalgamation of the analytic hierarchy process (AHP), the entropy method, and the minimal relative entropy theory, we formulated the CFFRI (composite forest fire risk index) and assessed forest fire risks spanning from 2010 to 2019 in Southwest China, which were validated with historical disaster data and MCD64. The findings revealed that the CFFRI yields consistently higher overall fire risk values, with 89% falling within the high-risk category and 11% within the moderate-risk category. In contrast, the FFDI designated 56% of cases as fourth-tier fire risks and 44% as third-tier fire risks. Notably, the CFFRI achieved an accuracy of 85% in its calculated results, while the FFDI attained 76%. These outcomes robustly demonstrate a superior applicability of the CFFRI compared with the traditional FFDI.

Evaluation of forest fire risk based on multicriteria decision analysis techniques for Changzhou, China

With the rapid development of China's economy, an increasing number of cities are building forest parks or landscape forests to improve the quality of people's lives and protect the environment. However, forest fires are notoriously difficult to extinguish and have caused tragic losses to cities. Therefore, it is particularly important to conduct a comprehensive assessment of forest fire risk. This study focuses on urban forest fire risk to develop an integrated fire risk model to prevent forest fires. A risk assessment model covering different multicriteria decision analysis (MCDA) techniques was developed to analyze safety vulnerabilities and determine fire risk levels with the spatial analysis functions of geographical information system (GIS) software. Moreover, the accuracy of the model was verified using fire data downloaded from the NASA-FIRMS website. The area under the ROC curve (AUC) was used to evaluate the accuracy of each model. The results show that the AUC value of the combination weighting of game theory (CWGT) method is 0.700. The method has good accuracy and can be used to predict the forest fire risk in Changzhou City. The towns with the highest forest fire risk in Changzhou City were Tianmuhu, Daibu, and Xuebu, with average areas of 8354.77 ha, 5125.92 ha, and 5256.67 ha, respectively, and proportions of 36.20 %, 37.15 %, and 23.43 %, respectively. The conclusion of this study provides a reference and guide for local government management. At the same time, this study considers the emergency rescue capability index and comprehensively evaluates the risk over a time series of pre- and postfire. This is a new attempt and has made new contributions to this field.

Policy narratives in forest fire management

As the risk of forest fires increases around the globe, the issues of how to control, suppress, and prevent them are the subjects of growing public and political attention. This study focuses on the political debate in Germany regarding forest fires and provides insights into the conceptualization of forest fires and forest fire management at the federal and state policymaking levels. By examining forest fire narratives through the policy lens, this case study takes an exemplary extreme weather event exacerbated by climate change as an opportunity to examine the policy response to this problem. In this way, we examine the role of policy narratives in civil and environmental protection and disaster management. The findings reveal that all politicians examined in this study agree that forest fire management is an urgent matter that needs to be supported. In the prevailing human-centered narrative, policymakers see active forest management and use as tools to improve forest resilience to fire and other calamities. Those who advocate a nature-based narrative assert that it is natural processes in protected forest areas that most effectively enhance resilience. The policy solutions derived from these views include financial support, recognition of the work of foresters, forest fire managers, and civil protection agencies as well as, depending on the type of narrative argument favored, either increased forest management or improved protection of forest ecologies. This suggests that narrative analysis may illuminate the rationales underlying previous policy decisions and the framework for future ones. This contribution throws light on how narratives shape policymaking and, by extension, disaster management. Future studies should therefore take into account the influence of prevailing narratives when it comes to evaluating the potential that policymaking can offer for disaster management in the future.

Modification and Comparison of Methods for Predicting the Moisture Content of Dead Fuel on the Surface of Quercus mongolica and Pinus sylvestris var. mongolica under Rainfall Conditions

The surface fine dead fuel moisture content (FFMC) is an important factor in predicting forest fire risk and is influenced by various meteorological factors. Many prediction methods rely on temperature and humidity as factors, resulting in poor model prediction accuracy under rainfall conditions. At the same time, there is an increasing number of methods based on machine learning, but there is still a lack of comparison with traditional models. Therefore, this paper selected the broad-leaved forest tree species Quercus mongolica and the coniferous forest species Pinus sylvestris var. mongolica in Northeast China. Taking surface dead fine fuel as the research object, we used indoor simulated rainfall experiments to explore the impact of rainfall on the surface dead fuel moisture content. The prediction model for surface dead fuel moisture content was modified by the direct estimation method. Finally, using field data, the direct estimation method and convolution neural network (CNN) model were used in the comparison. The rainfall simulation results showed that the indoor fuel moisture content had a logarithmic increasing trend. Rainfall and previous fuel moisture content had a significant impact on the fuel moisture content prediction model, and both the relational model and nonlinear model performed well in predicting fuel moisture content under indoor rainfall conditions. Under field conditions, humidity, temperature and rainfall played a significant role in fuel moisture content. Compared with the unmodified direct estimation method, the modified direct estimation method significantly improved the prediction accuracy and the goodness of fit (R2) increased from 0.85–0.94 to 0.94–0.96. Mean absolute error (MAE) decreased from 9.18–18.33% to 6.86–10.74%, and mean relative error (MRE) decreased from 3.97–17.18% to 3.53–14.48%. The modified direct estimation method has higher prediction accuracy compared with the convolutional neural network model; the R2 value was above 0.90, MAE was below 8.11%, and MRE was below 8.87%. The modified direct estimation method had the best prediction effect among them. This study has a certain reference value for the prediction model of surface fuel moisture content in post-rainfall fire risk assessment and is also of great significance for forest fire management in Northeast China.

