Forest Fire Management Research Articles

FOREST FIRE SUSCEPTIBILITY MAPPING OF BHUTAN USING LOGISTIC REGRESSION AND FREQUENCY RATIO MODEL

Forest fire is not only observed as one of the most significant sources of forest degradation in Bhutan but also a serious danger to national conservation efforts. As a result, forest fire susceptibility analysis is recognised as an important part of Bhutan's forest fire management strategy. The study's major goal is to create a forest fire susceptibility map for Bhutan using logistic regression (LR) and frequency ratio (FR) models. The study gathered number of fire influencing factors, evaluated them, and created susceptibility maps. Using the relative operating characteristics technique, the efficiency of each of the two models was analysed and compared to select the best model. The Receiver Operating Characteristics (ROC) curves with the area under the curve (AUC) was used to check the correctness of the maps produced by the modelling procedure. The prediction and success rates of the LR model were 88.8% and 87.5%, while for the FR model they were 85.4% and 85.1%, respectively. The results showed that both models are good predictors of forest fire with the LR model performing fairly better than the FR model. So, the LR model was chosen as an optimum model for forest fire susceptibility mapping. The susceptibility map obtained from the optimum LR model was classified into five categories such as; very low, low, moderate, high, and very high.. The findings of this study give useful spatial information for implementing forest management techniques.

Exploring the Effectiveness of Case Teaching Model in Graduate Forest Fire Management Course

In the context of the new era, forest fire management education must be combined with the characteristics and needs of students at this level to explore new teaching models. Teachers should adhere to the principle of promoting learning through application, starting from the reality, and cultivate and enhance the practical and comprehensive application ability of graduate students in forest fire prevention. The use of case teaching method in the teaching process can effectively cultivate students' thinking abilities in analysis, reasoning, induction, synthesis, and other aspects.

Smoldering charcoal detection in forest soil by multiple CO sensors

Cleaning up residual fires is an important part of forest fire management to avoid the loss of forest resources caused by the recurrence of a residual fire. Existing residual fire detection equipment is mainly infrared temperature detection and smoke identification. Due to the isolation of ground, temperature and smoke characteristics of medium and large smoldering charcoal in some forest soils are not obvious, making it difficult to identify by detection equipment. CO gas is an important detection index for indoor smoldering fire detection, and an important identification feature of hidden smoldering ground fires. However, there is no research on locating smoldering fires through CO detection. We studied the diffusion law of CO gas directly above covered smoldering charcoal as a criterion to design a detection device equipped with multiple CO sensors. According to the motion decomposition search algorithm, the detection device realizes the function of automatically searching for smoldering charcoal. Experimental data shows that the average CO concentration over the covered smoldering charcoal decreases exponentially with increasing height. The size of the search step is related to the reliability of the search algorithm. The detection success corresponding to the small step length is high but the search time is lengthy which can lead to search failure. The introduction of step and rotation factors in search algorithm improves the search efficiency. This study reveals that the average ground CO concentration directly above smoldering charcoal in forests changes with height. Based on this law, a CO gas sensor detection device for hidden smoldering fires has been designed, which enriches the technique of residual fire detection.

