Wildfire Locations Research Articles

Identifying the spatial patterns of population displacement during wildfires in Valparaíso, Chile

ABSTRACT We present a data visualisation approach to support the rapid humanitarian response during the 2024 Valparaíso wildfires in Chile. Combining Meta user location data for population displacement, NASA FIRMS satellite imagery for wildfire locations, and census data, we identify key origins and destinations of displaced people during recent wildfires in Valparaíso, Chile. Our choropleth maps reveal spatial patterns of movement and socioeconomic factors, demonstrating the value of integrating diverse data sources for near real-time crisis response.

Assessment of the effects of different variable weights on wildfire susceptibility

In this study, wildfire susceptibility is mapped using various multi-criteria decision analysis techniques (AHP, SAW and VIKOR) and machine learning algorithms (MaxEnt and logistic regression) to reveal the response of models for wildfires. In this study, it is suggested that using natural weights generated by machine learning algorithms instead of artificial weights in MCDA methods can increase the reliability of susceptibility maps because wildfires have very close relationship with climatic, topographic and environmental variables. The contribution rates (natural weights) were obtained using machine learning algorithms and incorporated into MCDA methods to make the spatial relationships between variables more obvious. As a result, eight susceptibility maps were generated using MCDA methods, MaxEnt and logistic regression algorithms. Correlation analysis showed that using natural weights instead of artificial weights increased the correlation between MCDA methods and machine learning algorithms. Each correlation value increased by 10% on average and the highest increase was determined between VIKOR and logistic regression from 0.6286 to 0.7580 when natural weights were used. In addition, 1035 existing wildfire locations were used to evaluate the reliability of generated maps. The results showed that the average risk values of 1035 wildfire locations increased from 6.04 to 7.23 using AHP, from 0.66 to 0.79 using SAW and from 0.35 to 0.25 using the VIKOR method. This indicates a significant increase in the accuracy and reliability of susceptibility maps produced when natural weights determined by machine learning algorithms are used in MCDA methods.

Wildfire Health Risks in Relation to Local Senior Population’s CRD Incidence Rate in the Greater Toronto Area

Wildfires as one of the most common and frequent environmental disasters in North America, Billions of government spending, public funds, resources, and personnel have been placed to cope with this natural disaster directly. The smoke, debris, particulates, ambient ultrafine particles, and other wildfire emissions have increasingly become a public health concern to which senior populations are particularly vulnerable, due to the populations' general behaviour and their physical health conditions. This essay investigates the implications of wildfires on public health by identifying the relationship between the exposure of wildfire emissions and the incidences of chronic respiratory disease in the senior population in the Greater Toronto Area. This research estimates the effects of wildfire emission exposure by using the cross-examination of the relevant health factors, including the incidence of wildfire; particulate level, ambulance service operation log and reported respiratory emergency disease to the incidences of wildfires. The Geographic Information Systems (GIS) have been placed to estimate the effect range and proximity of CRD incidences to the wildfire location.

Wildfire Susceptibility Mapping Using Deep Learning Algorithms in Two Satellite Imagery Dataset

Recurring wildfires pose a critical global issue as they undermine social and economic stability and jeopardize human lives. To effectively manage disasters and bolster community resilience, the development of wildfire susceptibility maps (WFSMs) has emerged as a crucial undertaking in recent years. In this research endeavor, two deep learning algorithms were leveraged to generate WFSMs using two distinct remote sensing datasets. Specifically, the Moderate-Resolution Imaging Spectroradiometer (MODIS) and Landsat-8 images were utilized to monitor wildfires that transpired during the year 2021. To develop an effective WFSM, two datasets were created by incorporating 599 wildfire locations with Landsat-8 images and 232 sites with MODIS images, as well as twelve factors influencing wildfires. Deep learning algorithms, namely the long short-term memory (LSTM) and recurrent neural network (RNN), were utilized to model wildfire susceptibility using the two datasets. Subsequently, four WFSMs were generated using the LSTM (MODIS), LSTM (Landsat-8), RNN (MODIS), and RNN (Landsat-8) algorithms. The evaluation of the WFSMs was performed using the area under the receiver operating characteristic (ROC) curve (AUC) index. The results revealed that the RNN (MODIS) (AUC = 0.971), RNN (Landsat-8) (AUC = 0.966), LSTM (MODIS) (AUC = 0.964), and LSTM (Landsat-8) (AUC = 0.941) algorithms demonstrated the highest modeling accuracy, respectively. Moreover, the Gini index was employed to assess the impact of the twelve factors on wildfires in the study area. The results of the random forest (RF) algorithm indicated that temperature, wind speed, slope, and topographic wetness index (TWI) parameters had a significant effect on wildfires in the study region. These findings are instrumental in facilitating efficient wildfire management and enhancing community resilience against the detrimental effects of wildfires.

