Fire Risk Map Research Articles

Suburban Forest Fire Risk Assessment and Forest Surveillance Using 360-Degree Cameras and a Multiscale Deformable Transformer

In the current context of climate change and demographic expansion, one of the phenomena that humanity faces are the suburban wildfires. To prevent the occurrence of suburban forest fires, fire risk assessment and early fire detection approaches need to be applied. Forest fire risk mapping depends on various factors and contributes to the identification and monitoring of vulnerable zones where risk factors are most severe. Therefore, watchtowers, sensors, and base stations of autonomous unmanned aerial vehicles need to be placed carefully in order to ensure adequate visibility or battery autonomy. In this study, fire risk assessment of an urban forest was performed and the recently introduced 360-degree data were used for early fire detection. Furthermore, a single-step approach that integrates a multiscale vision transformer was introduced for accurate fire detection. The study area includes the suburban pine forest of Thessaloniki city (Greece) named Seich Sou, which is prone to wildfires. For the evaluation of the performance of the proposed workflow, real and synthetic 360-degree images were used. Experimental results demonstrate the great potential of the proposed system, which achieved an F-score for real fire event detection rate equal to 91.6%. This indicates that the proposed method could significantly contribute to the monitoring, protection, and early fire detection of the suburban forest of Thessaloniki.

Mapping Forest Fire Risk Zones Using Machine Learning Algorithms in Hunan Province, China

Forest fire is a primary disaster that destroys forest resources and the ecological environment, and has a serious negative impact on the safety of human life and property. Predicting the probability of forest fires and drawing forest fire risk maps can provide a reference basis for forest fire control management in Hunan Province. This study selected 19 forest fire impact factors based on satellite monitoring hotspot data, meteorological data, topographic data, vegetation data, and social and human data from 2010–2018. It used random forest, support vector machine, and gradient boosting decision tree models to predict the probability of forest fires in Hunan Province and selected the RF algorithm to create a forest fire risk map of Hunan Province to quantify the potential forest fire risk. The results show that the RF algorithm performs best compared to the SVM and GBDT algorithms with 91.68% accuracy, 91.96% precision, 92.78% recall, 92.37% F1, and 97.2% AUC. The most important drivers of forest fires in Hunan Province are meteorology and vegetation. There are obvious differences in the spatial distribution of seasonal forest fire risks in Hunan Province, and winter and spring are the seasons with high forest fire risks. The medium- and high-risk areas are mostly concentrated in the south of Hunan.

Areas of the Terai Arc landscape in Nepal at risk of forest fire identified by fuzzy analytic hierarchy process

Forest fire frequency has increased in South Asia in recent decades, with a growing impact on forest ecosystems. Nepal's Terai Arc Landscape (TAL) is one of the most ecologically important landscapes in Asia, hosting a great diversity of endangered flora and fauna. To better predict the threat of fire to forest ecosystems in the TAL, we identified fire-prone areas using fuzzy AHP methods. We produced a fire risk map by applying the weighted linear combination method using topographic (slope, aspect, and elevation), climatic (temperature, precipitation, and wind speed), biophysical (normalised difference vegetation index and landcover classes), and anthropogenic variables (distance to road and proximity to settlements). We then validated the map with records of past fires by applying a confusion matrix. The accuracy of our technique of fire location prediction was above 95%, with a kappa coefficient of 0.93 and AUC value of 0.83. Locations of medium to very high forest fire risk were found in around 51% of the study area, usually in the vicinity of areas affected by anthropogenic factors. This landscape-level study shows the potential of multicriteria prediction models to inform the preparation of national and regional forest fire management strategies and plans.

