Forest Fire Management Research Articles

Forest fire management in the United States

With the intensification of climate change and human activities, megafires frequently occur, with serious impacts on ecosystems, atmospheric environment, and human health. The United States has accumulated a large amount of practical experience in forest fire management. A comprehensive review of the framework of forest fire management in the United States can provide an inspiring reference for forest fire prevention in China. Starting from the process of historical evolution of forest fire policy, we systematically introduced the four stages of policy evolution and the characteristics of each stage in the US. Moreover, a comprehensive analysis of forest fire management situation in the US from four aspects was conducted, including the management of combustible fuels, administrative responsibility, fire suppression and forest fire management research support. We summarized relevant literature and proposed improvement strategies for future combustibles management, policy politics and fire fighting in the United States. Through the comprehensive analysis of forest fire management in the United States, we put forward some inspiring opinions on forest fire management in China to promote the establishment of a sound forest fire management system with Chinese characteristics.

Identification and characterization of spatio-temporal hotspots of forest fires in South Asia.

Forest fire is considered as one of the major threats to global biodiversity and a significant source of greenhouse gas emissions. Rising temperatures, weather conditions, and topography promote the incidences of fire due to human ignition in South Asia. Because of its synoptic, multi-spectral, and multi-temporal nature, remote sensing data can be a state of art technology for forest fire management. This study focuses on the spatio-temporal patterns of forest fires and identifying hotspots using the novel geospatial technique "emerging hotspot analysis tool" in South Asia. Daily MODIS active fire locations data of 15years (2003-2017) has been aggregated in order to characterize fire frequency, fire density, and hotspots. A total of 522,348 active fire points have been used to analyze risk of fires across the forest types. Maximum number of forest fires in South Asia was occurring during the January to May. Spatial analysis identified areas of frequent burning and high fire density in South Asian countries. In South Asia, 51% of forest grid cells were affected by fires in 15years. Highest number of fire incidences was recorded in tropical moist deciduous forest and tropical dry deciduous forest. The emerging hotspots analysis indicates prevalence of sporadic hotspots, followed by historical hotspots, consecutive hotspots, and persistent hotspots in South Asia. Of the seven South Asian countries, Bangladesh has highest emerging hotspot area (34.2%) in forests, followed by 32.2% in India and 29.5% in Nepal. Study results offer critical insights in delineation of fire vulnerable forest landscapes which will stand as a valuable input for strengthening management of fires in South Asia.

Midterm Fire Danger Prediction Using Satellite Imagery and Auxiliary Thematic Layers

Wildfires constitute a significant environmental pressure in Europe, particularly in the Mediterranean countries. The prediction of fire danger is essential for sustainable forest fire management since it provides critical information for designing effective prevention measures and for facilitating response planning to potential fire events. This study presents a new midterm fire danger index (MFDI) using satellite and auxiliary geographic data. The proposed methodology is based on estimations of a dry fuel connectivity measure calculated from the Moderate Imaging Spectrometer (MODIS) time-series data, which are combined with biophysical and topological variables to obtain accurate fire ignition danger predictions for the following eight days. The index’s accuracy was assessed using historical fire data from four large wildfires in Greece. The results showcase that the index predicted high fire danger (≥3 on a scale within [ 1 , 4 ] ) within the identified fire ignition areas, proving its strong potential for deriving reliable estimations of fire danger, despite the fact that no meteorological measurements or forecasts are used for its calculation.

Characterizing Distribution of Forest Fires in Myanmar Using Earth Observations and Spatial Statistics Tool

This is the first of its kind work on the assessment of forest burnt area and fire hotspots of Myanmar using Landsat OLI data and spatial statistics tool. Burnt area analysis indicates 15.2% of vegetation area was affected by fires in 2017. Analysis of burnt area at state level indicates Kayah affected by more fires in 2017. Of the total vegetation fire occurrences from 2003 to 2017 about 44.7% were observed in the forested landscapes of Myanmar. The emerging hotspot analysis had shown the highest spatial extent of persistent hotspots followed by oscillating hotspots. Forest fire hotspots are mainly found in the states of Kayah, Shan, Bago, Nayi Pyi Taw, Magway, Mandalay, Chin, and Kayin. Overall earth observations based on 2003 to 2017 fire occurrences indicate a declining trend of fires in Myanmar. A comparison of the fire occurrences recorded by MODIS and VIIRS indicates that VIIRS is capable of detecting a greater number of fire incidences. The findings of the study would support in assessing the impact of fires on forest, its structure, composition, function, and provide valuable input for nationwide forest fire management.