Real-Time Wildfire Detection Algorithm Based on VIIRS Fire Product and Himawari-8 Data

Wildfires have a significant impact on the atmosphere, terrestrial ecosystems, and society. Real-time monitoring of wildfire locations is crucial in fighting wildfires and reducing human casualties and property damage. Geostationary satellites offer the advantage of high temporal resolution and are gradually being used for real-time fire detection. In this study, we constructed a fire label dataset using the stable VNP14IMG fire product and used the random forest (RF) model for fire detection based on Himawari-8 multiband data. The band calculation features related brightness temperature, spatial features, and auxiliary data as input used in this framework for model training. We also used a recursive feature elimination method to evaluate the impact of these features on model accuracy and to exclude redundant features. The daytime and nighttime RF models (RF-D/RF-N) are separately constructed to analyze their applicability. Finally, we extensively evaluated the model performance by comparing them with the Japan Aerospace Exploration Agency (JAXA) wildfire product. The RF models exhibited higher accuracy, with recall and precision rates of 95.62% and 59%, respectively, and the recall rate for small fires was 19.44% higher than that of the JAXA wildfire product. Adding band calculation features and spatial features, as well as feature selection, effectively reduced the overfitting and improved the model’s generalization ability. The RF-D model had higher fire detection accuracy than the RF-N model. Omission errors and commission errors were mainly concentrated in the adjacent pixels of the fire clusters. In conclusion, our VIIRS fire product and Himawari-8 data-based fire detection model can monitor the fire location in real time and has excellent detection capability for small fires, making it highly significant for fire detection.

Wildfire and prescribed burning impact moose forage availability and browsing levels in the northern boreal forest

ABSTRACT Fire regimes and herbivore densities have in parts of the boreal been heavily impacted by human activities with consequences for vegetation, forage supply and ungulate use. In this study, we evaluate the effects of natural wildfires and prescribed burning on moose forage availability, use, and browsing in northern Sweden. We studied three wildfire locations, adjacent controls, and five prescribed burns 12 years after fire. To compare forage availability and browsing levels we combined related variables with Principal Component Analyses and used the first axis in a regression analysis. The wildfire locations had a different composition of forage compared with the controls with less forage in the field layer and more woody browse and deciduous saplings. Forage availability was best explained by the occurrence of fire and differed between wildfire and the unburned control areas. Browsing levels were higher in wildfire locations compared with unburned controls. Prescribed burns varied substantially and could resemble both unburnt control and wildfire in terms of forage availability and browsing. We conclude that prescribed burning as a restoration action potentially can improve forage availability, particularly of deciduous species, if they mimic the impact of wildfires in terms of larger area burned and high fire severity.

Ultra-Low-Power Architecture for the Detection and Notification of Wildfires Using the Internet of Things

Wildfires kill and injure people, destroy residences, pollute the air, and cause economic loss. In this paper, a low-power Internet of Things (IoT)-based sensor network is developed, which automatically detects fires in forests and sends the location to a central monitoring station with smartphone notifications in a real-time setting. This action helps in the early detection of a fire and firefighters can be notified immediately—thus the spread of the fire and the harm caused by it can be reduced. The proposed system detects fires from the presence of smoke and a sudden increase in temperature. The system also logs the temperature, humidity, carbon dioxide, rain, light, and wind speed in different areas of the forest. The sensor nodes transmit the data to a hub using a long-range wireless transmitter and the hub then sends the data to the central monitoring station using the cellular Internet. The sensor nodes and hub are designed with ultra-low-power hardware and software architecture, consuming current of only 0.37 and 1.4 mA, respectively, so that they can be powered by solar panels throughout the year. The central server and smartphone app contain maps, and the wildfire locations are marked in the case of a fire. In the present study, a prototype of the proposed system is successfully developed and tested.