An innovative tool for mapping forest fire risk and danger: case studies from eastern Mediterranean

ABSTRACT Forest fires are one of the most important factors for forest ecosystem and cause ecosystem destruction such as decreasing forest area, biodiversity in the Mediterranean region. Türkiye located in Mediterranean region is exposed to hundreds of fires every year, which damage forest. Mapping forest fire risk and danger constitutes an important basis for preventing fire damages. Geographical Information System is used for mapping forest fire risk and making the accurate and fast decision. This study is designed to develop a GIS-based decision support systems (DSS) to produce a forest fire risk and danger map for Türkiye. DSS uses topography, stand structure and anthropogenic factors for mapping forest fire risk and danger. DSS was developed using the C-sharp (C#) programming language with the help of Add-in in the ArcGIS. DSS has been successfully tested on case study sites in Kozan and Milas Forest Enterprises in Türkiye. In conclusion, the DSS has contributed to the forest managers to fight forest fire effectively. This study will make an important contribution to both the General Directorate of Forestry, which is in the position of implementing it, and the scientific community.

Developing a forest fire prediction model based on deep learning and forecast a fire risk map of Turkey

Orman yangınları; doğal hayatı, biyolojik ekosistemi tehdit eden ve ormancılık faaliyetlerini sekteye uğratarak ekonomik kayıplara neden olan ana sorunlardan birisidir. Yangınlar; bitki örtüsü, su ve hava gibi doğal kaynakların zarar görmesine veya tamamen yok olmasına neden olabilir. Ayrıca ormanların çevresinde bulunan yerleşim veya tarım alanlarına da hasar vererek can ve mal kayıplarına sebep olabilir. Bu yüzden orman yangınlarıyla mücadele edilmesi ve tahribatın asgari düzeye indirilmesi için kaynakların kullanılmasında doğru kararların verilmesi ve etkili planlamaların yapılması önem arz etmektedir. Bu çalışmada orman yangınlarıyla mücadelede kullanılmak için Orman Yangını Analiz Tahmin (OYAT) modeli oluşturulmuştur. OYAT; resmi kaynaklardan elde edilen bitki örtüsü, iklim değişiklikleri, beşeri etmenler ve daha önceki yangın verilerine dayandırılarak geliştirilmiştir. Bu veriler derin öğrenme tekniği ile işlenerek OYAT modeli oluşturulmuştur. OYAT kullanılarak, analiz edilen veriler ile orman yangını tahmini yapılır ve bölgesel yangın risk haritası elde edilir. OYAT coğrafi bilgi sistemleri (CBS) ile uyumlu çalışabilmesi sayesinde kolay kullanıma sahip, dinamik veriler ile güncellenebilen ve görselleştirilerek saklanabilen bir yapıya sahiptir. Türkiye’deki 2013-2019 yılları arasındaki orman yangını verileri OYAT modelinin değerlendirilmesinde kullanılmıştır. OYAT ile oluşturulan risk haritalarının %98 doğruluk oranına sahip olduğu gözlenmiştir. İzleyen yıllarda veri havuzunun büyümesiyle birlikte, OYAT modelinin yangın önleme için yapılacak planlamalarda daha verimli ve başarılı olacağı öngörülmektedir.

Forest Fire Risk Assessment and Mapping Using Support Vector Machine Algorithm, A Case Study in Nghe An Province, Vietnam

Forest Fire Risk Assessment and Mapping Using Support Vector Machine Algorithm, A Case Study in Nghe An Province, Vietnam

Integrated approach for the assessment of forest fire risk and burn severity mapping using GIS, AHP method, and Google Earth Engine in Western Algeria

Integrated approach for the assessment of forest fire risk and burn severity mapping using GIS, AHP method, and Google Earth Engine in Western Algeria

Forest fire occurrence, distribution and risk mapping using geoinformation technology: A case study in the sub-tropical forest of the Meghalaya, India

