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Lessons Learned from Arson Wildfire Incidence in Reforestations and Natural Stands in Spain

German Aerospace Center (DLR), German Remote Data Center (DFD), Münchener Str. 20, 82234 Wessling, Germany
Department of Agriculture, Food, Environment and Forestry (DAGRI), University of Florence, Piazzale delle Cascine 18, 50144 Florence, Italy
Department of Agricultural and Forest Engineering, University of Lleida, Av. Alcalde Rovira Roure, 191, 25198 Lleida, Spain
Forest Sciences Centre of Catalonia, Ctra. Sant Llorenç de Morunys km 2, Solsona 25280, Spain
Author to whom correspondence should be addressed.
Forests 2019, 10(3), 229;
Received: 25 January 2019 / Revised: 22 February 2019 / Accepted: 28 February 2019 / Published: 5 March 2019
(This article belongs to the Section Forest Ecology and Management)


Wildfires are currently considered the major threat to forests in Mediterranean countries. It has been implied that a large percentage of arson-caused fires in Spain are connected with the extensive reforestation programs implemented between 1940 and 1970. However, no consistent studies have been conducted to study the relationships between arson-caused fires and stand origin. Therefore, the goal of this study was to analyze occurrences and model the influence of forest stand origin (artificial or not) on the development of wildfires in peninsular Spain. Twenty-one neural network models were trained to estimate fire incidence through fire type (surface or crown fire), burned area and total treed burned area, based on stand age (years), canopy cover (%), natural age class (from seedling to mature stages) and fuel type classification. Models were built for reforested stands and natural stands of Pinus pinaster Ait., the Mediterranean pines Pinus sylvestris L., Pinus nigra Arn., Pinus halepensis Mill. and Eucalyptus sp. L’Hér., or groups of these species, and the resulting models were compared. Reforested stands presented higher fire incidence than natural stands mainly for productive species like Pinus pinaster Ait. According to the fire type models, thickets had a large influence in the development of crown fires in reforested stands in a general model for all species, the model with the Mediterranean group of pines, and the Pinus pinaster Ait. model. Vertical continuity influenced crown fire propagation in natural Mediterranean pines and in Eucalyptus stands. Presence of shrubs, grasslands and wood slash was related to surface fires in models for both reforested and natural stands. The results suggested that stand origin was influential on fire incidence, at least with regard to fire type and commercial species in the northwestern region of Spain.

1. Introduction

During the last decades, more than 95% of the wildfires occurred in Spain and in other Southern European Mediterranean countries were of anthropogenic origin, and over 55% of them were classified as arson-caused fires [1,2]. Arson is not random, and many reasons have been put forward to explain why arsonists burn forests [3]. Among them, Simon et al. [4], Alvarez et al. [5] and Fuentes-Santos et al. [6] have cited resentment against reforestations as a motivation for arson-caused fires. A relationship between arson fires and reforestations is assumed in the Spanish Wildland Fire National Statistics of the Ministry of Agriculture, Fishing, Food and Environment, which includes in the fire reports a code for “Arson fires motivated by resentment against reforestations” [1]. In the past, these arson-caused fires would have been triggered by the discontent of many rural communities for the shifting of their productive and rangelands into forest [7] and towards the wood rights’ owner (and thus the beneficiary of the wood’s sale) when land and wood differed in ownership [8]. However, other motivations have risen over time; forcing land use changes is a potentially relevant, but difficult to prove, motivation in Southern European countries that depend on the tertiary sector [9]. Regardless of underlying motivation, the relations between arson wildfires and reforestations have received little attention in the literature. Previous studies on general human-caused fires, not specific for arson, have sometimes included forest-related variables like commercial value [10] or species composition [11,12,13,14,15]. Forest stand characteristics are usually accounted for and converted to fuel type categories [16]. Yet, very little scientific evidence exists on how stand origin may affect other drivers and influence arson fire incidence, despite decades-long controversies [8,17,18].
Reforestation has been frequent in many European countries in which clear-cutting [19] and natural disturbances such as windstorms [20] or wildfires [21] have affected large tracks of forest. In addition, socioeconomic drivers from the early 1940s onwards have prompted European Mediterranean countries to implement extensive governmental reforestation programs (i.e., 3 million ha in Spain during 1940–1970, 0.8 million ha after 1952 in Italy, 340,000 ha in Portugal in 1987–1999 [22]). The natural or artificial origin of stands, and later, the effects of natural and anthropogenic disturbances determine forest characteristics, namely, composition and structure. Stand composition and structure are supposed to differ widely between naturally expanding, colonizing stands, and artificially reforested stands that follow different ecological processes [23,24,25]. Forestry agencies have promoted monospecific and even-aged stands, selecting species for site restoration based on their fast growing and ground cover characteristics [26]. Consequently, reforested forest stands generally have higher even-age tree density and lesser variation of diameter at breast height (DBH), quantity of seedlings, and tree and genetic diversity [18,27].
In Southern European Mediterranean environments, stands arising from secondary succession often share characteristics with reforestations. Land abandonment and wildfires have contributed in Spain to a pervasive and fast expansion of shade-intolerant conifer species [28,29] like Aleppo pine (Pinus halepensis Mill.), also used in reforestation for their pioneer capacity. Several authors have stated that wildfires in South Europe have affected mainly conifers, with Pinus pinaster Ait., Pinus halepensis Mill., and Eucalyptus sp. L´Hér. stands reforested in the 1940’s and 1950’s being comparatively more affected than other species [11,12,13,14,15]. However, in terms of forest structure, wildfires tend to favor high-density even-aged forest stands [27,29], areas of low average tree diameter [12], or undermanaged or abandoned stands, which tend to develop vertical continuity of fuels [30]; it may be argued that both types of stands, either reforested or naturally arising from secondary-succession after abandonment since the 1950s, currently share these features and are both of them potentially highly susceptible to fire [31,32].
Our work aimed to compare the past incidence (occurrence and behavior) of arson-caused fires in reforested and nonreforested fire-prone stands in Spain. We explored fire incidence patterns to discredit or support the notion that reforestations are comparatively more affected by arson, due to resentment or other motivations. Similarity of fire incidence patterns for any stand origin would indicate arson is not specific to reforested stands, and that forest characteristics are alike across natural and artificial stands of fire-prone species determining fire behavior. Dissimilarity of patterns by origin could indicate differences in stand characteristics (behavior) and/or a differential pressure from arson (occurrence), requiring further analysis.
The hypothesis of our study was that stand origin influences ignitions, forest composition and stand structure to produce different patterns in fire occurrence, fire type and burned area. We analyzed fire occurrence, fire type and burned area in reforested and nonreforested stands, based on species composition, natural class age, canopy cover and fuel type.

