Evaluating the ecological benefits of wildfire by integrating fire and ecosystem simulation models

Fire managers are now realizing that wildfires can be beneficial because they can reduce hazardous fuels and restore fire-dominated ecosystems. A software tool that assesses potential beneficial and detrimental ecological effects from wildfire would be helpful to fire management. This paper presents a simulation platform called FLEAT (Fire and Landscape Ecology Assessment Tool) that integrates several existing landscape- and stand-level simulation models to compute an ecologically based measure that describes if a wildfire is moving the burning landscape towards or away from the historical range and variation of vegetation composition. FLEAT uses a fire effects model to simulate fire severity, which is then used to predict vegetation development for 1, 10, and 100 years into the future using a landscape simulation model. The landscape is then simulated for 5000 years using parameters derived from historical data to create an historical time series that is compared to the predicted landscape composition at year 1, 10, and 100 to compute a metric that describes their similarity to the simulated historical conditions. This tool is designed to be used in operational wildfire management using the LANDFIRE spatial database so that fire managers can decide how aggressively to suppress wildfires. Validation of fire severity predictions using field data from six wildfires revealed that while accuracy is moderate (30-60%), it is mostly dictated by the quality of GIS layers input to FLEAT. Predicted 1-year landscape compositions were only 8% accurate but this was because the LANDFIRE mapped pre-fire composition accuracy was low (21%). This platform can be integrated into current readily available software products to produce an operational tool for balancing benefits of wildfire with potential dangers.     

Linking 3D spatial models of fuels and fire: Effects of spatial heterogeneity on fire behavior

Crown fire endangers fire fighters and can have severe ecological consequences. Prediction of fire behavior in tree crowns is essential to informed decisions in fire management. Current methods used in fire management do not address variability in crown fuels. New mechanistic physics-based fire models address convective heat transfer with computational fluid dynamics (CFD) and can be used to model fire in heterogeneous crown fuels. However, the potential impacts of variability in crown fuels on fire behavior have not yet been explored. In this study we describe a new model, FUEL3D, which incorporates the pipe model theory (PMT) and a simple 3D recursive branching approach to model the distribution of fuel within individual tree crowns. FUEL3D uses forest inventory data as inputs, and stochastically retains geometric variability observed in field data. We investigate the effects of crown fuel heterogeneity on fire behavior with a CFD fire model by simulating fire under a homogeneous tree crown and a heterogeneous tree crown modeled with FUEL3D, using two different levels of surface fire intensity. Model output is used to estimate the probability of tree mortality, linking fire behavior and fire effects at the scale of an individual tree. We discovered that variability within a tree crown altered the timing, magnitude and dynamics of how fire burned through the crown; effects varied with surface fire intensity. In the lower surface fire intensity case, the heterogeneous tree crown barely ignited and would likely survive, while the homogeneous tree had nearly 80% fuel consumption and an order of magnitude difference in total net radiative heat transfer. In the higher surface fire intensity case, both cases burned readily. Differences for the homogeneous tree between the two surface fire intensity cases were minimal but were dramatic for the heterogeneous tree. These results suggest that heterogeneity within the crown causes more conditional, threshold-like interactions with fire. We conclude with discussion of implications for fire behavior modeling and fire ecology.     

Development of a framework for fire risk assessment using remote sensing and geographic information system technologies

Forest fires play a critical role in landscape transformation, vegetation succession, soil degradation and air quality. Improvements in fire risk estimation are vital to reduce the negative impacts of fire, either by lessen burn severity or intensity through fuel management, or by aiding the natural vegetation recovery using post-fire treatments. This paper presents the methods to generate the input variables and the risk integration developed within the Firemap project (funded under the Spanish Ministry of Science and Technology) to map wildland fire risk for several regions of Spain. After defining the conceptual scheme for fire risk assessment, the paper describes the methods used to generate the risk parameters, and presents proposals for their integration into synthetic risk indices. The generation of the input variables was based on an extensive use of geographic information system and remote sensing technologies, since the project was intended to provide a spatial and temporal assessment of risk conditions. All variables were mapped at 1 km² spatial resolution, and were integrated into a web-mapping service system. This service was active in the summer of 2007 for semi-operational testing of end-users. The paper also presents the first validation results of the danger index, by comparing temporal trends of different danger components and fire occurrence in the different study regions.     

