Risk Of Fire Occurrence Research Articles

Применение методов пространственного анализа при оценке степени горимости лесов Хабаровского края

Forest fires are the dominant factor in the transformation of natural landscapes in the boreal zone. The area of the Khabarovsk Territory, 93.6 % of which belongs to the forest fund lands, currently has a forest cover of only 66.5 %, which is largely due to forest fires. This makes urgent the problem of reducing the forest fire frequency in the region, including in terms of assessing the actual fire frequency for predicting the risks of landscape fires. The article shows the possibilities of using spatial analysis methods of the Spatial Analyst module of the ArcGIS 10.8 software package to assess the degree of actual forest fire frequency based on calculating the number of fires per 1 million and 250 thousand ha and the area of fires per 1000 ha for a sliding window of a given size. Based on the data from the Remote Monitoring Information System of the Federal Forestry Agency for 2000–2020 and the data on the number of fires from the Fire Information for Resource Management System website for 2000–2021, an assessment of the actual fire frequency for the Khabarovsk Territory has been carried out. The analysis of the results reflects the high spatial and seasonal heterogeneity of the spread of landscape fires in the territory and their proximity to transport and residential structures. Areas with high fire frequency often do not coincide with the boundaries of forestry enterprises, and the maximum indicators are typical for non-forest areas that are not covered by forestry statistics. For different seasons of the year, along with quantitative differences, spatial shifts of firing points and areas with their high density are also observed. Most of the lands in the region (88.3 %) in terms of fire danger belong to the “low” and “below medium” fire frequency categories, which is primarily due to the low degree of development of these areas. The assessment of the degree of fire frequency in the territory on the basis of annual and seasonal maps of the density of firing points, calculated using the sliding window method, makes it possible to fairly objectively and in detail reflect the average long-term risk of fire occurence outside of reference to forestry boundaries. In combination with a map of the distribution of forests by natural fire hazard classes based on pyrological characteristics, the results of the reseаrch can become the basis for calculating the background fire risk, reflecting the risk of fire occurrence and development.

A Combination of Human Activity and Climate Drives Forest Fire Occurrence in Central Europe: The Case of the Czech Republic

Central Europe is not a typical wildfire region; however, an increasingly warm and dry climate and model-based projections indicate that the number of forest fires are increasing. This study provides new insights into the drivers of forest fire occurrence in the Czech Republic, during the period 2006 to 2015, by focusing on climate, land cover, and human activity factors. The average annual number of forest fires during the study period was 728, with a median burned area of 0.01 ha. Forest fire incidence showed distinct spring (April) and summer (July to August) peaks, with median burned areas of 0.04 ha and 0.005 ha, respectively. Relationships between the predictors (climate data, forest-related data, socioeconomic data, and landscape-context data) and the number of forest fires in individual municipality districts were analyzed using Generalized Additive Models (GAM) on three time scales (annually, monthly, and during the summer season). The constructed GAMs explained 48.7 and 53.8% of forest fire variability when fire occurrence was analyzed on a monthly scale and during the summer season, respectively. On an annual scale, the models explained 71.4% of the observed forest fire variability. The number of forest fires was related to the number of residents and overnight tourists in the area. The effect of climate was manifested on monthly and summer season scales only, with warmer and drier conditions associated with higher forest fire frequency. A higher proportion of conifers and the length of the wildland–urban interface were also positively associated with forest fire occurrence. Forest fire occurrence was influenced by a combination of climatic, forest-related, and social activity factors. The effect of climate was most pronounced on a monthly scale, corresponding with the presence of two distinct seasonal peaks of forest fire occurrence. The significant effect of factors related to human activity suggests that measures to increase public awareness about fire risk and targeted activity regulation are essential in controlling the risk of fire occurrence in Central Europe. An increasing frequency of fire-conducive weather, forest structure transformations due to excessive tree mortality, and changing patterns of human activity on the landscape require permanent monitoring and assessment of possible shifts in forest fire risk.