BSEIFFS: Blockchain-secured edge-intelligent forest fire surveillance

This work aims to address two major issues in forest fire management: accurate real-time forecast and end-to-end secured data delivery between validated entities. To do so, we introduce BSEIFFS, a secured and intelligent forest fire prediction architecture realized through the convergence of Blockchain technology, mist computing, edge computing and machine learning paradigms. The proposed architecture renders Blockchain-backed security and Support Vector Regression based wildfire prediction at the edge by offloading the execution overheads to containerized cloud applications. Two algorithms namely Uncorrelated Data Transmission (UDT) algorithm and Blockchain based Audit Trail validation (BAT) algorithm are proposed to reduce number of repetitive packet transmissions and rendering a lightweight Blockchain based identity validation scheme at the edge respectively. The prediction model is trained using real-world meteorological data and tested on a steel manufacturing facility where controlled combustion and quenching processes closely resemble fire and rain scenarios. Apart from the Google Cloud Platform, tools such as IBM NodeRed, Siemens IBA PDA softwares are used to implement the BSEIFFS architecture. Extensive experimentation and comparative evaluation are performed with relevant baseline approaches. BSEIFFS achieves a prediction accuracy of 98.61%, ROC AUC score of 0.9668 with 0.1127 as false positive rate. The BSEIFFS SVR predictor with rbf kernel (MAE = 1.840, RMSE = 5.728) performs better than similar baseline approaches. The proposed UDT algorithm causes 82.02% reduction in packet transmissions through the network. BSEIFFS also achieves atleast 44.84% lesser energy consumption, 11.91% higher throughput and 71.91% lesser roundtrip time in comparison with baseline approaches. BSEIFFS can massively extend the benefits of the imminent B5G/6G technologies to perform fine-grained and faster computations.

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.

An Overview of Forest Fire Regimes and Their Constitution Basis

Prediction of future fire probability and severity lays the foundation for formulating ecological fire prevention and global environmental protection policies. The present work focuses on overview of the fire regimes and their constitution basis to promote the understanding on the fire regime parameters and the related mechanism operated in the ecosystem. Through the application of remote sensing technology and fire models in fire regime research and predictions, research progress and achievements were made in systematically quantifying the fire characteristic parameters, with the emphasis on the influence of forest stand characteristics on fire regimes under climate change. It has been well understood that the fire regimes have obvious spatiotemporal characteristics from regional to global scale, and the fire regimes are closely related to forest stands and climate. The future fire regimes are likely to be dominated by climate changes. For this reason, the future research on fire regimes needs to further study the spatiotemporal fire process and quantify the parameters of fire regimes through constantly exploring the mechanism of the fire regimes driven by climate changes and the control measures; meanwhile, the research on fire regimes will continue to serve as a basis for forest fire management.

Indoor Experiments on the Moisture Dynamic Response to Wind Velocity for Fuelbeds with Different Degrees of Compactness

The semiphysical method is presently the most widely used for predicting litter moisture content, but it produces some errors. These are mainly due to the simplification of the water loss process and not accounting for the fuelbed structure, which can have a serious impact on the accuracy of litter moisture content predictions and, consequently, on forest fire management. As such, in this study, we constructed fuelbeds with different degrees of compactness, and the moisture content is saturated at this time. The drying process is recorded every 10 min under different wind velocity, and the experiment is stopped when the moisture content is not changing. Taking the saturated fibers’ moisture content (30%) as the threshold value, the drying process was artificially divided into two stages (from the initial moisture content to 30%, it is a process of free water drying, and from 30% to the equilibrium moisture content, this is the process of drying of bound water), which is called the distinguishing drying process. The whole drying process (from the initial to the equilibrium moisture content) is called the undistinguishing drying process. Drying coefficient and effect factors were calculated by distinguishing and not distinguishing the drying process, respectively. This established a prediction model based on compactness and wind velocity. The results show that the drying coefficients, k2 and k, of the two litter types were significantly different: the k2 of the white oak fuelbed was significantly lower than its k, with a maximum variation difference of 57.10%. The k2 in the Masson pine fuelbed was significantly higher than its k, with a maximum variation difference of 72.76%. Wind velocity and compactness had significant effects on all the drying coefficients of the two litter types, but with changes in the effect factors. The changes in k2 were weaker than those of the other drying coefficients. Compared with the model that did not distinguish the drying process, the MRE of the prediction models for white oak and Masson pine decreased by 27.39% and 2.35%, respectively. The prediction accuracy of the model of the drying coefficient obtained by distinguishing the drying loss process was higher than that of the model that did not distinguish the drying process. This study was an indoor simulation experiment that elucidated the drying mechanism of litter and established a prediction model for the drying coefficient based on effect factors. It is of great significance for further field verification and for improving the accuracy of moisture content predictions based on the semiphysical method and will significantly improve the accuracy of fire risk and fire behavior prediction.