The study focuses on Meghalaya, the North Eastern State of India, for forest fire occurrence, distribution, and risk mapping using the different geospatial techniques is validated using different decision-making techniques such as MCDM, Space Time Cube Analysis (STC), and Emerging Hot Spot (EHS) Analysis. The archive data of Moderate Resolution Imaging Spectroradiometer (MODIS) near real time (NRT) fire data has been considered for this study. Fire points were obtained from Fire Information for Resource Management System (FIRMS), a web service provided by NASA. Using two decadal data, a NetCDF file was created which can store four-dimensional data. Based on this NetCFD file, forest fire trend and future prospect has been foreseen. After analyzing these points, a relationship with other fire triggering factors such as Human settlement, Roads, Climate, Elevation, Slope, Aspect, Forest Type, Forest Canopy density, Soil Moisture, Wind Speed, and Forest Health has been established. Forest risk map with four classes (extreme, severe, moderate, and mild) is also generated. These results are very effective for the managers, planners, and other stakeholders for disaster management. The suggested methodology can be implemented for the preparedness of forest fire management. The policy makers must act quickly to protect the Himalayan region with the highest biodiversity by adopting focused interventions and the strategic allocation of scarce resources in effective areas.

A UAV-Based Forest Fire Patrol Path Planning Strategy

The application of UAVs in forest fire monitoring has attracted increasing attention. When a UAV carries out forest fire monitoring cruises in a large area of the forest, one of the main problems is planning an appropriate cruise path so that the UAV can start from the starting point, cruise the entire area with little detour, and return to the initial position within its maximum cruise distance. In this paper, we propose a flight path planning method for UAV forest fire monitoring based on a forest fire risk map. According to the forest fire risk level, the method uses the ring self-organizing mapping (RSOM) algorithm to plan a corresponding flight path. In addition, since it is difficult for a single UAV to complete a single full-path cruise task in a large area within its maximum cruise time, a multi-UAV cruise scheme is proposed. First, the Gaussian mixture clustering algorithm is used to cluster the study area and divide it into several subareas. In combination with the RSOM algorithm, the corresponding path is planned for each UAV. A simulation with an actual dataset showed that the proposed method solves the problem of UAV patrol path planning for forest fire monitoring and can complete the task within a reasonable time.

Geospatial characteristics of Colorado wildfire occurrences from 2001 to 2020

Forest fires are a major environmental concern and can pose a direct threat to the environment, communities, and people living in fire-prone areas. Colorado has experienced an increase in wildfire frequency and severity in recent years and with the population increasing and communities expanding, it is imperative to understand the spatial patterns of wildfires and which areas are most at risk. Here we use geostatistical techniques to analyze the spatial distribution and seasonal patterns of fires in Colorado from 2001 to 2020 and assess fire risk for June 2021. Over the past 20 years, forest fires in Colorado have varied seasonally in concentration and dispersion, moving in a circular pattern from the southeast in the winter to the northwest in the summer. On an annual scale, the observed active fires varied latitudinally in recent years, with greater northward expansion. Several active fire hotspots were revealed in Colorado: two hotspots in the northwest towards Grand Junction and a large hotspot in the central north, of great concern for its expansion into the Rocky Mountain National Park and its location near more populated areas. Our fire risk model (which included topography, vegetation, drought intensity, and historical fire occurrences) showed that fire risk was greater along the western border of Colorado, and 45% of active fires occurred within the top two fire risk classes (100% occurred in the top three). Therefore, this fire risk map is a suitable tool to identify and locate areas that are at greater risk for fire occurrence in Colorado.

A Comparative Study of Forest Fire Mapping Using GIS-Based Data Mining Approaches in Western Iran

Mapping fire risk accurately is essential for the planning and protection of forests. This study aims to map fire risk (probability of ignition) in Marivan County of Kurdistan province, Iran, using the data mining approaches of the evidential belief function (EBF) and weight of evidence (WOE) models, with an emphasis placed on climatic variables. Firstly, 284 fire incidents in the region were randomly divided into two groups, including the training group (70%, 199 points) and the validation group (30%, 85 points). Given the previous studies and conditions of the region, the variables of slope percentage, slope direction, altitude, distance from rivers, distance from roads, distance from settlements, land use, slope curvature, rainfall, and maximum annual temperature were considered for zoning fire risk. Then, forest fire risk maps were prepared using the EBF and WOE models. The performance of each model was examined using the Relative Operating Characteristic (ROC) curve. The results showed that WOE and EBF are effective tools for mapping forest fire risks in the study area. However, the WOE model shows a slightly higher Area Under the Curve value (0.896) compared to that of the EBF model (0.886), indicating a slightly better performance. The results of this study can provide valuable information for preventing forest fires in the study area.