Utilizing Grid Data and Deep Learning for Forest Fire Occurrences and Decision Support: A Case Study in the Ningxia Hui Autonomous Region

In order to investigate the geographical distribution of forest fire occurrences in the Ningxia Hui Autonomous Region, this study employs advanced modeling techniques, utilizing diverse data sources, including fuel, Gross Domestic Product (GDP), population, meteorology, buildings, and grid data. This study integrates deep learning Convolutional Neural Networks (CNNs) to predict potential fire incidents. The research findings can be summarized as follows: (i) The employed model exhibits very good performance, achieving an accuracy of 84.35%, a recall of 86.21%, and an Area Under the Curve (AUC) of 87.67%. The application of this model significantly enhances the reliability of the forest fire occurrence model and provides a more precise assessment of its uncertainty. (ii) Spatial analysis shows that the risk of fire occurrence in most areas is low-medium, while high-risk areas are mainly concentrated in Longde County, Jingyuan County, Pengyang County, Xiji County, Yuanzhou District, Tongxin County, Xixia District, and Yinchuan City, which are mostly located in the southern, southeastern, and northwestern regions of Ningxia Hui Autonomous Region, with a total area of 2191.2 square kilometers. This underscores the urgent need to strengthen early warning systems and effective fire prevention and control strategies in these regions. The contributions of this research include the following: (i) The development of a highly accurate and practical provincial-level forest fire occurrence prediction framework based on grid data and deep learning CNN technology. (ii) The execution of a comprehensive forest fire prediction study in the Ningxia Hui Autonomous Region, China, incorporating multi-source data, providing valuable data references, and decision support for forest fire prevention and control. (iii) The initiation of a preliminary systematic investigation and zoning of forest fires in the Ningxia Hui Autonomous Region, along with tailored recommendations for prevention and control measures.

Optimal allocation model of forest fire detection towers in protected areas based on fire occurrence risk: Where and how to act?

Forest fire detection towers are crucial in supporting rapid firefighting actions in conservation units and thus reducing environmental, social, and economic damages. Thus, the aim was to evaluate scenarios for optimal allocation of forest fire detection towers, according to the risk of occurrence, in the Caparaó National Park, Brazil. Thus, by geotechnological analysis, the areas most susceptible to forest fires and the optimal locations for installation of detection and monitoring for these events were delimited. To run the proposed models, biological, physical, socioeconomic, and meteorological variables were used. From the application of the methodologies, it was observed that 76.70% of the study area was covered by low, moderate, and shallow fire risk classes, while high and very high-risk classes were concentrated in the buffer zone. The scenario with 45 towers was considered the most advantageous, given that they presented viewing levels above 70% and a lower cost per hectare viewed than the scenario with 48 towers. Results showed no critical risks of fire occurrence within the conservation unit, but preventive measures are still needed to avoid fire spread, particularly near the buffer zone. The study's methodologies can be applied in other areas to improve forest fire prevention and control efforts.

Ongoing climatic change increases the risk of wildfires. Case study: Carpathian spruce forests

The challenge to the sustainable development of forestry in the Eurasian temperate - boreal zone is the increase in the frequency and severity of natural disturbances due to global climate change. In this study, a mathematical model for predicting the risk of wildfires in spruce stands growing in the territory of Slovak Paradise National Park under climate change has been proposed and tested. Wildfire risk is described in terms of the observed probabilities of the destruction of spruce stands in relation to their age for a period of 10 years. As the indicators of assumed climate change, the time series of daily values of four fire weather indices (Angstrőm, Nesterov, Baumgartner, and the Meteorological Forest Fire Risk Index) for the period 1951–2019 have been analysed. The results obtained indicated the significant dependence of the observed increasing annual population proportions of burnt areas on the gradually increasing annual population proportions of risky days recorded and evaluated by using the common scales of risk classification. We found that ongoing climate change has a significant impact on increasing the risk of fires. The Meteorological Forest Fire Risk Index has proven to be the most suitable measure for predicting the probability of fire occurrence under the climate conditions in the experimental territory. The indicated risk of fire occurrence in spruce stands under the assumption of a climatic change is substantially higher than in the case when this assumption is neglected. This information can also serve as a basis for the formulation of efficient landscape fire protection measures focused on building the infrastructure to support the efficient retardation of propagation, including the quick suppression of this detrimental natural hazard.