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 Unsupervised Burned Area Mapping Approach Using Sentinel-2 Images

The frequency and severity of large, destructive fires have increased in the recent past, with extended impacts on the landscape, the human population, and ecosystems. Earth observations provide a means for the frequent, wide coverage and accurate monitoring of fire impacts. This study describes an unsupervised approach for the mapping of burned areas from Sentinel-2 satellite imagery, which is based on multispectral thresholding, and introduces an adaptive thresholding method. It takes into account the localized variability of the spectral responses in a two-phase approach. The first phase detects areas that are burned with a high probability, while the second phase adaptively adjusts this preliminary mapping by expanding and refining its boundaries. The resulting classification contains two main classes of interest: burned and unburned. The latter is further classified into four (4) fire impact severity classes, according to the Copernicus Emergency Management Service (CEMS) and the NASA United States Geological Survey (USGS)’s widely acknowledged nomenclature examples. Three distinct wildfire events are assessed, which occurred during the summers of 2020 and 2021 in Greece and Portugal. The classification accuracy is calculated by juxtaposing the classification outputs to burned area validation maps created through the photointerpretation of very high-resolution (VHR) satellite imagery. The corresponding CEMS On-Demand Mapping products are also juxtaposed against the validation maps for comparison purposes. The accuracy assessment showcases that the unsupervised approach closely follows the capacity provided by the CEMS maps (e.g., the kappa coefficient—k—of the proposed unsupervised approach is 0.91, 0.83 and 0.83 for the events processed, while the CEMS products achieve a k of 0.94, 0.93 and 0.8, respectively). The proposed approach considers the variability of the affected areas’ spectral response; thus, it generalizes well to different areas, e.g., areas characterized by different land cover types. It seems to offer an effective means of mapping the wildfire-induced changes, which can be further incorporated and used by forest fire management services and further decision support systems complementary to the CEMS maps.

Collaborative Governance on Natural Disaster Management A Study on Forest and Land Fires in Central Kalimantan

<em>Forest and land fires repeatedly occur each year in Indonesia, with a variety of sources that bring them about. There are 6 (six) provinces in Indonesia that have always experienced forest and land fires, because these six provinces have the largest peatland in comparison to other provinces. The Central Kalimantan Province experienced the largest forest and land fires in 2015 and 2019. Forest and land fires management involves multi-stakeholders both from the national and regional level. The research was conducted in Pulang Pisau Regency, to examine the collaborative collaboration process between stakeholders that have not functioned optimally. This is why forest and land fires keep on reoccurring annually. The authors conducted an analysis of several existing collaborative governance theories and based on the results of the subsequent analysis the authors recommended a collaboration governance model that is suitable to be applied to fire management on land specifically in Central Kalimantan and Indonesia in general. Here, the author reconstructs the collaborative governance theory of Ansell & Gash (2007) by adding a wisdom variable to become a new variable in implementing collaborative governance in forest and land fire disaste management in Central Kalimantan.</em>

The Political Law of Forest and Land Fire Management: A Case Study in Bengkalis and Meranti Islands Regency, Riau Province, Indonesia

The phenomenon of forest and land fires has had a major impact on everyone's life. Where the government has worked hard to solve this problem through policies, institutions, and funding. But this happens again and again throughout the year, so this is important to be addressed by all parties to help stop forest and land fires in Riau Province. This study uses an empirical juridical approach, where the analysis of research data is carried out through qualitative description. The results show that the Riau Provincial Government has issued an advisory on “Anticipating land and forest fires in the Province of Riau in 2020” to prevent land and forest fires disasters that occur repeatedly. In addition, all kinds of policies and funding have been prepared. However, due to overly complex technical problems and local regulations that have not been able to solve all problems, forest and land fires still occur in several places in Riau Province. Therefore, the Bengkalis Regency and Meranti Islands Regency Governments need to be dedicated and consistent in working to improve the work system and governance between agencies and institutions. In addition, there is a need for a regulation on forest and land fire management that is responsive to the situation and conditions that occur in the region.
 