Wildfire Risk in the Complex Terrain of the Santa Barbara Wildland–Urban Interface during Extreme Winds

Each year, wildfires ravage the western U.S. and change the lives of millions of inhabitants. Situated in southern California, coastal Santa Barbara has witnessed devastating wildfires in the past decade, with nearly all ignitions started by humans. Therefore, estimating the risk imposed by unplanned ignitions in this fire-prone region will further increase resilience toward wildfires. Currently, a fire-risk map does not exist in this region. The main objective of this study is to provide a spatial analysis of regions at high risk of fast wildfire spread, particularly in the first two hours, considering varying scenarios of ignition locations and atmospheric conditions. To achieve this goal, multiple wildfire simulations were conducted using the FARSITE fire spread model with three ignition modeling methods and three wind scenarios. The first ignition method considers ignitions randomly distributed in 500 m buffers around previously observed ignition sites. Since these ignitions are mainly clustered around roads and trails, the second method considers a 50 m buffer around this built infrastructure, with ignition points randomly sampled from within this buffer. The third method assumes a Euclidean distance decay of ignition probability around roads and trails up to 1000 m, where the probability of selection linearly decreases further from the transportation paths. The ignition modeling methods were then employed in wildfire simulations with varying wind scenarios representing the climatological wind pattern and strong, downslope wind events. A large number of modeled ignitions were located near the major-exit highway running north–south (HWY 154), resulting in more simulated wildfires burning in that region. This could impact evacuation route planning and resource allocation under climatological wind conditions. The simulated fire areas were smaller, and the wildfires did not spread far from the ignition locations. In contrast, wildfires ignited during strong, northerly winds quickly spread into the wildland–urban interface (WUI) toward suburban and urban areas.

Classifying surface fuel types based on forest stand photographs and satellite time series using deep learning

With the increasing threat of wildfires globally, improving the availability of accurate, spatially explicit fuel type information is critical for fire behavior predictions that can support management decisions to mitigate fire hazards. Since mapping surface fuel types using airborne or spaceborne sensors relies on ground truth data from laborious field assessments, here we propose a novel proximate sensing-based approach for classifying surface fuel types from in-forest RGB photographs using convolutional neural networks (CNNs). We test different configurations of deep learning models that integrate photographs of the forest stand and the forest floor as well as time series of multispectral satellite data from Sentinel-2 using long short-term memory (LSTM), and compare their performance in classifying understory and litter fuel types of Central European forests. We also investigate how ensemble approaches based on majority voting can help to improve classification results. We found that understory fuel types were classified with highest accuracy after cross-validation (0.78) using a combination of horizontal stand photos and forest floor photos. This accuracy was further improved by post-classification decision fusion of model predictions on multiple photographs of a forest stand and by considering the model’s confidence in its predictions (0.85). Litter fuel type classification based on forest photographs resulted in lower overall accuracy (0.60), but using model ensemble predictions on both photographs and Sentinel-2 time series significantly improved the results (0.72). We found that the accuracy of our models was mostly limited by naturally smooth transitions between the defined fuel type classes and the co-occurrence of multiple fuel types in a photograph. This study shows that deep learning methods can provide an efficient means to assess fuel types from GNSS-located photos of forest stands as a basis for generating and validating fuel type and finally fire risk maps. The necessary data can be readily collected by forest managers or citizen scientists.