Swin Transformer-Based Object Detection Model Using Explainable Meta-Learning Mining

In order to minimize damage in the event of a fire, the ignition point must be detected and dealt with before the fire spreads. However, the method of detecting fire by heat or fire is more damaging because it can be detected after the fire has spread. Therefore, this study proposes a Swin Transformer-based object detection model using explainable meta-learning mining. The proposed method merges the Swin Transformer and YOLOv3 model and applies meta-learning so as to build an explainable object detection model. In order for efficient learning with small data in the course of learning, it applies Few-Shot Learning. To find the causes of the object detection results, Grad-CAM as an explainable visualization method is used. It detects small objects of smoke in the fire image data and classifies them according to the color of the smoke generated when a fire breaks out. Accordingly, it is possible to predict and classify the risk of fire occurrence to minimize damage caused by fire. In this study, with the use of Mean Average Precision (mAP), performance evaluation is carried out in two ways. First, the performance of the proposed object detection model is evaluated. Secondly, the performance of the proposed method is compared with a conventional object detection method’s performance. In addition, the accuracy comparison using the confusion matrix and the suitability of real-time object detection using FPS are judged. Given the results of the evaluation, the proposed method supports accurate and real-time monitoring and analysis.

Superposition Risk Assessment and Calculation Model of the Working Position of Coal-Seam Fire Accidents in China

The coal-seam fire is one of the most significant disasters in the coal mining industry in China, affecting the safety of coal production in China. The working-position risk in coal mining has an important impact on the risk of fire occurrence, and thus it would be worthwhile to analyze working-position risks so as to effectively prevent and control coal-seam fires. Based on the kernel density estimation (KDE), this research puts forward an innovative calculation-model and assessment method of the superposition risk of the working position on coal-seam fire accidents. This research aims to evaluate the priority of risk management of working positions in coal-seam fire accidents. In order to achieve this research aim and objectives, this research carried out a statistical analysis of 100 classic cases of coal-seam fire accidents from 2000 to 2022, using the accident-tree-structure importance analysis method. This research contributed to the evaluation of the frequency and severity of various risk factors leading to fire accidents, and the development of the value at risk (VaR) of various risk factors in the coal-seam fire accidents. Integrating all the risk factors involved in each position and their risk values, and building a position-risk calculation model was carried out. In addition, in accordance with the kernel density estimation (KDE), a post-superposition risk model was established. Moreover, ArcGIS software was used to obtain the superimposed risk of posts and build a risk-distribution map. Based on the possibility of post-risk occurrence and the severity of the consequences, a risk-assessment matrix was developed, a post-risk grading standard was established, and risk levels of the working position were divided up in this research. Results indicated that (1) before risk superposition, working-position risks and risk levels are densely distributed, and nearly 80% of risk levels of the working position are focused on Level II and III, without Level I. (2) After risk superposition, the post-risk is affected by the surrounding post-risk, and the risk- and level-distribution is more hierarchical; the number of Level I risks in working positions increased to 12, which were mainly distributed among the comprehensive mining team, comprehensive excavation team and ventilation team, which accords more with the objective and actual production-conditions. The risk-distribution map directly showed that the post-fire risk at the mining face and shaft is higher, a result which will take on a significant guiding role in the effective control and prevention of risk in coal-seam fires in the future.