2. Materials and Methods

The study area encompassed the Spanish mainland, covering around 492,174 km² (Figure 1). The predominant Mediterranean climate in Spain, characterized by long summers of high temperatures and low rainfall, contributes to the high risk of fires in the country [33]. The fuel environment has undergone critical changes since the 1950’s, with a general increase in continuity and load due to land abandonment and socioeconomic changes, including reforestation [34]. Changes in the futures of fire regimes have been detected in past decades, with noteworthy shifts after the late 1980’s and the first half of the 1990’s [35]. After the extreme fire events in 1998 in Spain, wildfire policies and management (Law 43/2003, of November 21th) moved to reducing the gap between suppression and prevention budgets in the country, with a temporary increase in forest management interventions that was short-lived. The financial crisis that hit Spain in 2007–2008 [36] undermined the forestry sector, leading to the closing of many industries and, again, to pervasive forest abandonment. However, the crisis also produced a reduced pressure on the land resource for development and construction [37]. This factor may have influenced the occurrence arson wildfires, which decreased from an average of 10,587 arson-fires/year in 1998–2008 to 7336 fires/year in 2009–2015 [1]. The economic recovery of the country is currently leading to fire environmental conditions very similar to the conditions in the pre-financial-crisis period 1998–2008 [1]. Analysis of past patterns may provide clues for exploring future relations between arson fires and forest stand characteristics, determined by their origin, which justifies the selection of 1998–2008 as our study period.

2.1. Reforestation Historical Data

About 54% of Spain is covered by woodlands and shrublands, but only 35% can be considered forest [38]. Reforestations (Figure 1) represent almost 8% of the total land area of Spain [39].
The Forest Map of Spain used here was made between the years 1986 and 1997 through aerial photographs acquired in 1985 in tandem with field work (II National Forest Inventory, 1:200,000), which made it appropriate for the study period 1998–2008 [40]. According to the Forest Map of Spain, the main reforested species were Pinus pinaster Ait. (24.5% of the total reforested area), Pinus sylvestris L. (17.3%), Pinus halepensis Mill. (14.6%), Eucalyptus sp. L´Hér. (13.5%), Pinus nigra Arn. (11.8%), Pinus pinea L. (6.6%), Pinus radiata D. Don. (5.1%), Populus sp (1.6%), Pinus canariensis C. Sm. (0.8%) and Pinus uncinata Mill. (0.5%) with a total reforested area of 3.19 million ha [41].

2.2. Fire Historical Data

The fire registry was obtained from the General Fire Statistics of Spain (EGIF) of the Coordination Centre for National Information on Wildfires (CCINIF) of the Ministry of Agriculture and Fishing, Food and Environment [1]. The total amount of fires recorded during 1998–2008 was 417,406 but only arson fires were considered for the study (130,405 fires). Each fire record provided information on fire type, burned area, total treed-area burned, cause, motivation, species affected, stand mean age, natural age class, percentage canopy cover of the affected vegetation, fuel type, and many other variables related to fire suppression (detection date, arrival time, suppression resources, etc.) and losses caused (i.e. estimated monetary value of the forest products lost).
We selected several dependent (Fire Type—FT: surface/crown fire; Burned Area—BA in ha; and Treed Burned Area—TBA in ha) and independent variables (Table 1) from the EGIF database in agreement with the stated hypothesis. Mean stand age (AGE) was available in the fire reports, but not density, so we selected the percentage of canopy cover (CC) as independent variable. Stand structure was defined through five mutually exclusive natural age classes, from seedlings to mature forest (i.e., seedling—SD, sapling—SS, thicket—TH, pole—PS and high-forest—HFS). The fuel type (FTP1-15) classification used by the EGIF database was also included to compare the relative relevance of the fuel type class versus other forest stand characteristics on the incidence outcome. The EGIF fuel type classification is an adaptation of Rothermel’s [42] conducted by the Ministry of Agriculture and Fishing, Food and Environment.

2.3. Database for Model Building

Numerical and spatial analysis techniques applied with a dedicated GIS allowed matching ignition points to forest stands by origin. The number of fires located within reforestation polygons (RF) was 21,823 (Figure 2), while 108,582 fires started in nonreforested areas (NRF) in the study period. We removed missing data records and selected only those in which the Forest Map of Spain data was in agreement with the fire report in terms of forest characteristics, which considerably reduced the available data. The available data from 130,405 to 8022 usable records. We sampled arson fires in reforested stands by natural class age category to cover the whole range of characteristics, leaving 4810 RF observations and 3212 NRF observations in the database for analysis. The number of arson fire ignitions in RF and NRF stands were compared by species and weighted by area.
Additionally, RF and NRF databases were split by species before fire behavior modeling was carried out. Fire behavior features (Fire Type—FT, Burned Area—BA, and Treed Burned Area—TBA) were modelled separately for RF and NRF stands of the species listed in Section 2.1. RF and NRF P. pinaster and RF stands of Eucalyptus sp. were also analyzed in separate models. No arson fires in NRF stands of Eucalyptus sp. did exist, as this non-native species in Spain is always planted. The pine species Pi. halepensis, P. nigra and P. sylvestris were grouped for model building, based on their geographical distribution allowing mixing in the fringes of contiguous altitudinal levels in continental Mediterranean mountains, and because of a lower number of records available than for P. pinaster, the species most used in reforestation in Spain. Because the number of fire records of the other species used in reforestation (P. pinea, P. radiata, Populus, P. canariensis and P. uncinata) was likewise not large enough to build suitable models, they were also discarded for individual modeling. These species have been used in reforestation less frequently (<7%), but are affected by fire, so they were included in the global models in RF and NRF stands.
The number of fire observations varied for each modeling database (Table 2). The largest data set was for modeling Fire Type (surface/crown) in Reforested Stands (1524 cases) and the smallest was the dataset for modeling Treed Burned Area in Reforested pine stands (40). Databases were as much balanced as possible for numbers of observations in each category for classification.

