Forest Fire Risk Model Research Articles

Forest fire risk modeling in Mediterranean forests using GIS and AHP method: case of the high Rif forest massif (Morocco)

Forest fire risk modeling in Mediterranean forests using GIS and AHP method: case of the high Rif forest massif (Morocco)

DEVELOPING A MULTI-VARIABLE FOREST FIRE RISK MODEL AND FIRE RISK ZONE MAPPING

Abstract. Forest steppe and taiga regions with a dry climate, small fires occur frequently between large fires, and therefore groups of trees of various ages and structures are formed in these forests, which has the effect of increasing fire exposure and "fuel reserves". The main goal of this research is to develop a multi-variable forest fire risk model and to mapping forest fire risk. The study area is the forest community in Bulgan province. Forest cover maps are essential for current research of community. We used in this research Landsat OLI and Digital Elevation Model (DEM) data. Soil moisture index and land surface temperature data from Landsat satellite data and elevation data (DEM) were used to determine slope and aspect. A multi-variable forest fire risk model was developed in this research. The final output delineated fire risk zones in the study area in four categories that include high-risk, moderate-risk, low-risk, and non-fire risk zones. Accuracy assessments on the two Landsat scenes indicates that forest/non-forest maps derived using the forest index (FI) have high accuracy.

Modeling the forest fire risk by incorporating a new human activity factor from nighttime light data

Fire was a serious natural disturbance to forest ecosystems. In this study, a national-scale forest fire risk model (RF_NTL) was proposed in mainland China by incorporating a new human activity factor (NTL) from nighttime light data. For objective verification of model’s performance, the logistic regression model (LR) and random forest model (RF) were also built in this paper. Results showed that the RF_NTL model fitted with an AUC value up to 0.95, which was superior to the conventional LR model and RF model. The RF_NTL model also had good generalization ability and stability on annual and monthly scales, helping to accurately forecast forest fire risk. Moreover, the spatial distribution of forest fire risk based on RF_NTL model was detailed and reasonable on a national scale. Forest fire high-risk areas in China were mainly concentrated in the northeast and southwest regions. This study could provide references for future relevant research.

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.

Gis and fuzzy logic approach for forest fire risk modeling in the Cajamarca region, Peru

Forest fires are a potential threat to life, as they contribute to reducing forest areas, impact on the services we expect from ecosystems, the health of the inhabitants is affected by smoke and the economic costs for the recovery of affected areas is high. The objective of the study is to apply fuzzy logic to model the risk of forest fires in the Cajamarca-Peru region, incorporating variables that represent biological, topographic, socioeconomic, and meteorological factors. The analysis was based on the acquisition, editing and rasterization of the database, application of fuzzy membership functions and image fuzzification, fuzzy superposition and spatial reclassification of forest fire risk. The results obtained show that 71.68% of the area is under very low or medium forest fire risk. However, 28.32% of the study area has a high to very high fire risk, which makes the occurrence of fires susceptible to the lack of rain and water in the soil. It was found that biological, topographic, and socioeconomic factors with their respective variables are directly influenced by meteorological factor variables such as temperature, rainfall and water availability. Fuzzy logic offered flexibility in modeling wildfire risk in the region, proving to be a useful tool for predicting and mapping wildfire risk.

Gene Expression Programming For Forest Fire Risk Modeling In Western Himalayas

Gene Expression Programming For Forest Fire Risk Modeling In Western Himalayas

An integrated approach of artificial intelligence and geoinformation techniques applied to forest fire risk modeling in Gachsaran, Iran

