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Human-caused fire occurrence modelling in perspective: A review
Abstract
The increasing global concern about wildfires, mostly caused by people, has triggered the development of human-caused fire occurrence models in many countries. The premise is that better knowledge of the underlying factors is critical for many fire management purposes, such as operational decision-making in suppression and strategic prevention planning, or guidance on forest and land-use policies. However, the explanatory and predictive capacity of fire occurrence models is not yet widely applied to the management of forests, fires or emergencies. In this article, we analyse the developments in the field of human-caused fire occurrence modelling with the aim of identifying the most appropriate variables and methods for applications in forest and fire management and civil protection. We stratify our worldwide analysis by temporal dimension (short-term and long-term) and by model output (numeric or binary), and discuss management applications. An attempt to perform a meta-analysis based on published models proved limited because of non-equivalence of the metrics and units of the estimators and outcomes across studies, the diversity of models and the lack of information in published works.
... It is well established that wildfire activity has changed in a number of regions of the world during the past decades (Jones et al. 2022). Wildfires result from the combination of many factors including fuel configuration, fire-weather conditions, human-or naturally-triggered ignition source and the fire suppression and mitigation policies (Costafreda-Aumedes et al. 2017). As climate, vegetation and human settlement patterns are spatially diverse and evolving over time, the spatial and temporal trends of fire activity result from complex interactions between these factors that may sometimes act in opposite directions. ...
... The study of human-caused fire occurrence has therefore received much attention (Martínez et al. 2009; Ganteaume and Jappiot 2013) and modelling efforts (e.g. Oliveira et al. 2012;Rodriguez y Silva et al. 2014;Costafreda-Aumedes et al. 2017;Ruffault and Mouillot 2017). Results have shown that the 'wildland-urban interface' (WUI) and the 'wildland-agricultural interface' (WAI), as well as the density of human settlements (such as road density or building density), are among the most important drivers of fire occurrence in the EU-Med (Galiana-Martin et al. 2011;Martín et al. 2019), although the importance of each of these factors varies from one region to another (Moreira et al. 2011;Rodrigues et al. 2014). ...
... Overall, the LULC factors retained in our model of fire occurrence (>1 ha) were consistent with the findings of previous modelling studies in southern Europe. Indeed, we found that most prevalent factors in human-caused fire occurrence models were human-related, including population density, dwellings and access networks to forest and natural land areas (Costafreda-Aumedes et al. 2017). However, vegetation cover of different fuel types was not selected in our fire occurrence model, while fuel rating, usually not included in previous studies, was found to be a better predictor. ...
Background Identifying if and how climatic and non-climatic factors drive local changes in fire regimes is, as in many other human-dominated landscapes, challenging in south-eastern France where both heterogeneous spatial patterns and complex fire trends are observed. Aim We sought to identify the factors driving the spatial-temporal patterns of fire activity in southeastern France. Methods We incorporated several non-climatic variables into the probabilistic Firelihood model of fire activity and implemented an enhanced spatio-temporal component to quantitatively assess remaining unexplained variations in fire activity. Key results Several non-climatic drivers (i.e. orography, land cover and human activities) contributed as much as fire-weather to the distribution of fire occurrence (>1 ha) but less to larger fires (>10, 100 and 1000 ha). Over the past decades, increased fire-weather induced a strong increase in wildfire probabilities, which was actually observed on the western part of the region but not so in the east and Corsican Island, most likely due to reinforced suppression policies. Conclusions While spatial patterns in fire activity are driven by land-use and land-cover factors, temporal patterns were mostly driven by changes in fire-weather and unexplained effects potentially related to suppression policies but with large differences between regions.
... The research conducted so far has largely made it possible to identify the human factors that influence the fire occurrence. The first models only dealt with demographic indicators and forest availability (Costafreda-Aumedes et al. 2017). As technical capabilities have developed, spatial dependencies between fires and factors such as distance from roads and railroads, valuable natural features, recreation areas, and buildings have also been studied (Curt et al. 2016, Milanović et al. 2021. ...
... The most common is the binary logistic regression method. According to Costafreda-Aumedes et al. (2017), it is widely used because of its relative ease of application and understandability. Fire occurrence modeling has also been performed using machine learning methods, such as Random Forest (Milanović et al. 2021 Jain et al. (2020) noted that since 1990, more than 300 articles have been published using machine learning in modeling fire occurrence. ...
... Fire occurrence modeling has also been performed using machine learning methods, such as Random Forest (Milanović et al. 2021 Jain et al. (2020) noted that since 1990, more than 300 articles have been published using machine learning in modeling fire occurrence. Costafreda-Aumedes et al. (2017), who analyzed 152 papers in the field, showed that the modeling methods themselves were not identical. The researchers used both binary (absence or presence of fire) and numeric (number of fires) as dependent variables. ...
Climate is one of the main causes of forest fires in Europe. In addition, forest fires are influenced by other factors, such as the reconstruction of tree stands with a uniform species composition and increasing human pressure. At the same time, the increasing number of fires is accompanied by a steady increase in the number and quality of spatial information collected, which affects the ability to conduct more accurate studies of forest fires. The appropriate use of spatial information systems (GIS) together with all the collected information on fires could provide new insights into their causes and, in further steps, allow the development of new, more accurate predictive models. The objectives of the study were: (i) to estimate the probability of fire occurrence in the period 2007-2016; (ii) to evaluate the performance of the developed model; (iii) to identify and quantify anthropogenic, topographic and stand factors affecting the probability of fire occurrence in forest areas in Poland. To achieve these objectives, a statistical model based on a logistic regression approach was built using the nationwide forest fire database for the period from 2007 to 2016. The information in the database was obtained from the Polish State Forest Information System (SILP). Then it was supplemented with spatial, topo-graphic and socioeconomic information from various spatial and statistical databases. The results showed that fire probability is significantly positively affected by population density and distance from buildings. In addition, the further the distance from roads and railways, watercourses and water objects or the edge of the forest, height above sea level, and steep slopes, the lower is the fire probability. Analysis of spatial, ecological and socioeconomic factors provides new insights that contribute to a better understanding of fire occurrence in Poland.
... Human-caused wildland fires have caused growing levels of concern across the globe (e.g. Costafreda-Aumedes et al. 2017). In Canada, the threat of wildland fires to life, property and natural resources has increased over the last several years (Canadian Council of Forest Ministers Wildland Fire Management Working Group 2016). ...
... There are many different methods that have been used to model wildland fire occurrences. Recent summaries, reviews and discussions appear in Plucinski (2012), Taylor et al. (2013), Costafreda-Aumedes et al. (2017), Nadeem et al. (2020) and . These methods can be broadly viewed as coming from one of the following two dominant data modelling cultures: model-based (i.e. ...
... The 'black boxes' of BCTs, RFs and NNs provide no such reassurance to an end user in a fire management organisation. Fire management agencies are much more likely to make use of a model and value its outputs if they have some understanding of how the model arrived at its output, so the poor interpretability of the machine learning models may result in underutilisation of the model in practice (Costafreda-Aumedes et al. 2017). For this reason, there may be a practical improvement to fire management from using a logistic GAM for FOP instead of a machine learning model. ...
Wildland fire occurrence prediction (FOP) modelling supports fire management decisions, such as suppression resource pre-positioning and the routeing of detection patrols. Common empirical modelling methods for FOP include both model-based (statistical modelling) and algorithmic-based (machine learning) approaches. However, it was recently shown that many machine learning models in FOP literature are not suitable for fire management operations because of overprediction if not properly calibrated to output true probabilities. We present methods for properly calibrating statistical and machine learning models for fine-scale, spatially explicit daily FOP followed by a case-study comparison of human-caused FOP modelling in the Lac La Biche region of Alberta, Canada, using data from 1996 to 2016. Calibrated bagged classification trees, random forests, neural networks, logistic regression models and logistic generalised additive models (GAMs) are compared in order to assess the pros and cons of these approaches when properly calibrated. Results suggest that logistic GAMs can have similar performance to machine learning models for FOP. Hence, we advocate that the pros and cons of different modelling approaches should be discussed with fire management practitioners when determining which models to use operationally because statistical methods are commonly viewed as more interpretable than machine learning methods.
... Although the occurrence of fires in different parts of the world has been modeled for multiple purposes, few organizations as government agencies, forest companies, etc., have used these models to generate policies aiming at reducing the susceptibility of the landscape to these events (Costafreda-Aumedes et al., 2018). As pointed out by Chas-Amil et al. (2015), policies that incentivize cooperative forest management may reduce wildfire incidence, so innovative and integrated approaches are in increasing demand in the new world context of climate change. ...
... The most common way to model the fire occurrence is representing the absence or presence of fire (coded 0 or 1, respectively) (Moayedi et al., 2020). The most widely used method to model HCF binary occurrence is through binary logistic regression because it is easy to use and understand (Costafreda-Aumedes et al., 2018). However, in the last two decades, more complex Machine Learning (ML) techniques, such as Classification and Regression Trees (Amatulli et al., 2006), Support Vector Machines (Ghorbanzadeh et al., 2019), Artificial Neural Networks (De Vasconcelos et al., 2007), Random Forests (Massada et al., 2013;Oliveira et al., 2012), Fuzzy-metaheuristic ensembles (Moayedi et al., 2020), Generalised Additive Models (McWethy et al., 2018), or Convolution Neural Networks (Zhang et al., 2019) have been introduced as alternatives to traditional statistical methods, mainly powered by increased computational capacity, both in hardware and software. ...
... Through the use of Partial Dependence Plots (PDPs), we were able to find critical interactions between the most relevant variables to be managed by a landscape decision maker. Although the literature presents various models to predict fire occurrence, just a few use these models and tools to inform and establish preventive policies with the goal of minimizing risk that allow adaptation to new conditions (Costafreda-Aumedes et al., 2018). We found only a few articles that perform an one-dimensional sensitivity analysis to analyze the marginal effect of a variable with respect to risk of fire (McWethy et al., 2018;Yang et al., 2007;Kim et al., 2019;González et al., 2006;Miranda et al., 2020). ...
The strong link between climate change and increased wildfire risk suggests a paradigm change on how humans must co-exist with fire and the environment. Different studies have demonstrated that human-induced fire ignitions can account for more than 90 % of forest fires, so human coexistence with wildfires requires informed decision making via preventive policies in order to minimize risk and adapt to new conditions. In this paper, we address the multidimensional effects of three groups of drivers (human activity, geographic and topographic, and land cover) that can be managed to assist in territorial planning under fire risk. We found critical factors of strong interactions with the potential to increase the likelihood of starting a fire. Our solution approach included the application of a Machine Learning method called Random Undersampling and Boosting (RUSBoost) to assess risk (fire occurrence probability), which was subsequently accompanied by a sensitivity analysis that revealed interactions of various levels of risk. The prediction performance of the proposed model was assessed using several statistical measures such as the Receiver Operating Characteristic curve (ROC) and the Area Under the Curve (AUC). The results confirmed the high accuracy of our model, with an AUC of 0.967 and an overall accuracy over test data of 93.01 % after applying a Bayesian approach for hyper-parameter optimization. The study area to test our solution approach comprised the entire geographical territory of central Chile.
... Secondly, our work showed that there is an impact of the terrain on the appearance of fires in the study area. These results confirmed findings of previous studies carried out on the influence of topography (slope, altitude, aspect and type of soils) on the ignition of wildfires (Costafreda-Aumedes et al., 2017;Diouf et al., 2012;Lee et al., 2008;Oloukoi et al., 2014). Wildfires usually hatched on land suitable for agriculture (mid-elevation and shallow to mid-slope), characterised by deeper, coarse-textured soils and a continuous layer of mixed perennial and annual grass species (Diouf et al., 2012;Mbow et al., 2003). ...
... Also, the proximity of roads is a major cause of deforestation (Lambin & Meyfroidt, 2011). Road and rail networks can directly increase the risk of accidental ignition by reducing the closure of the forest canopy and increasing anthropogenic pressures as well as the human-forest interface (Costafreda-Aumedes et al., 2017;Ricotta et al., 2018;Stolle et al., 2003). Population density is the most widely used indicator for the occurrence of a human-caused fire (Knorr et al., 2014). ...
Wildfires are a significant threat to environmental, social, economic and agricultural systems. This study investigated the spatiotemporal trends in wildfire activity and its interactions with climate and environmental factors in Côte d'Ivoire through remotely sensed MODIS data associated with climate, biophysical and anthropogenic data. We analysed relationships between wildfire occurrence and climate drivers using cross-correlations, while Pearson chi-squared and Kruskal–Wallis tests were selected to identify linkages between qualitative and quantitative environmental data, respectively. Seasonal Kendall and Sen's slope approaches were applied for trend analysis. During the period 2001–2019, 8150 wildfires were recorded annually, burning 2.69 million hectares per year, representing about 8.34% of Côte d'Ivoire. Fire detections and burnt areas highlighted a downward trend in all ecoregions with a predominance in the Sudanian zone. Wildfire occurrence showed a significant and strongly negative link with relative humidity and visibility, as well as a significant and positive correlation with maximum temperature, thermal amplitude and vapour-pressure deficit. Also, the spatial distribution of wildfires was significantly conditioned by environmental factors. The findings of this study will help decision makers and managers to make decisions to reduce the vulnerability of local populations to current and future wildfire hazards.