2.4. The Modeling Approach: Artificial Neural Network (ANN) Models

Models for FT, BA and TBA were built based on any combination of the independent variables in Table 1, for the different species or groups of species (all species, continental Mediterranean mountain pine species, P. pinaster, Eucalyptus sp.), and forest stand origin (reforested/nonreforested). A total of 21 models (Table 2) were built.
Artificial neural networks (ANN) were chosen to explore the relationship between stand characteristics and fire behavior because they had proven useful in other wildfire studies, providing good accuracies and generalization capability (i.e., [29,43,44]). When dealing with non-normal distributions of the data, or nonlinear, complex relationships, these models are as reliable as any traditional statistical method, and often more robust [45]. An ANN is an information processing algorithm capable of learning patterns in data by iterative adjustment of the connections between processing elements (nodes) organized in layers. Our models were feed-forward multilayered non-linear fully connected cascade-correlation networks [46], built using NeuralWorks Predict® v.3.24 software [47]. The models were computed as in Alcázar et al. [48] and Debouk et al. [49]. The cascade-correlation algorithm starts model building with the simplest possible architecture, adding nodes until an optimal multi-layer structure is reached [46]. Training was based on an adaptative gradient learning rule, a variant of the general algorithm of back-propagation [50].
The FT models were based on a binary classification (with one-of-N encoding output either Surface Fire (1,0) or Crown Fire (0,1)). The BA and TBA models were predictive providing one output in hectares. Categorical independent variables were input to the network by using the same one-of-N encoding [29]. Interval variables (Age, CC) underwent several transformations that were tested with a genetic algorithm before the actual model training. As usual in ANN learning, each database was divided in three subsets: 10% of the observations were separated to validate the models with data not used in model building. From the rest, 70% were used for training and 30% for testing the accuracy of the model performance and to stop training [51]. Several replicates were simulated for each model. Convergence to a same or similar architecture between the replicates was checked to corroborate robustness of the models. Parsimonious models were favored because of the relatively low number of observations, which generally was below 1000 [48].
The best models were selected according to their capability to explain the dependent variable and performance in terms of total percentage correctly classified and confusion matrix (FT classification networks) and R correlation values between observed and predicted values (BA and TBA prediction networks).
A sensitivity analysis based on the partial derivatives of the outputs with respect to the inputs was run to establish which independent variable had the highest impact on the predicted variable for every model. A higher frequency of selection in the variable selection genetic algorithm was also indicative of the greater relevance of an independent variable.

3. Results

In terms of fire occurrence, the analysis of ignitions started in RF or NRF stands in the study period is presented in Table 3. It is remarkable that the ignition rate of P. pinaster is much higher in RF stands than in NRF stands, in a contrary pattern to the other Pinus species, with higher rates in natural stands. Planted species Eucaliptus and Populus also exhibited high rates of ignition, over one per 100 ha.
As for fire behavior dependent variables, unfortunately none of the prediction models developed for Burned Area and Treed Burned Area complied with the correlation requirements set (R > 0.50) or gave satisfactory enough results to reach significant conclusions. Consequently, only the best classification networks built for Fire Type classification are presented here.

3.1. Models RF_FT and NRF_FT: Fire Type in Reforested and Natural Stands (All Species)

According to Table 4, RF_FT and NFR_FT models present a simple architecture, with no nodes in the hidden layer in the RF_FT model (6-0-2) and only one in the NRF_FT model (8-1-2). The RF_FT model had a higher accuracy (around 0.85 in the training, testing and validation samples), whereas the NRF_FT accuracy model was around 0.7. In both models, the accuracy for crown fires was higher than that for surface fires, which was below 0.5 in the training, testing and validation samples for the nonreforested stands model NRF_FT.
The sensitivity analysis (Table 5) shows that FTP2 (shrubs) and AGE (Stand mean age) variables were selected in both models, being positively correlated to surface fires. FTP2 is the variable with higher weight. Its frequency of selection is very high in both models (with a percentage of 0.94 and 0.75, respectively). The best RF_FT model also shows that a higher proportion of TH (thickets) and PS (pole-stage forests) increases crown fire hazard, whereas AGE, FTP2 (shrubs) and FTP6 (forest with grasses) favor surface fires in reforested stands.
In the NRF_FT model for natural stands, only CC (canopy cover) is related with the development of crown fires. FTP1 (grasses), FTP2 (shrubs) and FTP4 (wood slash) were the most important variables for the development of surface fires in natural stands.

3.2. Models RFPINE_FT and NRFPINE_FT: Fire Type in Continental Mediterranean Mountain Pines in Reforested and in Natural Stands (P. halepensis, P. nigra, P. sylvestris)

RFPINE_FT and NFRPINE_FT models present a simple architecture (Table 6), with no nodes in the hidden layers. The RFPINE_FT model accuracy was 0.82 and the accuracy of the NFRPINE_FT model 0.67. In both models, crown fires accuracy was higher than surface fires accuracy, especially in the RFPINE_FT model.
Table 7 shows that FTP2 (Shrubs) was selected in the best RFPINE_FT and NRFPINE_FT as in the general (all species) models, being positively associated to surface fires. CC (canopy cover), FTP11 (forests with shrubs and grasses) and FTP15 (forests with shrubs, grasses and slash) were also selected in both models, but were positively correlated with crown fires.
According to Table 7, the best RFPINE_FT model shows that the development of crown fires in reforested pine stands is higher in those forests with a higher AGE, CC (canopy cover), TH (thicket stage), FTP11 (forests with shrubs and grasses) and FTP15 (forests with shrubs, grasses and slash). On the contrary, a higher probability of surface fires in RF pine stands is associated to a higher percentage of FTP2 (shrubs) and FTP4 (wood slash). The variables TH (1), AGE (0.92), and CC (0.90) had the higher frequency of selection by the genetic algorithm.
The highest probability of crown fires in the best NRFPINE_FT model is mainly related to a higher percentage of CC, FTP8 (bushes and forest), FTP11 (forests with shrubs and grasses) and FTP15 (forests with shrubs, grasses and slash). Surface fires are mainly related to FTP2 (shrubs). FTP2, FTP8, TH and CC are the variables most frequently selected for these models by the genetic algorithm applied to variable selection.