Research on the influencing factors and prevention measures of long-term forest fire risk in Northeast China

Forest fires damage ecological environment and increase carbon emissions. To reduce the adverse effects of fire on forests, the long-term (2002–2022) fires data were analyzed in the Great Xing'an Mountains region of Northeast China. The important influencing factors of forest fires were revealed by analyzing different combustibles, meteorological, climatic factors, and the spatial and temporal distribution characteristics of forest fires. The results showed that Ledum palustre, Pinus silvestris L., and Rhododendron dauricum L. were the main tree species with high probability of forest fires according to the flammability difference of combustibles. The young and middle-aged forests accounted for more than 60% of the total forests, which were high flammability. Based on the regression analysis of meteorological data, temperature and precipitation had significant effects on forest fires. Affected by the lightning strikes, the total number of fires in 2017–2022 was high, and the high frequency of fires was in June and August. Because the fires were influenced by factors such as surface vegetation combustibles, precipitation, temperature, and ground slope, the north and southwest slopes had the highest fire incidence rates. In addition to the resource utilization of dead branches and leaves, planting tree species with high fire resistance is an important measure. Artificial precipitations are taken in an extremely dry environment. The management of old trees are strengthened in the western region of Great Xing'an Mountains. The types of fires that cause negative impacts on forests are the focus of monitoring and prevention. This study is of great significance in reducing forest fire risk, improving forest carbon sequestration function, increasing wood resources, and improving the public climate environment.

Effects of Prescribed Burning on Surface Dead Fuel and Potential Fire Behavior in Pinus yunnanensis in Central Yunnan Province, China

Prescribed burning is a widely used fuel management employed technique to mitigate the risk of forest fires. The Pinus yunnanensis Franch. forest, which is frequently prone to forest fires in southwestern China, serves as a prime example for investigating the effects of prescribed burning on the flammability of surface dead fuel. This research aims to establish a scientific foundation for managing dead fuel in forests, as well as fire prevention and control strategies. Field data was collected from P. yunnanensis forests located in central Yunnan Province in 2021 and 2022. The study implemented a randomized complete block design with two blocks and three treatments: an unburned control (UB), one year after the prescribed burning (PB1a), and three years after the prescribed burning (PB3a). These treatments were evaluated based on three indices: surface dead-bed structure, physicochemical properties, and potential fire behavior parameters. To analyze the stand characteristics of the sample plots, a paired t-test was conducted. The results indicated no significant differences in the stand characteristics of P. yunnanensis following prescribed burning (p > 0.05). Prescribed burning led to a significant decrease in the average surface dead fuel load from 10.24 t/ha to 3.70 t/ha, representing a reduction of 63.87%. Additionally, the average fire−line intensity decreased from 454 kw/m to 190 kw/m, indicating a decrease of 58.15%. Despite prescribed burning, there were no significant changes observed in the physical and chemical properties of dead fuels (p > 0.05). However, the bed structure of dead fuels and fire behavior parameters exhibited a significant reduction compared with the control sample site. The findings of this study provide essential theoretical support for the scientific implementation of prescribed burning programs and the accurate evaluation of ecological and environmental effects post burning.

Geoinformatics and Machine Learning for Comprehensive Fire Risk Assessment and Management in Peri-Urban Environments: A Building-Block-Level Approach

Forest fires can result in loss of life, damage to infrastructure, and adverse environmental impacts. This study showcases an integrated approach for conducting high-detail fire risk assessment and supporting strategic planning and management of fire events in peri-urban areas that are susceptible to forest fires. The presented methodology encompasses fire hazard modeling, vulnerability and exposure assessment, and in situ observations. Numerous fire hazard scenarios were tested, simulating the spatiotemporal spread of fire events under different wind characteristics. The vulnerability of the studied areas was assessed by combining population data (density and age) and building characteristics, while the exposure parameter employed land value (EUR/m2) as an indicator for qualitatively estimating potential economic effects in the study area. Field campaigns facilitated the identification and recording of critical areas and points, including high-risk buildings and population gathering areas, which subsequently informed the mitigation and fire management planning suggestions. Moreover, field recordings acted as an iterative process for validating and updating the fire risk maps. This research work utilizes state-of-the-art techniques to achieve an analysis of fire risk at a building-block level. Overall, the study presents an applied and end-to-end methodology for effectively addressing forest fire risk in peri-urban areas.