Modelling fire hazard in the southern Mediterranean fire rim (Bejaia region, northern Algeria).

The southern rim of the Mediterranean Basin (MB) has a long fire history but fire hazard is poorly investigated in comparison to the northern rim. We built a fire database using MODIS data (2001-2015) for an area typical of the northern coastal Algeria (Bejaia region) in order to decipher the role of environmental and anthropic controls on the fire frequency and the area burnt. We found a high role of bioclimate, which controls the fuel dryness, ignitability, and biomass. Maximal fire frequency and burnt areas were recorded in northern sub-humid areas with high amounts of forests and shrublands, and fire was limited in the southern sub-arid area. Humans set most fires, and preferentially burn forests, shrublands, pastures, groves, and agricultural lands. The maximal fire frequency and burnt area occurs in wildland urban interfaces characterized by forest-shrublands mosaics with disseminated habitats. Fire activity is low to medium in rural-urban interfaces characterized by agropastoral areas with high habitat density and large habitat patches. Small to large crown fires occur in forests and shrublands, while small surface fires predominate in agropastoral areas and groves. Large fires (> 100 ha) are rare (10%) but contribute for ca. 50% to the total area burnt. These fire features are typical of many rural countries of the southern rim of the MB, and contrast with those on the northern rim. Based on this, we propose to improve the prevention, the detection, and the management of forest fires in the long term and to protect forests that host high biodiversity in Algeria.

Risk analysis in the management of forest fire in Russia

In the Russians forests, from 10 to 15 thousand forest fires are annually registered, and according to forecasts, the tendency of increasing damage from fires in the country’s forests will grow. The final annual damage from forest fires in 2010 amounted to 550.4 thousand hectares of forest covered by fire. The article discusses the risks of forest fires determined by a combination of natural, economic and social factors. Based on an expert survey, an assessment was made of the probability of occurrence of risk events taking into account the categories of forests in Russia. The expert assessment used two criteria - the probability of a forest fire and the extent of expected losses from the occurrence of risk. It is shown that the most vulnerable to forest fires are forests located near homes, social and industrial facilities, as well as recreational forests. Three categories of forests have been proposed for the purpose of continuous monitoring of fire danger, firstly, forests located near dwellings, social and industrial facilities, secondly, forests with a developed network of roads and having a high level of timber value.

Identifying forest fires causes in Kawah Kamojang Nature Reserve (Mount Guntur), Garut, West Java

Forest and land fires have caused losses on social, economic, and ecological aspects in Indonesia. Forest and land fires management will not be optimal without knowing factors causing them. Mount Guntur which belong to Kawah Kamojang Nature Reserve (Mount Guntur) (KKNR) is suffering from forest and land fires almost every year. This study aims to identify the factors causing forest and land fires in KKNR. The research used field observation, in-depth interviews, and field data collection. The results indicate that the factors causing forest and land fires are human factors from sand mining, land preparation, and bird poaching. Drought due to long dry season trigger the fire occurrences. Here, we strongly suggest increasing the knowledge and awareness of forest fire to the people in KKNR to prevent the area from the further destructive forest fire.

Comparative analysis of spatial variation in forest fire drivers between boreal and subtropical ecosystems in China

Drivers of forest fire are complex and may spatially vary between different ecosystems; thus their identification is critical for the development of effective fire management strategies. Here we identified the spatial distribution trend and drivers of forest fire over the period of 2000–2016 in the boreal and subtropical forest ecosystems in China using trend analysis and the geographically weighted negative binomial regression model (GWNBR). The results reveal that forest fires were generally shifting from the boreal to sub-tropical region and that there were significant (p < 0.05) spatial differences in fire drivers between boreal and subtropical forest ecosystems. Forest fires in the boreal ecosystem were driven by meteorological, topographic and human factors that had clear spatial variation. In contrast, forest fire occurrence in subtropical forest ecosystem was spatially stationary; i.e., constant throughout the area. Our results suggest that as socio-economy continues developing, the influence of human factors on forest fires in different ecosystems will be more complex. Future national forest fire management policies should consider local condition to establish region-specific management strategies due to significant spatial differences in major forest fire drivers between different ecosystems.