3.3. Models RFPP_FT and NRFPP_FT: Fire Type in P. pinaster in Reforested and in Natural Stands

RFPP_FT and NFRPP_FT models present a simple architecture (Table 8), with no nodes in the hidden layers (9-0-2 and 7-0-2, respectively).
The accuracy in both models followed the same pattern as in previous results (but better than the accuracies of previous models, Table 4 and Table 6), and were better for artificial stands (accuracy around 0.88 in RF and 0.80 in NRF stands) and crown fires (0.98–0.90 in crown fires and 0.80–0.70 in surface fires).
Table 9 shows that FTP2 was selected in the best RF and NRF P. pinaster models as in the previous models, also being associated to surface fires. The RFPP_FT model shows that the development of crown fires in P. pinaster stands is higher in those forests with higher percentage of TH (thicket), SS (sapling) and PS (pole) stages and the development of surface fires is more likely with a higher proportion of CC, SD (seedlings) and FTP2 (Shrubs). SD (0.86) and CC (0.64) are the variables with the highest frequency of selection in the RFPP_TF model.
The NFRPP_FT model identified high probability of surface fires (or low of crown fires) with higher presence of HFS (high-forest, mature stage), FTP1 (grasses), FTP2 (shrubs), FTP5 (grasses and shrubs) and FTP9 (shrubs and wood slash), with HFS (1) and FTP2 (0.92) being the most often selected variables.

3.4. Models RFE_FT: Eucalyptus Artificial Stands

The best RFE_FT model presents a simple architecture, with six inputs, no hidden nodes, and two outputs (Table 10).
The best model RFE accuracies were over 0.84 for the training, testing and validation subsets, with higher crown fires accuracy (over 0.92) than surface fires accuracy (over 0.80). The RFE_FT model (Table 11) shows that crown fires in Eucalyptus stands are directly related to FTP8 (shrubs and forest), FTP3 (forest) and FTP6 (grasses and forest). Surface fires are positively linked mainly to CC and FTP9 (shrubs and slash). In the RFE_FT variable selection, FTP3 (0.91), FTP8 (0.81) and AGE (0.8) were most often selected.

4. Discussion

This study focused on the past incidence of arson wildfires in artificial and natural stands, within a time frame providing a fire environment that is very likely to reproduce in the next few years in Spain. In order to learn our lessons, ignition points obtained from fire history records between 1998 and 2008 were overlapped with the National Forest Map of Spain at that time, to better understand occurrence and fire behaviour patterns in this study period.

4.1. Wildfire Incidence: Occurrence in Reforested and Nonreforested Stands

When considering the number of ignition points in relation to the origin of forest stands (reforested or natural) and species composition, we found that P. pinaster indeed presented relatively more arson ignitions in reforested than in nonreforested stands than expected based on its respective area of distribution. On the contrary, proportionally more arson-caused forest fires occurred in natural stands of P. halepensis, P. nigra. and P. pinea than in their reforested stands. P. sylvestris and P. uncinata presented a similar proportion of arson fire ignitions in stands of any origin.
Though some authors stated in previous work that wildfires in South Europe have affected mainly conifers reforested in the 1940’s and 1950’s (P. pinaster, P. halepensis, and Eucalyptus sp. stands) [11,12,13,14,15], and others [6,7,8] indicated reforestations as a major factor of occurrence of arson wildfires in Spain, we found this only true for a few of the most productive and fast-growing species. Reforestations per se were not comparatively more affected by arson unless we considered regional location. The spatial distribution of arson fires (Figure 2 and reference [1]) reveals that the highest percentage of these was located in the Northwestern (NW) region of Spain, where there was a differential pressure of arson on stands of different origin. In NW Spain, P. pinaster and Eucalyptus sp. are the most common species for reforestation, based on commercial value, and human-caused fire occurrence is high [6]. According to Cabana Iglesia [8], NW Spain has seen many occurrences of arson as a form of protest by rural communities in favor of the return of communal forests to the traditional silvopastoral use. Consequently, if this was the underlying motivation, arson occurrence may not have been species-specific, but location-specific, linked to wildlands ownership and land use issues.
On the other hand, the Mediterranean region, especially near the Eastern coastal mountains, has the highest percentage of monospecific and mixed Aleppo pine forest in the country [18], but a large part of the area can be considered as natural forests (from secondary succession), and arson seems to prefer them to reforested stands. Aleppo pine stands in the coastal Mediterranean region are more affected by human-caused fires [1] than Aleppo pine stands in the hinterland, so again, location-specific drivers could explain occurrence patterns. In the Mediterranean, drivers were probably linked to development demands for land, but the general difficulty in the certain identification of motivations and causes for arson makes risky to make assumptions, and precludes analysis beyond the scope of this work.
Thus, other risk factors related to human-caused fire occurrence, like low drought vegetation moisture [16], weather indices [54], traditional agricultural practices [15] or presence of long-term serial fire-setters as suggested by Fuentes-Santos et al. [6] could be better variables for explaining occurrence patterns than stand´s origin or composition.