Fire Risk Analysis in Large Multi-Compartment Structures Using a Hybrid Multiscale Approach

This paper proposes a hybrid multiscale approach to evaluate the fire performance of large multicompartment structures. A probabilistic network model is at the core of the proposed approach, whose inputs, namely the mean durations of the fire phases and fire transmission through the barriers between compartments (e.g., walls or ventilation ducts), are determined beforehand by a zone model, which is detailed in a companion paper and a one-dimensional computational fluid dynamics code. Next, a proof of concept is developed by applying the hybrid approach to different fire scenarios in a full-scale generic military corvette and a four-story office building. The simulation results highlight the strengths and limitations of the proposed approach. Regarding the latter, a field model is used to evaluate how the hybrid approach performs depending on the interaction between the entire building system and its ventilation and the fire. Finally, a statistical study is carried out to produce fire vulnerability and risk maps, ranking the fire compartments according to their vulnerability or propensity to generate serious fires.

Orman Yangını Riskinin CBS Kullanılarak Niğde Örneğinde Belirlenmesi

The main purpose of this study is to develop a statistical model to prepare forest fire risk map using GIS. In this study eight important factors were used to determining the forest fire risk such as land use/land cover type, slope, aspect, altitude, settlement, road, temperature and precipitation. The analytic hierarchy process (AHP) was used to evaluate the factors. Precipitation and temperature were the most important factors to determining the forest fire risk. The study area has approximately 10.72% low fire risk, 28.21% moderate fire risk, 43.50% high fire risk, 14.65% very high fire risk, and 2.92% extreme forest fire risk. 61.07% of the study area has a high, very high and extreme forest fire risk. In order to prevent forest fires, land cover/land use should be planned in a way that does not damage forests. Especially vehicle roads, expressways, etc. which are located near the forests, have a high fire risk. Therefore, these areas should be planned in a way that will not damage the forests. The climatic characteristics of the study area should be examined, the urban texture should not be in a way to prevent microclimatic factors such as wind and precipitation.

Fire Risk Probability Mapping Using Machine Learning Tools and Multi-Criteria Decision Analysis in the GIS Environment: A Case Study in the National Park Forest Dadia-Lefkimi-Soufli, Greece

Fire risk will increase in the upcoming years due to climate change. In this context, GIS analysis for fire risk mapping is an important tool to identify high risk areas and allocate resources. In the present study, we aimed to create a fire risk estimation model that incorporates recent land cover changes, along with other important risk factors. As a study area, we selected Dadia-Lefkimi-Soufli National Forest Park and the surrounding area since it is one of the most important protected areas in Greece. The area selected for the case study is a typical Mediterranean landscape. As a result, the outcome model is generic and can be applied to other areas. In order to incorporate land cover changes in our model, we used a support vector machine (SVM) algorithm to classify a satellite image captured in September 2021 and an image of the same period two years ago to obtain comparable results. Next, two fire risk maps were created with a combination of land cover and six other factors, using the analytic hierarchy process (AHP) on a GIS platform. The results of our model revealed noticeable clusters of extreme high risk areas, while the overall fire risk in the National Park Forest of Dadia-Lefkimi-Soufli was classified as high. The wildfires of 1st October 2020 and 9th July 2021 confirmed our model and contributed to quantification of their impact on fire risk due to land cover change.

A Control Algorithm for Early Wildfire Detection Using Aerial Sensor Networks: Modeling and Simulation

This work presents an algorithm for an Aerial Sensor Network (ASN) composed of fixed-wing Unmanned Aerial Vehicles (UAVs) that performs surveillance and detects the early signs of a wildfire in a given territory. The main goal is to cover a given area while prioritizing areas of higher fire hazard risk. The proposed algorithm is scalable to any number of aircraft and can use any kind of fire hazard risk map as long as it contains bounded and nonnegative values. Two different dynamical models associated with the movement of fixed-wing UAVs are proposed, tested, and compared through simulations. Lastly, we propose a workflow to size the ASN in order to maximize the probability of detection of wildfires for a particular risk profile.

