Fire Occurrence Data Research Articles

Modelling spatial patterns of forest fire occurrence in the Northwestern region of Vietnam

Forest fires present a significant threat to the tropical forest ecosystem in the northwestern region of Vietnam. Our study aimed to assess the impacts of environmental factors on forest fire occurrence and to map forest fire probability for the whole region. The forest fire occurrence data over the period 2003–2016, environmental factors (climate, fuel condition, topography, and human activity), and the MaxEnt approach were used for this study. The MaxEnt model performed better than the random model (AUC>0.88). Climatic factors (especially climatic seasonality: annual temperature range (bio_07), isothermality (bio_03), and precipitation of warmest quarter (bio_18)) had the highest contribution to the model, followed by population density and elevation. In contrast, fuel condition (Land cover type) had a small contribution to the model. While medium, high, and very high probabilities of forest fire occurred at medium to high elevations (e.g., Dien Bien, Son La, and Lai Chau provinces) throughout southern to northern and western areas, very low and low probability concentrated southeastern areas at lower elevations (mainly in Hoa Bình province). Our results may be helpful references for fire managers and policymakers to establish more effective fire management strategies for the region's forest.

Correction: Wang et al. Effectiveness in Mitigating Forest Fire Ignition Sources: A Statistical Study Based on Fire Occurrence Data in China. Fire 2022, 5, 215

In the original publication [...]

Spatial Prediction of Wildfire Susceptibility Using Hybrid Machine Learning Models Based on Support Vector Regression in Sydney, Australia

Australia has suffered devastating wildfires recently, and is predisposed to them due to several factors, including topography, meteorology, vegetation, and ignition sources. This study utilized a geographic information system (GIS) technique to analyze and understand the factors that regulate the spatial distribution of wildfire incidents and machine learning to predict wildfire susceptibility in Sydney. Wildfire inventory data were constructed by combining the fire perimeter through field surveys and fire occurrence data gathered from the visible infrared imaging radiometer suite (VIIRS)-Suomi thermal anomalies product between 2011 and 2020 for the Sydney area. Sixteen wildfire-related factors were acquired to assess the potential of machine learning based on support vector regression (SVR) and various metaheuristic approaches (GWO and PSO) for wildfire susceptibility mapping in Sydney. In addition, the 2019–2020 “Black Summer” fire acted as a validation dataset to assess the predictive capability of the developed model. Furthermore, the information gain ratio (IGR) method showed that driving factors such as land use, forest type, and slope degree have a large impact on wildfire susceptibility in the study area, and the frequency ratio (FR) method represented how the factors influence wildfire occurrence. Model evaluation based on area under the curve (AUC) and root average square error (RMSE) were used, and the outputs showed that the hybrid-based SVR-PSO (AUC = 0.882, RMSE = 0.006) model performed better than the standalone SVR (AUC = 0.837, RMSE = 0.097) and SVR-GWO (AUC = 0.873, RMSE = 0.080) models. Thus, optimizing SVR with metaheuristics improved the accuracy of wildfire susceptibility modeling in the study area. The proposed framework can be an alternative to the modeling approach and can be adapted for any research related to the susceptibility of different disturbances.

A data-driven model for Fennoscandian wildfire danger

Abstract. Wildfires are recurrent natural hazards that affect terrestrial ecosystems, the carbon cycle, climate and society. They are typically hard to predict, as their exact location and occurrence are driven by a variety of factors. Identifying a selection of dominant controls can ultimately improve predictions and projections of wildfires in both the current and a future climate. Data-driven models are suitable for identification of dominant factors of complex and partly unknown processes and can both help improve process-based models and work as independent models. In this study, we applied a data-driven machine learning approach to identify dominant hydrometeorological factors determining fire occurrence over Fennoscandia and produced spatiotemporally resolved fire danger probability maps. A random forest learner was applied to predict fire danger probabilities over space and time, using a monthly (2001–2019) satellite-based fire occurrence dataset at a 0.25∘ spatial grid as the target variable. The final data-driven model slightly outperformed the established Canadian Forest Fire Weather Index (FWI) used for comparison. Half of the 30 potential predictors included in the study were automatically selected for the model. Shallow volumetric soil water anomaly stood out as the dominant predictor, followed by predictors related to temperature and deep volumetric soil water. Using a local fire occurrence record for Norway as target data in a separate analysis, the test set performance increased considerably. This demonstrates the potential of developing reliable data-driven models for regions with a high-quality fire occurrence record and the limitation of using satellite-based fire occurrence data in regions subject to small fires not identified by satellites. We conclude that data-driven fire danger probability models are promising, both as a tool to identify the dominant predictors and for fire danger probability mapping. The derived relationships between wildfires and the selected predictors can further be used to assess potential changes in fire danger probability under different (future) climate scenarios.