... environment to maintain ecosystem services and optimum use of resources (Ahamed et al. 2000;Collins et al. 2001;Malczewski 2004;Amiri and Shariff 2012;Zolekar and Bhagat 2015). The prominent methods in the generation of wildfire risk maps are geographical information systems (GIS) tools (Jaiswal et al. 2002;Setiawan et al. 2004;Xu et al. 2005;Hernandez-Leal et al. 2006;Chuvieco et al. 2010;Sowmya and Somashekar 2010;Puri et al. 2011;Adab et al. 2013;Ajin et al. 2016;Pourghasemi, 2016;Baqer Rasooli and Bonyad 2019;Austin et al. 2020;Kayet et al. 2020;Ziccardi et al. 2020), multi-criteria decision analysis (MCDA) (Iwan et al. 2004;Vadrevu et al. 2010;Eugenio et al. 2016;Suryabhagavan et al. 2016;Güngöroğlu 2017;You et al. 2017;Sakellariou et al. 2019), and generalized linear models (Syphard et al. 2008;Martinez et al. 2009;Kwak et al. 2012;Vilar et al. 2016;Costafreda-Aumedes et al. 2017Li et al. 2019). ...
... The relationship between fire ignition and environmental causes has been analyzed using different models. Most studies used logistic regression-based models (Chuvieco et al. 2009;Martinez et al. 2009) and generalized linear models (Syphard et al. 2008;Martinez et al. 2009;Kwak et al. 2012;Vilar et al. 2016;Costafreda-Aumedes et al. 2017Li et al. 2019) and the random forest algorithm (Archibald et al. 2009;Aldersley et al. 2011;Olivera et al. 2012;Arpaci et al. 2014; Rahmati et al. 2016) due to the presence of nonlinear and nonparametric variables. Another approach is the machine learning algorithms of the MaxEnt method to discover the relationship between variables and ignition possibilities (Parisien and Moritz 2009;Bar-Massada et al. 2012, 2013Renard et al. 2012;De Angelis et al. 2015;Vacchiano et al. 2018;Yago et al. 2019;Bekar et al. 2020;Banerjee, 2021;Tariq et al. 2022). ...
Turkey has a high potential for wildfires along its Mediterranean coast because of its dense forest cover and mild climate. An average of 250 wildfires occurs every year with more than 10,000 hectares destroyed due to natural and human-related causes. The study area is sensitive to fires caused by lightning, stubble burning, discarded cigarette butts, electric arcing from power lines, deliberate fire setting, and traffic accidents. However, 52% of causes could not be identified due to intense wildfires occurring at the same time and insufficient equipment and personnel. Since wildfires destroy forest cover, ecosystems, biodiversity, and habitats, they should be spatially evaluated by separating them according to their causes, considering environmental, climatic, topographic and forest structure variables that trigger wildfires. In this study, wildfires caused by lightning, the burning of agriculture stubble, discarded cigarette butts and power lines were investigated in the provinces of Aydın, Muğla and Antalya, where 22% of Turkey’s wildfires occurred. The MaxEnt method was used to determine the spatial distribution of wildfires to identify risk zones for each cause. Wildfires were used as the species distribution and the probability of their occurrence estimated. Additionally, since the causes of many wildfires are unknown, determining the causes is important for fire prediction and prevention. The highest wildfire occurrence risks were 9.7% for stubble burning, 30.2% for lightning, 4.5% for power lines and 16.9% by discarded cigarette butts. In total, 1,266 of the 1,714 unknown wildfire causes were identified by the analysis of the cause-based risk zones and these were updated by including cause-assigned unknown wildfire locations for verification. As a result, the Area under the ROC Curve (AUC) values were increased for susceptibility maps.
... Humans cause and suppress most wildfires in Mediterranean areas, and ignition locations present strong aggregation clusters with very distinctive spatiotemporal patterns (González-Olabarria et al., 2015;Salis et al., 2015). Previous studies used wildfire occurrence modeling to predict ignition probability (IP) with different methods such as logit models or random forest algorithms (Galizia and Rodrigues, 2019;Rodrigues and de la Riva, 2014) using geospatial variables correlated to anthropic factors, land cover types, topographic features, and weather conditions (Costafreda-Aumedes et al., 2017). However, only a few fires escape from initial attack (IA), which essentially depends on firefighting resource availability, deployment time or distance to fire ignitions, and fire-weather conditions . ...
... Geospatial information about wildfire drivers was used to generate a human-caused fire (HCF) ignition probability raster grid using machinelearning algorithms as proposed by Rodrigues and de la Riva (2014). The method builds upon historical fire records, compiled in the Spanish fire database (EGIF; MAAyMA, 2015), coupled to spatial raster layers (at 40 m resolution) of fire drivers (Costafreda-Aumedes et al., 2017;Leone et al., 2003) depicting accessibility (distance to paved roads, forest tracks and walking trails), human pressure on wildlands (WUI), presence of agricultural activities (Wildland-Agricultural interface, WAI) and sparks from power lines (distance to power lines). The modeling approach relied on Random Forest (RF; Breiman, 2001), a powerful machine learning algorithm very popular in wildfire science due to its high predictive performance (Bar Massada et al., 2012). ...
Despite the abundant firefighting resources deployed to reinforce the fire exclusion policy, extreme events continue to cause substantial losses in Mediterranean regions. These catastrophic wildfires question the merely-reactive response, while science-based decision-making advocates for a paradigm shift towards a long-term solution to coexist with fire. Comprehensive management solutions integrate multiple efforts to minimize the number of escaped wildfires in fire ignition hotspots, restrict large fire spread across the landscape, and prevent losses to valued resources and assets. This study develops a wildfire management zone (WMZ) delineation framework to inform decision-making in fire-prone Mediterranean landscapes. First, we combined modeling outcomes of wildfire occurrence, initial attack success, and wildfire transmission to communities to segment the landscape in WMZ blocks. We assumed the worst-case scenario in terms of fire simultaneity and weather conditions to implement the models. The geospatial outcomes were assembled and classified into four primary archetypes, and we then designated the most suitable risk mitigation strategies for each management unit. The WMZs included (1) comprehensive management, (2) human ignition prevention, (3) intensive fuel management, and (4) fire reintroduction areas. Finally, we downscaled within zones to assign specific management prescriptions to the different areas. The results were presented in a set of cross-scale maps to assist in designing risk management plans and raise social awareness. The methodological framework developed in this study may be valuable to help mitigate risk in fire-prone Mediterranean areas, but also in other regions in which similar total suppression policies fail to reduce catastrophic wildfire losses.
... Although in wildfire research the number of fire ignitions is commonly used as an essential variable, recent studies [35][36][37] have progressively highlighted the importance of also knowing the other characteristics of fires such as their extension and the ratio of hectares burned/number of wildfires. Some authors [37,38] have used statistical and econometric models (panel data and predictive models) to demonstrate the fire frequency and extension originated by socio-economic variables related to social vulnerability. ...
... Although in wildfire research the number of fire ignitions is commonly used as an essential variable, recent studies [35][36][37] have progressively highlighted the importance of also knowing the other characteristics of fires such as their extension and the ratio of hectares burned/number of wildfires. Some authors [37,38] have used statistical and econometric models (panel data and predictive models) to demonstrate the fire frequency and extension originated by socio-economic variables related to social vulnerability. ...
This paper studied the effect of the socio-economic variables related to social vulnerability on wildfire characteristics (ignitions, hectares burned, and ratio hectares burned/ignitions) in Galicia, Spain. The study recognized that wildfires present threats to people and communities, so actions might be taken to address vulnerabilities in ways that mitigate the negative impacts of such fires. Our final aim was to identify those variables that are relevant to the starting and spreading of wildfires that can help improve the prevention and mitigation of wildfires. Panel data collected over 15 years (2001–2015) for the municipalities of Galicia were used in this study. The results show that vulnerability-related socio-economic factors affect the number of wildfires and the extent of the destruction they cause. Indeed, the progressive abandonment of rural areas is one of the most important problems that increases the occurrence of wildfires. This abandonment is connected to population factors such as aging or low density of population, economic factors such as the decrease in income or low cadastral value, and territorial factors such as the decrease in rustic hectares and ranches. We conclude that prevention and mitigation focused on areas prone to wildfires could be enhanced by taking into account these variables.
... The important factors influencing both the fire ignition and propagation are also a human element together with the irresponsible behaviour of a local population. Due to this fact, wildfire risk levels are often dependent on the distance to roads and buildings, so as it is discussed by Costafreda-Aumedes et al. (2018). However, the construction of new roads and buildings within the territory of a national park is strictly prohibited since its declaration in the 1988 year. ...
The challenge to the sustainable development of forestry in the Eurasian temperate - boreal zone is the increase in the frequency and severity of natural disturbances due to global climate change. In this study, a mathematical model for predicting the risk of wildfires in spruce stands growing in the territory of Slovak Paradise National Park under climate change has been proposed and tested. Wildfire risk is described in terms of the observed probabilities of the destruction of spruce stands in relation to their age for a period of 10 years. As the indicators of assumed climate change, the time series of daily values of four fire weather indices (Angstrőm, Nesterov, Baumgartner, and the Meteorological Forest Fire Risk Index) for the period 1951–2019 have been analysed. The results obtained indicated the significant dependence of the observed increasing annual population proportions of burnt areas on the gradually increasing annual population proportions of risky days recorded and evaluated by using the common scales of risk classification. We found that ongoing climate change has a significant impact on increasing the risk of fires. The Meteorological Forest Fire Risk Index has proven to be the most suitable measure for predicting the probability of fire occurrence under the climate conditions in the experimental territory. The indicated risk of fire occurrence in spruce stands under the assumption of a climatic change is substantially higher than in the case when this assumption is neglected. This information can also serve as a basis for the formulation of efficient landscape fire protection measures focused on building the infrastructure to support the efficient retardation of propagation, including the quick suppression of this detrimental natural hazard.
... The literature includes several attempts to model ignition events with a variety of causes at a fine spatio-temporal resolution. The general approach has been to formulate the problem as a bernoulli process, where the two mutually exclusive outcomes are ignition and no ignition [7]. One study [8] explored the potential of Logistic Regression and Decision Tree algorithms to convert satellite-derived Live Fuel Moisture Content (LFMC) into ignition probability for the Iberian Peninsula territory of Spain. ...
Electrical infrastructure is one of the major causes of bushfire in Australia alongside arson and lightning strikes. The two main causes of electrical-infrastructure-initiated fires are asset failure and powerline vegetation interactions. In this paper, we focus on powerline–vegetation interactions that are caused by vegetation falling onto or blowing onto electrical infrastructure. Currently, there is very limited understanding of both the spatio-temporal variability of these events and their causative factors. Bridging this knowledge gap provides an opportunity for electricity utility companies to optimally allocate vegetation management resources and to understand the risk profile presented by vegetation fall-in initiated fires, thereby improving both operational planning and strategic resource allocation. To bridge this knowledge gap, we developed a statistical rare-event modelling and simulation framework based on Endeavour Energy’s fire start and incident records from the last 10 years. The modelling framework consists of nested, rare-event-corrected, conditional probability models for vegetation events and consequent ignition events that provide an overall model for vegetation-initiated ignitions. Model performance was tested on an out-of-time test set to determine the predictive utility of the models. Predictive performance was reasonable with test set AUC values of 0.79 and 0.66 for the vegetation event and ignition event models, respectively. The modelling indicates that wind speed and vegetation features are strongly associated with vegetation events, and that Forest Fire Danger Index (FFDI) and soil type are strongly associated with ignition events. The framework can be used by energy utilities to optimize resource allocation and prepare future networks for climate change.
... Population is another major driver of fire regimes. Fire ignitions are often associated with human presence, agricultural activity, and livestock grazing [7,9,16,20,22,[29][30][31][32][33][34]. Intentional or negligent/accidental ignitions frequently result from using fire for agricultural and grazing purposes, i.e., harvest waste removal and brushwood/abandoned land clearance [20,30]. ...
Fire regimes in Mediterranean countries have been shifting in recent decades, including changes in wildfire size and frequency. We sought to describe changes in fire regimes across two periods (1975-1995 and 1996-2018) in a fire-prone region of central Portugal, explore the relationships between these regimes and territorial features, and check whether these associations persisted across periods. Two independent indicators of fire regimes were determined at parish level: fire incidence and burn concentration. Most parishes presented higher values of both indicators in the second period. Higher values of fire incidence were associated with lower population densities, lower proportions of farmland areas and higher proportions of natural vegetation. Higher levels of burn concentration were associated with smaller areas of farmland and natural vegetation. These associations differed across periods, reflecting contrasting climatic and socioeconomic contexts. Keeping 40% of a parish territory covered by farmland was effective to buffer the increased wildfire risks associated with different management and climate contexts. The effectiveness of higher population densities in keeping fire incidence low decreased in the last decades. The results can improve the knowledge on the temporal evolution of fire regimes and their conditioning factors, providing contributions for spatial planning and forest/wildfire management policies.