4.2. Wildfire Incidence: Neural Network Models for Fire Behaviour

Modelling the relationship between fire type and a set of different independent variables was successfully achieved by means of cascade-correlation artificial neural networks. The neural networks were parsimonious, robust, and converged in all cases to same/similar solutions when the observations were randomly interchanged in the training, test and validation subsets, and when the initial random weights were shifted [45]. However, no robust and reliable models were found for total burned area and treed burned area. This was probably the consequence of having one single set of forest structure parameters for all the burned area in one single fire. There may be a high diversity in forest structures typically affected by any single fire in Spain, but only one description is input in the fire reports, which prevented the successful development of these models.
The fire type models did allow us to explore stand-origin influences; models for artificial and natural stands were different in accuracy, structure, and variables selected. Table 4, Table 6, Table 8 and Table 10 showed that crown fires exhibited better classification accuracy than surface fires in all the databases.
Out of all datasets and corresponding models, the best accuracy was obtained for the P. pinaster models, the species most extensively used for reforestation (24% of the reforested area), especially in the NW Spain, and the most affected by human-caused fires. Artificial seedlings of this species experimented surface fires, but thickets, young forest and pole-forest were linked to crown fires. Closed canopy cover seemed to favor surface fires [29], a trend contrary to the Mediterranean pines group [25], but understandable by the higher foliage density and thickness of this pine needles and their shadowing effects. Surface fires were related to the presence of shrubs, grasses, and slash in the under-story of high-forest in natural maritime pine stands.
The analysis performed on the continental Mediterranean pines indicated an opposite trend with regards to stand age. Crown fires were more likely to occur in pine reforestations when the stand age increased with closed canopy cover and vertical fuel continuity. Natural stands showed similar trends for canopy cover and verticality in fuels, but thickets were not selected as a crown fire factor, neither was age an input to the model. This may be explained by the fact that more natural conditions in the establishment of these stands mean that age is not coetaneous, competition among individuals is lower and thickets are not so homogeneous spatially [55].
Eucalyptus planted stands appeared to be more prone to crown fires when vertical continuity was present (forest, or forest with grasses and shrubs) but also a somewhat lower canopy cover. Surface fires seemed more likely with denser canopy cover, increasing age of the stands, and presence of shrubs and slash. In any case, presence of shrubs and slash is quite common in Eucalyptus stands, as the combination of abundant understory vegetation due to the Atlantic geographic location of the Eucalyptus plantations often combined with woody debris coming from silvicultural activities, frequently applied due to importance of the pulp market in the area (Galicia). When a flammable young tree layer is added over this understory, crown fires result [56].
Since different variables were selected in each model, the formulated hypothesis stated that the origin of a forest stand influences species, age, density and structure, and in turn fire type was upheld. According to the obtained results, fire type was influenced by different variables in stands of different origin and species. Thickets were influential on crown fires in reforestations within the models for all species. Crown fire probability on a forest stand also increased in previous work with smaller tree diameters, larger basal areas and an increasing variation in tree diameter, which suggest thicket structures with high densities [12,14,57]. According to Moreno et al. [58], vertical continuity between understory vegetation (with or without grasses) and trees in pine and eucalypt stands was also linked to crown fires. The variables related to stand age, canopy cover, stand structure, and other fuel types showed variable patterns, but shrubs, grasses and wood slash proved influential for surface fires in all the models. Similar results were found by [59,60], in which fire type in mature stands was linked to the quantity and availability of fuel in their understories. The association of shrubs and surface fires, which was detected in most of the models, is a well-known fact in Spain, and is the basis for the fuel treatments usually applied for hazard reduction (i.e., [61]). General trends for all forest types and origins indicate that the presence of grasses and slash, without brushes and thickets, also favors surface fires (Table 4, Table 6 and Table 8).
Management implications useful for silvicultural treatments and for supporting restoration strategies and policies could be derived from the historical analysis of arson incidence, reinforcing some recommendations by other authors (e.g., [62]) and introducing other actions that may be quite relevant if future conditions evolve as expected in the socioeconomic and ecological environment of Spain. Fuel management is technically well developed in European Mediterranean countries and in Spain, and abundant literature exists on the application of prescribed burning, mechanical thinning or mastication [63,64], but treatments and effects are usually analyzed for individual species (i.e., P. nigra, Piqué and Domenech [65]; P. halepensis, Palmero-Iniesta [66]; P. pinaster, Molina et al. [67] without considering stand origin. In agreement with these findings, aged, more mature forest structures and closed canopy cover generally lead to less severe fires in our models, even in Eucalyptus or P. pinaster stands, but not in P. halepensis, P. nigra and P. sylvestris forests if vertical fuel connectivity was present. However, our results indicate that controlling understory vegetation influences the development of surface fires especially for shrubs in artificial stands, so treatments should be prioritized in them. Previous work has often grouped several pine species [63], but we found that beyond species, also origin makes a difference in fire behavior. Thickets in artificial stands are the most hazardous structures, especially for our grouped pine species, but this does not seem to be the case for natural stands. Consequently, at the landscape planning scale, thinning in thicket structures should be prioritized for reforested over natural stands.
Dissimilarity of models for stands of different origin (fire type explained by different explanatory variables) seemed to indicate differences in stand characteristics ruled fire behavior. However, and mainly in terms of occurrence, a differential pressure from arson can also explain incidence, so further analysis should be conducted in this regard.

5. Conclusions

This study aimed to learn from the historical (1998–2008) relationship between arson fire incidence (occurrence and behavior) and reforestations in Spain, to anticipate similar future conditions in the fire environment. We intended to discredit or support the notion that reforestations were comparatively more affected by arson. The hypothesis of our study was that stand origin influenced ignitions, forest composition and stand structure to produce different patterns in fire occurrence, fire type and burned area. We analyzed fire occurrence, fire type and burned area in reforested and nonreforested stands, based on species composition, natural class age, canopy cover, and fuel type.
Arson-caused ignitions had a higher rate of occurrence in reforestations of P. pinaster, than in its natural stands, but this preference applied only to these commercial species located NW Spain, not to all considered, everywhere.
According to ANN models built for fire type, thickets had a large influence in the development of crown fires in reforested stands in a general model for all species, the model with the Mediterranean group of pines, and the P. pinaster model. Vertical continuity influenced crown fire propagation in natural Mediterranean pines and Eucalyptus stands. Presence of shrubs, grasses, and wood slash was related to surface fires in models for both reforested and natural stands. The results from this study could not confirm a direct relationship between arson fires and reforestations. However, overall outcomes suggested that stand origin and composition were influential on fire incidence, at least in regards to number of ignitions and fire type.
Future studies should consider additional variables related to location, such as ownership, land uses, topography or weather to improve the accuracy and reliability of models. Understanding arson fire incidence can provide valuable information to support restoration strategies and policies, and also differential management actions over natural and artificial stands. Environmental benefits when implementing restoration programs should consider fire impacts over time, and past lessons.

Author Contributions

E.D.P. processed the wildfire data, generated the neural network models, analyzed the results and contributed to the writing of the manuscript. S.C.-A. contributed to the processing of fire data and the writing of the manuscript. C.V.-G. was involved in research design; develop of the manuscript, methodological guidance for data processing and a critical revision of the manuscript.


This research received no external funding.