Effectiveness in Mitigating Forest Fire Ignition Sources: A Statistical Study Based on Fire Occurrence Data in China

Control of forest fire ignition sources is the top priority in fire management practices. China has gained great success in reducing forest fires in recent years, and the relevant safety measures taken during this process are worthy of investigation and publicity. Based on fire statistical data through the years between 2003 and 2017, we analyzed the detailed classification of fire ignition sources and their contribution to the annual forest fire occurrence. The role of different ignition sources in altering fire occurrence was quantified and ranked by defining a contribution extent parameter. A statistical tool was also applied to conduct correlation analysis to identify variation patterns of time series data from individual fire causes. The annual fire numbers declined after 2008 and stabilized at a level < 2000 in recent years, pointing to the containment of several major ignition sources. Starting from the legislative development, an accountability system was established at all levels from administrative heads to local residents, paving the way for the multifaceted and full-coverage fire prevention publicity and education as well as the fire use restriction in particular seasons. The effectiveness of management measures in lessening forest fire occurrence was interpreted using the results of correlation analysis among the fire numbers initiated by individual ignition sources.

Spatial Clustering of Vegetation Fire Intensity Using MODIS Satellite Data

This work analyses the spatial clustering of fire intensity in Zimbabwe, using remotely sensed Moderate Resolution Imaging Spectroradiometer (MODIS) active fire occurrence data. In order to investigate the spatial pattern of fire intensity, MODIS-derived fire radiative power (FRP) was utilized. A local indicator of spatial autocorrelation method, the Getis-Ord (Gi*) spatial statistic, was applied to show the spatial distribution of high and low fire intensity clusters. Analysis of the relationship between topographic variables, vegetation type, agroecological zones and fire intensity was done. According to the study’s findings, the majority (44%) of active fires detected in the study area in 2019 were of low-intensity (cold spots), and the majority (49.3%) of them occurred in shrubland. High-intensity fires (22%) primarily occurred in the study area’s eastern and western regions. The study findings demonstrate the utility of spatial statistics methods in conjunction with satellite fire data in detecting clusters of high and low-intensity fires (hot spots and cold spots).

Fire probability mapping and prediction from environmental data: What a comprehensive savanna-forest transition can tell us

The Cerrado-Amazon transition (CAT) is located between the two largest biogeographic domains of the Neotropical region, coinciding with the “Arc of Deforestation”, an agricultural frontier that expands towards the Amazon. Fire plays an important role in the landscape changes that have been occurring in this transition. Thus, the objectives of this study were (i) evaluate the accuracy of different algorithms/techniques in predicting fires in the CAT; (ii) investigate the season of highest fire probabilities in the CAT; and (iii) identify, among anthropic, climatic, topographic and soil variables, the main drivers of fires spatial distribution in the CAT. Fire occurrence data (active fire pixels) from the MODIS sensor were used as input data in species distribution models. The SDM allowed the construction of monthly fire probability models for the study area. The model's quality was assessed by R-squared, RMSE, AUC and TSS metrics. In addition, we assessed the weight of variables in building the models. To obtain the fire seasonality, we used the bootstrap technique to calculate the 95% confidence interval (CI) about the mean. As result, we found that all algorithms obtained satisfactory performance, as well as the ensemble model technique used, which obtained an average AUC of 0.812 ± 0.001. The algorithm with the best individual performance was Random Forest, with a mean AUC 0.851 ± 0.002. Similar patterns were found when considering TSS metrics. The predicted and observed fire occurrences showed strong association (R2 = 0.81). The high fire probability area was higher during the dry season (15304.37 ± 41.65), showing a significant difference (p-value ≤ 0.05) from the rainy season. The variables that most explained the fire occurrences along the CAT were 'Silt Fraction', 'Distance from Agriculture' and 'Maximum Temperature'. In addition, ‘Distance from Roads’, ‘Distance from Protected Areas’ and Elevation’ were relevant for the monthly models. Anthropogenic variables were more important during the dry season. The results demonstrate that SDMs can be adequately used in fire prediction. Protected areas have shown an important role in acting as a fire barrier. However, many of these areas are vulnerable, surrounded by high fire probabilities during the dry season, predominantly anthropic, putting the natural dynamics of fire in the region and the local biodiversity at risk. Our approach can be useful in environmental protection and monitoring, in addition to facilitating the understanding about the CAT ecological complexity and the high anthropogenic pressure happening there.