Simulating dynamic and mixed-severity fire regimes: A process-based fire extension for LANDIS-II

Fire regimes result from reciprocal interactions between vegetation and fire that may be further affected by other disturbances, including climate, landform, and terrain. In this paper, we describe fire and fuel extensions for the forest landscape simulation model, LANDIS-II, that allow dynamic interactions among fire, vegetation, climate, and landscape structure, and incorporate realistic fire characteristics (shapes, distributions, and effects) that can vary within and between fire events. We demonstrate the capabilities of the new extensions using two case study examples with very different ecosystem characteristics: a boreal forest system from central Labrador, Canada, and a mixed conifer system from the Sierra Nevada Mountains (California, USA). In Labrador, comparison between the more complex dynamic fire extension and a classic fire simulator based on a simple fire size distribution showed little difference in terms of mean fire rotation and potential severity, but cumulative burn patterns created by the dynamic fire extension were more heterogeneous due to feedback between fuel types and fire behavior. Simulations in the Sierra Nevada indicated that burn patterns were responsive to topographic features, fuel types, and an extreme weather scenario, although the magnitude of responses depended on elevation. In both study areas, simulated fire size and resulting fire rotation intervals were moderately sensitive to parameters controlling the curvilinear response between fire spread and weather, as well as to the assumptions underlying the correlation between weather conditions and fire duration. Potential fire severity was more variable within the Sierra Nevada landscape and also was more sensitive to the correlation between weather conditions and fire duration. The fire modeling approach described here should be applicable to questions related to climate change and disturbance interactions, particularly within locations characterized by steep topography, where temporally or spatially dynamic vegetation significantly influences spread rates, where fire severity is variable, and where multiple disturbance types of varying severities are common.     

Using a fire propagation model to assess the efficiency of prescribed burning in reducing the fire hazard

We examined how fire hazard was affected by prescribed burning and fuel recovery over the first six years following treatment. Eight common Mediterranean fuel complexes managed by means of prescribed burning in limestone Provence (South-Eastern France) were studied, illustrating forest and woodland, garrigue and grassland situations. The coupled atmosphere-wildfire behaviour model FIRETEC was used to simulate fire behaviour (ROS, intensity) in these complex vegetations. The temporal threshold related to the effectiveness of prescribed burning in reducing the fire hazard was assessed from derivated fuel dynamics after treatment. The study showed that prescribed burning treatment was effective for the first two years in most of the Mediterranean plant communities analysed. Thereafter, all forests and shrublands were highly combustible with a fire line intensity of more than 5000 kW/m except for pine stands with or without oak (medium intensity of 2000 kW m⁻¹ 3 years after treatment). Low fire line intensity (900 kW m⁻¹) was obtained for grassland which was entirely treatment-independent since the resprouter hemicryptophyte, Brachypodium retusum, is highly resilient to fire. Fire behaviour was greatly affected by fuel load accumulation of Quercus ilex in woodland, and by standing necromass of Rosmarinus officinalis in treated garrigue. Pure pine stands with shrub strata similar to garrigue showed a lower fire intensity due to wind speed decrease at ground level under tree canopy, underlining the advantage of maintaining a proportion of canopy cover in strategic fuel-break zones.     

Human influence on California fire regimes.

Periodic wildfire maintains the integrity and species composition of many ecosystems, including the mediterranean-climate shrublands of California. However, human activities alter natural fire regimes, which can lead to cascading ecological effects. Increased human ignitions at the wildland-urban interface (WUI) have recently gained attention, but fire activity and risk are typically estimated using only biophysical variables. Our goal was to determine how humans influence fire in California and to examine whether this influence was linear, by relating contemporary (2000) and historic (1960-2000) fire data to both human and biophysical variables. Data for the human variables included fine-resolution maps of the WUI produced using housing density and land cover data. Interface WUI, where development abuts wildland vegetation, was differentiated from intermix WUI, where development intermingles with wildland vegetation. Additional explanatory variables included distance to WUI, population density, road density, vegetation type, and ecoregion. All data were summarized at the county level and analyzed using bivariate and multiple regression methods. We found highly significant relationships between humans and fire on the contemporary landscape, and our models explained fire frequency (R2 = 0.72) better than area burned (R2 = 0.50). Population density, intermix WUI, and distance to WUI explained the most variability in fire frequency, suggesting that the spatial pattern of development may be an important variable to consider when estimating fire risk. We found nonlinear effects such that fire frequency and area burned were highest at intermediate levels of human activity, but declined beyond certain thresholds. Human activities also explained change in fire frequency and area burned (1960-2000), but our models had greater explanatory power during the years 1960-1980, when there was more dramatic change in fire frequency. Understanding wildfire as a function of the spatial arrangement of ignitions and fuels on the landscape, in addition to nonlinear relationships, will be important to fire managers and conservation planners because fire risk may be related to specific levels of housing density that can be accounted for in land use planning. With more fires occurring in close proximity to human infrastructure, there may also be devastating ecological impacts if development continues to grow farther into wildland vegetation.     