The Approach to Fire Safety in the Curriculum Map-Matrix of Engineering Courses at a Federal University According to The Parameters of law 13.425: a Systematic Review

Objective: the aim of this study is to analyze how and how often the fire protection approach is included in the curriculum of engineering courses. Theoretical reference: fire protection has gained the spotlight, in large part, as a result of some disasters that have occurred in recent years. The subject has evolved and gained considerable prominence, thus requiring changes in current legislation, as well as the formulation of new Standards and Laws, capable of mitigating the risk of fire occurrence. It is necessary to understand the role of the future engineer on the fire protection policy, to avoid possible fire occurrences in institutions. So, several studies from literature about fire have been used to a theorical basis of this research. Methodology: a systematic literature review whit Prism protocol have been used to a review literature. The databases used and selected for this study was Google Academic, Scielo and Eric was carried out in a quantitative analysis. Secondly, in a qualitative analysis, a case study was carried out, starting with an investigation of the curriculum of nine engineering courses at a Brazilian university, to verify the consistency of the fire protection approach and compliance with the “Kiss Law” in the courses studied. Results and conclusion: the results indicate a certain insufficiency of disciplines when analyzing the curricular matrices of each course. In most cases, the adoption of contents or disciplines that address the topic present fire protection generically and inconsistently. It is concluded that there is still a lack of knowledge about the relevance of the topic and effective non-compliance with legislation on the subject. There is, therefore, a need for implementation measures in the learning topics of engineering curricula. Originality/ value: There are few studies on fire protection in the school environment. So, the originality of this study is in analyzing how engineering schools are implementing fire protection legislation and how this practice is effective for collective safety.

Estimating Next Day’s Forest Fire Risk via a Complete Machine Learning Methodology

Next day wildfire prediction is an open research problem with significant environmental, social, and economic impact since it can produce methods and tools directly exploitable by fire services, assisting, thus, in the prevention of fire occurrences or the mitigation of their effects. It consists in accurately predicting which areas of a territory are at higher risk of fire occurrence each next day, exploiting solely information obtained up until the previous day. The task’s requirements in spatial granularity and scale of predictions, as well as the extreme imbalance of the data distribution render it a rather demanding and difficult to accurately solve the problem. This is reflected in the current literature, where most existing works handle a simplified or limited version of the problem. Taking into account the above problem specificities, in this paper, we present a machine learning methodology that effectively (sensitivity > 90%, specificity > 65%) and efficiently performs next day fire prediction, in rather high spatial granularity and in the scale of a country. The key points of the proposed approach are summarized in: (a) the utilization of an extended set of fire driving factors (features), including topography-related, meteorology-related and Earth Observation (EO)-related features, as well as historical information of areas’ proneness to fire occurrence; (b) the deployment of a set of state-of-the-art classification algorithms that are properly tuned/optimized on the setting; (c) two alternative cross-validation schemes along with custom validation measures that allow the optimal and sound training of classification models, as well as the selection of different models, in relation to the desired trade-off between sensitivity (ratio of correctly identified fire areas) and specificity (ratio of correctly identified non-fire areas). In parallel, we discuss pitfalls, intuitions, best practices, and directions for further investigation derived from our analysis and experimental evaluation.

Analysis of Forest Fire Dynamics, Distribution and Main Drivers in the Atlantic Forest

The fire susceptibility of the Atlantic Forest has largely increased over the past two decades due to a combination of climate change and anthropogenic factors such as land cover change and human modification. High rates of forest fragmentation have contributed to escalating fires in this imperilled global biodiversity hotspot. Understanding fire patterns is essential to developing an effective forest fire management strategy. In this research, we utilized the Random Forest (RF) machine learning approach for identifying the role of climatic and anthropogenic factors in influencing fire occurrence probability and mapping the spatial distribution of fire risk. We found that the Normalized Difference Vegetation Index value and climate variables (i.e., temperature and solar radiation) were significant drivers of fire occurrence risk. Results also confirm that forest fragmentation increases with fire density in the region.