Modeling the Susceptibility of Forest Fires Using a Genetic Algorithm: A Case Study in Mountain Areas of Southwestern China

Modeling fire susceptibility in fire-prone areas of forest ecosystems was essential for providing guidance to implement prevention and control measures of forest fires. Traditional models were developed on the basis of random selection of absence data (i.e., nonfire data from unburned areas), which could bring uncertainties to modeling results. Here, a new model with the genetic algorithm for Rule-set Production (GARP) algorithm and 10 environmental layers was proposed to process presence-only data in the susceptibility modeling of forest fires in Chongqing city. To do this, 70% of 684 fire occurrence data (479) during the period of 2000–2018 were applied to train the proposed model. And, 30% of these fire occurrence data (205) and the same amount of no-fire data (205) were emerged as validation dataset. The results showed that, for some environmental layers (i.e., distance to the nearest road, land cover, precipitation, distance to the nearest settlement, aspect, relative humidity, elevation, wind speed, and temperature), their P values were less than 0.05, indicating that these 9 environmental layers have significant influence on the spatial distribution of fire susceptibility in Chongqing city. On the contrary, with a higher P value (i.e., 0.126), the slope layer has an insignificant effect on fire susceptibility in the study area. Furthermore, the results of receiver operating characteristic analysis (ROC) showed that the proposed model has a good performance with an AUC value of 0.869, an accuracy value of 0.732, a sensitivity value of 0.59, a specificity value of 0.873, a positive predictive value of 0.823, and a negative predictive value of 0.681. This study revealed the validity of the proposed model in modeling the susceptibility of forest fires.

Livestock exposure to bushfires and meat, offal and carcase quality: Is there an association?

The occurrence of bushfires (wildfires) is increasing with climate change in many areas of the world. In Australia, the 2019/20 bushfire season involved a particularly severe and widespread fire emergency (the ‘Black Summer’ bushfires). Understanding of how exposure to bushfires affects specific disease processes in livestock is limited. This research investigated spatiotemporal relationships between exposure to bushfires and observations of pneumonia and pleurisy in slaughtered sheep, and meat quality in slaughtered cattle. Two related cross-sectional studies were undertaken using historical abattoir monitoring data from the National Sheep Health Monitoring Project and the Meat Standards Australia Program. The study area involved the Australian states of New South Wales and Victoria, and the Australian Capital Territory, which were heavily affected by the ‘Black Summer’ bushfires. Carcase data were matched to fire occurrence data and to potential confounders including rainfall, pasture growth and pasture biomass indices for the farm of origin. The predictive approach to modelling included generalised additive mixed effects models and a generalised linear mixed model. Consistent though imprecise trends in pneumonia occurrence in sheep carcases were observed across time and distance since exposure to fire, with sheep slaughtered in the immediate aftermath of exposure to high intensity fires at a close distance having the highest occurrence (3.78 cases per 1000 sheep slaughtered 5 days after exposure to medium-to-high intensity fire at 0.5 km distance (95% CI 0.48, 30.02), compared to 0.387 cases per 1000 slaughtered sheep (95% CI 0.147, 1.02) across the study population). However, the economic implications of this for producers and processors are considered to be very limited. No such trends were observed in regards to pleurisy occurrence in sheep. Consistent trends were observed in meat quality in cattle carcases, with lower meat quality scores observed in cattle slaughtered after close proximity to fire (mean MSA index of 57.12 for cattle slaughtered 5 days after exposure to medium-to-high intensity fire at 0.5 km distance (95% CI 56.91, 57.34), compared to a mean of 57.65 (95% CI 57.60, 57.71) across the study population). In the aftermath of exposure to mid-to-high intensity fires, this may warrant consideration in withholding cattle from slaughter from an economic perspective, after decisions based on welfare grounds have been made. These observations will inform practical recommendations to improve health and productivity outcomes in management of bushfire-affected livestock. The observed trends may reflect causal relationships, but this requires further investigation with specific explanatory modelling studies.