Integrating models to predict regional haze from wildland fire

Visibility impairment from regional haze is a significant problem throughout the continental United States. A substantial portion of regional haze is produced by smoke from prescribed and wildland fires. Here we describe the integration of four simulation models, an array of GIS raster layers, and a set of algorithms for fire-danger calculations into a modeling framework for simulating regional-scale smoke dispersion. We focus on a representative fire season (2003) in the northwestern USA, on a 12 km domain, and track the simulated dispersion and concentration of PM_2.5 over the course of the season. Simulated visibility reductions over national parks and wilderness areas are within the ranges of measured values at selected monitoring sites, although the magnitudes of peak events are underestimated because these include inputs other than fire. By linking the spatial and temporal patterns of haze-producing emissions to climatic variability, particularly synoptic weather patterns, and the stochastic nature of fire occurrence across the region, we can provide a robust method for estimating the quantity and distribution of fire-caused regional haze under climate-warming scenarios.     

Grass feedbacks on fire stabilize savannas

Savannas commonly consist of a discontinuous cover of overstory trees and a groundcover of grasses. Savanna models have previously demonstrated that vegetation feedbacks on fire frequency can limit the density of overstory trees, thereby maintaining savannas. Positive feedbacks of either savanna trees alone or trees and grasses together on fire frequency have been shown to result in a stable savanna equilibrium. Grass feedbacks on fire frequency, in contrast, have resulted in stable equilibria in either a grassland or forest state, but not in a savanna. These results, however, were derived from a system of differential equations that assumes that fire occurrence is strictly deterministic and that vegetation losses due to fire are continuous in time. We develop an alternative formulation of the grass-fire feedback model that assumes that fires are discrete and occur stochastically in time to examine the influence of these assumptions on the predicted state of the system. We show that incorporating fire as a discrete event can produce a recurring temporal refuge in which both grass and trees co-occur in a stable, bounded savanna. In our model, tree abundance is limited without invoking demographic bottlenecks in the transition from fire-sensitive to fire-resistant life history stages. An increasing strength of grass feedback on fire results in regular, predictable fires, which suggests that the system can also be modeled using a set of difference equations. We implement this discrete system using modified Leslie/Gower difference equations and demonstrate the existence of a bounded savanna state in this model framework. Our results confirm the potential for grass feedbacks to result in stable savannas, and indicate the importance of modeling fire as a discrete event rather than as a loss rate that is continuous in time.     

Assessment of forest fire vulnerability zones in Missouri,United States of America

Forest fire is one of the major disasters that distresses the terrestrial environment and causes economic disruptions for people and communities in areas prone to forest fire. Information on forest fire risk zones is therefore essential for effective and sound decision-making in forest management. Forest fire risk assessment is a critical part and the most important step in forest management because it enables us to know where the risk is higher in order to minimize threats to life, property and natural resources. This study used a hazard assessment model to assess forest fire risk in Missouri based on several measurable environmental parameters influencing forest fire risk vulnerability. Using the four ecological zones in Missouri as the basis of analysis, three forest risk zones were identified. These were high forest fire risk zones, moderate forest fire risk zone and low forest fire risk zone. Strategies for the mitigation of the hazard of forest fire in the state were also recommended.     