 Received: 18 September 2022 / Accepted: 28 December 2022 / Published: 5 January 2023

Drivers of wildfire burn severity in the montane rainforests of northern Vietnam

Background Fire impacts and drivers of wildfire burn severity remain poorly understood for tropical forests. Aims To assess variation and environmental drivers of burn severity for nine forest fires in northern Vietnam. Methods Burn severity was estimated from satellite image analyses, and associations with a remotely sensed index of annual fuel production, topographic factors (elevation, slope, aspect) and weather variables (temperature, rainfall, relative humidity, wind speed) were evaluated. Key results High severity burn areas were found to be fairly uncommon and were associated with steeper, south-west facing slopes, higher elevations and lower fuel abundance. There was a weak tendency for higher burn severity on days with lower relative humidity. Conclusions Conditions that increase fire intensity and the dryness and flammability of fuels are important contributors to high severity fires in wet tropical systems. However, the pattern of higher burn severity at high elevation, where forests tend to be denser and more humid, is counter to this interpretation and may be due to species compositional changes and greater vulnerability of high-elevation forests to fire impacts. Implications Better understanding of fire risk and where in the montane forests of northern Vietnam fires are most likely to burn at high severity will assist forest fire management and recovery strategies.

A Review on Spatio-temporal Pattern Evolution of Shrub Fire Caused by Human Disturbance in Yunnan Province

Artificial fire is the main cause of forest fires, and human intervention can reduce the occurrence of forest fires. Yunnan province is a province with a high incidence of forest fires. A large number of flammable shrubbery are distributed in many regions. The study on the time scale and space scale of shrubbery fires caused by human disturbance is conducive to the study on the evolution of the space-time pattern of shrubbery fires and the reduction of forest fires. Based on Yunnan province from 1990 to 2018 shrubbery fire data, combined with ArcGIS geographic information system, BP neural network analysis of the impact of human interference on Yunnan shrubbery fire and shrubbery fire time and space changes. The research results can provide some theoretical support for forest fire management departments.

The Extension and Connotation of Forest Fire Ecology Under Constant Innovation

Forest fire ecology is a discipline to study the ecological consequences of forest fires and the interrelationships among them. With the intensification of climate change and the deterioration of forest fire status, forest fire ecology has become a very active branch of ecology. In this paper we reviewed the development history of forest fire ecology and expounded the extension and connotation of forest fire ecology, fire factors and regimes as well as their interaction with the ecological systems. Previous ecologists have conducted a lot of research on the impact of forest fire on the ecological environment and the adaptability of ecosystems to forest fire. The development of forest fire ecology can be divided into three periods: in the initial period the destruction of forest fire was recognized with the study objective being focused on forest fire control; in the expansion period, efforts were made to the application of forest fire in the fire management and the third period was symbolized as the founding of the entire discipline. With the application of the advanced technology and mathematical models, forest fire ecology provides successful examples in carrying out long-term prediction on the forest community dynamics after fire intrusion. Planned burning have been developed as effective means of forest fire management, whereas ecological fire mitigation is considered as a new concept in forest fire management.