We are thankful to Jesús Pemán and Domingo Molina, from the University of Lleida, for helpful suggestions and Marta López, from the Coordination Centre for National Information on Wildfires (CCINIF) of the Ministry of Agriculture and Fishing, Food and Environment (MAPAMA), for helping us in understanding the EGIF database.

Conflicts of Interest

The authors declare no conflicts of interest.


  1. Ministerio de Agricultura y Pesca Alimentación y Medio Ambiente (MAPAMA) Estadística General de Incendios Forestales. Memoria 2017, 32. Available online: (accessed on 5 February 2018).
  2. Camia, A.; Durrant, T.; San-Miguel Ayanz, J. Harmonized classification scheme of fire causes in the EU adopted for the European Fire Database of EFFIS; Scientific and Technical Research Report for UN: Luxemburg, 2013; pp. 1–51. [Google Scholar] [CrossRef]
  3. Prestemon, J.P.; Butry, D.T. Wildland arson management. In The Economics of Forest Disturbances; Holmes, T., Prestemon, J.P., Abt, K., Eds.; Springer Science & Business Media: Dordrecht, The Netherlands, 2008; pp. 123–147. [Google Scholar]
  4. Simon, L.; Clément, V.; Pech, P. Forestry disputes in provincial France during the nineteenth century: The case of the Montagne de Lure. J. Hist. Geogr. 2007, 33, 335–351. [Google Scholar] [CrossRef]
  5. Álvarez, P.; De la Fuente, E.I.; García, J.; De la Fuente, L. Psychosocial variables in the determination of the verdict object in trials for environmental crimes in Spain. Environ. Behav. 2009, 41, 509–525. [Google Scholar] [CrossRef]
  6. Fuentes-Santos, I.; Marey-Pérez, M.F.; González-Manteiga, W. Forest fire spatial pattern analysis in Galicia (NW Spain). J. Environ. Manag. 2013, 128, 30–42. [Google Scholar] [CrossRef] [PubMed]
  7. Molina, D.M. Origins of Arson in Northwestern Spain. Fire Manag. Notes 1997, 57, 18–23. [Google Scholar] [CrossRef]
  8. Cabana Iglesia, A. Los incendios en el monte comunal gallego. Lugo durante el primer franquismo. Hist. Agrar. 2007, 17, 555–577. [Google Scholar]
  9. Ganteaume, A.; Camia, A.; Jappiot, M.; San-Miguel-Ayanz, J.; Long-Fournel, M.; Lampin, C. A review of the main driving factors of forest fire ignition over Europe. Environ. Manag. 2013, 51, 651–662. [Google Scholar] [CrossRef] [PubMed]
  10. Vega-Garcia, C.; Woodard, P.M.; Titus, S.J.; Adamowicz, W.L.; Lee, B.S. A Logit Model for Predicting the Daily Occurrence of Human Caused Forest-Fires. Int. J. Wildl. Fire 1995, 5, 101–111. [Google Scholar] [CrossRef]
  11. Nunes, M.C.S.; Vasconcelos, M.J.; Pereira, J.M.C.; Dasgupta, N.; Alldredge, R.J.; Rego, F.C. Land Cover Type and Fire in Portugal: Do Fires Burn Land Cover Selectively? Landsc. Ecol. 2005, 20, 661–673. [Google Scholar] [CrossRef]
  12. González, J.R.; Palahí, M.; Trasobares, A.; Pukkala, T. A fire probability model for forest stands in Catalonia (North-East Spain). Ann. For. Sci. 2006, 63, 169–176. [Google Scholar] [CrossRef]
  13. Kalabokidis, K.D.; Koutsias, N.; Konstantinidis, P.; Vasilakos, C. Multivariate analysis of landscape wildfire dynamics in a Mediterranean ecosystem of Greece. Area 2007, 39, 392–402. [Google Scholar] [CrossRef]
  14. Garcia-Gonzalo, J.; Zubizarreta-Gerendiain, A.; Ricardo, A.; Marques, S.; Botequim, B.; Borges, J.G.; Oliveira, M.M.; Tomé, M.; Pereira, J.M.C. Modelling wildfire risk in pure and mixed forest stands in Portugal. Allg. Forst und Jagdzeitung 2012, 183, 238–248. [Google Scholar]
  15. Barreal, J.; Loureiro, M.L. Modelling spatial patterns and temporal trends of wildfires in Galicia (NW Spain). For. Syst. 2015, 24, e022. [Google Scholar] [CrossRef][Green Version]
  16. Padilla, M.; Vega-García, C. On the comparative importance of fire danger rating indices and their integration with spatial and temporal variables for predicting daily human-caused fire occurrences in Spain. Int. J. Wildl. Fire 2011, 20, 46–58. [Google Scholar] [CrossRef]
  17. Álvarez, R.; Muñoz, A.; Pesqueira, X.M.; García-Duro, J.; Reyes, O.; Casal, M. Spatial and temporal patterns in structure and diversity of Mediterranean forest of Quercus pyrenaica in relation to fire. For. Ecol. Manag. 2009, 257, 1596–1602. [Google Scholar] [CrossRef]
  18. Ruiz-Benito, P.; Gómez-Aparicio, L.; Zavala, M.A. Large-scale assessment of regeneration and diversity in Mediterranean planted pine forests along ecological gradients. Divers. Distrib. 2012, 18, 1092–1106. [Google Scholar] [CrossRef]
  19. Barbero Bonin, G.; Loisel, R.; Quézel, P.M. Changes and disturbances of forest ecosystems caused by human activities in the western part of the Mediterranean basin. Vegetatio 1990, 87, 151–193. [Google Scholar] [CrossRef]
  20. Schelhaas, M.J.; Hengeveld, G.; Moriondo, M.; Reinds, G.J.; Kundzewicz, Z.W.; ter Maat, H.; Bindi, M. Assessing risk and adaptation options to fires and windstorms in European forestry. Mitig. Adapt. Strateg. Glob. Chang. 2010, 15, 681–701. [Google Scholar] [CrossRef][Green Version]
  21. Sheffer, E. A review of the development of Mediterranean pine-oak ecosystems after land abandonment and afforestation: Are they novel ecosystems? Ann. For. Sci. 2012, 69, 429–443. [Google Scholar] [CrossRef]