The fire frequency analysis branch of the pyrostatistics tree: sampling decisions and censoring in fire interval data

Statistical characterization of past fire regimes is important for both the ecology and management of fire-prone ecosystems. Survival analysis—or fire frequency analysis as it is often called in the fire literature—has increasingly been used over the last few decades to examine fire interval distributions. These distributions can be generated from a variety of sources (e.g., tree rings and stand age patterns), and analysis typically involves fitting the Weibull model. Given the widespread use of fire frequency analysis and the increasing availability of mapped fire history data, our goal has been to review and to examine some of the issues faced in applying these methods in a spatially explicit context. In particular, through a case study on the massive Cedar Fire in 2003 in southern California, we examine sensitivities of parameter estimates to the spatial resolution of sampling, point- and area-based methods for assigning sample values, current age surfaces versus historical intervals in generating distributions, and the inclusion of censored (i.e., incomplete) observations. Weibull parameter estimates were found to be roughly consistent with previous fire frequency analyses for shrublands (i.e., median age at burning of ~30–50 years and relatively low age dependency). Results indicate, however, that the inclusion or omission of censored observations can have a substantial effect on parameter estimates, far more than other decisions about specifics of sampling.

Wildfire smoke and health impacts: A closer look at fire attributes and their marginal effects

Existing studies on the economic impact of wildfire smoke have focused on single fire events or entire seasons without considering the marginal effect of daily fire progression on downwind communities. Neither approach allows for an examination of the impact of even the most basic fire attributes, such as distance and fuel type, on air quality and health outcomes. Improved knowledge of these effects can provide important guidance for efficient wildfire management strategies. This study aims to bridge this gap using detailed information on 24 large-scale wildfires that sent smoke plumes to the Reno/Sparks area of Northern Nevada over a 4-year period. We relate the daily acreage burned by these fires to daily data on air pollutants and local hospital admissions. Using information on medical expenses, we compute the per-acre health cost of wildfires of different attributes. We find that patient counts can be causally linked to fires as far as 200–300 miles from the impact area. As expected, the marginal impact per acre burned generally diminishes with distance and for fires with lighter fuel loads. Our results also highlight the importance of allowing for temporal lags between fire occurrence and pollutant levels.     

Integrated remote sensing and GIS techniques for landslide hazard zonation: a case study Wadi Watier area,South Sinai,Egypt

Natural hazard analysis involves mapping and identifying future hazardous zones through the analysis of the controls influencing hazard initiation and occurrence. One of such natural hazard is the landslide. Landslides are amongst the most costly and damaging natural hazards especially in mountain regions and are triggered mainly by seismic activity and/or rainfall. The aim of the present study is to integrate Remote Sensing (RS) and Geographic Information System (GIS) tools to create thematic layers for assessment and the estimation of landslide hazard zones in and surrounding the Wadi Watier area, South Sinai, Egypt. Various factors, variables and/or parameters can be derived from thematic layers such as lithology, structural lineaments, land-cover/land-use, terrain analysis and earthquakes. Intensity risk layers were created by using ERDAS Imagine 9.2, ARC GIS 9.2 and ARC INFO 7.2.1 software. Enhanced Thematic Mapper (ETM+7) Landsat satellite images were used to discriminate and extract structural lineaments, lithology and land-use/land-cover variables for the study area. The Digital Elevation Model (DEM) was generated from digitized topographic maps to produce terrain analysis maps such as; slope, aspect, height elevation, and 3D. The weighting score rating system based on the relative importance of various causal factors derived from RS data and other thematic layers was used for landslide hazard zonation (LHZ). Based on these data, a simple algorithm was created to classify the area into different risk zones. By overlaying all hazard layers a final landslide hazard map was produced. Using trial and error and statistical methods the weight score rating values have been readjusted. GIS integration with RS data can greatly facilitate classifying landslide hazard zones into low risk, moderate risk and high risk by using a slicing operation. Seismic data are integrated with final the LHZ to generate a LHZ scenario map for the future and to draw up an action plan of mitigation measures to avoid the damage, loss of life and socio-economic impacts in the study area.     

Hierarchical space-time models for fire ignition and percentage of land burned by wildfires

Policy responses for local and global fire management as well as international green-gas inventories depend heavily on the proper understanding of the annual fire extend as well as its spatial variation across any given study area. Proper statistical models are important tools in quantifying these fire risks. We propose Bayesian methods to model jointly the probability of ignition and fire sizes in Australia and New Zeland. The data set on which we base our model and results consists of annual observations of several meteorological and topographical explanatory variables, together with the percentage of land burned over a grid with resolution of 1° across Austalia and New Zealand. Our model and conclusions bring improvements on the results reported by Russell-Smith et al. in Int J Wildland Fire, 16:361–377 (2007) based on a similar data set.     