Design and Development of Forest Fire Management System

Design and Development of Forest Fire Management System

The use of aircraft tracking GPS data to develop models of the use of airtankers in forest fire management

The Ontario Ministry of Natural Resources and Forestry uses amphibious airtankers to help control forest fires in the province of Ontario. Their airtankers are based at airports from which they are dispatched to fires to assist FireRanger crews that work on the ground. Airtanker systems can be modelled as spatial queueing systems with fires as customers that arrive at rates that vary over both time and space and airtankers as mobile servers that fight fires at spatially dependent service rates. In order to model the airtanker service process one must know how much time airtankers spend travelling to and from and dropping water on fires. We developed a methodology for processing historical fire, airport location and on-board airtanker tracking GPS data to identify the key events that take place while airtankers are involved in fire suppression operations. We used that data to develop detailed models that describe how airtankers participate in the initial attack process. Those models can be incorporated in strategic, tactical and operational airtanker management decision support systems. We conclude by suggesting that our methodology be extended to model the use of transport helicopters, trucks and heavy equipment in fire suppression, and the activities of FireRanger crews on the ground.

Variation in the Canadian Fire Weather Index Thresholds for Increasingly Larger Fires in Portugal

Forest fire management relies on the fire danger rating to optimize its suite of activities. Limiting fire size is the fire management target whenever minimizing burned area is the primary goal, such as in the Mediterranean Basin. Within the region, wildfire incidence is especially acute in Portugal, a country where fire-influencing anthropogenic and landscape features vary markedly within a relatively small area. This study establishes daily fire weather thresholds associated to transitions to increasingly larger fires for individual Portuguese regions (2001–2011 period), using the national wildfire and Canadian fire weather index (FWI) databases and logistic regression. FWI thresholds variation in relation to population density, topography, land cover, and net primary production (NPP) metrics is examined through regression and cluster analysis. Larger fires occur under increasingly higher fire danger. Resistance to fire spread (the fire-size FWI thresholds) varies regionally following biophysical gradients, and decreases under more complex topography and when NPP and occupation by flammable forest or by shrubland increase. Three main clusters synthesize these relationships and roughly coincide with the western north-central, eastern north-central and southern parts of the country. Quantification of fire-weather relationships can be improved through additional variables and analysis at other spatial scales.

Forest Fire Susceptibility Modeling Using a Convolutional Neural Network for Yunnan Province of China

Forest fires have caused considerable losses to ecologies, societies, and economies worldwide. To minimize these losses and reduce forest fires, modeling and predicting the occurrence of forest fires are meaningful because they can support forest fire prevention and management. In recent years, the convolutional neural network (CNN) has become an important state-of-the-art deep learning algorithm, and its implementation has enriched many fields. Therefore, we proposed a spatial prediction model for forest fire susceptibility using a CNN. Past forest fire locations in Yunnan Province, China, from 2002 to 2010, and a set of 14 forest fire influencing factors were mapped using a geographic information system. Oversampling was applied to eliminate the class imbalance, and proportional stratified sampling was used to construct the training/validation sample libraries. A CNN architecture that is suitable for the prediction of forest fire susceptibility was designed and hyperparameters were optimized to improve the prediction accuracy. Then, the test dataset was fed into the trained model to construct the spatial prediction map of forest fire susceptibility in Yunnan Province. Finally, the prediction performance of the proposed model was assessed using several statistical measures—Wilcoxon signed-rank test, receiver operating characteristic curve, and area under the curve (AUC). The results confirmed the higher accuracy of the proposed CNN model (AUC 0.86) than those of the random forests, support vector machine, multilayer perceptron neural network, and kernel logistic regression benchmark classifiers. The CNN has stronger fitting and classification abilities and can make full use of neighborhood information, which is a promising alternative for the spatial prediction of forest fire susceptibility. This research extends the application of CNN to the prediction of forest fire susceptibility.