Conceptual optimization of remotely piloted amphibious aircraft for wildfire air attack

In this study, a methodology for the high-level conceptual design, optimization, and evaluation of amphibious remotely piloted and autonomous fixed-wing aircraft to support wildfire air attack strategies is presented. Of particular interest are questions of scale, water source utilization, and optimization of high-level aircraft parameters in a regional context. The Canadian province of British Columbia is used as a case study due to the relevance of wildfire control in that region. The present strategy incorporates a detailed analysis of available water bodies, tanker base locations, and their distance from historical wildfire locations and explores how these regionally specific details impact optimal aircraft design parameters. Results are obtained for optimal lake size as well as the primary design characteristics of the corresponding optimal aircraft. Two filling strategies are evaluated, namely, a “stop-and-go” strategy and a traditional skimming strategy. The results indicate the potential of fleets of optimized aircraft to supply high flow rates while capitalizing on the established benefits of using remotely piloted and autonomous systems. It is hoped this work will encourage future study into improved models and the further development of drone technology for this application, including necessary beyond visual line-of-sight technology and infrastructure.

Threatened by wildfires: What do firms disclose in their 10‐Ks?

AbstractWe examine the determinants of firms’ 10‐K disclosures on the threat of and exposure to wildfires. We match the location of wildfires in the United States to firms in the same county as the wildfire. We first establish that wildfire disclosure is far from widespread. On average, 6.1% of firms with wildfires in their headquarters county mention wildfire information in their 10‐Ks. Second, we find that the number of wildfire days in a company's headquarters county is a key determinant of wildfire disclosure. The sensitivity of wildfire disclosure to wildfire exposure has also increased in recent years, emanating mainly from firms having experienced wildfires impacting their past operations and in the western states and the utility and banking industries, and from those exhibiting a high level of tangible assets. Third, we find that wildfire exposure has no bearing on stock price, whereas more wildfire‐related disclosure lowers stock price.

Multitask Allocation Framework With Spatial Dislocation Collision Avoidance for Multiple Aerial Robots

Multitask allocation and trajectory planning for multiple unmanned aerial vehicles (UAVs) have been extensively used in various real-world applications. This article presents a framework of multi-UAV multitask allocation and trajectory planning with collision avoidance. The scenario of interest is one where multiple UAVs are launched in order to investigate selected targets in a massive wildfire disaster relief terrain. Initially, one UAV is launched to search wildfire locations and wildfire lines by a developed informative path planning algorithm. An informative exploratory search mechanism is developed that provides the exploration trajectories to precisely locate the wildfire positions in the wildfire environments. Afterward, with the investigated environmental information including GPS coordinates of wildfire positions and distribution as targets, UAVs are deployed to multiple target positions. In order to perform effective collision avoidance, a spatial dislocation scheme is developed by introduction of an additional dimension for UAVs at different altitudes, whereas UAVs avoid collision at the same altitude using a proposed velocity profile paradigm. Concurrent multitask allocation, trajectory planning, and collision avoidance are successfully carried out with unequal numbers of UAVs and targets. The proposed framework has been validated by simulation studies and comparative analyzes.

Forecasting wildfires in major forest types of India

Severity of wildfires witnessed in different parts of the world in the recent times has posed a significant challenge to fire control authorities. Even when the different fire early warning systems have been developed to provide the quickest warnings about the possible wildfire location, severity, and danger, often it is difficult to deploy the resources quickly to contain the wildfire at a short notice. Response time is further delayed when the terrain is complex. Early warning systems based on physics-based models, such as WRF-FIRE/SFIRE, are computationally intensive and require high performance computing resources and significant data related to fuel properties and climate to generate forecasts at short intervals of time (i.e., hourly basis). It is therefore that when the objective is to develop monthly and yearly forecasts, time series models seem to be useful as they require lesser computation power and limited data (as compared to physics-based models). Long duration forecasts are useful in preparing an efficient fire management plan for optimal deployment of resources in the event of forest fire. The present research is aimed at forecasting the number of fires in different forest types of India on a monthly basis using “Autoregressive Integrated Moving Average” time series models (both univariate and with regressors) at 25 km × 25 km spatial resolution (grid) and developing the fire susceptibility maps using Geographical Information System. The performance of models was validated based on the autocorrelation function (ACF), partial ACF, cumulative periodogram, and Portmanteau (L-Jung Box) test. Both the univariate- and regressor-based models performed equally well; however, the univariate model was preferred due to parsimony. The R software package was used to run and test the model. The forecasted active fire counts were tested against the original 3 years monthly forecasts from 2015 to 2017. The variation in coefficient of determination from 0.94 (for year 1 forecast) to 0.64 (when all the 3-year forecasts were considered together) was observed for tropical dry deciduous forests. These values varied from 0.98 to 0.89 for tropical moist deciduous forest and from 0.97 to 0.88 for the tropical evergreen forests. The forecasted active fire counts were used to estimate the future forest fire frequency ratio, which has been used as an indicator of forest fire susceptibility.