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 Comparative Study on the Drivers of Forest Fires in Different Countries in the Cross-Border Area between China, North Korea and Russia

The occurrence and spread of forest fires are the result of the interaction of many factors. In cross-border areas, different fire management systems may lead to different forest fire driving factors. A comparative analysis of the forest fire driving factors in different countries can help to provide ideas for fire prevention and control. In this study, based on the logistic regression (LR) model and standardized coefficients, we determined the relative impact of forest fire driving factors in different countries, in the cross-border area between China, North Korea and Russia from 2001 to 2020, and established a forest fire probability and fire risk level division using a Kriging interpolation. The results show that the climate is the most important factor affecting the probability of forest fires in the cross-border area, followed by the topography and vegetation factors; human activities have the least influence. From a country-by-country perspective, the forest fires on the Chinese side were more affected by humans than on the North Korean and Russian sides and they were mainly concentrated in areas with a low altitude and high population density. The forest fires on the North Korean side and the Russian side were more affected by nature than on the Chinese side and were mainly concentrated in areas with a low altitude, high temperature and little rainfall. The high-risk areas for forest fires were mostly concentrated near the border between China, North Korea and Russia, where transboundary fires pose a great threat to forest resources and rare animals. This study shows that there is a significant difference between the impact of different forest fire management systems on fire conditions, and that active forest fire control policies can effectively reduce the damage caused by forest fires. Due to the complexity of the geopolitics in cross-border areas, transboundary firefighting faces certain difficulties. In the future, it will be necessary to strengthen the cooperation between countries and establish transboundary joint defenses against forest fires in order to protect the declining forest resources and the habitats of rare animals.

Drivers, trends and management of forest plantation fires in Tanzania

During the last two decades there has been an increase in investments in exotic woodlots and commercial forest plantations in Tanzania. However, fire has repeatedly been singled out as number one risk to forestry plantations investments and improved management is urgently needed to reverse the trend and ensure sustainable supply of forest products. Unfortunately, little has been documented on major drivers, trends and management of exotic woodlots and forest plantation fires in Tanzania. Lack of such information has slowed down and diminished efforts to reduce the negative impacts of these fires and upscaling of commercial forestry. This paper analyses few available published information and gray literature as well as satellite imagery for 2016–2020 to put together drivers, trends and management of forest plantation fires. To effectively control fires, it is important that forest policy is enforced, education and awareness on fires is enhanced, local communities support is promoted and fire prevention and suppression is strengthened in Tanzania.

Challenges in forest fire management in the Himalaya: experiences from Nepal

Nepal faces challenges in forest fire management that are aggravated by climate change, and there is an urgent need for financial, institutional and technological resources and for capacity development to effectively implement fire management. The problems of forest fires in the region are complex, and cannot be addressed at a single sectoral level. This article explains that to overcome the country’s limited capacity in fire management, there is a need to strengthen the human and technical resources of agencies and local communities that deal with fire prevention and response. In addition, transboundary cooperation in fire management is needed to share the most appropriate knowledge of advanced approaches in fire management.

THE IMPLEMENTATION OF FOREST AND LAND FIRE MANAGEMENT POLICY IN INDONESIA DURING THE COVID-19 PANDEMIC

The coronavirus outbreak (COVID-19) has raised questions about changes in subsequent environmental effects, mainly forest and land fires. This paper evaluates the implementation of land and forest fire management policies in Indonesia during the COVID-19 pandemic, particularly in 2020. A qualitative approach was conducted in policy implementation analysis based on George Edward III's theory by looking at bureaucratic structure, resources, communication, and disposition factors. The research focused on the operational work of the Forest and Land Fire Brigade, known as Manggala Agni, in Indonesia. The results showed that the collaborative work of Manggala Agni and the other forest and land fire task forces successfully reduced the hotspots; hence there was a significant decrease in the burned area. It is also inseparable from climatic factors. During this period there was no haze disaster although the task of controlling forest and land fires still encountered several obstacles during the pandemic. This is because of training, technology transfer, budget support, and synergy between stakeholders and Manggala Agni’s team members, so a significant reduction in forest and land fires during 2020 can be achieved.

Union Datasets for Information Support in the Risk Management of Forest Fires

In this paper the sources of forest fi re monitoring data are analyzed. It is required to form a union interdepartmental information space and use intelligent data processing tools to support management. Th e proposed method for linking sets of heterogeneous data in the process of forming management decisions is discussed. An algorithm for applying diff erent data to manage fire safety in rural areas of Siberia is given.