Fire Risk Areas Research Articles

Enhancing Grid Resiliency in High Fire or Flood Risk Areas: Integrating Protective Relay Settings, Broken Conductor Detection, and Grid Hardening for Climate-Induced Event Preparedness

Enhancing Grid Resiliency in High Fire or Flood Risk Areas: Integrating Protective Relay Settings, Broken Conductor Detection, and Grid Hardening for Climate-Induced Event Preparedness

서울시 화재 취약지역 예측 및 소방력 공간 최적화 연구

서울시 화재 취약지역 예측 및 소방력 공간 최적화 연구

Mapping opportunities for the use of land management strategies to address fire risk in Europe

Many parts of Europe face increasing challenges managing wildfires. Although wildfire is an integral part of certain ecosystems, fires in many places are becoming larger and more intense, driven largely by climate change, land abandonment, and changes in fuel management with important socioeconomic, environmental, and ecosystem services consequences for Europe. In order to envision a comprehensive fire risk mitigation strategy for Europe, a spatial assessment of opportunities to manage fuels at the landscape-scale is needed. Our study explored the suitability of three land management strategies (LMS)—herbivory, mechanical fuel removal, and prescribed burn—which can create more heterogenous fuelscapes, thereby reducing an element of fire risk. We created suitability maps for each of the LMS using adoption factors identified in a systematic literature review (n = 123). We compared these maps with areas of historical fire occurrence as a proxy for fire risk to prioritize key areas for intervention. We found that over a quarter of Europe was suitable for multiple LMS within areas of greater fire risk, creating opportunities for concurrent and synergistic use of the strategies. Options were more limited in areas of southern Europe, where prescribed burn was found to be uniquely viable amongst the LMS evaluated. Opportunities were also restricted in some areas of high fire risk in northern Europe, where herbivory was found to be the only suitable LMS. Our findings take a wide-view of fuel management to target landscape-scale decision making focused on reducing fire risk. However, many other factors must be taken into account to successfully manage fuels at local scales, including the socio-cultural appropriateness of the LMS, the viability of incentive schemes, and possible trade-offs with other management goals, such as carbon storage and biodiversity.

Forest fire in Thailand: Spatio-temporal distribution and future risk assessment

Understanding the spatiotemporal distribution of forest fires and future predictions is very important for management strategies. To identify the present status of forest fires in the Kingdom of Thailand and their risk in the future, ten-year forest fire data were used, and a forest fire hotspot was prepared. A geospatial technique was used in the study to characterize the parameters of forest fires in the country and identify future forest fire risk areas. Most of the forest fires in the country were found to be seasonal. Deciduous forests in higher elevations and on moderate slopes were most vulnerable to forest fire. The level of aridity, soil moisture, temperature, precipitation, vegetation status, and topography influenced the spatiotemporal distribution of forest fires in the country. Greater than 50% of fire risks were observed in 22 administrative divisions, and 17 of the 209 protected areas are also in the high-risk category. The final forest fire hotspot map can be used in policy development and successful management strategies. A better monitoring strategy should be used in the fire hotspot areas as a precautionary measure to minimize the anthropogenic causes of forest fires.

Current and future land fire risk mapping in the southern region of Sumatra, Indonesia, using CMIP6 data and GIS analysis

Land fires lead to a deterioration of environmental quality and have perilous social and economic aspects. One method for mitigating this hazard is to determine the range of fire risk areas. Thus, this study aimed to analyze the current and potential future fire risk in the Ogan Komering Ilir, southern Sumatra region of Indonesia, using CMIP6 data and geospatial technology. In order to analyze fire risk areas, the Landsat 8 satellite image from 2021 (as a baseline period) was used. Variables like the normalized difference moisture index (NDMI), the normalized difference vegetation index (NDVI), and the land surface temperature (LST) were extracted from the satellite image. Then, the standard precipitation index (SPI) was determined using rainfall data from meteorological stations. For future period prediction (periods 2021–2040 and 2041–2060), this study used future climate data from the CMIP6. By overlaying these variables, current and future fire risk zone maps were produced. The results obtained showed that in the current period, around 726.4 km2 (4.2%) of the area was at high fire risk, then 5,621.3 km2 (32.7%) and 10,835.3 km2 (63.1%) were at moderate and low fire risk zones, respectively. Furthermore, for the future period, this study estimated there was an increase in high fire risk areas of about 14% during 2021–2040 and 30% during 2041–2060. This was recorded in the southern and northern parts of the study area. Fire risk areas were strongly associated with NDMI, NDVI, and LST. The study's findings may be useful for land fire management in fire-prone areas.