... It was divided into grids of 1 km*1 km for study. Previous research has indicated that wildfire occurrence not only correlates to local meteorological conditions but also is affected by physiography, land cover, or socioeconomic features (Costafreda-Aumedes et al., 2018). Our study collected and aggregated data from 14 wildfire-related features over three categories (physiographical, meteorological, and anthropological). ...
To reduce the impact of wildfires on the operation of power systems, a back-propagation neural network (BPNN) model is used to evaluate the wildfire risk distribution after feature selection. Data from 14 types of wildfire-related features, including anthropogenic, geographical, and meteorological factors, were collected from public data websites and local departments. The weight ranking was calculated using filtering and wrapper methods to form five feature subsets. These are used as the input sets of the BPNN model training, and network parameters are optimized by genetic algorithm (GA). Finally, the optimal feature subset is chosen to establish the optimal BPNN model. With the optimal model, the prediction results are graded to draw a wildfire risk distribution map. Situated in medium-, high-, and very-high-risk zones are 90.26% of new fire incidents, indicating the applicability of the proposed BPNN model.
... We found that explaining wildfire ignitions was possible by using Maxent and a limited number of ancillary variables. This is an addition to the many statistical and modelling tools used until now to model ignitions [78], including resource selection functions (e.g., [79]), negative binomial models (e.g., [37]), logistic and Poisson regression (e.g., [80][81][82]), or machine learning algorithms (e.g., [83]), including Maxent (e.g., [84,85]). The accuracy with which ignitions can be modelled can vary as a function of seasonality [85] or fire size (this study), among other factors. ...
Managing protected areas requires knowing what factors control fire ignitions and how likely they are compared to non-protected ones. Here, we modelled fire ignition likelihood in west-central Spain as a function of biophysical and anthropogenic variables in 172 protected areas (PA) of the Natura 2000 network, their buffer zones (BZ, 1500 m area surrounding PA), and non-protected areas (NP). Ignition coordinates from fire statistics (2001–2015 period) were overlaid over maps of relevant biophysical and socioeconomic variables. Models were built for four different fire sizes, small (1–5 ha), medium (5–50 ha), large (50–500 ha), and very large (≥500 ha), using Maxent software. Additionally, PA were classified based on their land use and land cover types by cluster analysis. Mean ignition probabilities were compared between PA, BZ and NP, as well as among different types of PA, by generalized linear models. Maxent models’ accuracy increased as fires were of larger size. Ignitions of small fires were associated with anthropogenic variables, while those of larger fires were more associated with biophysical ones. Ignition likelihood for the small and medium fire sizes was highest in BZ, while being the lowest in PA. Conversely, the likelihood of large and, particularly, very large fires was highest in PA. Mean ignition likelihood varied among types of PA, being highest for very large fires in PA, dominated by pine and mixed forests. Our results support the hypothesis that PAs are at the highest risk of large fire ignition, but BZ were also at high risk for the rest of the fire sizes. This largely reflects the more hazardous nature of PA landscapes. This work provides the needed tools to identify critical fire ignition areas within and nearby protected areas, which should be considered in their conservation and management plans.
... However, human factors such as fuel management, fire prevention, detection and suppression capability are also critical [4][5][6][7]. Notably, anthropogenic ignitions dominate heavily over lightning ignitions in most regions [8,9]. At regional levels, fire occurrence therefore tends to correlate positively with anthropogenic parameters such as population- [10][11][12] and road density [13][14][15]. ...
Organization of successful wildfire prevention and suppression require detailed information on ignition causes, size distributions and relations to weather. From a large and highly detailed dataset of Swedish wildfire incidents (n = 124 000) we assess temporal, geographical and human-related patterns over a 25 year-period (1996–2020). We find strong positive correlations between population density and wildfire occurrence, primarily caused by a wide spectrum of human activities. However, fires >10 ha mostly occurred in sparsely populated regions and were more often ignited by lightning or heavy machinery. Further, large fires had a history of long response times and insufficient mop-up, in turn intimately linked to low population density. We detect no trend over the period 25-year-period in either fire weather, number of ignitions or burned area, but a dramatic decline in wildfire caused by children's play as well as by springtime burning of dead grass, a traditional fire use in rural areas. Our results indicate that irrespective of climate change, societal changes such as rural depopulation and cultural shifts are imminently important for the future fire regime in this intensely managed part of the boreal, and may warrant more attention worldwide.
... Forests IOP Publishing doi:10.1088/1755-1315/1111/1/012005 2 play an essential role in human life, proven in the protection, shade, and products humans need for survival [3]. Describes the data on forest fires that occurred in the last five years in Indonesia and also in 2022, which shows that information on forest fires has increased and decreased in forest fires and areas where forest fires occurred in Indonesia [4]. Forest supervision and protection aim to prevent and minimize forest damage, maintain state rights to forests and their products, and have strategic value in people's and state's lives where forests function as biological natural resources, support life, and are regional assets that have ecological and economic benefits. ...
Jambi Province is one of the areas most prone to forest destruction in Indonesia. The case of forest fires is an annual disaster that regularly occurs in Indonesia, including one in Jambi province. The problem of forest fires often occurs in Indonesia, whether human activities cause it or are caused by a long dry season. This study analyzes Sustainable Forest Governance from the point of view of the New Policy Strategy in Handling Forest Fires in Jambi Province. This research focuses on Jambi Province, one of Indonesia’s regions with the largest forest. This research method is qualitative exploratory with secondary data carried out to describe and describe the research. Data were collected from field interviews, online media, and related literature related to the research topic. The analysis technique uses the NVivo 12 plus data processing application, a qualitative document analysis tool with the help of a computer. Equipment. They are easy to use and can word-process and explore word frequency, attributes, and cases from big data. They also generate factor or sub-factor categories in journalistic and research applications related to the research topic. The study results show that the government’s program in making new policy strategies for handling forest fires in the province has several indicators, then the cooperation of actors in forest fire prevention in the region.
... These or similar dimensions are used by the authors to identify relevant variables (i.e., indicators) describing exposure and sensitivity associated with fire risk in different geographical contexts Ager, Preisler, Arca, Spano, & Salis, 2014;Vallejo-Villalta et al., 2019;Costafreda-Aumedes et al., 2017). ...
O período entre 2018 e 2022 mostrou-nos que o problema dos incêndios à escala global não está a diminuir, antes pelo contrário. Parece que as consequências das alterações climáticas já estão a afectar a ocorrência de incêndios florestais em várias partes do Mundo, de uma forma que só esperaríamos que acontecesse vários anos mais tarde. Em muitos países do Sul da Europa, bem como em algumas regiões dos EUA, Canadá e Austrália, onde estamos habituados a enfrentar a presença de incêndios muito grandes e devastadores, continuamos a ter eventos que quebram recordes. Alguns países, como os da Europa Central e do Norte, que não estavam habituados a ter grandes incêndios, experimentaram-nos durante estes anos. Os anos anteriores foram muito exigentes para todo o Mundo, também noutros aspectos que nos afectaram a todos. Referimo-nos às restrições impostas pela pandemia que limitaram as nossas reuniões e viagens, afectando em muitos casos a saúde dos membros da Comunidade Científica Wildfire. Felizmente, conseguimos encontrar novas formas de comunicação, ultrapassar essas limitações e manter-nos em contacto uns com os outros. Durante semanas e meses, para muitos de nós, as reuniões pessoais e o trabalho de grupo foram substituídos por ligações em linha. Apesar da economia de dinheiro e tempo, e da facilidade de reunir uma grande variedade de pessoas que estas reuniões desde que nos apercebêssemos de que não substituem as reuniões presenciais, que trazem consigo outras dimensões inestimáveis, que fazem parte da comunicação pessoal e ajudam a construir uma comunidade científica.
... The year 2017 was the most devastating to date in Portugal, with the largest recorded burned area (~ 500 000 ha) and more than hundred fatalities in the June and October fires (Comissão Técnica Independente, 2018; Instituto da Conservação da Natureza e das Florestas, 2017). These events renovated the need to improve the protection of human communities and to increase the coping capacity of the population (Alcasena et al., 2019;Costafreda-Aumedes et al., 2017;Gonçalves et al., 2021;O'Connor et al., 2016;Palaiologou et al., 2019). Since then, several initiatives have been developed in Portugal to deal with these challenges; one such measure is the Safe Village Safe People program, initiated in 2018 and coordinated by the National Emergency and Civil Protection Authority (ANEPC). ...
A presente obra resulta das contribuições de quase 1000 investigadores internacionais sobre questões relacionadas com os incêndios florestais. A obra tem a sua origem na “9th International Conference on Forest Fire Research”, uma conferência internacional sobre a mesma temática, que reúne de 4 em 4 anos na região de Coimbra os mais prestigiados e reconhecidos investigadores de todo o Mundo que se dedicam a estudar os mais diversos aspetos relacionados com a gestão dos incêndios florestais. A obra, em inglês, divide-se em 6 áreas temáticas: 1) Decision Support Systems and Tools, 2) Fire at the Wildland Urban Interface, 3) Risk Assessment, 4) Risk reduction, 5) Risk adaptation e 6) Wildfire management and safety. O prefácio é da autoria do Chairman da Conferência, um dos dois editores da obra, Professor Doutor Domingos Xavier Viegas, docente aposentado do Departamento de Engenharia Mecânica da Faculdade de Ciências e Tecnologia da Universidade de Coimbra
... Forest canopies impact microclimate conditions, which in turn alter ecosystem processes, including species interactions, disturbance dynamics, and successional trajectories post-disturbance (Costafreda-Aumedes et al., 2018;North et al., 2019;Zellweger et al., 2020). With climate change, fire activity is increasing in Western North America (Westerling et al. 2006), with uncertain effects on forests. ...
Forest canopies can buffer seedlings from extreme climate conditions. Yet, how disturbed forest canopies influence microclimate is not well understood, despite the important implications of microclimate for seedling establishment and post-disturbance successional trajectories. Better understanding of the relationship between a forest canopy and sub-canopy temperature and moisture conditions requires easily acquired and continuous forest canopy data, which is increasingly available due to new technology. Here, we measured canopy height using a remotely piloted aircraft (RPA) and monitored microclimate with low-cost temperature and soil moisture sensors in a sub-boreal forest impacted by fires of variable severity. We used regression models to investigate how differences in canopy height influenced microclimate variables. Mean growing season temperatures at -8 cm (soil), 0 cm (surface), and 15 cm (near-surface) relative to the ground surface were higher under shorter more disturbed canopies. Soil temperature was most sensitive to canopy height differences: linear models for the observed data range predicted a 2.0 °C increase in mean growing season soil temperature with every 10 m decrease in canopy height. We observed a weak negative relationship between canopy height and mean growing season soil moisture. We found that canopy height summarized at moderate resolution (15 m) better explained differences in temperature in our disturbed landscape. This work informs future methods to produce gridded microclimate datasets and outlines the impact of disturbed forest structure on microclimate variables. Our results show that the characteristics of the forest canopy remaining after a burn impact microclimates, which has important implications for post-fire ecosystems.
... Several aspects of wildfire research linked to mathematical modelling and simulation have also been subjects of review articles in this literature (Bakhshaii and Johnson, 2019;Costafreda-Aumedes et al., 2017;Seidl et al., 2011;Sullivan et al., 2003). For example, Paugam et al. (2016) reviewed how landscape-scale fire smoke plume injection heights are represented in larger-scale atmospheric transport models, Perry (1998) considered the development of modelling approaches designed to predict the spread and spatial behaviour of wildland fire events, and Parisien et al. (2019) reviewed applications of burn-probability modelling approaches and applications in risk analysis and land management. ...
Along with the increase in the frequency of disastrous wildfires and bushfires around the world during the recent decades, scholarly research efforts have also intensified in this domain. This work investigates divisions and trends of the domain of wildfire/bushfire research. Results show that this research domain has been growing exponentially. It is estimated that the field, as of 2021, it has grown to larger than 13,000 research items, with an excess of 1,200 new articles appearing every year. It also exhibits distinct characteristics of a multidisciplinary research domain. Analyses of the underlying studies reveal that the field is made up of five major divisions. These divisions embody research activities around (i) forest ecology and climate, (ii) fire detection and mapping technologies, (iii) community risk mitigation and planning, (iv) soil and water ecology, and (v) atmospheric science. Research into the sub-topics of reciprocal effects between climate change and fire activities, fire risk modelling/mapping (including burned area modelling), wildfire impact on organic matter, biomass burning, and human health impacts currently constitute trending areas of this field. Amongst these, the climate cluster showed an explosion of activities in 2020 while the human health cluster is identified as the most recent emerging topic of this domain. On the other hand, dimensions of wildfire research related to human behaviour—particularly issues of emergency training, risk perception and wildfire hazard education—seem to be notably underdeveloped in this field, making this one of its most apparent knowledge gaps. A scoping review of all reviews and meta-analysis of this field demonstrates that this sub-topic is also virtually non-existent on the research synthesis front. This meta-synthesis further reveals how a western, deductive view excludes socioecological and traditional knowledge of fire.
... Studies regarding the characterization of wildfire behavior have experienced a notable increase in recent years concomitant with a rising concern for the increase in the frequency and severity of fires in different regions of the world (Costafreda-Aumedes et al., 2017;Keeley et al., 2008;Roldán-Zamarrón et al., 2006). Such research supports initiatives for the prevention, management and combat of fires and the development of methods for post fire damage evaluation. ...