  22. Food and Agriculture Organization. Food and Agriculture Organization Global Forest Resources Assessment 2005: Progress towards Sustainable Forest Management; Food and Agriculture Organization: Rome, Italy, 2006; ISBN 9251054819. [Google Scholar]
  23. Nolet, P.; Béland, M. Long-term susceptibility of even-and uneven-aged northern hardwood stands to partial windthrow. Forests 2017, 8. [Google Scholar] [CrossRef]
  24. Schall, P.; Gossner, M.M.; Heinrichs, S.; Fischer, M.; Boch, S.; Prati, D.; Jung, K.; Baumgartner, V.; Blaser, S.; Böhm, S.; et al. The impact of even-aged and uneven-aged forest management on regional biodiversity of multiple taxa in European beech forests. J. Appl. Ecol. 2018, 55, 267–278. [Google Scholar] [CrossRef]
  25. Elia, M.; Lafortezza, R.; Lovreglio, R.; Sanesi, G. Developing Custom Fire Behavior Fuel Models for Mediterranean Wildland–Urban Interfaces in Southern Italy. Environ. Manag. 2015, 56, 754–764. [Google Scholar] [CrossRef] [PubMed]
  26. Zavala, M.A.; Zea, E. Mechanisms maintaining biodiversity in Mediterranean pine-oak forests: Insights from a spatial simulation model. Plant Ecol. 2004, 171, 197–207. [Google Scholar] [CrossRef]
  27. Gömöry, D.; Paule, L. Management implications of intrapopulation and nterpopulation genetic variation in Norway spruce. In Close to Nature Forestry; Novotny, J., Ed.; Forest Research Institute: Zvolen, Slovakia, 2004; pp. 129–137. [Google Scholar]
  28. Pausas, J.G.; Llovet, J.; Rodrigo, A.; Vallejo, R. Are wildfires a disaster in the Mediterranean basin?—A review. Int. J. Wildl. Fire 2008, 17, 713. [Google Scholar] [CrossRef]
  29. Vega-García, C.; Chuvieco, E. Applying local measures of spatial heterogeneity to Landsat-TM images for predicting wildfire occurrence in Mediterranean landscapes. Landsc. Ecol. 2006, 21, 595–605. [Google Scholar] [CrossRef]
  30. Martínez, J.; Vega-Garcia, C.; Chuvieco, E. Human-caused wildfire risk rating for prevention planning in Spain. J. Environ. Manag. 2009, 90, 1241–1252. [Google Scholar] [CrossRef] [PubMed]
  31. Badia, A. Causality and management of forest fires in Mediterranean environments: an example from Catalonia. Glob. Environ. Chang. Part B Environ. Hazards 2002, 4, 23–32. [Google Scholar] [CrossRef]
  32. González, J.R.; Pukkala, T. Characterization of forest fires in Catalonia (north-east Spain). Eur. J. For. Res. 2007, 126, 421–429. [Google Scholar] [CrossRef]
  33. Moreno, J.M.; Vazquez, A.; Velez, R. Recent history of forest fires in Spain. In Large forest fires; Moreno, J., Ed.; Backhuys Publishers: Leiden, The Netherlands, 1998; pp. 159–185. [Google Scholar]
  34. Pausas, J.G.; Fernandez-Muñoz, S. Fire regime changes in the Western Mediterranean Basin: from fuel-limited to drought-driven fire regime. Clim. Change 2012, 110, 215–226. [Google Scholar] [CrossRef]
  35. Jiménez-Ruano, A.; Rodrigues Mimbrero, M.; de la Riva, J. Exploring spatial—Temporal dynamics of fire regime features in mainland Spain. Nat. Hazards Earth Syst. Sci. 2017, 17, 1697–1711. [Google Scholar] [CrossRef]
  36. Claessens, S.; Dell’Ariccia, G.; Igan, D.; Laeven, L. Cross-country experiences and policy implications from the global financial crisis. Glob. linkages Glob. policies 2010, 25, 267–293. [Google Scholar] [CrossRef]
  37. Collantes, F.; Pinilla, V.; Sáez, L.A.; Silvestre, J. Reducing depopulation in rural Spain: The impact of immigration. Popul. Space Place 2014, 20, 606–621. [Google Scholar] [CrossRef]
  38. WWF/Adena ¿Recuperando bosques o plantando incendios? Un análisis de WWF sobre el futuro de los grandes incendios forestales en España. 2010. Available online: (accessed on 15 June 2018).
  39. Ruiz de la Torre, J. Memoria general del Mapa Forestal de España, 1:200000. Ministerio de Agricultura, Pesca y Alimentación. ICONA, 1990; 191. [Google Scholar]
  40. Costafreda-Aumedes, S.; Garcia-Martin, A.; Vega-Garcia, C. The relationship between landscape patterns and human-caused fire occurrence in Spain. For. Syst. 2013, 22, 71–81. [Google Scholar] [CrossRef]
  41. Instituto Nacional para la Conservación de la Naturaleza (Espanya). Memoria sobre las actividades de este Instituto, ICONA (1973-1982); Ministerio de Agricultura: Madrid, Spain, 1983.
  42. Rothermel, R.C. A Mathematical Model for Predicting Fire Spread in Wildland Fuels. USDA For. Serv. Res. Pap. INT USA 1972, 40. Available online: (accessed on 20 June 2018).
  43. Costafreda-Aumedes, S.; Vega-Garcia, C. ANN multivariate analysis of factors that influence human-caused multiple fire starts. In Advances in Forest Fire Research; Imprensa da Universidade de Coimbra, 2014; Volume 4, pp. 1787–1798. [Google Scholar] [CrossRef]
  44. Vasconcelos, M.J.P.; Silva, S.; Tomé, M.; Alvim, M.; Cardoso Pereira, J.M. Spatial Prediction of Fire Ignition Probabilities: Comparing Logistic Regression and Neural Networks. Photogramm. Eng. Remote Sens. 2001, 67, 73–81. [Google Scholar]
  45. Scrinzi, G.; Marzullo, L.; Galvagni, D. Development of a neural network model to update forest distribution data for managed alpine stands. Ecol. Modell. 2007, 206, 331–346. [Google Scholar] [CrossRef]
  46. Fahlman, S.E.; Lebiere, C. The Cascade-Correlation Learning Architecture. In Advances in Neural Information Processing Systems 2; Morgan Kaufmann Publisher: San Francisco, CA, USA, 1990; pp. 524–532. [Google Scholar]