Evaluation of Fire Prevention and Control System in dr. R. Koesma Regional General Hospital of Tuban Regency in 2021

Introduction: Fires in the workplace can have consequences that adversely affect many parties, both for companies of the workers and the wider community, including institutions such as hospitals. In this research, hospitals are considered to be at high risk of causing fatalities in the event of a fire. The purpose of this research is to evaluate the prevention and control of fire in dr. R. Koesma Hospital Tuban based on the regulation of Minister of Health No.66 of 2016 about Occupational Health and Safety of Hospital. Method: This research is observational research. Data collection was done by interview and observation. The assessment of the evaluation of fire prevention and control is done by using a scoring formula made independently. Result: The evaluation is done on the identification of fire and explosion risk areas as well as on the mapping of high-risk areas of fire and explosion in dr. R. Koesma Hospital based on regulation of Minister of Health No.66 of 2016. The evaluation results on both aspects are 4% out of 6%. The evaluation result of the risk reduction of fire and explosion hazards shows a score of 15% out of 18%. The evaluation result of fire control is 22%. The evaluation result of the fire simulation shows a score of 38% out of 48%. Conclusion: This research concludes that the evaluation results of the fire prevention and control system in dr. R. Koesma Hospital based on regulation of Minister of Health No.66 of 2016 show a score of 83%.

Automatic Processing for Identification of Forest Fire Risk Areas along High-Voltage Power Lines Using Coarse-to-Fine LiDAR Data

This work focuses on the automatic identification of forest fire risk areas along high-voltage power lines through the development of a tool and its validation on a real forest area. The tool allows one to automate the whole process, which includes the classification of the point cloud, the computation of the catenary of the wires using different calculation methods, the estimation of the vegetation growth and the identification of the risk areas. To this end, a coarse-to-fine approach is proposed, so that a preliminary analysis is performed with public airborne LiDAR data, and then a more detailed inspection is provided with drone LiDAR data over those areas classified as high risk. The tool and the methodology developed were validated along a high-voltage power line of 53 km in a real forest area. The results show that although the preliminary analysis based on public airborne LiDAR data is more conservative, it is very useful for selecting those areas of higher risk for further analysis with drone LiDAR data.

Assessment of the Forest Fire Risk and Its Indicating Significances in Zhaoqing City Based on Landsat Time-Series Images

An efficient monitoring of forest fire risk and its dynamic changes is an important way to prevent and reduce forest fire hazards. This study integrated the ignition effect of fire sources and the impact of landform into the calculation of forest fire risk using digital-terrain-slope factor, and developed an optimized forest fire risk model (fire-potential-index slope, FPIS). Combined with Landsat 8 satellite images, the study retrieved and analyzed the variations of forest fire risk in Zhaoqing City, Guangdong province, for four consecutive periods in the dry season, 2019. It was found that the high forest fire risk area was mainly distributed in the valley plains of Huaiji district, Fengkai district and Guangning district, the depressions of the Sihui district, and mountain-edge areas of Dinghu district and Gaoyao district, and accounted for 8.9% on 20 October but expanded to 19.89% on 7 December 2019. However, the further trend analysis indicated that the forest fire risk with significant increasing trend only accounted for 6.42% in Zhaoqing. Compared to the single high forest fire risk results, the changing trend results effectively narrowed the key areas for forest fire prevention (2.48%–12.47%) given the actual forest fires in the city. For the four forest fire events (Lingshan mountain, Hukeng industrial area, Xiangang county and Huangniuling ridge forest fires), it was found that the forest fire risk with significant increasing trend in these regions accounted for 26.63%, 35.84%, 54.6% and 73.47%, respectively, which further proved that the forest fire risk changing trend had a better indicated significance for real forest fire events than the high forest fire risk results itself (1.89%–71.69%). This study suggested that the forest fire risk increasing trend could be well used to reduce the probability of misjudgment and improve the accuracy of the early-warning areas when predicting forest fires.