Forest fires are a major issue worldwide, and especially in Mediterranean ecosystems where the frequency, extension and severity of wildfire events have increased related to longer and more intense droughts. Open access remote sensing and climate datasets make it possible to describe in detail the precursory environmental conditions triggering major fire events under drought conditions. In this study, a probabilistic methodological approach is proposed and tested to evaluate extreme drought conditions prior to the occurrence of a wildfire in Central Chile, an area suffering an unprecedented prolonged drought. Using 21 years of monthly records of gridded climate and remotely sensed vegetation water status data, we detected that vegetation at the ground level, by means of fine and dead fuel moisture (FDFM), and canopy level, by means of the enhanced vegetation index (EVI) were extremely dry for a period of about 8 months prior to the fire event, showing records that fall into the 2.5% of the lowest values recorded in 21 years. These extremely dry conditions of the vegetation, consequence of low air humidity and precipitation, favored the ignition and horizontal and vertical propagation of this major wildfire. Post fire, we found high severity values for the native vegetation affected by the fire, with dNBR values >0.44 3 days after the fire and significant damage to the Mediterranean sclerophyllous and deciduous forest present in the burned area. The proposed probabilistic model is presented as an innovation and an alternative to evaluate not only anomalies of the meteorological and vegetation indices that promote the generation of extreme events, but also how unusual or extreme these conditions are. This is achieved by placing the abnormal values in the context of the reference historical frequency distribution of all available records, in this case, more than 20 years of remote sensing and climate data. This methodology can be widely applied by fire researchers to identify critical precursory fire conditions in different ecosystems and define environmental indicators of fire risk.
... There are many different methods that have been used to model wildland fire occurrences. Recent summaries, reviews, and discussions in [44][45][46][47][48] revealed that these methods can be broadly viewed as coming from one of two dominant data modeling cultures: model-based (i.e., statistical modeling/learning) or algorithmic-based (i.e., machine learning). ...
As the climate changes with the population expansion in Pakistan, wildfires are becoming more threatening. The goal of this study was to understand fire trends which might help to improve wildland management and reduction in wildfire risk in Pakistan. Using descriptive analyses, we investigated the spatiotemporal trends and causes of wildfire in the 2001–2020 period. Optimized machine learning (ML) models were incorporated using variables representing potential fire drivers, such as weather, topography, and fuel, which includes vegetation, soil, and socioeconomic data. The majority of fires occurred in the last 5 years, with winter being the most prevalent season in coastal regions. ML models such as RF outperformed others and correctly predicted fire occurrence (AUC values of 0.84–0.93). Elevation, population, specific humidity, vapor pressure, and NDVI were all key factors; however, their contributions varied depending on locational clusters and seasons. The percentage shares of climatic conditions, fuel, and topographical variables at the country level were 55.2%, 31.8%, and 12.8%, respectively. This study identified the probable driving factors of Pakistan wildfires, as well as the probability of fire occurrences across the country. The analytical approach, as well as the findings and conclusions reached, can be very useful to policymakers, environmentalists, and climate change researchers, among others, and may help Pakistan improve its wildfire management and mitigation
... Wildfire risk is mainly associated with the number of ignitions that potentially result in large fires, considering that risk is a factor of hazard by the number of elements at risk, ignitions add to hazard, even though most don't result in large fires [20,21]. Ignition prevention associated with an adequate monitoring strategy represents a key element in fire risk reduction [22,23]. Several risk projection maps and techniques are based mostly on meteorological characteristics such as wind, humidity, and temperature; however, vegetation density and leaf litter, along with chlorophyll content and dryness, are factors of major importance, as has been thoroughly addressed in the literature [11,21,[24][25][26]. ...
Countries unaccustomed to wildfires are currently experiencing wildfire as a new climate-change reality. Understanding how fire ignition and propagation are correlated with temperature, orography, humidity, wind, and the mixture and age of individual plants must be considered when designing prevention strategies. While wildfire prevention focuses on fire ignition avoidance, firefighting success depends on early ignition detection, meaning that, in either case, ignition plays a major role. The current case study considered three Portuguese municipalities that annually observe frequent fire ignitions (Tomar, Ourém, and Ferreira do Zêzere) as the testing ground for the Modernized Dynamic Ignition Risk (MDIR) strategy, thus evaluating the efficiency of MDIR and the efficacy of the variables used. This methodology uses geographic information systems technology sustained by open-source satellite imagery, along with the Habitat Risk Assessment model from the InVEST software package, as drivers for the MDIR application. The MDIR approach grants frequent update capabilities and fully open-sourced high ignition risk area identification, producing monthly ignition risk maps. The advantage of using this method is the ease of adaptation to any current monitoring strategy, awarding further efficiency and efficacy in reducing ignitions. The approach delivered adequate results in estimating ignitions for the three Portuguese municipalities, achieving, for several months, prediction accuracy percentages of over 70%. For the studied area, MDIR clearly identifies areas of high ignition risk and delivers an average of 62% success in predicting ignitions, thus showing potential for analyzing the impact of policy implementation and monitoring through the strategy design.
... El análisis de peligro de incendios forestales toma especial relevancia por el aumento de la severidad de este tipo de eventos y la cada vez más estrecha relación de la propagación del fuego con la presencia o cercanía de centros poblados (Castillo, 2015;Costafreda et al., 2017). De esta manera, la importancia de estudiar la vulnerabilidad en zonas de interfaz urbano-forestal debe iniciarse, primero, con un catastro detallado de la condición de Peligro frente al comportamiento del fuego (CONAF, 2016). ...
Se analizó la condición de vulnerabilidad de edificaciones frente al impacto de los incendios forestales en áreas de interfaz localizadas en San José de Maipo, Región Metropolitana, Chile. Para este propósito recopilamos datos de población, estadísticas de incendios, normas de construcción, legislación vigente y en general una extensa red de información desde el año 2000 en adelante. Con estos antecedentes evaluamos las áreas con mayor presencia de viviendas en bosques y las principales normas de construcción vigentes, como también las características de resguardo frente al fuego para distintas categorías de construcción: viviendas, colegios, hospitales, y en general todo tipo de infraestructura crítica que cumple funciones de defensa y protección frente a catástrofes. Los resultados muestran una alta exposición de prácticamente todas las categorías de edificaciones, debido a que se localizan en áreas de máxima vulnerabilidad. Respecto a las construcciones existentes, se realizan propuestas para mejorar el entorno inmediato a las áreas con mayor vulnerabilidad. Se trata de una experiencia piloto en Chile, basada en los resultados del Proyecto FONDEF it16i10003 (Chile), sobre protección y defensa de edificaciones frente a incendios forestales en zonas de interfaz urbano forestal.
... Human activities are the primary source of wildfire ignitions globally (Costafreda-Aumedes et al. 2017), and human ignitions are increasing in the western United States where wildfires are increasing in frequency, size and severity (Williams et al. 2010;Dennison et al. 2014;Balch et al. 2017;Nagy et al. 2018). Models of wildfire under future climatic conditions predict dramatic increases in ignitions and area burned (Hawbaker and Zhu 2012) and consequently, greater societal exposure to extreme fire events in the Wildland Urban Interface (WUI) (Bowman et al. 2017). ...
Most wildfires are started by humans, however, geographic variation of potential ignition sources is not often explicitly accounted for in wildfire simulation modelling or risk assessments. In this study, we investigated how patterns of human and lightning ignitions can influence modelled fire simulations and demonstrate how these data can be used to assess post-fire flooding and sediment transport.Weusedhistoricalignitiondata(1992–2015) to characterize ignition patterns for thirteen mountain ranges in southern Arizona, United States, and developed FlamMap burn probability (BP) models for three scenarios: human ignition, lightning ignition, and random ignition. We then developed a watershed-scale case study assessing the impacts of ignition scenarios on post-fire hydrology using the KINEROS2 model that simulates runoff and erosion. BP models illustrated considerable differences in landscape fire risk between the three ignition scenarios. Results from the watershed model indicate the greatest impacts from the post-fire human ignition scenario, with a 10-fold increase in sediment discharge and four-fold increase in peak flow compared to pre-fire conditions. Our results show that consideration of ignition source and location is important for assessing fire risk, and our modelling approach provides a planning mechanism to identify locations most at risk to fire-induced flood hazards, where prevention and mitigation activities can be focused.
... Sineiro 2006, Gómez-Vazquez et al., 2009: Marey et al. 2010Prestemon et al., 2012;Barreal y Loureiro, 2013;Fuentes-Santos et al., 2013;Moreno et al., 2014;Chas-Amil et al., 2015;Boubeta et al., 2015;López-Rodríguez y Marey, 2017;Da Ponte et al., 2019). La mezcla, en un espacio reducido, de usos urbanos, agrícolas y forestales, crea interfaces que suponen áreas de fricción de intereses que favorecen la proliferación de incendios Costafreda et al., 2017). El influjo de la IUF en la aparición de igniciones es un hecho también documentado en otros estudios a diferentes escalas (Syphard et al., 2007;Vilar et al., 2008Vilar et al., , 2011Vilar et al., , 2016Rodrigues et al., 2014;Gallardo et al., 2015;Costafreda et al., 2018) si bien parece que sin una tendencia clara de cambio en el tiempo para el conjunto de España ( Vilar et al., 2016;Rodrigues et al., 2018). ...
... Nowadays, fire ignitions are mostly due to humans activities (i.e. accidental or intentional ignitions) (Curt et al. 2016;Costafreda-Aumedes et al. 2018). Associated to the warmer and dryer climate, these conditions will strongly contribute to increase wildfire frequency and intensity in the Mediterranean region and likely the occurrence of large fires and megafires (Mouillot et al. 2002;Pausas 2004;Giannakopoulos et al. 2005;Tedim et al. 2013;Batllori et al. 2013;Lahaye et al. 2018;Lestienne et al. 2020b). ...
In the Mediterranean basin, Corsica (French island) harbours among the best-preserved Mediterranean forest ecosystems. However, its high biodiversity could be threatened by the climate and disturbance-regime changes due to the global warming. This study aims (i) to estimate the future climate-related fire hazard in Corsica for the current century (2020–2100) based on two RCP scenarios (RCP4.5 and RCP8.5) and (ii) to compare the predicted trends with the entire Holocene period for which fire hazard has previously been assessed. An ensemble of future climate simulations from two IPCC RCP scenarios has been used to compute the Monthly Drought Code (MDC) and the Fire Season Length (FSL) and to assess the level of fire hazard. Here, we show that the MDC and the FSL would both strongly increase over the next decades due to the combined effect of temperature increase and precipitation decrease in the Corsica region. Moreover, the maximum Holocene FLS (7000 to 9000 years ago) will be reached (and even exceeded depending upon the scenario) after 2040. For the first time in the Holocene, we may be confronted to an increase in the number of fire-prone months driven by climate combined with many human-caused ignitions. This combination should increase the burned area from 15 to 140% according to scenarios. For the next 30 years, the game seems to be already played as both RCP scenarios resulted in similar increase in fire hazard in terms of drought and duration. It is thus mandatory to reconsider fire-management and fire-prevention policy to mitigate the future fire risk and its catastrophic consequences for ecosystems, population, and economy.
... Nowadays, re ignitions are mostly due to humans activities (i.e. accidental or intentional ignitions) (Curt et al. 2016;Costafreda-Aumedes et al. 2018). Associated to the warmer and dryer climate, these conditions will strongly contribute to increase wild re frequency and intensity in the Mediterranean region, and likely the occurrence of large res and mega res (Mouillot et al. 2002;Pausas 2004;Giannakopoulos et al. 2005;Tedim et al. 2013;Batllori et al. 2013;Lahaye et al. 2018;Lestienne et al. 2020b). ...
In the Mediterranean basin, Corsica (French island) harbours among the best-preserved Mediterranean forest ecosystems. However, its high biodiversity could be threatened by the climate and disturbance-regime changes due to the global warming. This study aims (i) to estimate the future climate-related fire hazard in Corsica for the current century (2020–2100) based on two RCP scenarios (RCP4.5 and RCP8.5) and (ii) to compare the predicted trends with the entire Holocene period for which fire hazard has previously been assessed. An ensemble of future climate simulations from two IPCC RCP scenarios has been used to compute the Monthly Drought Code (MDC) and the Fire Season Length (FSL) and to assess the level of fire hazard. Here, we show that the MDC and the FSL would both strongly increase over the next decades due to the combined effect of temperature increase and precipitation decrease in the Corsica region. Moreover, the maximum Holocene FLS (7000 to 9000 years ago) will be reached (and even exceeded depending upon the scenario) after 2040. For the first time in the Holocene, we may be confronted to an increase in the number of fire-prone months driven by climate combined with many human-caused ignitions. This combination should increase the burned area from 15 to 140% according to scenarios. For the next 30 years, the game seems to be already played as both RCP scenarios resulted in similar increase in fire hazard in terms of drought and duration. It is thus mandatory to reconsider fire-management and fire-prevention policy to mitigate the future fire risk and its catastrophic consequences for ecosystems, population, and economy.