Das Konzept «Waldbrand 2020» des Kantons Tessin

The “forest fire 2020” program of Canton Ticino The Canton of Ticino has a long-lasting experience in facing forest fires. As a result, a tradition in forest fire documentation and analysis exists and the forest fire management approach is continuously reviewed and improved with the aim to preserve the forest protection functions and to keep the mountain areas safe for the inhabitants. The fire regime has been reduced in Ticino since the seventies of last century thanks to improvement of the firefighting organization and fire control techniques (e.g. systematic use of helicopters for the aerial fire control) and the possibility of declaring a total fire ban in the open. However the demand in terms of protection of human lives and goods of the modern society is raising and as consequence of the climate change fire risk may increase in the future. For this reason two years ago the forest service of Canton Ticino developed the “forest fire 2020” program, in collaboration with the cantonal fire brigades association and the federal research Institute WSL. The program consists of four interdependent activity modules, which are 1) prevention, 2) organizational and technical measures, 3) firefighting and control, 4) burnt area restoration. The forest service is responsible for the fire-danger rating, the fire-ban release, the mentoring of local authorities in forest management questions and for planning pre-suppression facilities (e.g. water points for helicopters). It is also responsible for defining the mission rules for aerial firefighting, for collecting the data for the statistics, and for planning the post-fire forest restoration measures. The fire brigades are in charge of the firefighting tasks, by first intervening with the urban fire brigades and in case of need requiring the support of specialized forest-fire brigades. During the firefighting actions the forest service takes a consulting role. The first two years of implementation confirmed the suitability of the “forest fire 2020” program. Potential improvements have been however detected and are under implementation, such as the completion of the pre-suppression infrastructures, a better coordination between aerial and terrestrial firefighting and the strengthening of the specialized forest-fire brigades.

Risk management of forest fire occurrence

This academic paper is devoted to the issue of how to reduce forest fire damage through affecting the fire causes. Forest fire causes are a subject of random probability. Adequate forest management is not possible when there is lack of precise information about when and where a forest fire may occur. When there are no ways and methods to influence the reasons of forest fires occurrence, the likelihood of a wrong decision is directly proportional to the negative consequences of forest fires. The research purpose was to develop mechanisms of risk management taking into account the cause and effect relation between human activities, natural phenomena and occurrence of forest fire. The risk as economic category is the target of the research. The scope of the research encompasses the process of decision-taking in forest management with regards to forest preservation from fires under the conditions of unpredictability. The research was based on a comprehensive approach to the problem and used contemporary regulations of sustainable forest management and global experience in forestry relationship. It was suggested to classify the causes of forest fires in groups. Probability of forest fires occurrence was estimated in accordance with various causes. There was introduced an approach of forest fire risk management based on strategic risk management. The results of the research can be used by scientific institutions, as well as by forestry companies while planning environmental protection measures. The research can proceed towards assessment and forecasting of fire risks in forestry at the national and international levels.

SN Based Forest Fire Detection and Early Warning System

This paper proposes a generic Sensor Network (SN) based forest fire detection and management system, which is scalable and readily deployable for all environments and terrains. SN are being deployed in critical and hazardous areas, for monitoring as well as for collection of useful environmental data for analysis. In these particular contexts, active fire detection and management in forest prove to be challenging, especially in areas which are remote, unapproachable or at the epicenter of such incident. The proposed system caters to the above said problem by allowing a visual representation of status of the sensor nodes (in real-time) through the use of web map system, connected to the strategically deployed nodes based on geography. This deployment is enhanced with the usage of a low power, high range and low data rate wireless protocol, for which the lifetime of the nodes can be further increased through an appropriate scheduling. Moreover, a system with environmental sensors and 3 level hierarchical network covering a total of 4 sq. km has been designed, to investigate the feasibility of the underlying system with the experimental scenarios of fire incident at certain nodes. Software modules providing detection functionalities have been implemented as prototype in the proposed system. The real time system has proved to provide a better visualization and real time tracking of the fire incidents, which in turn facilitates the fire management system as a whole.

Fog-assisted IoT-enabled scalable network infrastructure for wildfire surveillance

Forest fires frequently termed as wildfires are fiercely destructive disasters causing enormous ecological and economic damage, as well as the loss of human lives. Global predictions for increased incidence and destructiveness of forest fires due to warming climate, drought conditions, urbanization and arson highlight the importance of an effective forest fire mitigation and management approach. Internet of Things (IoT) is well suited to ubiquitously assess the time-critical parameters for effective and reliable prediction of forest fires. This paper presents a novel Fog-assisted IoT-enabled framework for early prediction and forecasting of wildfires. The framework includes proposals for efficient energy utilization of the resource-constrained sensors responsible for wildfire monitoring by adapting the sampling rate of Wildfire Causing Attributes (WCAs) at Fog Layer. Moreover, the time enriched sampled data is further analyzed at Cloud Layer for predicting and forecasting the susceptibility of a forest block to wildfire outbreak. In addition, the forest area (in hectares) that could possibly be burnt in the event of wildfire outbreak is also predicted. Experimentation and performance analysis of the proposed system reveal that high values of accuracy, sensitivity, specificity, and precision averaging to 95.45%, 96.08%, 94.63%, and 95.64% respectively are registered for wildfire susceptibility prediction. Furthermore, Mean Absolute Error (MAE), Mean Square Error (MSE) and Root Mean Square Error (RMSE) values averaging to 0.25, 0.25 and 0.5 respectively are registered for wildfire susceptibility forecasting. Lastly, the efficacy of the proposed framework can also be derived from the real-time alert generation in the event of high wildfire susceptibility level.