Integrating multiple factors to optimize watchtower deployment for wildfire detection

Traditional human-vision-based watchtower systems are being gradually replaced by the machine-vision-based watchtower system. The visual range of machine-vision-based watchtower is smaller than the range of traditional human-vision-based watchtower, which has led to a sharp increase in the number of towers that should be deployed. Consequently, the overlapping area between watchtowers is larger and overlaps are more frequent than in conventional watchtower networks. This poses an urgent challenge: identifying the optimal locations for deployment. If the number of required watchtowers must be increased to extend the detection coverage, overlaps among watchtowers are inevitable and result in viewshed redundancy. However, this redundancy of the viewshed resources of the watchtowers has not been utilized in the design of fire detection systems. Moreover, fire ignition factors, such as climatic factors, fuels, and human behaviour, cause the fire occurrence risk to differ among forest areas. Thus, the fire risk map of the area should also be considered in watchtower deployment. A fire risk model is used as the first step in producing the fire risk map, which is used to propose a new watchtower deployment model for optimizing the watchtower system by integrating viewshed analysis, location allocation, and multi-coverage of the high-fire-risk area while considering the budget constraints for providing optimal coverage. We use a real dataset of a forest park to evaluate the applicability of our approach. The proposed approach is evaluated against the FV-NB (Full coVerage with No Budget constraint) algorithm and the XV-B (maXimum possible coVerage with a Budget constraint) algorithm in terms of performance. The evaluation results demonstrate that our approach realizes higher coverage gain and excellent multiple-coverage of the fire risk area by integrating the viewshed and the fire risk level into location allocation while satisfying requirements on the overall coverage and budget. The proposed approach is more suitable in the environments with moderate watchtower density, in which overlapping areas are frequent. It offers as much as 8.9–17.3% improvement of multiple-coverage of the high-fire-risk area.

Fire history and its drivers based on peatland charcoal analysis in the Changbai Mountains, north-east China, during the last 13 000 years

The Changbai Mountains forest ecosystem is one of the best-preserved temperate mountain forest ecosystems in Asia. Since the establishment of the reserve in 1960, extensive forest fires have been excluded as a result of strict regulation and suppression efforts and thus fuels have accumulated for several decades. Due to the lack of historical fire records in the area, the risk of fire occurrence cannot be properly estimated. In this study, we used charcoal records to reconstruct the palaeo-fire frequency in the Changbai Mountains during the last 13000 years. The results indicate that fires were frequent during 13.0–11.0, 7.0–6.0, and 2.5–2.0 calender (cal) kyr before present (BP) and from 1.0 cal kyr BP to the present. The fire frequencies are largely consistent with other palaeoenvironmental records from the study area and we conclude that since 13.0 cal kyr BP fires were more frequent at the regional scale during intervals of a weak summer monsoon, caused by a cold and dry climate and the abundance of flammable trees. Archaeological data from the study area since 3.0 cal kyr BP indicate increasing human activity, which dominated the occurrence of local fires. In addition, intermittent volcanic activity influenced the occurrence of fires during the past millennium.

Forest fire hazard zoning in Mato Grosso State, Brazil

This work aimed to map areas of forest fire risk in the state of Mato Grosso, Brazil. Study area is located in Brazilian Midwest region. Defined risk factors were: land-use, vegetation, slope, terrain orientation, proximity to roads and trails, altitude, precipitation, water deficit, evapotranspiration and temperature. Generated risk map was validated by overlapping it with fire occurrence points. It was observed that the highest risks of forest fires in Mato Grosso State are distributed in central and south / southwest regions. The majority of Mato Grosso area possesses high to extreme fire occurrence risk (55.76%). Adopted methodology was accurate in zoning fire risk in the evaluated area. Forest fire risk zoning provides subsidies for planning preventive and forest firefighting actions in the state. This methodology can be applied to other Brazilian or global regions.

Temporal patterns of active fire density and its relationship with a satellite fuel greenness index by vegetation type and region in Mexico during 2003\u20132014