... Given the complexity of factors influencing a wildfire spread, diverse features have been explored. Anthropogenic factors like population, roads, power lines, and developments and geophysical variables such as meteorological and climatic factors (e.g., temperature, precipitation, drought, wind), vegetation type and density, and topography are considered in most of the existing studies (Amraoui et al. 2015;Costafreda-Aumedes et al. 2018;Rodrigues et al. 2018;Shen et al. 2019). ...
Wildfire spread is a stochastic phenomenon driven by a multitude of geophysical and anthropogenic factors. In this study, we propose a spatiotemporal data-driven risk assessment framework to understand the effect of various geophysical/anthropogenic factors on wildfire size, leveraging a systematic machine learning approach. We apply this framework in the state of California–the most vulnerable US state to wildfires. Using county-level annual wildfire data from 2001–2015, and various geophysical (e.g., land cover, wind, surface temperature) and anthropogenic features (e.g., population density, housing type), we trained, tested, and validated a suite of ensemble tree-based learning algorithms to identify and evaluate the key factors associated with wildfire size. The Extreme Gradient Boosting (XGBoost) algorithm outperformed all the other models in terms of generalization performance, categorization of important features, and risk performance. We found that standard deviations of meteorological variables with long-tailed distributions play a key role in predicting wildfire size. Specifically, the top ten factors associated with high risk of larger wildfires include larger standard deviations of surface temperature and vapor pressure deficit, higher wind gust, more grassy and barren land covers, lower night-time boundary layer height and higher population density. Our proposed risk assessment framework will help federal/state decision-makers to adequately plan for wildfire risk mitigation and resource allocation strategies.
... Collecting field data at a local level is time-consuming. The synthesis of data from multiple local studies into a global data set could provide one route to obtaining sufficient data to parameterise models, whether these are ABMs, economic models or fire-enabled vegetation models, but the quality of such a synthesis depends to some extent on whether the same information has been collected and whether the same data-collection methods have been applied (Costafreda-Aumedes et al., 2017). Many placebased models of fire knowledge are not framed in the quantitative way required for use by global fire models, for example. ...
Although it has long been recognised that human activities affect fire regimes, the interactions between humans and fire are complex, imperfectly understood, constantly evolving, and lacking any kind of integrative global framework. Many different approaches are used to study human-fire interactions, but in general they have arisen in different disciplinary contexts to address highly specific questions. Models of human-fire interactions range from conceptual local models to numerical global models. However, given that each type of model is highly selective about which aspects of human-fire interactions to include, the insights gained from these models are often limited and contradictory, which can make them a poor basis for developing fire-related policy and management practices. Here, we first review different approaches to modelling human-fire interactions and then discuss ways in which these different approaches could be synthesised to provide a more holistic approach to understanding human-fire interactions. We argue that the theory underpinning many types of models was developed using only limited amounts of data and that, in an increasingly data-rich world, it is important to re-examine model assumptions in a more systematic way. All of the models are designed to have practical outcomes but are necessarily simplifications of reality and as a result of differences in focus, scale and complexity, frequently yield radically different assessments of what might happen. We argue that it should be possible to combine the strengths and benefits of different types of model through enchaining the different models, for example from global down to local scales or vice versa. There are also opportunities for explicit coupling of different kinds of model, for example including agent-based representation of human actions in a global fire model. Finally, we stress the need for co-production of models to ensure that the resulting products serve the widest possible community.
... A specific fire risk model may not be widely applicable on a global scale due to the different environmental conditions under which the model is developed [58]. Utilizing appropriate publicly available data to develop a model, the generality of the model application and developing method may be enhanced to some extent [59][60][61]. In other words, the model development method could be applied in other places when developing such a model with its publicly available data. ...
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%.
... Sineiro 2006, Gómez-Vazquez et al., 2009: Marey et al. 2010Prestemon et al., 2012;Barreal y Loureiro, 2013;Fuentes-Santos et al., 2013;Moreno et al., 2014;Chas-Amil et al., 2015;Boubeta et al., 2015;López-Rodríguez y Marey, 2017;Da Ponte et al., 2019). La mezcla, en un espacio reducido, de usos urbanos, agrícolas y forestales, crea interfaces que suponen áreas de fricción de intereses que favorecen la proliferación de incendios Costafreda et al., 2017). El influjo de la IUF en la aparición de igniciones es un hecho también documentado en otros estudios a diferentes escalas (Syphard et al., 2007;Vilar et al., 2008Vilar et al., , 2011Vilar et al., , 2016Rodrigues et al., 2014;Gallardo et al., 2015;Costafreda et al., 2018) si bien parece que sin una tendencia clara de cambio en el tiempo para el conjunto de España ( Vilar et al., 2016;Rodrigues et al., 2018). ...
Every year, an average of 250 forest fires occur in Turkey and 10,000 hectares of forest area are destroyed by natural and human-caused forest fires. Moreover, 90% of the world's pine honey production is produced in red pine (Pinus brutia) forests infested with Marchalina Hellenica. However, the limited production sites for pine honey are destroyed by forest fires and most of the sites overlap with the regions where susceptibility to forest fires is highest. In particular, in 2021 and 2022, pine honey production in Muğla province decreased by half due to the large forest fires.
In this study, susceptibility to forest fires caused by lightning, cigarette butts, stubble burning and power lines was modeled separately for all pine honey production sites via MaxEnt. Each risk map overlapped with the Marchalina Hellenica distribution map to determine which fire causes put each region at risk. When the results were examined, 1357.6 km² (56.6%) of the 2396 km² pine honey production site was found to be at risk from lightning-caused forest fires. For human-caused forest fires, 184.7 km² (7.7%) were at risk from power lines and 136 km² (5.7%) from stubble fires. 116.8 km² of pine honey production areas are threatened by forest fires caused by cigarette butts, which is the least threatening cause in the study area. The findings obtained in this study provide important information on the measures that can be taken against forest fires and on the planning of early intervention procedures to protect pine honey production areas.
Forest fires are becoming a serious concern in Central European countries such as Austria (AT) and the Czech Republic (CZ). Mapping fire ignition probabilities across countries can be a useful tool for fire risk mitigation. This study was conducted to: (i) evaluate the contribution of the variables obtained from open-source datasets (i.e., MODIS, OpenStreetMap, and WorldClim) for modeling fire ignition probability at the country level; and (ii) investigate how well the Random Forest (RF) method performs from one country to another. The importance of the predictors was evaluated using the Gini impurity method, and RF was evaluated using the ROC-AUC and confusion matrix. The most important variables were the topographic wetness index in the AT model and slope in the CZ model. The AUC values in the validation sets were 0.848 (AT model) and 0.717 (CZ model). When the respective models were applied to the entire dataset, they achieved 82.5% (AT model) and 66.4% (CZ model) accuracy. Cross-comparison revealed that the CZ model may be successfully applied to the AT dataset (AUC = 0.808, Acc = 82.5%), while the AT model showed poor explanatory power when applied to the CZ dataset (AUC = 0.582, Acc = 13.6%). Our study provides insights into the effect of the accuracy and completeness of open-source data on the reliability of national-level forest fire probability assessment.
Wildfires are becoming an increasing challenge to the sustainability of boreal peatland (BP) ecosystems and can alter the stability of boreal carbon storage. However, a quantitative understanding of natural and anthropogenic influences on the changes in BP fires remains elusive. Here, we quantified the predictability of BP fires and their primary controlling factors from 1997 to 2016 using a two-step correcting machine learning (ML) framework that combines multiple ML classifiers, regression models, and an error-correcting technique. We found that (1) the adopted oversampling algorithm effectively addressed the unbalanced data and improved the recall rate by 26.88 %–48.62 % when using multiple datasets, and the error correcting technique tackled the overestimation of fire sizes during fire seasons, (2) non-parametric models outperformed parametric models in predicting fire occurrences, and the machine learning model of Random Forest performed the best with the area under the Receiver Operating Characteristic curve ranging from 0.83 to 0.93 across multiple fire data sets, and (3) four sets of factor-control simulations consistently indicated the dominant role of temperature, air dryness, and climate extreme (i.e., frost) for boreal peatland fires, overriding the effects of precipitation, wind speed, and human activities. Our findings demonstrate the efficiency and accuracy of ML techniques in BP fire prediction and disentangle the primary factors determining BP fires, which are critical for predicting future fire risks under climate change.
The appropriate planning of infrastructure protects people’s lives and property. Fire stations are an essential part of a city’s infrastructure and they must be precisely located to shorten emergency response times and reduce casualties. Recently, the focus of the city emergency service has shifted from fire suppression to technical rescues. We compared the spatial distribution of fire suppression and technical rescues at a city scale to show the variation in their influences. An integrated road-network accessibility and location-allocation model (RNALA) for the location planning of a fire station was proposed. Specific sites for fire stations were identified using the L-A model. Then, the spatial design network analysis was performed to quantify areas around the selected site with high road network accessibility. The RNALA model was used to extend the selection from a point to a region by introducing road network accessibility to accomplish coverage and efficiency requirements. A quantitative and universal approach that focuses on fire station location planning based on emergency services is proposed. This methodology provides a practical solution for implementation, as a specific identified location might not be available for implementation. These results can serve as a reference for identifying fire station locations in cities.
Lesná pôda je už dlho uznávaná ako primárny vstup do výroby a ako zásobáreň bohatstva. Napriek jej zásadnej úlohe v takmer všetkých hospodárskych činnostiach neexistuje aktuálny a úplný odhad hodnoty lesnej pôdy. Preto sme vytvorili publikáciu, ktorá zapĺňa túto medzeru. Čítate publikáciu, v ktorej sa navrhuje nová inovatívna metóda oceňovania lesnej pôdy v podmienkach klimatických zmien. Klimatické zmeny majú zásadný vplyv na špecifické riziká lesného hospodárstva. Včasná a presná identifikácia týchto rizík pre lesné hospodárstvo môže znížiť ekonomické straty vlastníkov lesov, lesníkov a ďalších záujemcov o investície do lesného hospodárstva, ale aj celého lesníckeho sektora. Z tohto dôvodu vám prinášame publikáciu s názvom: Oceňovanie lesnej pôdy v podmienkach rizika výskytu požiarov na lesnej pôde a klimatických zmien, v ktorej sa dočítate všetky aspekty, ktoré potrebujete poznať, aby ste ich mohli aplikovať na zhodnotenie kapitálovej hodnoty lesnej pôdy v podmienkach rizika, ktoré prináša so sebou klimatická zmena. Kniha si nájde čitateľov aj medzi študentmi lesníctva a ekonómie lesníctva v rámci ich doktorandského štúdia. Veríme, že obsah by mohol byť užitočný a použiteľný v oblasti poistenia lesov proti požiarom a pri tvorbe nových postupov oceňovania lesných pozemkov.
Background Wildfire is a major environmental threat worldwide and climate change is expected to increase its severity. Galicia has suffered high wildfire incidence during the last decades, most wildfires being from arson, in contrast with the low rate of natural wildfires. Aim This work aims to characterise the spatiotemporal dynamics of human-caused and natural fires in Galicia. Methods We apply first- and second-order non-parametric inference to spatiotemporal wildfire point patterns. Key results The distribution of natural wildfires remained stable over years, with high incidence in summer and in the eastern area of Galicia. Arson wildfires had aggregated patterns, with strong interaction between outbreaks and fires, and their distribution varied both over and within years, with high incidence shifting between the southern and western areas, and high hazard in early spring and late summer. Negligence wildfire patterns showed short-distance aggregation, but large-distance aggregation between outbreaks and fires; their spatial distribution also varied between and within years. Conclusions Different models and covariates are required to predict the hazard from each wildfire type. Natural fires are linked to meteorological and environmental factors, whereas socioeconomic covariates are crucial in human-caused wildfires. Implications These results are the basis for the future development of predictive spatiotemporal point process models for human-caused wildfires.
Wildfires have changed in recent decades. The catastrophic wildfires make it necessary to have accurate predictive models on a country scale to organize firefighting resources. In Mediterranean countries, the number of wildfires is quite high but they are mainly concentrated around summer months. Because of seasonality, there are territories where the number of fires is zero in some months and is overdispersed in others. Zero-inflated negative binomial mixed models are adapted to this type of data because they can describe patterns that explain both number of fires and their non-occurrence and also provide useful prediction tools. In addition to model-based predictions, a parametric bootstrap method is applied for estimating mean squared errors and constructing prediction intervals. The statistical methodology and developed software are applied to model and to predict number of wildfires in Spain between 2002 and 2015 by provinces and months.