Fire hazard forecast by the regional climate change projection using the ETA model: a case study in Bahia, Brazil

This article proposes a method for predicting fire occurrence, considering regional climate change projection using the Eta model, with a 20 km resolution, for the RCP4.5 and RCP8.5 scenarios. Fire occurrence in the state of Bahia was calculated as a function of the three main sensitivity factors on a daily time-scale: days without precipitation, precipitation, and maximum temperature. Historical fire occurrences from 1998 to 2018 and meteorological data from 1960 to 2018 were obtained from official institutes, and weather forecast parameters from 2018 to 2050 were downscaled from the web platform PROJETA. The correlations between the meteorological factors and fire occurrence were calculated for the historical data and a weight factor corresponding to a control simulation. Afterwards, a correction factor was determined, based on the historical fire occurrence data used for the forecast in the two scenarios. The results indicate that between 2018 and 2050, risk of fire will have an average increase of 27% at the RCP4.5 and 38% at the RCP8.5 scenario.

Characteristics of Spatiotemporal Changes in the Occurrence of Forest Fires

The purpose of this study is to understand the characteristics of the spatial distribution of forest fire occurrences with the local indicators of temporal burstiness in Korea. Forest fire damage data were produced in the form of areas by combining the forest fire damage ledger information with VIIRS-based forest fire occurrence data. Then, detrended fluctuation analysis and the local indicator of temporal burstiness were applied. In the results, the forest fire occurrence follows a self-organized criticality mechanism, and the temporal irregularities of fire occurrences exist. When the forest fire occurrence time series in Gyeonggi-do Province, which had the highest value of the local indicator of temporal burstiness, was checked, it was found that the frequency of forest fires was increasing at intervals of about 10 years. In addition, when the frequencies of forest fires and the spatial distribution of the local indicators of forest fire occurrences were compared, it was found that there were spatial differences in the occurrence of forest fires. This study is meaningful in that it analyzed the time series characteristics of the distribution of forest fires in Korea to understand that forest fire occurrences have long-term temporal correlations and identified areas where the temporal irregularities of forest fire occurrences are remarkable with the local indicators of temporal burstiness.

Forest fire probability under ENSO conditions in a semi-arid region: a case study in Guanajuato

Fires can pose a threat to forest ecosystems when those ecosystems are not fire-adapted or when forest community conditions make them vulnerable to wildfires. Thus, investigating fire-prone environmental conditions is urgently needed to create action plans that preserve these ecosystems. In this sense, climate variables can determine the environmental conditions favorable for forest fires. Our study confirms that vapor pressure deficit (VPD) is an essential climate indicator for forest fires, as it is related to maximum temperatures and low humidity, representing the stress conditions for vegetation prone to fires. This study explores the extent to which ENSO phases can modulate climatic conditions that lead to high VPD over Guanajuato, a semi-arid region in central Mexico, during the dry season (March-April-May). Using fire occurrence data from MODIS (2000-2019) and Landsat 5 (1998-1999), we developed a climatic probability model for the occurrence of forest fires using VPD estimated from ERA5 reanalysis for each ENSO phase. We found that VPD and the occurrence of forest fires were higher during El Niño than under Neutral and La Niña years, with a higher risk of forest fire occurrence in Guanajuato's southern region. This study concludes that it is necessary to implement regional and local fire management plans, especially where the largest number of natural protected areas is located.