BackgroundUnderstanding the temporal patterns of fire occurrence and their relationships with fuel dryness is key to sound fire management, especially under increasing global warming. At present, no system for prediction of fire occurrence risk based on fuel dryness conditions is available in Mexico. As part of an ongoing national-scale project, we developed an operational fire risk mapping tool based on satellite and weather information.ResultsWe demonstrated how differing monthly temporal trends in a fuel greenness index, dead ratio (DR), and fire density (FDI) can be clearly differentiated by vegetation type and region for the whole country, using MODIS satellite observations for the period 2003 to 2014. We tested linear and non-linear models, including temporal autocorrelation terms, for prediction of FDI from DR for a total of 28 combinations of vegetation types and regions. In addition, we developed seasonal autoregressive integrated moving average (ARIMA) models for forecasting DR values based on the last observed values. Most ARIMA models showed values of the adjusted coefficient of determination (R2 adj) above 0.7 to 0.8, suggesting potential to forecast fuel dryness and fire occurrence risk conditions. The best fitted models explained more than 70% of the observed FDI variation in the relation between monthly DR and fire density.ConclusionThese results suggest that there is potential for the DR index to be incorporated in future fire risk operational tools. However, some vegetation types and regions show lower correlations between DR and observed fire density, suggesting that other variables, such as distance and timing of agricultural burn, deserve attention in future studies.

A stochastic programming model with endogenous uncertainty for incentivizing fuel reduction treatment under uncertain landowner behavior

Reducing the potential damage caused by a wildfire is a problem of significant importance to land and fire managers. Fuel reduction treatment is a well-known method of reducing the risk of fire occurrence and spread on landscapes. However, officials seeking fuel reduction treatments on privately owned lands can only encourage it through incentive programs such as cost-share programs. This research developed a methodology that provides the basis for a decision-making tool to help managers allocate limited cost-share resources among a set of landowners to maximize wildfire risk reduction by implementing a hazardous fuel reduction treatment. A key feature of the methodology is that it incorporates uncertainty in the landowners’ decision of whether or not to implement treatment on their lands. The methodology is based on a stochastic programming model with endogenous uncertainty where the probability that a landowner accepts a cost-share offer to implement a fuel reduction treatment on their land depends on the offer amount. To estimate the probability that a landowner accepts a given cost-share offer amount, we used a predictive modeling technique to analyze landowner survey data. The results provide insight about the effects of different cost-share allocation strategies on the expected damage. Numerical experiments show that the risk-based allocation provides up to 37.3% more reduction in damage compared to other strategies that allocate equal cost-share amounts among landowners. Additionally, the results show that the solution quality is substantially sensitive to changes in the number of resource allocation levels.

FIRE SAFETY IN THE BUILT-ENVIRONMENT: A CASE STUDY IN A RESIDENTIAL FACILITY

Abstract In light of the spread of residential fires worldwide, this paper aims to identify the current fire safety code requirements for providing the minimum level of safety in residential facilities, hence, mitigating the risk of fire occurrence. The paper also presents the findings of a case study to demonstrate the compliance level of a sample residential facility, with code requirements. The case study building is composed of three floors and a basement. It can accommodate 214 persons. It was built in 2013, and it is located in Khobar, Saudi Arabia. The study revealed that the assessed facility had an adequate distribution of fire detection and notification systems. Moreover, the level of the housekeeping practices and concentration of hazardous material was found to be acceptable. However, the means of egress, fire doors and fire suppression systems were not up to code requirements. The study recommended measures to improve the level of safety in the building. These include increasing the number of fire exits; installing sprinkler heads on each floor; distributing fire extinguishers sufficiently; and installing fire rated doors. This study aims at raising awareness about the incidences of fires accidents in residential facilities and their catastrophic implications. It serves to provide a standardized fire safety management checklist, for the use of design professionals and property managers, towards the effective design and maintenance of residential facilities.

Economic Assessment of Fire Damage to Urban Forest in the Wildland–Urban Interface Using Planet Satellites Constellation Images