O período entre 2018 e 2022 mostrou-nos que o problema dos incêndios à escala global não está a diminuir, antes pelo contrário. Parece que as consequências das alterações climáticas já estão a afectar a ocorrência de incêndios florestais em várias partes do Mundo, de uma forma que só esperaríamos que acontecesse vários anos mais tarde. Em muitos países do Sul da Europa, bem como em algumas regiões dos EUA, Canadá e Austrália, onde estamos habituados a enfrentar a presença de incêndios muito grandes e devastadores, continuamos a ter eventos que quebram recordes. Alguns países, como os da Europa Central e do Norte, que não estavam habituados a ter grandes incêndios, experimentaram-nos durante estes anos. Os anos anteriores foram muito exigentes para todo o Mundo, também noutros aspectos que nos afectaram a todos. Referimo-nos às restrições impostas pela pandemia que limitaram as nossas reuniões e viagens, afectando em muitos casos a saúde dos membros da Comunidade Científica Wildfire. Felizmente, conseguimos encontrar novas formas de comunicação, ultrapassar essas limitações e manter-nos em contacto uns com os outros. Durante semanas e meses, para muitos de nós, as reuniões pessoais e o trabalho de grupo foram substituídos por ligações em linha. Apesar da economia de dinheiro e tempo, e da facilidade de reunir uma grande variedade de pessoas que estas reuniões desde que nos apercebêssemos de que não substituem as reuniões presenciais, que trazem consigo outras dimensões inestimáveis, que fazem parte da comunicação pessoal e ajudam a construir uma comunidade científica.
The first statewide wildland-urban interface (WUI) maps were created for Alaska in 2000 and 2010. • Alaska experienced expanding WUI area and rapid housing growth within WUI from 2000 to 2010. • Housing density was the dominant contributor to WUI change. • As the distance from WUI increased, both human and lightning ignition density decreased but the percentage of fire perimeters increased within 30 km from WUI. A B S T R A C T Climate change is exacerbating the fire activity in Alaska, which exposes lives and properties to great risk, especially residents living in Wildland-Urban Interface (WUI). Therefore, it is crucial to characterize the spatial distribution and temporal dynamics of WUI and assess its impacts on fire activity. However, existing WUI delineations in Alaska do not cover all communities and apply different mapping approaches, making it difficult to examine the WUI distribution and dynamics across the state. This study created the first statewide WUI map using census data and National Land Cover Database, and characterized the dynamics of WUI from 2000 to 2010. Furthermore, the relationship between WUI and fire was identified using fire ignition and fire perimeter datasets from Alaska Interagency Coordination Center. The findings showed WUI that only covered 0.22 % of the total area in Alaska contained 73.45 % of the housing units. Nearly 85 % of newly added WUI housing units were found in WUI, and the growth rates in WUI housing units far exceeded that in non-WUI. As the distance from WUI increased, both human and lightning ignition density decreased but the percentage of fire perimeters increased within 30 km from WUI. Our results demonstrated the importance of tracking the dynamics of WUI and characterizing the social change behind the pattern to strengthen wildfire preparedness and facilitate community-adapted management.
Background: In the Spanish region of Galicia, one of the most fire-prone areas in Europe, most wildfires are directly or indirectly related to human activities, so socioeconomic conditions and population characteristics become determinant in wildfire management. Socioeconomic factors could also help explain the causes and distribution of wildfires spatially and temporally within the region. We sought to improve the temporal and spatial understanding of the causes of forest land wildfires in Galicia by analyzing the importance of socioeconomic and natural variables over the wildfire ignitions and hectares burned during 2001-2015. We established the municipality as the smallest geographical section with readily available information on socioeconomic factors and forest land wildfires.
Results: We used clustering to analyze the spatial dimension and regression analysis of panel data to investigate the temporal dimension. Through the cluster analysis, we divided the region interterritorially according to its socio-economic behavior; nevertheless, our results suggest that the geographical distribution of the municipalities belonging to the four clusters has a similar pattern to that of the Galician provinces. Our regression models for each cluster indicate that several socioeconomic factors are at least correlated with and may tend to influence wildfire occurrence and burned area in Galicia. We also found discernable patterns related to our identified clusters, confirming that differences between territories exist regarding the likely influence of socioeconomic factors on the number of wildfire ignitions and hectares burned.
Conclusions: Results suggest that explanatory socioeconomic variables are as crucial as meteorological variables in wildfire ignitions and burned area and that an accurate knowledge of inter-territorial socioeconomic differences could help to design wildfire prevention policies best suited to the socioeconomic, cultural, and environmental circumstances of each territory.
In the last decades, natural fire regimes have experienced significant alterations in terms of intensity, frequency and severity in fire prone regions of the world. Modelling forest fire susceptibility has been essential in identifying areas of high risk to minimize threats to natural resources, biodiversity and life. There have been significant improvements in forest fire susceptibility modelling over the past two decades 2001–2021. In this study, we conducted a systematic literature review of literature covering forest fire susceptibility modelling published during this period. The review provides insights on the main themes of forest fire susceptibility modelling research, the main base input factors used in models to map forest fire susceptibility, the main researchers, the areas where this type of research were implemented, technology and models used. It also highlights collaboration opportunities, and regions, such as Central America and Africa, where mapping of forest fire susceptibility is needed. We argue that such knowledge is crucial in order to identify critical factors and opportunities which can aid in improving factor selection and forest fire management.
Spatial wildfire ignition predictions are needed to ensure efficient and effective wildfire response, and robust methods for modeling new wildfire occurrences are ever-emerging. Here, ignition locations of natural and human-caused wildfires across the state of Montana (USA) from 1992 to 2017 were intersected with static, 30 m resolution spatial data that captured topography, fuel availability, and human transport infrastructure. Once combined, the data were used to train several simple and multiple logistic generalized linear models (GLMs) and generalized additive models (GAMs) to predict the spatial likelihood of natural and human-caused ignitions. Increasingly more complex models that included spatial smoothing terms were better at distinguishing locations with and without natural and human-caused ignitions, achieving area under the receiver operating characteristic curves (AUCs) of 0.84 and 0.89, respectively. Whilst both ignition types were more likely to occur at intermediate fuel loads, as characterized by the local maximum Normalized Difference Vegetation Index (NDVI), naturally-ignited wildfires were more locally influenced by slope, while human-caused wildfires were more locally influenced by distance to roads. Static maps of ignition likelihood were verified by demonstrating that mean annual ignition densities (# yr−1 km−1) were higher within areas of higher predicted probabilities. Although the spatial models developed herein only address the static component of wildfire hazard, they provide a foundation upon which dynamic data can be superimposed to forecast and map wildfire ignition probabilities statewide on a timely basis.
This chapter compares fuzzy-machine learning algorithms for predicting fire outbreaks using temperature, smoke, and flame datasets. The datasets are preprocessed using interval type-2 fuzzy logic (IT2FL). Min-max normalization and principal component analysis (PCA) are used to predict, normalize, and select relevant feature labels in the dataset. The preprocessed datasets are used to train (80%) and test (20%) K-nearest neighbor (KNN), support vector machine (SVM), random forest (RF), linear discriminant analysis (LDA), and classification and regression tree (CART). K-fold cross-validation is used to evaluate the performance of the models using the receiver operating curve (ROC), specificity and sensitivity matrices. The validation result shows that KNN performs better with ROC values of 0.99878 against 0.99753, 0.997265, 0.997073, and 0.958693 for SVM, RF, LDA, and CART, respectively. It is also observed that KNN outperforms SVM, RF, LDA, and CART in predicting fire outbreaks with the highest degree of accuracy of 0.9643 against 0.9571, 0.95, 0.9429, and 0.9571, respectively.
Wildfires not only severely damage the natural environment and global ecological balance but also cause substantial losses to global forest resources and human lives and property. Unprecedented fire events such as Australia’s bushfires have alerted us to the fact that wildfire prediction is a critical scientific problem for fire management. Therefore, robust, long‐lead models and dynamic predictions of wildfire are valuable for global fire prevention. However, despite decades of effort, the dynamic, effective and accurate prediction of wildfire remains problematic. There is great uncertainty in predicting the future based on historical and existing spatiotemporal sequence data, but with advances in deep learning algorithms, solutions to prediction problems are being developed. Here, we present a dynamic prediction model of global burned area of wildfire employing a deep neural network (DNN) approach that produces effective wildfire forecasts based on historical time series predictors and satellite‐based burned area products. A hybrid DNN that combines long short‐term memory and a two‐dimensional convolutional neural network (CNN2D‐LSTM) was proposed, and CNN2D‐LSTM model candidates with four different architectures were designed and compared to construct the optimal architecture for fire prediction. The proposed model was also shown to outperform convolutional neural networks (CNNs) and the fully connected long short‐term memory (FcLSTM) approach using the refined index of agreement and evaluation metrics. We produced monthly global burned area spatiotemporal prediction maps and adequately reflected the seasonal peak in fire activity and highly fire‐prone areas. Our combined CNN2D‐LSTM approach can effectively predict the global burned area of wildfires one month in advance and can be generalized to provide seasonal estimates of global fire risk.
Forest fires have become a habitual threat in all types of ecosystems, which is the reason why it is necessary to improve management of the territories and optimization of prevention and means of extinction. This study compares three machine learning techniques: logistic regression, logistic decision tree, and multivariate adaptive regression spline to identify areas susceptible to forest fires in the Loja canton. In the training of the machine learning models, a multitemporal database with 1436 points was used, fed with the information from seven variables related to fuel moisture, proximity to anthropic activities, and ground elevation. After analyzing the performance of the three models, better results were observed with the LMT, thus offering application ease for local decision-makers. The results show that the technique used allowed generating a model with a good predictive capacity and that the maps resulting from the model can be updated in short periods of time. However, it is necessary to highlight the lack meteorological data availability at the local level and to encourage future researchers to implement improvements in this regard.
The damage and effects of fire on the native vegetation of Central Chile have been increasing in recent years due to drought conditions and the state of abandonment of the Mediterranean forests. The objective of this research was to study the damage and effects of fire in four regions of Central Chile, in order to establish adequate post-fire restoration proposals. The method consisted of the analysis of eight data sampling areas on native plant species affected by fire and sectors for statistical comparison (not burned). Consequently, it was possible to establish physical restoration measures and recovery proposals based on planting native species of the place affected by the fire. In conclusion, it can be indicated that Mediterranean landscapes need measures to accelerate the process of ecological restoration, due to the scarce recovery of vegetation due to the increase in the recurrence and intensity of forest fires.
Forest policy increasingly mobilizes the forest sector to address environmental concerns. Owing to the forest sector’s complexity and time scales involved, simulation models are often used as research methods to explore the future. This thesis investigates the contributions of Forest Sector Models (FSM), bio-economic simulation models commonly used for prospective analysis, to this transition.We first adopt a conceptual perspective and, through a parallel exploration of the early literature in forest economics and the epistemology of model use, we show that forest policy has been, and still is, a strong driver of FSM research, influencing representation processes in models as well as narratives used to drive research. We also highlight that the nature of facts within the forest sector, the local context, data availability and past practices are other important determinants of model-based research. We subsequently review more recent literature to assess the extent to which environmental issues have been addressed. While originally focused on timber production and trade, a majority of the research now focuses on goals such as renewable energy production or the conservation of biodiversity. The treatment of such objectives has however been unequal, and those closer to the models’ original target are treated more often and more deeply. On the contrary, modelling is hindered when economic values are hard to estimate or when models cannot handle spatialized data, hence objectives related to cultural and some regulation services are less commonly studied.The remainder of the thesis addresses two aspects of climate change, namely mitigation and adaptation, and brings methodological contributions by leveraging two ways of overcoming obstacles to the investigation of environmental objectives with large-scale bio-economic models: model couplings and the consideration of local environmental conditions. Both chapters focus on France, where the diversity of local contexts makes analyses focused on the upstream forest sector relevant, and use the French Forest Sector Model (FFSM).First, using the FFSM and Hartman’s model for optimal rotations with non-timber amenities, we investigate consequences for forestry and landscapes of management practices aiming at both producing timber and sequestering carbon. We show that, while postponing harvests can increase carbon stocks in the short-term, changes in management regimes and species choice yield additional benefits in the long-term. Over time, these changes lead to more diverse forest landscapes in terms of composition and structure, with potential implications for policy and environmental co-benefits. However, trends show a high level of spatial variability across and within regions, highlighting the importance of considering the local context.In-situ carbon stocks are however exposed to risks of non-permanence. We assess implications for the forest sector of climate-induced changes in wildfire regimes, as well as implications for model projections of uncertainties related to these changes. To do so, we use a probabilistic model of wildfire activity, which we couple to the FFSM, and we carry out multiple simulations using various radiative forcing levels and different climate models. Although locally significant, wildfires’ impacts remain limited at the sectoral scale. Fires affect a limited amount of the resource every year but in a cumulative manner, and the influence of climate change is mostly witnessed in the latter half of the century. Inter-annual fluctuations in fire activity only marginally propagate to the forest sector, and most uncertainty comes from the choice of climate models and scenarios. Stochasticity in the fire process, although never predominant, accounts for a significant share of uncertainty. These results stress the importance of considering multiple possible outcomes and the inherent variability in environmental processes in large-scale model projections.
Overall decline of global burned area paradoxically hides a number of economic realities that have increased the likelihood and costs of wildfire-caused disasters. In this critical review, we address the pressing need to identify and incorporate economic elements shaping global wildfire activities. To synthesize our current understanding of economic drivers of wildfires, we leverage the DPSIR framework to structure the issues related to wildfires to establish coherent causal pathways between Drivers (D), Pressures (P), States (S), Impacts (I) and Responses (R). We identified global patterns of worsening wildfire risks with the double-exposure to globalization and climate change. Current developments call for a paradigm shift in how we understand and manage wildfires to promote an adaptation-mitigation-resilience strategy. We propose expanding the science-policy interface to global scale with new indicators for assessing and communicating the impacts of global economic drivers on wildfire activities, such as “Virtual wildfire trade” accounting to monitor delocalized fire activity—exported fires and land transformation from developed to developing regions with weak governance. We also identified the areas where research is lacking, highlighting future research areas in wildfire economics to advance effective, efficient, and equitable global governance of wildfires.