Forest fires are a multidimensional phenomenon that affects many parts of the world, including the Zagros region of Iran. They are often caused by various factors that can have natural-, anthropogenic-, or combined origins. Considering the significant environmental and socio-economic impacts of forest fires, it is essential to take necessary measures to identify the areas that are prone to forest fires and develop plans and policies for crisis management and risk mitigation accordingly. In this study, we applied an integrated geoinformation (remote sensing and GIScience) approach to analyze and map forest fire risk in Gachsaran, Iran, which is highly prone to forest fires. For the forest fire risk mapping (FFRM), we employed a GIS-based multi-criteria decision analysis method in combination with fuzzy and analytical network process (ANP) methods to identify the forest areas with a high fire risk. To distinguish the vulnerable sites, we employed 13 independent variables encompassing geomorphological factors, land surface characteristics, climatological factors, and anthropological factors. To develop initial criteria maps, we determined the criteria weights using the ANP and used the fuzzy technique for standardization. Finally, the forest fire risk map was produced using the multi-layer perceptron artificial neural network. Our results were also validated against the historical forest fire data using the operating characteristics. Our results showed that 18.417% of the province is subject to a very high forest fire risk. These are areas that should be prioritized when designing precautionary and protective measures. Among the criteria examined in this study, the land surface temperature, soil moisture, and distance from historical forest fire sites received the highest scores in the ANP. The results of this study can be used to identify vulnerable areas, take appropriate planning measures to deal with forest fire risk, and make informed decisions regarding the allocation of facilities in high-risk areas.

GIS-Based Forest Fire Risk Model: A Case Study in Laoshan National Forest Park, Nanjing

Fire risk prediction is significant for fire prevention and fire resource allocation. Fire risk maps are effective methods for quantifying regional fire risk. Laoshan National Forest Park has many precious natural resources and tourist attractions, but there is no fire risk assessment model. This paper aims to construct the forest fire risk map for Nanjing Laoshan National Forest Park. The forest fire risk model is constructed by factors (altitude, aspect, topographic wetness index, slope, distance to roads and populated areas, normalized difference vegetation index, and temperature) which have a great influence on the probability of inducing fire in Laoshan. Since the importance of factors in different study areas is inconsistent, it is necessary to calculate the significance of each factor of Laoshan. After the significance calculation is completed, the fire risk model of Laoshan can be obtained. Then, the fire risk map can be plotted based on the model. This fire risk map can clarify the fire risk level of each part of the study area, with 16.97% extremely low risk, 48.32% low risk, 17.35% moderate risk, 12.74% high risk and 4.62% extremely high risk, and it is compared with the data of MODIS fire anomaly point. The result shows that the accuracy of the risk map is 76.65%.

Forest Fire Hazards Vulnerability and Risk Assessment in Sirmaur District Forest of Himachal Pradesh (India): A Geospatial Approach

The Himachal Pradesh district’s biggest natural disaster is the forest fire. Forest fire threat evaluation, model construction, and forest management using geographic information system techniques will be important in this proposed report. A simulation was conducted to evaluate the driving forces of fires and their movement, and a hybrid strategy for wildfire control and geostatistics was developed to evaluate the impact on forests. The various methods we included herein are those based on information, such as knowledge-based AHP-crisp for figuring out forest-fire risk, using such variables as forest type, topography, land-use and land cover, geology, geomorphology, settlement, drainage, and road. The models for forest-fire ignition, progression, and action are built on various spatial scales, which are three-dimensional layers. To create a forest fire risk model using three different methods, a study was made to find out how much could be lost in a certain amount of time using three samples. Precedent fire mapping validation was used to produce the risk maps, and ground truths were used to verify them. The accuracy was highest in the form of using “knowledge base” methods, and the predictive value was lowest in the use of an analytic hierarchy process or AHP (crisp). Half of the area, about 53.92%, was in the low-risk to no-risk zones. Very-high- to high-risk zones cover about 24.66% of the area of the Sirmaur district. The middle to northwest regions are in very-high- to high-risk zones for forest fires. These effects have been studied for forest fire suppression and management. Management, planning, and abatement steps for the future were offered as suitable solutions.