Annual precipitation drives fire occurrence across sub-humid and semi-arid ecological gradients

Fire is an integral part of semiarid to moderately humid ecosystem dynamics in North America. The biogeographical settings in which fires readily occur are affected by global processes like climate change, as well as local and regional characteristics such as terrain, proximity to human infrastructure, and vegetation structure. Increasing numbers and severity of fires today requires high-resolution and accurate predictions of fire probability. Species distribution models (SDM) allow researchers to identify environmental predictors of fire and depict the probability of fire occurrence. We applied a Maximum Entropy (Maxent) SDM to identify fire predictors and fire risk across a broad biogeographic humid to semi-arid climate gradient within the state of Texas. We used 15 years (2001-2016) of remotely sensed fire occurrence data, along with 13 biophysical variables representing climate, terrain, human activity, and landcover to generate multiple models. Annual precipitation was the primary predictor of fire occurrence, followed by elevation and landcover. After projecting fire probability onto three climate scenarios, we found moderate change in fire distribution. Humid and sub-humid areas had higher probabilities of fire occurrence while arid regions had lower probabilities under those scenarios. Overall, the linkage between fire occurrence and annual precipitation suggests that climate-driven fire probabilities will be variable under projected future climates.

Using the Landsat Burned Area Products to Derive Fire History Relevant for Fire Management and Conservation in the State of Florida, Southeastern USA

Development of comprehensive spatially explicit fire occurrence data remains one of the most critical needs for fire managers globally, and especially for conservation across the southeastern United States. Not only are many endangered species and ecosystems in that region reliant on frequent fire, but fire risk analysis, prescribed fire planning, and fire behavior modeling are sensitive to fire history due to the long growing season and high vegetation productivity. Spatial data that map burned areas over time provide critical information for evaluating management successes. However, existing fire data have undocumented shortcomings that limit their use when detailing the effectiveness of fire management at state and regional scales. Here, we assessed information in existing fire datasets for Florida and the Landsat Burned Area products based on input from the fire management community. We considered the potential of different datasets to track the spatial extents of fires and derive fire history metrics (e.g., time since last burn, fire frequency, and seasonality). We found that burned areas generated by applying a 90% threshold to the Landsat burn probability product matched patterns recorded and observed by fire managers at three pilot areas. We then created fire history metrics for the entire state from the modified Landsat Burned Area product. Finally, to show their potential application for conservation management, we compared fire history metrics across ownerships for natural pinelands, where prescribed fire is frequently applied. Implications of this effort include increased awareness around conservation and fire management planning efforts and an extension of derivative products regionally or globally.

Livestock Exposure to Bushfires and Meat, Offal and Carcass Quality: Is There an Association?

The occurrence of bushfires (wildfires) is increasing with climate change in many areas of the world. In Australia, the 2019/20 bushfire season involved a particularly severe and widespread fire emergency (the ‘Black Summer’ bushfires). Understanding of how exposure to bushfires affects specific disease processes in livestock is limited. This research investigated spatiotemporal relationships between exposure to bushfires and observations of pneumonia and pleurisy in slaughtered sheep, and meat quality in slaughtered cattle. Two related cross-sectional studies were undertaken using historical abattoir monitoring data from the National Sheep Health Monitoring Project and the Meat Standards Australia Program. The study area involved the Australian states of New South Wales and Victoria, and the Australian Capital Territory, which were heavily affected by the ‘Black Summer’ bushfires. Carcase data were matched to fire occurrence data and to potential confounders including rainfall, pasture growth and pasture biomass indices for the farm of origin. The predictive approach to modelling included generalised additive mixed effects models and a generalised linear mixed model. Consistent though imprecise trends in pneumonia occurrence in sheep carcases were observed across time and distance since exposure to fire, with sheep slaughtered in the immediate aftermath of exposure to high intensity fires at a close distance having the highest occurrence (3.78 cases per 1,000 sheep slaughtered 5 days after exposure to medium-to-high intensity fire at 0.5 km distance (95% CI 0.48, 30.02), compared to 0.387 cases per 1,000 slaughtered sheep (95% CI 0.147, 1.02) across the study population). However, the economic implications of this for producers and processors are considered to be very limited. No such trends were observed in regards to pleurisy occurrence in sheep. Consistent trends were observed in meat quality in cattle carcases, with lower meat quality scores observed in cattle slaughtered after close proximity to fire (mean MSA index of 57.12 for cattle slaughtered 5 days after exposure to medium-to-high intensity fire at 0.5 km distance (95% CI 56.91, 57.34), compared to a mean of 57.65 (95% CI 57.60, 57.71) across the study population). In the aftermath of exposure to mid-to-high intensity fires, this may warrant consideration in withholding cattle from slaughter from an economic perspective, after decisions based on welfare grounds have been made. These observations will inform practical recommendations to improve health and productivity outcomes in management of bushfire-affected livestock. The observed trends may reflect causal relationships, but this requires further investigation with specific explanatory modelling studies.