Application of remote sensing and geographical information system in mapping forest fire risk zone at Bhadra wildlife sanctuary, India

Fire is the most spectacular natural disturbance that affects the forest ecosystem composition and diversity. Fire has a devastating effect on the landscape and its impact is felt at every level of the ecosystem and it is possible to map forest fire risk zone and thereby minimize the frequency of fire. There is a need for supranational approaches that analyze wide scenarios of factors involved and global fire effects. Fires can be monitored and analyzed over large areas in a timely and cost effective manner by using satellite imagery. Also Geographical Information System (GIS) can be used effectively to demarcate the fire risk zone map. Bhadra wildlife Sanctuary located in Kamataka, India was selected for this study. Vegetation, slope, distance from roads, settlements parameters were derived for a study area using topographic maps and field information. The Remote Sensing (RS) and Geographical Information System (GIS)-based forest fire risk model of the study area appeared to be highly compatible with the actual fire-affected sites. The temporal satellite data from 1989 to2006 have been analyzed to map the burnt areas. These classes were weighted according to their influence on forest fire. Four categories of fire risk regions such as Low, Moderate, High and Very high fire intensity zones were identified. It is predicted that around 10.31% of the area falls undermoderate risk zone.     

Combined effect of fire and water scarcity on vegetation patterns in arid lands

In many arid zones around the word, the vegetation spontaneously forms regular patterns to optimize the use of the scarce water resources. The patterns act as early warning signal that fragile ecosystems may suddenly undergo irreversible shifts, thus, interpreting the structural shape of vegetation patterns is crucial to deciphering the ecosystem history and its expected further development. The sudden and irreversible shift of delicate ecosystems as a consequence of minor variation of the climatic forcing has been studied extensively in the past. The attitude of the ecosystem to recover after a catastrophic event, such as fire, did not receive as much attention so far. Here we modelled fire, as a sudden shift of the ecosystem state variables and functionality and evaluated post-fire scenarios under the hypothesis that two major feedbacks shaped the vegetation patterns: a positive feedback between preferential infiltration and plant growth, and a second feedback between infiltration and vegetation burning. A simple model solving a set of partial differential equations for soil moisture, plant biomass, surface water and dead biomass balance predicted significantly diverse post-fire vegetation patterns depending on the fire severity and on the degree of soil water repellency induced by the vegetation burning.     

Spatial modelling of socioeconomic data to understand patterns of human-caused wildfire ignition risk in the SW of Madrid (central Spain)

The majority of wildfires in the Mediterranean Basin are caused directly or indirectly by human activity. Many biophysical and socioeconomic factors have been used in quantitative analyses of wildfire risk. However, the importance and effects of socioeconomic factors in spatial modelling have been given inadequate attention. In this paper, we use different approaches to spatially model our data to examine the influence of human activity on wildfire ignition in the south west of the Madrid region, central Spain. We examine the utility of choropleth and dasymetric mapping with both Euclidean and functional distance surfaces for two differently defined wildfire seasons. We use a method from Bayesian statistics, the Weights of Evidence model, and produce ten predictive maps of wildfire risk: (1) five maps for a two-month fire season combining datasets of evidence variables and (2) five maps for the four-month fire season using the same dataset combinations. We find that the models produced from a choropleth mapping approach with spatial variables using Euclidian and functional distance surfaces are the best of the ten models. Results indicate that spatial patterns of wildfire ignition are strongly associated with human access to the natural landscape. We suggest the methods and results presented will be useful to optimize wildfire prevention resources in areas where human activity and the urban-forest interface are important factors for wildfire ignition.     

Assessing spatial uncertainty of the Portuguese fire risk through direct sequential simulation

Forest fires have a significant economic, social, and environmental impact in Portugal. For that its fire risk was assessed through Bayes Formalism, where the main component of the risk of fire was assessed by the conditional probability of fire I(u,t) given a class of the daily severity rating (DSR) for a specific period of time—P[I(u,t)|R(u,t)]. The evaluation of this a posterior probability, P[I(u,t)|R(u,t)], was based on the update of marginal local probability of fire in each chosen region u (Durão, 2006).DSR values were used to calculate fire's risk, taking into account historical data, I(s,t), in a given region s, and also to define DSR's local thresholds in order to have P [I(u,t)|R(u,t)] ≥ 0.65.In this paper we characterize these posterior probabilities using direct sequential simulation models (DSS models) to obtain the spatial distribution of these probabilities over the entire Portugal, in order to assess the risk of fire and associated spatial uncertainty. Local probability density functions (pdfs) and spatial uncertainty are evaluated by a set of equiprobable simulated images of these posterior probabilities.Results are presented and discussed for the Portuguese fire seasons of the 2-year period, 2003-2004. The conditional probabilities reproduced reasonably well what was officially published for the studied fire seasons. We expect that a better understanding of both spatial and temporal patterns of fire in Portugal together with uncertainty measures constitutes an important tool for managers, helping to improve the effectiveness of fire prevention, detection and fire fighting resources allocation in critical social and environmental areas.     