Developing a New Hourly Forest Fire Risk Index Based on Catboost in South Korea

Forest fires can cause enormous damage, such as deforestation and environmental pollution, even with a single occurrence. It takes a lot of effort and long time to restore areas damaged by wildfires. Therefore, it is crucial to know the forest fire risk of a region to appropriately prepare and respond to such disastrous events. The purpose of this study is to develop an hourly forest fire risk index (HFRI) with 1 km spatial resolution using accessibility, fuel, time, and weather factors based on Catboost machine learning over South Korea. HFRI was calculated through an ensemble model that combined an integrated model using all factors and a meteorological model using weather factors only. To confirm the generalized performance of the proposed model, all forest fires that occurred from 2014 to 2019 were validated using the receiver operating characteristic (ROC) curves and the area under the ROC curve (AUC) values through one-year-out cross-validation. The AUC value of HFRI ensemble model was 0.8434, higher than the meteorological model. HFRI was compared with the modified version of Fine Fuel Moisture Code (FFMC) used in the Canadian Forest Fire Danger Rating Systems and Daily Weather Index (DWI), South Korea’s current forest fire risk index. When compared to DWI and the revised FFMC, HFRI enabled a more spatially detailed and seasonally stable forest fire risk simulation. In addition, the feature contribution to the forest fire risk prediction was analyzed through the Shapley Additive exPlanations (SHAP) value of Catboost. The contributing variables were in the order of relative humidity, elevation, road density, and population density. It was confirmed that the accessibility factors played very important roles in forest fire risk modeling where most forest fires were caused by anthropogenic factors. The interaction between the variables was also examined.

Integrating Remote Sensing and GIS to Model Forest Fire Rik in Virunga Massif, Central - Eastern Africa

This study aimed at developing a forest fire risk model using a combination of GIS and Remote sensing techniques, which helped to identify the level of forest fire vulnerability in Virunga Massif, located at the edge of central and eastern Africa. The Analytic Hierarchical Process (AHP) approach was employed to rank and weigh the key variables and combine them into different fire risk input factors which were later integrated into the main forest fire risk model. The main input datasets, which were linked with a potential source of a forest fire, include the land cover (specifically vegetation type data generated through the Landsat 8 image classification); topographic variables such as slope, elevation and aspect retrieved from the existing Digital Elevation Model (DEM) of Rwanda; the concentration of illegal activities and proximity to beehives sites; as well as visibility from the road and human settlements. Input factor maps were generated, assigned weights and combined in a GIS environment to produce a Virunga massif fire risk model map, which was validated using the existing burnt areas map, and ground truth points recorded using GPS. The study found that the ignition factors are the most forest fire triggering factors in Virunga massif, followed by topographic factors which play a major role in the fire spreading across the ecosystem. The high forest fire risk areas were found in steep slope location around the peaks of the volcanoes, whereas areas with the lowest risk of forest fire were found inside the forest at gentle slopes. The model was validated at 75% accuracy using ground truth data. The study proposes measure to halt the ignition factors through prevention of illegal activities in the Virunga massif for the successful prevention of the forest fire risk in the ecosystem, with much effort invested during the dry season, along with the relocation of beehives to a farther distance from the ecosystem’s edge.
 Keywords: Forest Fire Risk Modelling, Biodiversity, Illegal Activities, Ignition Factors, Topographic Factors, Analytic Hierarchy Process

Remote sensing and GIS-based forest fire risk zone mapping: The case of Manisa, Turkey

The aim of this research is to map the potential forest fire risk zones using various landscape analysis techniques in Manisa province, Turkey. Forest fire, which is defined as an ecological disaster caused by natural processes or as a result of human activities, causes environmental degradation and fragmentation of the landscape. Therefore, it is very important to produce a fire risk zone map that can be used to minimize the frequency of fire, to prevent damage, to provide a prediction for the problems that may cause fire and to form a decision mechanism for the solution methods. This research utilized CORINE 2018 produced under the framework of the Copernicus Program which is the European Union's Earth Observation Programme coordinated and managed by the European Commission, ASTER Global DEM digital elevation model data obtained from the website of NASA Earthdata, fire archive records based on MODIS satellite images, digital stand map displaying the spatial distribution of tree species, and the OpenStreetMap (OSM) which were used for mapping the existing road network. Vegetation cover, slope, aspect, elevation, distance to settlement, and distance to road variables were used to determine risk zones. The specific weights were assigned to each thematic map layer according to their capacity on fire ignition. The slope, aspect, and elevation maps were generated from the digital elevation model. The distance to settlement map was generated from the CORINE database while the distance to road map was produced from OSM. The Fire Risk Zone Index (FRZI) was utilized to determine forest fire risk zones. According to the generated final fire risk map, almost 25.8% of the study area was predicted to be under very high and highrisk zones. The final forest fire risk model was validated with past fire incidents data that was acquired from MODIS images as fire points. The result of this research showed that out of 149 fire incidents in Manisa between 2001 and 2018, 97 incidents had occurred in very high and highrisk areas. This finding supports that the presented methodology based on RS and GIS techniques is reliable and can be effectively used in the process of delineation of the forest fire risk zones.