We implemented a fire risk assessment framework that combines spatially-explicit burn probabilities, post-fire mortality models and public auction timber prices, to estimate expected economic losses from wildfires in 155 black pine stands covering about 450 ha in the Juslapeña Valley of central Navarra, northern Spain. A logit fire occurrence model was generated from observed historic fires to provide required fire ignition input data. Wildfire likelihood and intensity were estimated by modeling 50,000 fires with the minimum travel time algorithm (MTT) at 30 m resolution under 97th percentile fire weather conditions. Post-fire tree mortality due to burning fire intensity at different successional stages ranged from 0.67% in the latest stages to 9.22% in the earliest. Stands showed a wide range of potential economic losses, and intermediate successional stage stands presented the highest values, with about 124 € ha− 1 on average. A fire risk map of the target areas was provided for forest management and risk mitigation purposes at the individual stand level. The approach proposed in this work has a wide potential for decision support, policy making and risk mitigation in southern European commercial conifer forests where large wildfires are the main natural hazard.
Identifying areas that have a high risk of burning is a main component of fire management planning. Although the available tools can predict the fire risks, these are poor in accommodating uncertainties in their predictions. In this study, we accommodated uncertainty in wildfire prediction using Bayesian belief networks (BBNs). An influence diagram was developed to identify the factors influencing wildfire in arid and semi-arid areas of Iran, and it was populated with probabilities to produce a BBNs model. The behavior of the model was tested using scenario and sensitivity analysis. Land cover/use, mean annual rainfall, mean annual temperature, elevation, and livestock density were recognized as the main variables determining wildfire occurrence. The produced model had good accuracy as its ROC area under the curve was 0.986. The model could be applied in both predictive and diagnostic analysis for answering “what if” and “how” questions. The probabilistic relationships within the model can be updated over time using observation and monitoring data. The wildfire BBN model may be updated as new knowledge emerges; hence, it can be used to support the process of adaptive management.
Human impact on wildfires, a major earth system component, remains
poorly understood. While local studies have found more fires close
to settlements and roads, assimilated charcoal records and analyses
of regional fire patterns from remote-sensing observations point to
a decline in fire frequency with increasing human population. Here,
we present a global analysis using three multi-year satellite-based
burned-area products combined with a parameter estimation and
uncertainty analysis with a non-linear model. We show that at the
global scale, the impact of increasing population density is mainly
to reduce fire frequency. Only for areas with up to 0.1 people
per km2, we find that fire frequency increases by 10 to
20% relative to its value at no population. The results are
robust against choice of burned-area data set, and indicate that at
only very few places on earth, fire frequency is limited by human
ignitions. Applying the results to historical population estimates
results in a moderate but accelerating decline of global burned area
by around 14% since 1800, with most of the decline since 1950.
Human-caused wildfires are often regarded as unpredictable, but usually occur in patterns aggregated over space and time. We analysed the spatio-temporal configuration of 7790 anthropogenic wildfires (2007–2013) in nine study areas distributed throughout Peninsular Spain by using the Ripley’s K-function. We also related these aggregation patterns to weather, population density, and landscape structure descriptors of each study area. Our results provide statistical evidence for spatio-temporal structures around a maximum of 4 km and six months. These aggregations lose strength when the spatial and temporal distances increase. At short time lags after a wildfire (<1 month), the probability of another fire occurrence is high at any distance in the range of 0–16 km. When considering larger time lags (up to two years), the probability of fire occurrence is high only at short distances (>3 km). These aggregated patterns vary depending on location in Spain. Wildfires seem to aggregate within fewer days (heat waves) in warm and dry Mediterranean regions than in milder Atlantic areas (bimodal fire season). Wildfires aggregate spatially over shorter distances in diverse, fragmented landscapes with many small and complex patches. Urban interfaces seem to spatially concentrate fire occurrence, while wildland-agriculture interfaces correlate with larger aggregates.
The socio-economic factors are of key importance during all phases of wildfire management that include prevention, suppression and restoration. However, modeling these factors, at the proper spatial and temporal scale to understand fire regimes is still challenging. This study analyses socio-economic drivers of wildfire occurrence in central Spain. This site represents a good example of how human activities play a key role over wildfires in the European Mediterranean basin. Generalized Linear Models (GLM) and machine learning Maximum Entropy models (Maxent) predicted wildfire occurrence in the 1980s and also in the 2000s to identify changes between each period in the socio-economic drivers affecting wildfire occurrence. GLM base their estimation on wildfire presence-absence observations whereas Maxent on wildfire presence-only. According to indicators like sensitivity or commission error Maxent outperformed GLM in both periods. It achieved a sensitivity of 38.9% and a commission error of 43.9% for the 1980s, and 67.3% and 17.9% for the 2000s. Instead, GLM obtained 23.33, 64.97, 9.41 and 18.34%, respectively. However GLM performed steadier than Maxent in terms of the overall fit. Both models explained wildfires from predictors such as population density and Wildland Urban Interface (WUI), but differed in their relative contribution. As a result of the urban sprawl and an abandonment of rural areas, predictors like WUI and distance to roads increased their contribution to both models in the 2000s, whereas Forest-Grassland Interface (FGI) influence decreased. This study demonstrates that human component can be modelled with a spatio-temporal dimension to integrate it into wildfire risk assessment.
Fire regimes are strongly dependent on human activities. Understanding the relative influence of human factors on wildfire is an important ongoing task especially in human-dominated landscapes such as the Mediterranean, where anthropogenic ignitions greatly surpass natural ignitions and human activities are modifying historical fire regimes. Most human drivers of wildfires have a temporal dimension, far beyond the appearance of change, and it is for this reason that we require an historical/temporal analytical perspective coupled to the spatial dimension. In this paper, we investigate and analyze spatial–temporal changes in the contribution of major human factors influencing forest fire occurrence, using Spanish historical statistical fire data from 1988 to 2012. We hypothesize that the influence of socioeconomic drivers on wildfires has changed over this period. Our method is based on fitting yearly explanatory regression models—testing several scenarios of wildfire data aggregation—using logit and Poisson generalized linear models to determine the significance thresholds of the covariates. We then conduct a trend analysis using the Mann–Kendall test to calculate and analyze possible trends in the explanatory power of human driving factors of wildfires. Finally, Geographically Weighted Regression Models are explored to examine potential spatial–temporal patterns. Our results suggest that some of the explanatory factors of logistic models do vary over time and that new explanatory factors might be considered (such as arson-related variables or climate factors), since some of the traditional ones seem to be losing significance in the presence–absence models, opposite to fire frequency models. In particular, the wildland–agricultural interface and wildland–urban interface appear to be losing explanatory power regarding ignition probability, and protected areas are becoming less significant in fire frequency models. GWR models revealed that this temporal behavior is not stationary neither over space nor time.
Context
Fire is an important driver of ecological processes in semiarid systems and serves a vital role in shrub-grass interactions. In desert grasslands of the southwestern US, the loss of fire has been implicated as a primary cause of shrub encroachment. Where fires can currently be re-introduced given past state changes and recent restoration actions, however, is unknown and controversial.
Objectives
Our objective was to evaluate the interactive effects of climate, urban development, and topo-edaphic properties on fire distribution in the desert grassland region of the southwestern United States.
Methods
We characterized the spatial distribution of fire in the Chihuahuan Desert and Madrean Archipelago ecoregions and investigated the influence of soil properties and ecological site groups compared to other commonly used biophysical variables using multi-model inference.
Results
Soil-landscape properties significantly influenced the spatial distribution of fire ignitions. Fine-textured bottomland ecological site classes experienced more fires than expected in contrast to upland sites with coarse soil textures and high fragment content that experienced fewer fire ignitions than expected. Influences of mean annual precipitation, distance to road/rail, soil available water holding capacity (AWHC) and topographic variables varied between ecoregions and political jurisdictions and by fire season. AWHC explained more variability of fire ignitions in the Madrean Archipelago compared to the Chihuahuan Desert.
Conclusions
Understanding the spatiotemporal distribution of recent fires in desert grasslands is needed to manage fire and predict responses to climate change. The use of landscape units such as ecological sites presents an opportunity to improve predictions at management scales.
The costly interactions between humans and wildfires throughout California demonstrate the need to understand the relationships between them, especially in the face of a changing climate and expanding human communities. Although a number of statistical and process-based wildfire models exist for California, there is enormous uncertainty about the location and number of future fires, with previously published estimates of increases ranging from nine to fifty-three percent by the end of the century. Our goal is to assess the role of climate and anthropogenic influences on the state's fire regimes from 1975 to 2050. We develop an empirical model that integrates estimates of biophysical indicators relevant to plant communities and anthropogenic influences at each forecast time step. Historically, we find that anthropogenic influences account for up to fifty percent of explanatory power in the model. We also find that the total area burned is likely to increase, with burned area expected to increase by 2.2 and 5.0 percent by 2050 under climatic bookends (PCM and GFDL climate models, respectively). Our two climate models show considerable agreement, but due to potential shifts in rainfall patterns, substantial uncertainty remains for the semiarid inland deserts and coastal areas of the south. Given the strength of human-related variables in some regions, however, it is clear that comprehensive projections of future fire activity should include both anthropogenic and biophysical influences. Previous findings of substantially increased numbers of fires and burned area for California may be tied to omitted variable bias from the exclusion of human influences. The omission of anthropogenic variables in our model would overstate the importance of climatic ones by at least 24%. As such, the failure to include anthropogenic effects in many models likely overstates the response of wildfire to climatic change.
We applied logistic regression and Random Forest to evaluate drivers of fire occurrence on a provincial scale. Potential driving factors were divided into two groups according to scale of influence: 'climate factors', which operate on a regional scale, and 'local factors', which includes infrastructure, vegetation, topographic and socioeconomic data. The groups of factors were analysed separately and then significant factors from both groups were analysed together. Both models identified significant driving factors, which were ranked in terms of relative importance. Results show that climate factors are the main drivers of fire occurrence in the forests of Fujian, China. Particularly, sunshine hours, relative humidity (fire seasonal and daily), precipitation (fire season) and temperature (fire seasonal and daily) were seen to play a crucial role in fire ignition. Of the local factors, elevation, distance to railway and per capita GDP were found to be most significant. Random Forest demonstrated a higher predictive ability than logistic regression across all groups of factors (climate, local, and climate and local combined). Maps of the likelihood of fire occurrence in Fujian illustrate that the high fire-risk zones are distributed across administrative divisions; consequently, fire management strategies should be devised based on fire-risk zones, rather than on separate administrative divisions.
Predictive models of fire frequency conditional on weather and land cover are essential to assess how future cover-type distributions and weather conditions may influence fire regimes. We modelled the effects of bottom-up variables (e.g. land cover) and top-down variables (e.g. fire weather) simultaneously with data aggregated or interpolated to spatial and temporal units of 100 km2 and 1yr in the boreal forest of Québec, Canada. For models of human-caused fires, we used road density as a surrogate for human access and behaviour. We exploited the additive property of Poisson distributions to estimate cover-type specific fire count rates, which would normally not be possible with data of this spatial resolution. We used piecewise linear functions to model nonlinear relations between fire weather and fire frequency for each cover-type simultaneously. The estimated conditional rates may be considered as expected mean counts per unit area and time. It follows that these rates can be rescaled to arbitrary spatial and temporal extents. Our results showed fire frequency increased nonlinearly as aridity increased and more quickly in disturbed areas than other types. Road density exerted the strongest influence on the frequency of human-caused fires, which were positively correlated with road density. The estimates may be used to parameterize the fire ignition component of spatial simulation models, which often have a resolution different from that at which the data were collected. This is an essential step in incorporating biotic and abiotic feedbacks, land-cover dynamics, and climate projections into ecological forecasting. The insight into the power of Poisson additivity to reveal high-resolution ecological processes from low-resolution data could have applications in other areas of ecology.
Over recent decades, Land Use and Cover Change (LUCC) trends in many regions of Europe have reconfigured the landscape structures around many urban areas. In these areas, the proximity to landscape elements with high forest fuels has increased the fire risk to people and property. These Wildland-Urban Interface areas (WUI) can be defined as landscapes where anthropogenic urban land use and forest fuel mass come into contact. Mapping their extent is needed to prioritize fire risk control and inform local forest fire risk management strategies. This study proposes a method to map the extent and spatial patterns of the European WUI areas at continental scale. Using the European map of WUI areas, the hypothesis is tested that the distance from the nearest WUI area is related to the forest fire probability. Statistical relationships between the distance from the nearest WUI area, and large forest fire incidents from satellite remote sensing were subsequently modelled by logistic regression analysis. The first European scale map of the WUI extent and locations is presented. Country-specific positive and negative relationships of large fires and the proximity to the nearest WUI area are found. A regional-scale analysis shows a strong influence of the WUI zones on large fires in parts of the Mediterranean regions. Results indicate that the probability of large burned surfaces increases with diminishing WUI distance in touristic regions like Sardinia, Provence-Alpes-Côte d'Azur, or in regions with a strong peri-urban component as Catalunya, Comunidad de Madrid, Comunidad Valenciana. For the above regions, probability curves of large burned surfaces show statistical relationships (ROC value > 0.5) inside a 5000 m buffer of the nearest WUI. Wise land management can provide a valuable ecosystem service of fire risk reduction that is currently not explicitly included in ecosystem service valuations. The results re-emphasise the importance of including this ecosystem service in landscape valuations to account for the significant landscape function of reducing the risk of catastrophic large fires.