Predicting electricity infrastructure induced wildfire risk in California

This paper examines the use of risk models to predict the timing and location of wildfires caused by electricity infrastructure. Our data include historical ignition and wire-down points triggered by grid infrastructure collected between 2015–2019 in Pacific Gas & Electricity territory along with various weather, vegetation, and very high resolution data on grid infrastructure including location, age, and materials. With these data we explore a range of machine learning methods and strategies to manage training data imbalance. The best area under the receiver operating characteristic we obtain is 0.776 for distribution feeder ignitions and 0.824 for transmission line wire-down events, both using the histogram-based gradient boosting tree algorithm with under-sampling. We then use these models to identify which information provides the most predictive value. After line length, we find that weather and vegetation features dominate the list of top important features for ignition or wire-down risk. Distribution ignition models show more dependence on slow-varying vegetation variables such as burn index, energy release content, and tree height, whereas transmission wire-down models rely more on primary weather variables such as wind speed and precipitation. These results point to the importance of improved vegetation modeling for feeder ignition risk models, and improved weather forecasting for transmission wire-down models. We observe that infrastructure features make small but meaningful improvements to risk model predictive power.

Combining community observations and remote sensing to examine the effects of roads on wildfires in the East Siberian boreal forest

The paper is aimed at assessing the associations between the road networks geography and dynamics of wildfire events in the East Siberian boreal forest. We examined the relationship between the function of roads, their use, and management and the wildfire ignition, propagation, and termination during the catastrophic fire season of 2016 in the Irkutsk Region of Russia. Document analysis and interviews were utilized to identify main forest users and road infrastructure functional types and examine wildfire management practices. We combined community observations and satellite remotely sensed data to assess relationships between the location, extent, and timing of wildfires and different types of roads as fire sources, barriers, and suppression access points. Our study confirms a strong spatial relationship between the wildfire ignition points and roads differentiated by their types with the highest probability of fire ignition near forestry roads and the lowest near subsistence roads. Roads also play an important role in wildfire suppression, working as both physical barriers and access points for firefighters. Our research illustrates the importance of local and Indigenous observations along the roads for monitoring and understanding wildfires, including “zombie fires”. It also has practical implications for fire management collectively developed by authorities and local communities.

Estimation of fire-induced CO plume age from NAST–I during the FIREX-AQ field campaign

Ultra-spectrally resolved infrared measurements from aircraft and space-based observations contain information about tropospheric carbon monoxide (CO) and ozone (O3), as well as other trace species. A methodology for retrieving these tropospheric trace species from such remotely sensed spectral data has been developed and validated for the National Airborne Sounder Testbed-Interferometer (NAST–I). The Fire Influence on Regional to Global Environments and Air Quality (FIREX-AQ) field campaign was conducted during August 2019 to investigate the impact of wildfire and biomass smoke on air quality and weather in the continental United States. NAST–I CO and O3 measurements from the recent FIREX-AQ field campaign are presented and used to estimate wildfire plume age. Results show enhanced levels of CO in the evolving plume as it is transported away from the fire ground site, and its plume age is associated with the plume distance in both the vertical and horizontal directions from the wildfire location. These results are enabled by the moderate-vertical and high-horizontal resolution obtained from the NAST–I IR spectrometer onboard the NASA ER-2 aircraft. This study advances our knowledge of fire-induced plumes with their evolution and age characterized in three-dimensional space using information from NAST–I retrieved CO and O3 and relative changes in their concentrations.