MODELING OF PARAMETERS FOR FOREST FIRE RISK ZONE MAPPING

Abstract. Forest fire has been regarded as one of the major reasons for the loss of biodiversity and dreadful conditions of environment. Global warming is also increasing the incidence of forest fire at an alarming rate. That’s why, one need to understand the complex biophysical parameters, which are responsible for this disaster. As it is difficult to predict forest fire, fire risk zone map can be useful for combating the forest fire. So the main aim of this study is to generate a Fire risk model to map fire risk zone using Remote Sensing & GIS technique. Pauri Garhwal District, located in Uttarakhand, India, has been selected for this study as it continually faces the problem of forest fire. Landsat-8 data of 18th April, 2016 have been used for land use land cover mapping. Slope and other information have been derived from topographic maps and field information. For thematic and topographic information analysis ArcGIS and ERDAS Imagine software have been used. Forest fire risk model was generated by using AHP method, where each category was assigned subjective weight according to their sensitivity to fire. Three categories of forest fire risk ranging from very high to low were derived. The generated forest fire risk model was found to be in strong agreement with actual fire-affected sites.

Forest fire risk analysis via integration of GIS, RS and AHP: The Case of Çanakkale, Turkey

Forest fire is one of the high-risk natural disasters in the north-western Anatolia section of Turkey. This paper suggests a new approach based on Geographic Information Systems (GIS), Remote Sensing (RS) and Analytical Hierarchy Process (AHP) for the development of forest fire-risk model. The proposed approach includes human factors as well as environmental factors. In this context, the 12 variables defined under anthropogenic and physical factors in the proposed model are the slope, elevation, aspect, vegetation type, crown closure, Normalized Difference Vegetation Index (NDVI), distance to road, settlement, and agricultural areas, population density, previous fires, and Canadian Forest Fire Weather Index (FWI). For each variable, a layer was created in the GIS database environment. GIS-layers were classified, considering the risk of potentially generating forest-fire of the relevant variables. In addition, to generate risk maps, the weights used in these GIS-layers were obtained by applying the AHP technique. One of the major results of the study shows that the rates of “extreme”, “very high”, “high”, and “moderate” risk areas are 3.87%, 63.46%, 32.13% and 0.53%, respectively. Another important result is that there are not observed the so called “no risk" and "low risk" classes in the region. The results let us to make a conclusion that the natural and human factors having significant contributions the region to be fire-prone. Yet, these results also indicate that rather than emphasizing forest-fire preparedness and mitigation, policy-makers manage forest-fires through reactive, crisis-oriented approaches. In contrast to crisis-based management plans, this study suggests that risk-based preventive plans should be developed and implemented.

MODELOS MATEMÁTICOS DE PREVISÃO DO TEOR DE UMIDADE DOS MATERIAIS COMBUSTÍVEIS FLORESTAIS FINOS E MORTOS

O presente artigo busca descrever, por meio de uma revisão da literatura, os principais modelos matemáticos existentes para estimar o teor de umidade dos materiais combustíveis florestais finos e mortos, ou seja, os materiais da classe de 1-h de timelag, com base em variáveis meteorológicas. A determinação desses valores compreende uma importante informação para o delineamento de ações de prevenção e combate a incêndios florestais, e de realização de queimadas controladas, já que respondem pela probabilidade de ignição e comportamento do fogo. Com base na análise realizada, percebe-se que o Fine Fuel Moisture Code (FFMC), um dos componentes do Fire Weather Index (FWI) canadense, constitui o modelo de previsão do teor de umidade mais utilizado no mundo. Porém, considerando-se que trabalhos na literatura relatam limitações e imprecisão tanto no FFMC quanto nos demais modelos analisados nesse artigo, é essencial a validação dos mesmos antes de serem utilizados de forma operacional. Em função da pequena quantidade de estudos envolvendo essa temática no Brasil, recomenda-se a validação ou desenvolvimento de novos modelos, a fim de se aprimorar os programas de prevenção e de delineamento de risco de incêndios florestais em nível nacional.