Static Risk Mapping of Forest Fires in the Case of the Province of Chefchaouen (Morocco)

Abstract. Forest fire is responsible for a lot of problems as it destroys the landscape. Such spaces are valuable and take very long to recover. Hence, the risk of forest fire consists primarily of both the risk of an outbreak and of propagation which depend, in addition to the weather, to a number of environmental factors including: the type of vegetation (structure and composition), its state of desiccation as well as the slope and exposure to prevailing winds. Therefore, the goal is to develop static hazard maps of a 100 m resolution related to the province of Chefchaouen where the focus is on three maps; Surface threatened; Annual pressure of fire; Probability map. The production of these maps is based on various data including statistics on the fire, meteorological references, and flammability. In addition, several data were generated, namely, wind direction, wind speed, humidity, the slope in percent, aspect, etc. The production of these maps will make it possible to orient and optimize the means of investment, in particular with regard to infrastructures, equipment and forest fire management operations.

Desa’a national forest reserve susceptibility to fire under climate change

Climate change influences spreading and occurrence of forest fire. Forest fire modeling is one of the most important tasks to fight forest fires. Climate data of current (1980–2010) was projected to near (2010–2039), mid (2040–2069) and end-term (2070–2099) using Representative Concentration Pathway (RCP4.5 and 8.5) of an ensemble of twenty General Circulation Models using R-software. Current and projected climate data were used to determine the impact of climate change on current and future forest fire using Keetch-Byram Drought Index. Current and future forest fire-vulnerable areas were mapped and weighed using Inverse Distance Weighting. The result indicates that, while no forest fire occurrence in the current, there might be a high forest fire risk in near-term. It might be become very high in mid and end-term. The size of forest fire-vulnerable areas might be increased to 12.85, 18.8, 17.1 and 46.26% in Mid-RCP4.5, Mid-RCP8.5, End-RCP4.5 and End-term-RCP8.5 respectively. Fire may occurred in winter and spring seasons. The risk might be move to higher elevation of the forest. This directs increase of forest fire occurrence and spread due to climate change. The study recommends that forest fire management should be applied before fire happened to sustain the forest and its products.

Development of a Spatial Decision Support System (SDSS) for the active forest-urban fires management through location planning of mobile fire units

ABSTRACTCritical time of response constitutes the foundation for effective and timely forest fires management. It is the milestone for strategic and operational fire planning. Aim of the paper is the development of a Spatial Decision Support System (SDSS) adopting a top-down approach for the immediate containment of forest and urban fires. The objectives lie in the coupling of strategic and operational nature of fire management, so that the proposed model to be used for real-time purposes. The strategic dimension focus on the location-allocation of fire vehicles to establish effective location schemes adjusted to population dynamics (Wildland Urban Interface). Four spatial patterns of optimal locations were developed based on ideal and realistic critical time of response as well as the current and desired capacity of fire agency. The operational dimension supports the strategic planning objectives through a series of additional sub-models for a second phase optimization (activating and leading the nearest fire vehicle(s) to the affected area through the determination of optimal route; and/or the rerouting in case of road closure due to evolving fires). Therefore, the current SDSS handles the forest and urban fires management in a more comprehensive way, creating an effective link between strategic and operational level.

Forest Fire Risk Assessment Model Using Remote Sensing and GIS Techniques in Northwest Algeria

Abstract Algeria loses more than 20,000 hectares of forest to fire every year. The losses are costly both in terms of life and property damage, which weighs heavily on the environment and the local economy. Geomatics can complement the conventional methods used in fire hazard prevention and management. The objective of our study is to use the geographic information system (GIS) and the Remote Sensing (RS) technology to develop the fire risk assessment map of the forest massif of Zelamta located in Southeast Mascara province (Northwest Algeria). The methodology employed was an empirical model involving three parameters that can control fire behaviour: geomorphology, vegetal cover combustibility, and human activity. The obtained results can help in the decision-making process as well as provide cartographic support for forest fire prevention and management.