MANAGEMENT STRATEGIES FOR PREVENTION OF FOREST FIRE AND ENVIRONMENTAL DEGRADATION IN TROPICS WITH SPECIAL REFERENCE TO WESTERN GHATS OF KERALA REGION, INDIA

Management of forest fire is one of the top priority activities of forest departments, both in tropics and temperate regions. Extensive amount and intensive efforts are deployed to combat fire. The results of a case study on forest fire prone mapping conducted at Periyar tiger reserve in Western Ghats of Kerala, India, was highlighted, and based on the study, the implementation of fire management strategies were discussed. The fire risk map was validated with the forest fire inventory points of Forest Survey of India. Forest fire is a frequent occurrence in tropical forests, especially in moist deciduous and dry deciduous regions. Though forest fire management is one of the major activities of forest departments, the forest fire mitigation is not 100% successful. The magnitude of forest fire, its frequency, potential location, etc., can be assessed using Geoinformatics techniques, by giving different weightage status to fire controlling factors. A case study was conducted on these lines and control measures were highlighted using different management strategies. The implementation of the desired action plan for sustainable development and conservation of forests can be successful only by getting the full support and confidence of the stakeholders. The stakeholder support for environmental management aspects is possible only by convincing them that they are directly or indirectly getting the benefits by the proposed action. Usually, most of the environmental management activities may lead to nontangible benefits, which is apparently non-visible, hence getting stakeholder support may not be an easy task. Various marketing strategies needs to be used to convince the beneficiaries to implement good conservation practices for the betterment of the local inhabitants.

Analyzing the Forest Fire Risk of a Tropical Deciduous Forest in Southern Western Ghats Region in Wayanad Wildlife Sanctuary, Kerala, India

In recent years, the forest fire has increased at an alarming rate in India. It has become a common incident in many areas of Wayanad Wildlife Sanctuary situated along the North-Eastern part of Kerala, lying in the Western Ghats. The forest fire incidents happen mainly in the pre-monsoon period ranging from February to April. Using various forest risk parameters, the study analyses the forest fire risk zone in the Wayanad Wildlife Sanctuary. A weighted index method using GIS and Remote sensing was acquired to obtain and distinguish the forest fire-risk map by including the risk variables such as vegetation, topographic factors, land surface temperature and proximity to the road and settlements. The forest fire risk zone map integrates the observed variable with the help of the GIS platform. Out of the study area's total of 412 sq km, 24.65 % falls in very high and 33.39 % comprises the high fire risk zones. Validation of the forest fire risk zone map with the fire locations illustrates that most fire locations (72 %) fall under high and high-risk zones. The result of the study could help the decision-makers to implement fire management plans and strategies.

A spatially explicit analytical framework to assess wildfire risks on brown bear habitat and corridors in conservation areas

BackgroundHumans have altered fire regimes across ecosystems due to climate change, land use change, and increasing ignition. Unprecedented shifts in fire regimes affect animals and contribute to habitat displacement, reduced movement, and increased mortality risk. Mitigating these effects require the identification of habitats that are susceptible to wildfires. We designed an analytical framework that incorporates fire risk mapping with species distribution modeling to identify key habitats of Ursus arctos with high probability of fire in Iran. We applied the random forest algorithm for fire risk mapping. We also modeled brown bear habitats and predicted connectivity between them using species distribution models and connectivity analysis, respectively. Finally, the fire risk map, critical habitats, and corridors were overlaid to spatially identify habitats and corridors that are at high risk of fire.ResultsWe identified 17 critical habitats with 5245 km2 of corridors connecting them, 40.06% and 11.34% of which are covered by conservation areas, respectively. Our analysis showed that 35.65% of key habitats and 23.56% of corridors are at high risk of fire.ConclusionsSince bears habitat in this semi-arid landscape rely on forests at higher altitudes, it is likely that shifting fire regimes due to changing climate and land use modifications reduce the extent of habitats in the future. While it is not well known how fire affects bears, identifying its key habitat where wildfires are likely to occur is the first step to manage potential impacts from increasing wildfires on this species.