A new approach for forest fire risk modeling using fuzzy AHP and GIS in Hyrcanian forests of Iran

The presented research was performed in order to model the fire risk in a part of Hyrcanian forests of Iran. The fuzzy sets integrated with analytic hierarchy process (AHP) in a decision-making algorithm using geographic information system (GIS) was used to model the fire risk in the study area. The used factors included four major criteria (topographic, biologic, climatic, and human factors) and their 17 sub-criteria. Fuzzy AHP method was used for estimating the importance (weight) of the effective factors in forest fire. Based on this modeling method, the expert ideas were used to express the relative importance and priority of the major criteria and sub-criteria in forest fire risk in the study area. The expert ideas mean was analyzed based on fuzzy extent analysis. Then, the fuzzy weights of criteria and sub-criteria were obtained. The major criteria models and fire risk model were presented based on these fuzzy weights. On the other hand, the spatial data of 17 sub-criteria were provided and organized in GIS to obtain the sub-criteria maps. Each sub-criterion map was converted to raster format and it was reclassified based on risk of its classes to fire occurrence. Then, all sub-criteria maps were converted to fuzzy format using fuzzy membership function in GIS. The fuzzy map of each major criterion (topographic, biologic, climatic, and human criteria) was obtained by weighted overlay of its sub-criteria fuzzy maps considering to major criterion model in GIS. Finally, the fuzzy map of fire risk was obtained by weighted overlay of major criteria fuzzy maps considering to fire risk model in GIS. The actual fire map was used for validation of fire risk model and map. The results showed that the fuzzy estimated weights of human, biologic, climatic, and topographic criteria in fire risk were 0.301, 0.2595, 0.2315, and 0.208, respectively. The results obtained from the fire risk map showed that 38.74% of the study area has very high and high risk for fire occurrence. Results of validation of the fire risk map showed that 80% of the actual fires were located in the very high and high risk areas in fire risk map. It can show the acceptable accuracy of the fire risk model and map obtained from fuzzy AHP in this study. The obtained fire risk map can be used as a decision support system for predicting of the future fires in the study area.

An easy, accurate and efficient procedure to create forest fire risk maps using the SEXTANTE plugin Modeler

To prevent, detect, and protect against forest fires, forest personnel need to define rules for determining forest fire risk. In Portugal, all municipalities must annually produce forest fire risk (FFR) maps. To produce more reliable FFR maps more easily, we developed an open source model using the Modeler plugin of SEXTANTE in the program QGIS version 2.0 Dufour. The model provides all the maps involved in the FFR model (susceptibility map, hazard map, vulnerability map, economic value map, and potential loss map) and was produced according to Portuguese Forest Authority’s (AFN, Autoridade Florestal Nacional) rules for determining the FFR. This model was tested for the Portuguese municipality Santa Maria da Feira, where 40 % of the total municipality area falls in the category “very high” or “high” fire risk. The “very high” fire risk area is mainly classified as broad-leaved forest and has the steepest slopes (>15 %). The distance of burned areas to roads was also analyzed; the proportion of burned areas increased with increasing distance to the main roads. In addition, 92.6 % of the “high” and “very high” risk zones were located in areas with lower elevation. These results confirmed that forest fire is strongly influenced not only by environmental factors but also by anthropogenic factors. The procedure implemented here was compared with our open source application already available in QGIS and also to the same procedure implemented in GIS proprietary software. Although the results were obviously the same, the model developed here presents several advantages over the other two approaches. Besides being faster, it is easy to change the model parameters according to user needs (i.e., to the rules of different countries), and can be modified and adapted to other variables and other areas to create risk maps for different natural phenomena (e.g., floods, earthquakes, landslides). The model is easy to use and to create risk and hazard maps rapidly in a free, open source environment that does not require any programming knowledge.