Assessment of social vulnerability to forest fire and hazardous facilities in Germany

In recent years, Germany has seen an increase in forest fires, and many fires have occurred in military training areas that are difficult to access for firefighting. While casualties are still low and mostly restricted to firefighting personnel, settlements are also increasingly threatened. Increasing impacts from extreme events due to climate change will likely increase the ignition and spread of fires. More people are being affected by fires, and in need of external help to evacuate and cope with the resulting damages and losses. Forest fires also threaten military training site, with additional risks created by the presence of ammunition depots.Despite this hazard scenario, Germany so far lacks an overview of the spatial occurrence of forest fires and the related risk. This study develops a spatial exposure and social vulnerability assessment of settlements to forest fires in Germany. The results reveal that social vulnerability is an important variable in determining which settlements are potentially exposed to fire. Areas exposed to fire risk are characterised by having a higher proportion of women, a higher-than-average age, a higher number of young people under 18 years, a higher number of persons over 65 years and foreigners than the national average. Furthermore, exposed communities are characterised by higher rates of unoccupied housing units and lower living space per dwelling, as well as living in areas with high population densities within forested areas. The results can help to improve emergency management and spatial planning to prevent the development of fire risk areas.

Stakeholder perceptions of wildfire management strategies as nature-based solutions in two Iberian biosphere reserves

Increased large and high-intensity wildfires cause large socioeconomic and ecological impacts, which demand improved landscape management approaches in which both ecological and societal dimensions are integrated. Engaging society in fire management requires a better understanding of stakeholder perceptions of wildfires and landscape management. We analyze stakeholder perceptions about wildfire-landscape interactions in abandoned rural landscapes of southern Europe, and how fire and the land should be managed to reduce wildfire hazard and ensure the long-term supply of ecosystem services in these fire-prone regions. To do so, a structured online questionnaire was sent to the stakeholders of two transboundary biosphere reserves in Spain-Portugal. Our analysis also questioned to what extent fuel management strategies can be considered nature-based solutions (NbS) using the IUCN standard. Overall, stakeholders state that fire should be managed and support fire prevention in lieu of fire suppression policies. Rural abandonment is perceived as the main cause of large wildfires, with high-intensity fires impacting the study regions more than in the recent past, a trend which they expect to continue in the future in the absence of management. All the suggested fuel management strategies, except chemical treatments, were accepted by the stakeholders who perceive more positive than negative effects of fuel management on forest ecosystem services. Transboundary coordination was rated as inadequate or even nonexistent. We did not find differences among stakeholder sectors and biosphere reserves, indicating that in the study area, there is a general agreement on perceptions about wildfire and associated impacts at the landscape level. Finally, we showed that promoting agricultural and livestock uses, modifying forest species composition to increase fire resistance, and introducing large herbivores have the potential to become effective NbS in the regions. This study represents a first-step analysis representing a base for future co-design and implementation of NbS to improve fuel management, contributing to the understanding of the stakeholder support for their application in addressing the socioeconomic challenges in high fire-risk areas.

Analysis of the Threat of Forest Fires to Ancient Cities by GIS and Remote Sensing Methods

Forest fires have been more common in recent years and caused extensive damage. Not only settlements and natural life but also historical places and ancient cities are at risk of forest fires. This study discussed forest fires in Turkey in the summer of 2021. Forest fire risk classifications were determined using Landsat-8 images. The Normalized Burn Ratio (NBR) and Differenced Normalized Burn Ratio (dNBR) indices were used to assess the area impacted by fire and to create fire risk classes. Furthermore, the burned and unburned areas in different zones from the Amos ancient city in the Marmaris were calculated using remote sensing methods. Thus, areas that should be protected from the fire were determined in future studies for fire risk areas.

An adaptive quadtree-based approach for efficient decision making in wildfire risk assessment

Rapidly identifying high-risk areas for potential wildfires is crucial for preparedness, disaster management, and operational logistics decisions. With the advancement of technologies such as Cloud computing, high-risk areas can be determined ahead of time by simulating several possible fires based on forecast conditions. However, such systems may take longer and delay decision-making. We introduce a novel approach that harnesses the benefits of quadtree-based search strategies and conditional probability to enable rapid identification of high fire-risk areas and produces an increasingly detailed risk map within a given time frame. We also present a comprehensive performance analysis of different search strategies to investigate the trade-off between risk areas coverage and time efficiency showcasing how decision-makers can modify parameters based on time requirements. Experimental results show that up to 80% of high fire-risk areas in Tasmania can be identified with the proposed approach in about 20% less time than conventional comprehensive sweep methods.