Characterising the holdover phase of lightning-ignited wildfires in Catalonia

In the present work, we analysed fuel moisture conditions during ignition, holdover, and detection of flaming combustion of lightning-ignited wildfires in Catalonia (north-eastern Spain) between 2003 and 2018. First, we identified the most probable lightning candidate for each wildfire, implementing a matching algorithm between historical lightning-caused fire data and cloud-to-ground lightning records. The bulk of lightning-fire ignitions (80%) occurred between June and September during the warm season. Conifer forests concentrate almost half of the lightning-ignited wildfires. Then, we spatially interpolated air temperature and relative humidity data from automatic weather stations to calculate a weather index describing the evolution of fuel moisture content at the specific wildfire location, from the time of the lightning-caused ignition to the time of the fire detection. Results showed that fuel moisture content drives lightning-ignited wildfires since most ignitions around midday turn into flaming combustion almost immediately, when fuel moisture content reaches the minimum of the day. The holdover duration increased in late afternoon lightning-ignited fires, which remain smouldering overnight and evolve to flaming combustion in the next solar cycle. We found that latent fires above 24 h were rare (15%), and only 3% of the fires had a holdover period above three days. Only 1 in ∼840 cloud-to-ground flashes started a wildland fire. Our outcomes provide valuable insight to improve the modeling and management of natural wildfires in the Mediterranean areas.

Wildfire detection through deep learning based on Himawari-8 satellites platform

ABSTRACT The accurate identification of the location, intensity, and spread of wildfires is an essential early-stage precaution for reducing wildfire damage. Satellite imaging platforms, particularly those with high revisiting frequencies and fine spatial resolutions, represent the most efficient possible means of monitoring wildfires dynamically. However, the extraction of accurate fire-related information from satellite images remains challenging, and few studies have investigated the use of remote sensing data from satellites with geostationary orbits. The present work addresses these issues by applying over 5,000 images obtained from the geostationary Himawari-8 satellite of a severe Australian wildfire occurring from November 2019 to February 2020 to train and test a fully connected convolutional neural network (CNN) for identifying the location and intensity of wildfires. The proposed CNN model obtains a detection accuracy greater than 80%, which greatly exceeds that of other machine learning algorithms, such as support vector machine and k-means clustering. Moreover, the CNN model can be trained in a relatively short period, even when employing large training datasets, and predictions can be made in just one or two minutes. The proposed model provides insight into the application of deep learning methodologies for wildfire monitoring based on the imagery provided by geostationary satellites, and support for developing similar satellite missions.

Identifying anthropogenic and natural causes of wildfires by maximum entropy method-based ignition susceptibility distribution models

Turkey has a high potential for wildfires along its Mediterranean coast because of its dense forest cover and mild climate. An average of 250 wildfires occurs every year with more than 10,000 hectares destroyed due to natural and human-related causes. The study area is sensitive to fires caused by lightning, stubble burning, discarded cigarette butts, electric arcing from power lines, deliberate fire setting, and traffic accidents. However, 52% of causes could not be identified due to intense wildfires occurring at the same time and insufficient equipment and personnel. Since wildfires destroy forest cover, ecosystems, biodiversity, and habitats, they should be spatially evaluated by separating them according to their causes, considering environmental, climatic, topographic and forest structure variables that trigger wildfires. In this study, wildfires caused by lightning, the burning of agriculture stubble, discarded cigarette butts and power lines were investigated in the provinces of Aydın, Muğla and Antalya, where 22% of Turkey’s wildfires occurred. The MaxEnt method was used to determine the spatial distribution of wildfires to identify risk zones for each cause. Wildfires were used as the species distribution and the probability of their occurrence estimated. Additionally, since the causes of many wildfires are unknown, determining the causes is important for fire prediction and prevention. The highest wildfire occurrence risks were 9.7% for stubble burning, 30.2% for lightning, 4.5% for power lines and 16.9% by discarded cigarette butts. In total, 1,266 of the 1,714 unknown wildfire causes were identified by the analysis of the cause-based risk zones and these were updated by including cause-assigned unknown wildfire locations for verification. As a result, the Area under the ROC Curve (AUC) values were increased for susceptibility maps.