Building probabilistic models of fire occurrence and fire risk zoning using logistic regression in Shanxi Province, China

Fires are a recurrent environmental and economic emergency throughout the world. Fire risk analysis and forest fire risk zoning are important aspects of forest fire management. MODIS remote sensing datasets for Shanxi Province from 2002 to 2012 were used to build a spatial logistic forest fire risk model, based on the spatial distribution of forest fires and forest fire-influencing factors, using geographic information system technology. A forest fire risk zoning study was conducted at a large temporal scale and a provincial spatial scale. The resulting logistic model of forest fire risk, built with spatial sampling, showed a good fit (p < 0.05) between the distribution of forest fires and forest fire impact factors. The relative operating characteristic value was 0.757, and a probability distribution map for forest fire was developed, using layer computing. The forest fire area of Shanxi Province was divided into zones of zero, low, moderate, high and extremely high fire risk. The influences of altitude (GC), land-use type (LT), land surface temperature (LST), normalized difference vegetation index (NDVI) and global vegetation moisture index (GVMI) on fire events presented significant spatial variability, whereas the influences of slope and distance to the nearest path exhibited insignificant spatial variability in Shanxi Province. The influences of NDVI and LST on fire events were significant throughout Shanxi Province, whereas the influences of GC, LT and GVMI were only significant locally. Seven fire-prevention regions were delineated, based on the fire-influencing factors. Different fire-prevention policies and emphases should be taken into consideration for each of the seven fire-prone regions.

Forest fire risk mapping of Kolli Hills, India, considering subjectivity and inconsistency issues

Forest fires have adverse ecological, economic, and social impacts. In this light, the present research aimed, first, to construct a fire risk model using a GIS-based multi-criteria analysis and second, to derive a forest fire risk modeling strategy that alleviates the problem of inconsistency in the assigning of scores and weights to forest fire categories and layers. Third, the local-orientation effects and causes, which are relevant to the subjectivity problem, were investigated by comparing the risk scoring and weighting outcomes from Indian and Korean expert groups (IEG and KEG). Fourth, forest fire factors that can be considered regional and global also were investigated. Kolli Hills, India, was selected as the study area in this research. In the interests of alleviating the inconsistency problem, a weighting and scoring scheme based on the analytic hierarchy process was applied. The experiences from the existence of prevailing westerly winds, the most common forest types (i.e., in Korea: pine trees), and the different anthropogenic pressures between Korea and India resulted in the different scoring and weighting decisions of the two expert groups. Among the five fire risk factors, slope, road, and settlement can be considered to be global factors. On the other hand, forest cover and aspect are regional factors that can be more influenced by local environmental conditions. When considering the producer’s accuracy, the approach of the IEG together with the natural breaks thresholding method provided the best fire risk mapping result. On the other hand, the model from the IEG with equal interval provided the best result from the viewpoint of user’s accuracy and overall accuracy. Overall, this paper proposes a forest fire risk mapping procedure as basis for developing a global forest fire risk modeling in the future, where a series of standardized modeling steps and variables should be defined.

A territorial fire vulnerability model for Mediterranean ecosystems in South America

A forest fire risk model was designed and applied to a South American Mediterranean ecosystem, taking into consideration three analysis groups: fire risk; danger of fire spread, or propagation; and damage potential over economic threat values. The study area for development and validation of the model was the Mediterranean zone of central Chile and employed data from historical records spanning a 14year period (1997–2010). Territorial data layers, combined with analysis of the statistical database and wildfire simulation have enabled areas of highest vulnerability to be defined with greater precision, especially in sectors associated with the urban–wildland interface (defined as the zone where man-made structures meet wildland). Maps generated by this model have enabled improvements to be made to the traditional mapping of fires currently undertaken in South American countries. The results shown here are applicable to other Mediterranean areas, where modifications are made to the entrance variables in the risk model.