Exploring spatially varying relationships between forest fire and environmental factors at different quantile levels

The effect of driving factors on forest fire occurrence at various risk levels beyond average fire risk is of great interest to forest fire managers in practice. Using forest fire occurrence data collected in Fujian province, China, global quantile regression (QR) and geographically weighted quantile regression (GWQR) were applied to investigate the spatially varying relationships between forest fire and environmental factors at different quantiles (e.g. 0.50, 0.75, 0.90 and 0.99) of fire occurrence. These results indicated that: (1) at each quantile, the regression coefficients of both global QR and GWQR were negative for elevation, slope and Normalised Difference Vegetation Index, and positive for settlement density, national road density and grass cover; (2) low number of pixels with high fire occurrence in space might dramatically affect the analysis and modelling of the relationship between fire occurrence and a specific environmental factor; (3) according to GWQR, the relationships between forest fire and environmental factors significantly varied across the study area at different quantiles of fire occurrence; and (4) the GWQR models performed better in model fitting and prediction than the QR models at all quantiles. Therefore, the GWQR models could help decision makers to better plan for forest fire management and prevention strategies.

Planned and unplanned fire regimes on public land in south-east Queensland

Land management agencies in Queensland conduct planned burning for a variety of reasons, principally for management of fuels for human asset protection and biodiversity management. Using Queensland Parks and Wildlife Service’s archived manually derived fire reports, this study considered the individual components of the fire regime (extent, frequency and season) to determine variation between planned and unplanned fire regimes in south-east Queensland. Overall, between 2004 and 2015, planned fire accounted for 31.6% and unplanned fire 68.4% of all fire on Queensland Parks and Wildlife Service state-managed land. Unplanned fire was more common in spring (September–October), and planned fire was more common in winter (June–August). Unplanned fire affected 71.4% of open forests and woodlands (148563ha), whereas 58.8% of melaleuca communities (8016ha) and 66.6% of plantations (2442ha) were burnt with planned fire. Mapping fire history at a regional scale can be readily done with existing publicly available datasets, which can be used to inform the assessment of planned burning effectiveness for human asset protection and the management of biodiversity. Fire management will benefit from the continued recording of accurate fire occurrence data, which allows for detailed fire regime mapping and subsequent adaptive management of fire regimes in the public domain.

A Probabilistic Methodology for Assessing Post-Earthquake Fire Ignition Vulnerability in Residential Buildings