Effects of Forest Fire Prevention Policies on Probability and Drivers of Forest Fires in the Boreal Forests of China during Different Periods

Fire prevention policies during different periods may lead to changes in the drivers of forest fires. Here, we use historical fire data and apply the boosted regression tree (BRT) model to analyze the spatial patterns and drivers of forest fires in the boreal forests of China from 1981 to 2020 (40 years). We divided the fire data into four periods using the old and new Chinese Forest Fire Regulations as a dividing line. Our objectives here were: to explore the influence of key historical events on the drivers of forest fires in northern China, establish a probability model of forest fire occurrence, and draw a probability map of forest fire occurrence and a fire risk zone map, so as to interpret the differences in the drivers of forest fires and fire risk changes over different periods. The results show that: (1) The model results from 1981 to 2020 (all years) did not improve between 2009 and 2020 (the most recent period), indicating the importance of choosing the appropriate modeling time series length and incorporating key historical events in future forest fire modeling; (2) Climate factors are a dominant factor affecting the occurrence of forest fires during different periods. In contrast with previous research, we found that here, it is particularly important to pay attention to the relevant indicators of the autumn fire prevention period (average surface temperature, sunshine hours) in the year before the fire occurrence. In addition, the altitude and the location of watchtowers were considered to have a significant effect on the occurrence of forest fires in the study area. (3) The medium and high fire risk areas in our three chosen time periods (1981–14 March 1988; 15 March 1988–2008; 2009–2020) have changed significantly. Fire risks were higher in the east and southeast areas of the study area in all periods. The northern primeval forest area had fewer medium-risk areas before the new and old regulations were formulated, but the medium-risk areas increased significantly after the old regulations were revised. Our study will help understand the drivers and fire risk distribution of forest fires in the boreal forests of China under the influence of history and will help decision-makers optimize fire management strategies to reduce potential fire risks.

Identification of Land Fire Risk Areas with Random Forest Using Landsat Image Data 8 OLI

Land fire is a complex and serious problem affecting several human life sectors. Stimulating the occurrence of land fires caused by long dry seasons and erratic rainy seasons causes plants to dry up and die. Illegal land burning by irresponsible people for expansion and other interests. This study uses quantitative methods. The variables used are vegetation index values ​​obtained from Landsat 8 OLI images. The result of this research is that data classified using the random forest algorithm at RStudio produces a default number of 500 trees, and the number of variables tested in each split is 2. So, the estimated error rate of 2 out of the bag is 0.47%. Prediction and confusion matrix - train 403 data are less risky "0" (middle risk) of a fire, and there are 22 data of high risk of "1" (high risk) of a fire, with an accuracy value of 0.9914, 1. While the second prediction of the confusion matrix - test data, there are 167 data less risky "0" (middle risk) of fire. There is 1 data with a high risk of “1” (high risk) of a fire. There are seven high-risk data "1" (high risk) of fire, with an accuracy value of 0.9943, indicating a more accurate assessment of model accuracy, and the range is still good, around 96% to 99%. There are several incorrect classifications in the prediction and confusion matrix – train, which is slightly higher than the predictions of the two-confusion matrix – test, and sensitivity: 1.0000, which is the same in both prediction and confusion matrix – train & tes.

Wildfire evacuation decision modeling using GPS data

The threat of wildfires is increasing at an alarming rate due to climate change and the expansion of the wildland–urban interface. It is critical to improve understanding of people’s evacuation decisions during wildfire emergencies. Therefore, this study proposes a novel methodology to model evacuation rates using large-scale GPS data generated by mobile devices. We first overlay socio-demographic and built environment attributes—aggregated at the census-block-group-level—to the inferred home locations of the mobile device users. We then develop a linear regression model to examine how the socio-demographic and built environment variables affect evacuation rates across census block groups. We apply the GPS data (44.2 million signal records from over 5000 devices) collected during the 2019 Kincade Fire in Sonoma County, California to evaluate the proposed methodology. The results of the model are generally consistent with findings of a prior survey of the same fire event. We also include factors in our model that are rarely measured through previous methods and find several built environment factors such as distance to the fire, land parcel size, and residing in a high fire risk area to be correlated with evacuation rates. Another notable finding is that people living in urban block groups, block groups with a higher median age, and block groups with a higher average level of educational attainment are more likely to evacuate. This research shows that the use of GPS data is a valuable complement to existing methods in wildfire evacuation research, and provides new insights to improve evacuation planning.