We applied a classic logistic regression (LR) model together with a geographically weighted logistic regression (GWLR) model to determine the relationship between anthropogenic fire occurrence and potential driving factors in the Chinese boreal forest and to test whether the explanatory power of the LR model could be increased by considering geospatial information of geographical and human factors using a GWLR model. Three tests, “all variables”, “significant variables”, and “cross-validation”, were applied to compare model performance between the LR and GWLR models. Our results confirmed the importance of distance to railway, elevation, length of fire line, and vegetation cover on fire occurrence in the Chinese boreal forest. In addition, the GWLR model performs better than the LR model in terms of model prediction accuracy, model residual reduction, and spatial parameter estimation by considering geospatial information of explanatory variables. This indicates that the global LR model is incapable of identifying underlying causal factors for wildfire modeling sufficiently. The GWLR model helped identify spatial variation between driving factors and fire occurrence, which can contribute better understanding of forest fire occurrence over large geographic areas and the forest fire management practices may be improved based on it. © 2016, National Research Council of Canada. All rights reserved.
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.
Fires have been threatening green forestry all over the world. In Lebanon, green areas declined dramatically during the last decades, what imposes an urgent intervention with strict governmental policies and support of non-governmental organizations. The orientation is towards techniques that predict high fire risks, allowing for precautions to preclude fire occurrences or at least limit their consequences. Two data mining techniques are used for the purpose of prediction and decision-making: Decision trees and back propagation forward neural networks. Four meteorological attributes are utilized: temperature, relative humidity, wind speed and daily precipitation. The obtained tree drawn from applying the first algorithm could classify these attributes from the most significant to the least significant and better foretell fire incidences. Adopting neural networks with different training algorithms shows that networks with 2 inputs only (temperature and relative humidity) retrieve better results than 4-inputs networks with less mean squared error. Feed forward and Cascade forward networks are under scope, with the use of different training algorithms.
Understanding the spatial distribution and driving factors of forest fire facilitates local forest fire management planning and optimization of resource allocation for fire prevention geographically. In this study, we analyzed the spatial pattern and drivers of forest fire in Fujian province, southeastern China, during 2000-2008 using Ripley's K-function and logistic regression (LR) model. The likelihood of fire occurrence was mapped based on the resultant model. The data regarding fire ignitions, weather conditions, vegetation, topography, infrastructure, and socioeconomic factors were extracted from ArcGIS environment. The study revealed that fire ignition was mainly clustered in space due to the comprehensive influence of different factors. Elevation, daily precipitation, and daily relative humidity were negatively associated with fire ignitions, whereas distance to settlement, population density, and per capita gross domestic product (GDP) impacted fire occurrence positively. The spatial distribution of fire occurrence likelihood was highly variable in Fujian: high fire likelihood was prevalent in the northern and southeastern parts of Fujian, whereas it was relatively low in the western province. Fire risk may be underestimated in some areas of Fujian according to the spatial patterns of the model residual, which should be paid more attention to in the forest fire management practice.
Regional analysis of large wildfire potential given climate change scenarios is crucial to understanding areas most at risk in the future, yet wildfire models are not often developed and tested at this spatial scale. We fit three historical climate suitability models for large wildfires (i.e. ≥ 400 ha) in Colorado and Wyoming using topography and decadal climate averages corresponding to wildfire occurrence at the same temporal scale. The historical models classified points of known large wildfire occurrence with high accuracies. Using a novel approach in wildfire modeling, we applied the historical models to independent climate and wildfire datasets, and the resulting sensitivities were 0.75, 0.81, and 0.83 for Maxent, Generalized Linear, and Multivariate Adaptive Regression Splines, respectively. We projected the historic models into future climate space using data from 15 global circulation models and two representative concentration pathway scenarios. Maps from these geospatial analyses can be used to evaluate the changing spatial distribution of climate suitability of large wildfires in these states. April relative humidity was the most important covariate in all models, providing insight to the climate space of large wildfires in this region. These methods incorporate monthly and seasonal climate averages at a spatial resolution relevant to land management (i.e. 1 km2) and provide a tool that can be modified for other regions of North America, or adapted for other parts of the world.
An improved understanding of the relative influences of climatic and landscape controls on multiple fire regime components is needed to enhance our understanding of modern fire regimes and how they will respond to future environmental change. To address this need, we analyzed the spatio-temporal patterns of fire occurrence, size, and severity of large fires (> 405 ha) in the western United States from 1984–2010. We assessed the associations of these fire regime components with environmental variables, including short-term climate anomalies, vegetation type, topography, and human influences, using boosted regression tree analysis. Results showed that large fire occurrence, size, and severity each exhibited distinctive spatial and spatio-temporal patterns, which were controlled by different sets of climate and landscape factors. Antecedent climate anomalies had the strongest influences on fire occurrence, resulting in the highest spatial synchrony. In contrast, climatic variability had weaker influences on fire size and severity and vegetation types were the most important environmental determinants of these fire regime components. Topography had moderately strong effects on both fire occurrence and severity, and human influence variables were most strongly associated with fire size. These results suggest a potential for the emergence of novel fire regimes due to the responses of fire regime components to multiple drivers at different spatial and temporal scales. Next-generation approaches for projecting future fire regimes should incorporate indirect climate effects on vegetation type changes as well as other landscape effects on multiple components of fire regimes.
Tree growth depends, among other factors, largely on the prevailing climatic conditions. Therefore, changes to tree growth patterns are to be expected under climate change. Here, we analyze the tree-ring growth response of three major European tree species to projected future climate across a climatic (mostly precipitation) gradient in northeastern Germany. We used monthly data for temperature, precipitation, and the standardized precipitation evapotranspiration index (SPEI) over multiple time scales (1, 3, 6, 12, and 24 months) to construct models of tree-ring growth for Scots pine (Pinus sylvestris L.) at three pure stands, and for common beech (Fagus sylvatica L.) and pedunculate oak (Quercus robur L.) at three mature mixed stands. The regression models were derived using a two-step approach based on partial least squares regression (PLSR) to extract potentially well explaining variables followed by ordinary least squares regression (OLSR) to consolidate the models to the least number of variables while retaining high explanatory power. The stability of the models was tested through a comprehensive calibration-verification scheme. All models were successfully verified with R²s ranging from 0.21 for the western pine stand to 0.62 for the beech stand in the east. For growth prediction, climate data forecasted until 2100 by the regional climate model WETTREG2010 based on the A1B Intergovernmental Panel on Climate Change (IPCC) emission scenario was used. For beech and oak, growth rates will likely decrease until the end of the 21st century. For pine, modeled growth trends vary and range from a slight growth increase to a weak decrease in growth rates. The climatic gradient across the study area will possibly affect the future growth of oak with larger growth reductions towards the drier east. For beech, site-specific adaptations seem to override the influence of the climatic gradient. We conclude that Scots pine has great potential to remain resilient to projected climate change without any greater impairment, whereas common beech and pedunculate oak will likely face lesser growth under the expected warmer and dryer climate conditions. The results call for an adaptation of forest management to mitigate the negative effects of climate change for beech and oak.
In Spain, the established fire control policy states that all fires must be controlled and put out as soon as possible. Though budgets have not restricted operations until recently, we still experience large fires and we often face multiple-fire situations. Furthermore, fire conditions are expected to worsen in the future and budgets are expected to drop. To optimize the deployment of firefighting resources, we must gain insights into the factors affecting how it is conducted. We analyzed the national data base of historical fire records in Spain for patterns of deployment of fire suppression resources for large fires. We used artificial neural networks to model the relationships between the daily fire load, fire duration, fire type, fire size and response time, and the personnel and terrestrial and aerial units deployed for each fire in the period 1998-2008. Most of the models highlighted the positive correlation of burned area and fire duration with the number of resources assigned to each fire and some highlighted the negative influence of daily fire load. We found evidence suggesting that firefighting resources in Spain may already be under duress in their compliance with Spain's current full suppression policy.
Significant relationships were found between high-temperature days and wildland fire occurrence in the 1978-2011 period in Aragón (NE Spain). Temperature was analyzed at 850 hPa to characterize the low troposphere state, avoiding problems that affect surface reanalysis and providing regional coverage. A high-temperature day was established when air temperature was higher than 20 °C at 850 hPa. The number of these days increased significantly in the study period, increasing the frequency of adverse weather conditions that could facilitate extreme fire behavior. Specifically, these high-temperature days are more frequent in June than they used to be. The effects of those high-temperature days in wildland fire patterns were significant in terms of burned area, number of wildland fires, and average size. Fires larger than 60 ha were the subject of this study. These wildland fires have been increasing in number and size in the last years of the series. Abstract Significant relationships were found between high-temperature days and wildland fire occurrence in the 1978-2011 period in Aragón (NE Spain). Temperature was analyzed at 850 hPa to characterize the low troposphere state, avoiding problems that affect surface reanalysis and providing regional coverage. A high-temperature day was established when air temperature was higher than 20 °C at 850 hPa. The number of these days increased significantly in the study period, increasing the frequency of adverse weather conditions that could facilitate extreme fire behavior. Specifically, these high-temperature days are more frequent in June than they used to be. The effects of those high-temperature days in wildland fire patterns were significant in terms of burned area, number of wildland fires, and average size. Fires larger than 60 ha were the subject of this study. These wildland fires have been increasing in number and size in the last years of the series.
Forest fires have not been considered as a significant threat for mountain forests of the European Alpine Space so far. Climate change and its effects on nature, ecology, forest stand structure and composition, global changes according to demands of society and general trends in the provision of ecosystem services are potentially going to have a significant effect on fire ignition in the future. This makes the prediction of forest fire ignition essential for forest managers in order to establish an effective fire prevention system and to allocate fire fighting resources effectively, especially in alpine landscapes. This paper presents a modelling approach for predicting human-caused forest fire ignition by a range of socio-economic factors associated with an increasing forest fire danger in Austria. The relationship between touristic activities, infrastructure, agriculture and forestry and the spatial occurrence of forest fires have been studied over a 17-year period between 1993 and 2009 by means of logistic regression. 59 independent socio-economic variables have been analysed with different models and validated with heterogeneous subsets of forest fire records. The variables included in the final model indicate that railroad, forest road and hiking trail density together with agricultural and forestry developments may contribute significantly to fire danger. The final model explains 60.5% of the causes of the fire events in the validation set and allows a solid prediction. Maps showing the fire danger classification allow identifying the most vulnerable forest areas in Austria and are used to predict the fire danger classes on municipality level.
Fire regimes are shifting worldwide because of global changes. The relative contribution of climate, topography and vegetation greatly determines spatial and temporal variations in fire regimes, but the interplay of these factors is not yet well understood. We introduce here a novel classification of fires according to dominant fire spread pattern, an approach considered in operational firefighting, to help understand regional-scale spatial variability in fire regimes. Here, we studied whether climate, topography and fuel variables allowed the prediction of occurrences from different fire spread patterns in Catalonia, NE Spain. We used a correlative modelling approach based on maximum entropy methods, and examined, through variation partitioning, the relative contribution of different factors on determining their occurrence. Our results accurately predicted the occurrence of different fire spread patterns, and the results were consistent when temporal validation was conducted. Although forest fuel factors made a higher contribution to the occurrence of convective fires, wind-driven fires were strongly related to topographic and climate factors. These findings may have a strong impact on investigations into how fire regimes may be projected into the future under forecast global change as they suggest that future environmental changes may affect different fire spread patterns in an idiosyncratic manner.
Aim of study: The goal of this paper is to analyse the importance of the main contributing factors to the occurrence of wildfires.
Area of study: We employ data from the region of Galicia during 2001-2010; although the similarities shared between this area and other rural areas may allow extrapolation of the present results.
Material and Methods: The spatial dependence is analysed by using the Moran’s I and LISA statistics. We also conduct an econometric analysis modelling both, the number of fires and the relative size of afflicted woodland area as dependent variables, which depend on the climatic, land cover variables, and socio-economic characteristics of the affected areas. Fixed effects and random effect models are estimated in order to control for the heterogeneity between the Forest Districts in Galicia.
Main results: Moran’s I and LISA statistics show that there is spatial dependence in the occurrence of Galician wildfires. Econometrics models show that climatology, socioeconomic variables, and temporal trends are also important to study both, the number of wildfires and the burned-forest ratio.
Research highlights: We conclude that in addition to direct forest actions, other agricultural or social public plans, can help to reduce wildfires in rural areas or wildland-urban areas. Based on these conclusions, a number of guidelines are provided that may foster the development of better forest management policies in order to reduce the occurrence of wildfires.