Modis reflectance and active fire data for burn mapping and assessment at regional level

This paper presents an algorithm for rapid and accurate burn mapping that is applied here to the forest fires occurred in Galicia (northwest Spain) in August 2006, when nearly 930 km² were almost entirely burned over the course of eight days. The algorithm synergistically combines remotely-sensed reflectance and active fire data as measured by the MODIS (MODerate resolution Imaging Spectrometer) sensor on board Terra and Aqua NASA (National Aeronautics and Space Administration) satellites. Burned area data collected from this work was compared to official fires statistics from the Spanish Ministry of Environment and to perimeters that were derived using a high spatial resolution satellite image. In a later step, burn patches area analyzed using information from the National Forest Map (1:50,000) and some Geographical Information Systems (GIS) tools.     

Simulating a natural fire regime on an Atlantic coast barrier island complex in Florida, USA

The HFire fire regime model was used to simulate the natural fire regime (prior to European settlement) on Kennedy Space Center, Merritt Island National Wildlife Refuge, Canaveral National Seashore, and Cape Canaveral Air Force Station, Florida. Model simulations were run for 500 years and the model was parameterized using information generated from previously published empirical studies on these properties (e.g., lightning fire ignition frequencies and ignition seasonality). A mosaic pattern of frequent small fires dominated this fire regime with rare but extremely large fires occurring during dry La Niña periods. This simulated fire size distribution very closely matched the previously published fire size distribution for lightning ignitions on these properties. A sensitivity analysis was performed to establish which parameters were most influential and the range of variation surrounding empirically parameterized model output. Dead fuel moisture and wind speed had the largest influence on model outcome. A wide range of variance was observed surrounding the composite simulation with the least being 6% in total burn frequency and the greatest being 49% in total area burned. Because simulation modeling is the best option for fire regime reconstruction in many rapidly growing shrub dominated systems, these results will be of interest to scientists and fire managers for delineating the natural fire regime on these properties, the southeastern United States and other fire adapted shrub systems worldwide.     

Mapping lightning/human-caused wildfires occurrence under ignition point location uncertainty

Fire managers need to study fire history in terms of occurrence in order to understand and model the spatial distribution of the causes of ignition. Fire atlases are useful open sources of information, recording each single fire event by means of its geographical position. In such cases the fire event is considered as point-based, rather than area-based data, completely losing its surface nature. Thus, an accurate method is needed to estimate continuous density surfaces from ignition points where location is affected by a certain degree of uncertainty. Recently, the fire scientific community has focused its attention on the kernel density interpolation technique in order to convert point-based data into continuous surface or surface-data. The kernel density technique needs a priori setting of smoothing parameters, such as the bandwidth size. Up to now, the bandwidth size was often based on subjective choices still needing expert knowledge, eventually supported by empirical decisions, thus leading to serious uncertainties. Nonetheless, a geostatistical model able to describe the point concentration and consequently the clustering degree is required. This paper tries to solve such issues by implementing the kernel density adaptive mode. Lightning/human-caused fires occurrence was investigated in the region of Aragón's autonomy over 19 years (1983–2001) using 3428 and 4195 ignition points respectively for the two causes of fire origin. An analytical calibration procedure was implemented to select the most reliable density surfaces to reduce under/over-density estimation, overcoming the current drawbacks to define it by visual inspection or personal interpretation. Besides, ignition point location uncertainty was investigated to check the sensitivity of the proposed model. The different concentration degree and the dissimilar spatial pattern of the two datasets, allow testing the proposed calibration methodology under several conditions. After having discovered the slight sensitivity of the model to the exact point position, the obtained density surfaces for the two causes were combined to discover hotspot areas and spatial patterns of the two causes. Evident differences in spatial location of the origin causes were noted and described. The general trend follows the geographical features and the human activity of the study areas. The proposed technique should be promising to support decision-making in wildfire prevention actions, because of the occurrence map can be used as a response variable in fire risk predicting models.