Detection of high fire risk areas in Zagros Oak forests using geospatial methods with GIS techniques

Detection of high fire risk areas in Zagros Oak forests using geospatial methods with GIS techniques

Observational Analyses of Dry Intrusions and Increased Ozone Concentrations in the Environment of Wildfires

In this study, atmospheric dynamical processes, which govern the intensification of wildfire activity and the associated increase in low-level ozone concentrations, were studied using images, advanced products and vertical profiles derived from satellite observations. The analyses confirm that the influence of deep stratospheric intrusions, identified in the satellite water vapor imagery, on a fire-risk area contributes to the increase in fire activity. The depth of dry stratospheric intrusions, the associated synoptic evolution and the enhanced low-level ozone concentrations caused by vertical transport of stratospheric air and/or related to biomass burning emissions were analyzed using satellite measurements from SEVIRI, IASI and CrIS instruments, complemented with surface observations near the wildfires’ locations. It is shown that the spatial and vertical resolutions of these soundings provide a way of identifying areas of enhanced ozone downwind of wildfires. Influences of the upper-troposphere dynamics and the wind field evolution as factors of uncertainty and complexity in studying the ozone production from wildfire emissions are considered. The combination of satellite soundings and satellite estimations of fire radiative energy and WV imagery may contribute to better understand the ozone enhancement associated with stratospheric intrusion and wildfire emissions.

Numerical Simulation Analysis of Fire Hazard from Leakage and Diffusion of Vinyl Chloride in Different Atmospheric Environments

In this paper, using the numerical simulation analysis software MATLAB and Fire Dynamics Simulation (FDS), the corresponding gas leakage diffusion model and turbulence leakage diffusion module were used to study the diffusion law and fire hazard of vinyl chloride monomer after leakage in different atmospheric environments. The concentration distribution results for vinyl chloride diffusion obtained by numerical simulation were compared and analyzed. The fire hazard area of leakage diffusion is defined by the hot fire concentration of vinyl chloride gas, and the influence of the gas-phase oxidation reaction of vinyl chloride on leakage diffusion is also discussed. The conclusion shows that as the atmospheric environment stabilizes, the peak concentration of vinyl chloride leakage and the diffusion area decrease correspondingly, the fire risk area gradually shrinks, the fire risk decreases, the affected area gradually expands, and the toxicity risk increases. When it is not in a stable state, the formaldehyde derived from the gas-phase oxidation reaction aggregates together with vinyl chloride to form a mixed gas cloud. Although the scope of influence is reduced, the concentration of harmful gases in the area is higher, and the fire risk of the mixed gas is increased.

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

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

Prioritising areas for wildfire prevention and post-fire restoration in the Brazilian Pantanal

In 2020, fires in the Pantanal, the world's largest continuous tropical wetland, made global news. The flames destroyed almost one-third of the biome. Furthermore, 43% of the affected area was burnt for the first time in 20 or even more years. As the combination of extreme drought and anthropogenic actions that caused these extreme wildfires is still prevalent, scientifically informed actions are necessary to prevent catastrophic fires in the future. Fire prevention, as well as restoration need to be spatially prioritised, as it is unfeasible to plan actions for the whole extent (150,355 km2) of the Brazilian Pantanal. In this study, we identified areas of high fire risk based on meteorological fire risk tendency for 1980–2020, fire intensity, last year with fire, the recurrence of fires for 2003–2020, and remaining areas of natural forest vegetation around watercourses. These native remnants include unburnt areas that can serve as refuges for fire-sensitive species and are important for fire prevention. We identified 246 km2 with high fire risk, i.e., high probability of megafires, with vegetation types that support fire-sensitive plant species. We found that while 179 km2 had high or medium natural regeneration potential, 66 km2 had low potential and needed active restoration. Over 3120 km2 have been severely degraded by recent fires. About 93% of these areas have high or medium potential for natural regeneration, where the suggested actions are passive restoration and Integrated Fire Management. We estimated the cost of post-fire restoration for areas with high and medium potential for natural regeneration to be around 123 million USD. In areas with low regeneration potential (219 km2), we suggest active restoration. The cost to restore these areas using transplanted seedlings or enrichment planting is estimated between 28 and 151 million USD.