The wildland-urban interface (WUI)—the area where wildland vegetation and urban buildings intermix—is at a greater risk of fire occurrence because of extensive human activity in that area. Although satellite remote sensing has become a major tool for assessing fire damage in wildlands, it is unsuitable for WUI fire monitoring due to the low spatial resolution of the images from satellites that provide frequent information which is relevant for timely fire monitoring in WUI. Here, we take advantage of frequent (i.e., ca. daily), high-spatial-resolution (3 m) imagery acquired from a constellation of nano-satellites operated by Planet Labs (“Planet”) to assess fire damage to urban trees in the WUI of a Mediterranean city in Israel (Haifa). The fire occurred at the end of 2016, consuming ca. 17,000 of the trees (152 trees ha−1) within the near-by wildland and urban parts of the city. Three vegetation indices (GNDVI, NDVI and GCC) from Planet satellite images were used to derive a burn severity map for the WUI area after applying a subpixel discrimination method to distinguish between woody and herbaceous vegetation. The produced burn severity map was successfully validated with information acquired from an extensive field survey in the WUI burnt area (overall accuracy and kappa: 87% and 0.75, respectively). Planet’s vegetation indices were calibrated using in-field tree measurements to obtain high spatial resolution maps of burned trees and consumed woody biomass in the WUI. These were used in conjunction with an ecosystem services valuation model (i-Tree) to estimate spatially-distributed and total economic loss due to damage to urban trees caused by the fire. Results show that nearly half of the urban trees were moderately and severely burned (26% and 22%, respectively). The total damage to the urban forest was estimated at ca. 41 ± 10 M USD. We conclude that using the method developed in this study with high-spatial-resolution Planet images has a great potential for WUI fire economic assessment.

Assessment of fire danger vulnerability using McArthur’s forest and grass fire danger indices

McArthur’s fire danger index (FDI) is an integrated measure of the risk of fire occurrence, spreading speed and containment difficulty. The climatic parameter-dependent McArthur forest and grass fire danger indices were used to categorize fire danger severity. This paper explored the characteristics of four different climatic stations in Victoria, Australia, to determine their spatial and temporal variations impacting FDIs and the risk of fire occurrence. Sensitivity analysis was performed to develop nomograms to relate the variation of FDIs with key climatic parameters. These nomograms combine temperature, relative humidity and wind speed to determine fire risk categories in assisting field-based emergency and fire management authorities to identify danger and response plans to mitigate risks by mobilizing human and mechanical resources.

Energy-Efficient Forest Fire Prediction Model Based on Two-Stage Adaptive Duty-Cycled Hybrid X-MAC Protocol.

This paper proposes an adaptive duty-cycled hybrid X-MAC (ADX-MAC) protocol for energy-efficient forest fire prediction. The Asynchronous sensor network protocol, X-MAC protocol, acquires additional environmental status details from each forest fire monitoring sensor for a given period, and then changes the duty-cycle sleep interval to efficiently calculate forest fire occurrence risk according to the environment. Performance was verified experimentally, and the proposed ADX-MAC protocol improved throughput by 19% and was 24% more energy efficient compared to the X-MAC protocol. The duty-cycle was shortened as forest fire probability increased, ensuring forest fires were detected at faster cycle rate.

Developing Models to Predict the Number of Fire Hotspots from an Accumulated Fuel Dryness Index by Vegetation Type and Region in Mexico

Understanding the linkage between accumulated fuel dryness and temporal fire occurrence risk is key for improving decision-making in forest fire management, especially under growing conditions of vegetation stress associated with climate change. This study addresses the development of models to predict the number of 10-day observed Moderate-Resolution Imaging Spectroradiometer (MODIS) active fire hotspots—expressed as a Fire Hotspot Density index (FHD)—from an Accumulated Fuel Dryness Index (AcFDI), for 17 main vegetation types and regions in Mexico, for the period 2011–2015. The AcFDI was calculated by applying vegetation-specific thresholds for fire occurrence to a satellite-based fuel dryness index (FDI), which was developed after the structure of the Fire Potential Index (FPI). Linear and non-linear models were tested for the prediction of FHD from FDI and AcFDI. Non-linear quantile regression models gave the best results for predicting FHD using AcFDI, together with auto-regression from previously observed hotspot density values. The predictions of 10-day observed FHD values were reasonably good with R2 values of 0.5 to 0.7 suggesting the potential to be used as an operational tool for predicting the expected number of fire hotspots by vegetation type and region in Mexico. The presented modeling strategy could be replicated for any fire danger index in any region, based on information from MODIS or other remote sensors.