Post-earthquake fire (PEF) ignition events constitute a secondary consequence of an earthquake and may result in the loss of life and substantial property damage, especially in urban areas where the potential for fire spread and conflagration exists. These secondary hazards can cause severe structural and non-structural damage, potentially more significant damage than the direct damage caused by the initial earthquake, and can lead to catastrophic structural failures, devastating economic losses, and casualties. To manage the impact of PEF in urban areas, it is important to identify the potential ignition sources and quantify the vulnerabilities of these ignition sources as a result of earthquake-induced structural damage. The results of such analyses can be used to offer resiliency improvement investments and mitigation strategies in urban areas located in seismically active regions. Most of the previous PEF studies are data-driven, utilizing ignitions reported following recent earthquakes. However, in areas for which historical PEF data are not available, such as the Midwestern United States, a different methodology for developing a PEF model is needed. This paper describes an analytical model for quantifying the vulnerabilities of residential buildings to PEF by estimating the failure of ignition sources upon a probable seismic event. The underlying concept in developing the method is that (1) ignition sources in residential buildings remain unchanged before and after an earthquake, and (2) the total probability of PEF occurrence can be estimated by adjusting the probabilistic fire occurrence data for normal conditions (everyday operation of ignition sources) to account for the effect of the earthquake. This paper’s contribution to state of the art is in developing a new framework for estimating the probability of PEF for areas in which historical PEF data is unavailable. The developed framework uses the likelihood of ignition occurrence during normal condition as a baseline; this baseline is then adjusted using certain key parameters to capture spatial characteristics, ignitability, and potential seismic intensity of the study area to estimate the probability of PEF as a function of projected earthquake characteristics. The model was tested for St. Louis City as a populated area with potential future earthquake hazard because of its proximity to the New Madrid Fault zone. Using the National Fire Incident Reporting System dataset, the frequency of normal condition ignitions was determined as 1.97E−03 ignition per unit per year. Using the proposed PEF model considering PEFs caused by damage to drift and acceleration sensitive equipment and human actions, the projected frequency of PEF was estimated between 2.79E−06 and 2.81E−06 ignitions per household per year. Using this model, and the average number of households between 2010 to 2015, 175,854 households, it was estimated that in the next 50 years, approximately 25 households would experience fires related to probable earthquake events in St. Louis City.

Can satellite-based data substitute for surveyed data to predict the spatial probability of forest fire? A geostatistical approach to forest fire in the Republic of Korea

To assess which data type is more effective for spatial modeling in the Republic of Korea, we conducted geostatistical analysis based on frequency, intensity, and spatial autocorrelation using two types of forest fire occurrence data: that collected through field survey of the Korea Forest Service (KFS) and satellite active fire data of Moderate Resolution Imaging Spectroradiometer (MODIS). The maximum entropy (MaxEnt) model was used with environmental factors in the spatial modeling of fire probability to compare the accuracy of the two data types based on 10 years of historical data. The results showed a clear difference in fire frequency and similar fire intensity patterns. The spatial autocorrelation between the fire frequency and intensity of the two data types was analyzed using a semi-variogram. Fire intensity was significantly correlated, with the MODIS data having a higher correlation than the KFS data. Examination of the spatial autocorrelation and related factors by fire source also indicated that MODIS data had higher spatial autocorrelation, with remarkable distinction found in climate factors. In spatial the modeling, MODIS data showed a similar outcome to that of hotspot analysis, with higher accuracy and better model performance attributable to high spatial autocorrelation. Even though the KFS data were collected from post-fire surveys, they resulted in low spatial autocorrelation and reduced model accuracy owing to the wide distribution of data. MODIS had many detection errors. With spatial filtering, however, the model accuracy can be improved with relatively high spatial autocorrelation.

Towards a comprehensive wildfire management strategy for Mediterranean areas: Framework development and implementation in Catalonia, Spain

Southern European countries rely largely on fire suppression and ignition prevention to manage a growing wildfire problem. We explored a more wholistic, long-term approach based on priority maps for the implementation of diverse management options aimed at creating fire resilient landscapes, restoring cultural fire regimes, facilitating safe and efficient fire response, and creating fire-adapted communities. To illustrate this new comprehensive strategy for fire-prone Mediterranean areas, we developed and implemented the framework in Catalonia (northeastern Spain). We first used advanced simulation modeling methods to assess various wildfire exposure metrics across spatially changing fire-regime conditions, and these outputs were then combined with land use maps and historical fire occurrence data to prioritize different fuel and fire management options at the municipality level. Priority sites for fuel management programs concentrated in the central and northeastern high-hazard forestlands. The suitable areas for reintroducing fires in natural ecosystems located in scattered municipalities with ample lightning ignitions and minimal human presence. Priority areas for ignition prevention programs were mapped to populated coastal municipalities and main transportation corridors. Landscapes where fire suppression is the principal long-term strategy concentrated in agricultural plains with a high density of ignitions. Localized programs to build defensible space and improve self-protection on communities could be emphasized in the coastal wildland-urban interface and inner intermix areas from Barcelona and Gerona. We discuss how the results of this study can facilitate collaborative landscape planning and identify the constraints that prevent a longer term and more effective solution to better coexist with fire in southern European regions.