Environmental indicators combined with risk analysis to evaluate potential wildfire incidence on the Dadu Plateau in Taiwan

Wildfire is a common disaster in the world, and it has a considerable impact on the safety of residents and ecological disturbance. Periodic wildfires are an urgent problem to be solved. This research uses big data from relevant departments to extract environmental indicators that affect wildfires, including satellite images, meteorological observations, and field surveys and establishes a risk model for the Spatio-temporal distribution of wildfires based on risk analysis. Previous studies using Differenced Normalized Burn Ratio (dNBR) to assess fire severity and distinguish wildfire ruins did not deal with the impact of atmospheric humidity on dNBR values. In this study, an adjustable fire threshold was developed to enable dNBR to improve the accuracy of identifying wildfire locations. Regarding the temporal distribution of wildfire risks, environmental vulnerability cannot specifically reflect the frequency of actual wildfires. If the hazard degree is introduced to calculate the wildfire risk, the coefficient of determination can be increased from 0.49 to 0.79. The verification of the village boundary zone depicts that the risk analysis can effectively show the temporal and spatial distribution of wildfire hotspots. On this basis, a village-level wildfire disaster prevention strategy can be formulated.

A Google Earth Engine Approach for Wildfire Susceptibility Prediction Fusion with Remote Sensing Data of Different Spatial Resolutions

The effects of the spatial resolution of remote sensing (RS) data on wildfire susceptibility prediction are not fully understood. In this study, we evaluate the effects of coarse (Landsat 8 and SRTM) and medium (Sentinel-2 and ALOS) spatial resolution data on wildfire susceptibility prediction using random forest (RF) and support vector machine (SVM) models. In addition, we investigate the fusion of the predictions from the different spatial resolutions using the Dempster–Shafer theory (DST) and 14 wildfire conditioning factors. Seven factors are derived separately from the coarse and medium spatial resolution datasets for the whole forest area of the Guilan Province, Iran. All conditional factors are used to train and test the SVM and RF models in the Google Earth Engine (GEE) software environment, along with an inventory dataset from comprehensive global positioning system (GPS)-based field survey points of wildfire locations. These locations are evaluated and combined with coarse resolution satellite data, namely the thermal anomalies product of the moderate resolution imaging spectroradiometer (MODIS) for the period 2009 to 2019. We assess the performance of the models using four-fold cross-validation by the receiver operating characteristic (ROC) curve method. The area under the curve (AUC) achieved from the ROC curve yields 92.15% and 91.98% accuracy for the respective SVM and RF models for the coarse RS data. In comparison, the AUC for the medium RS data is 92.5% and 93.37%, respectively. Remarkably, the highest AUC value of 94.71% is achieved for the RF model where coarse and medium resolution datasets are combined through DST.

Associations between Wildfire Risk and Socio-Economic-Demographic Characteristics Using GIS Technology

Wildfire is a natural hazard caused mostly by the interaction of human systems and natural phenomena. This research aims to investigate how extreme wildfire events and disasters that occurred in California in the recent three decades are related to socio-economic-demographic characteristics at the levels of census tracts and counties. In addition, this research will use the data of historic wildfires to show counties and census tracts vulnerable to the natural hazard as well as the cyclical changes such as seasonal and annual fluctuations in the wildfire occurrences in the state. To decide how those variables correlate, this research used a Geographic Information System (GIS) designed to collect, analyze, query, and display geographical information. Two types of secondary data were used to conduct the research. One is the geospatial data showing each location of wildfires. The other is the data about such sociodemographic characteristics as race, ethnicity, level of education, and income, which can be collected through the Bureau of Census. In particular, the research employing GIS-based spatial analysis created maps that represent information on the geographic locations of the wildfires at the different geographic levels as well as demographic and socioeconomic factors influenced by the potential risk of wildfires. There are several researching findings. First, this research showed the wildfire-prone communities have comparatively higher level of representation for the populations such as the White and Native Americans. Second, it reveals that Asian people would prefer to reside in communities with a lower level of wildfire risk. In contrast with previous research reporting the Black, Hispanic or Native American people are more vulnerable to wildfire, this research showed only the census tracts with the higher number of the Native Americans are more exposed to the wildfire risk, compared with other census tracts. Third, it revealed that people with a higher level of educational attainment would prefer to reside in communities with a lower level of chemical risk. Forth and lastly, this research indicates that the census tracts that have a higher median household income and median housing price have a negative relationship with the wildfire risk, meaning that people with a higher level of the income or a relatively higher-priced home prefer residing in communities less subject to the natural hazard. Therefore, it can be concluded that associations exist between wildfire risk and certain socio-economic and demographic characteristics.