  47. NeuralWare NeuralWorks Predict, The Complete Solution for Neural Data Modelling; User’s Guide, Version 3.24; NeuralWare: Carnergie, PA, USA, 2009.
  48. Alcázar, J.; Palau, A.; Vega-Garcia, C. A neural net model for environmental flow estimation at the Ebro River Basin, Spain. J. Hydrol. 2008, 349, 44–55. [Google Scholar] [CrossRef]
  49. Debouk, H.; Riera-Tatché, R.; Vega-García, C. Assessing Post-Fire Regeneration in a Mediterranean Mixed Forest Using Lidar Data and Artificial Neural Networks. Photogramm. Eng. Remote Sens. 2013, 79, 1121–1130. [Google Scholar] [CrossRef]
  50. Werbos, P.J. The Roots of Backpropagation: From Ordered Derivatives to Neural Networks and Political Forecasting; Wiley-Interscience: New York, NY, USA, 1994; ISBN 0-471-59897-6. [Google Scholar]
  51. Jutras, P.; Prasher, S.O.; Mehuys, G.R. Prediction of street tree morphological parameters using artificial neural networks. Comput. Electron. Agric. 2009, 67, 9–17. [Google Scholar] [CrossRef]
  52. Castroviejo, S.; García-Dorny, M.A.; Martínez, S.; Prieto, F. Política forestal en España (1940–1985). Quercus 1985, 19, 5–51. [Google Scholar]
  53. Ministerio de Agricultura y Pesca Alimentación. Segundo Inventario Forestal Nacional 198-1995. Explicaciones y métodos; ICONA, Ed.; EGRAF S.A.: Madrid, Spain, 2007; ISBN 84-85496-60-4. [Google Scholar]
  54. Cardil, A.; Molina, D. Large wildland fires in three diverse regions in Spain from 1978 to 2010. For. Syst. 2013, 22, 526–534. [Google Scholar] [CrossRef]
  55. Dimitrakopoulos, A.P. Mediterranean fuel models and potential fire behaviour in Greece. Int. J. Wildl. Fire 2002, 11, 127–130. [Google Scholar] [CrossRef]
  56. Fernandez, C.; Vega, J.A.; Gras, J.M.; Fonturbel, T. Changes in water yield after a sequence of perturbations and forest management practices in an Eucalyptus globulus Labill. watershed in Northern Spain. For. Ecol. Manag. 2006, 234, 275–281. [Google Scholar] [CrossRef]
  57. Prasad, V.K.; Badarinath, K.V.S.; Eaturu, A. Biophysical and anthropogenic controls of forest fires in the Deccan Plateau, India. J. Environ. Manag. 2008, 86, 1–13. [Google Scholar] [CrossRef] [PubMed]
  58. Moreno, J.M.; Viedma, O.; Zavala, G.; Luna, B. Landscape variables influencing forest fires in central Spain. Int. J. Wildl. Fire 2011, 20, 678–689. [Google Scholar] [CrossRef]
  59. Finney, M.A. Mechanistic modeling of landscape fire patterns. In Spatial Modeling of Forest Landscapes: Approaches and Applications. Cambridge University Press, Cambridge; Mladenoff, D.J., Baker, W., Eds.; University Press: Cambrige, MA, USA, 1999; pp. 186–209. [Google Scholar]
  60. Pollet, J.; Omi, P.N. Effect of thinning and prescribed burning on crown fire severity in ponderosa pine forests. Int. J. Wildl. Fire 2002, 11, 1–10. [Google Scholar] [CrossRef]
  61. Tragsatec. Plan de selvicultura preventiva de incendios en los sistemas forestales de la Comunidad Valenciana. Memoria 1996. 420 pags. Available online: (accessed on 10 September 2018).
  62. Pasalodos-Tato, M.; Pukkala, T.; Rojo Alboreca, A. Optimal management of Pinus pinaster in Galicia (Spain) under risk of fire. Int. J. Wildl. Fire 2010, 19, 937–948. [Google Scholar] [CrossRef]
  63. Casals, P.; Valor, T.; Besalú, A.; Molina-Terrén, D. Understory fuel load and structure eight to nine years after prescribed burning in Mediterranean pine forests. For. Ecol. Manag. 2016, 362, 156–168. [Google Scholar] [CrossRef]
  64. Fernandes, P.M. Scientific support to prescribed underburning in southern Europe: What do we know? Sci. Total Environ. 2018, 630, 340–348. [Google Scholar] [CrossRef] [PubMed]
  65. Piqué, M.; Domènech, R. Effectiveness of mechanical thinning and prescribed burning on fire behavior in Pinus nigra forests in NE Spain. Sci. Total Environ. 2018, 618, 1539–1546. [Google Scholar] [CrossRef] [PubMed]
  66. Palmero-Iniesta, M.; Domènech, R.; Molina-Terrén, D.; Espelta, J.M. Fire behavior in Pinus halepensis thickets: Effects of thinning and woody debris decomposition in two rainfall scenarios. For. Ecol. Manag. 2017, 404, 230–240. [Google Scholar] [CrossRef]
  67. Molina, J.R.; García, J.P.; Fernández, J.J.; Rodríguez y Silva, F. Prescribed fire experiences on crop residue removal for biomass exploitations. Application to the maritime pine forests in the Mediterranean Basin. Sci. Total Environ. 2018, 612, 63–70. [Google Scholar] [CrossRef] [PubMed]
Figure 1. Map of reforested areas in Spain. (Source: adapted from Ruiz de la Torre, 1990)
Figure 1. Map of reforested areas in Spain. (Source: adapted from Ruiz de la Torre, 1990)
Forests 10 00229 g001
Figure 2. Frequency of ignition points within reforestations in Spain.
Figure 2. Frequency of ignition points within reforestations in Spain.
Forests 10 00229 g002
Table 1. Description of the 22 independent variables. The description of the fuel types is according to the EGIF database.
Table 1. Description of the 22 independent variables. The description of the fuel types is according to the EGIF database.
AGEMean AgeFTP5Grasses and bushes
CCCanopy coverFTP6Grasses and forest
SDSeedling stageFTP7Grasses and slash
SSSapling stage FTP8Bushes and forest
THThicket stageFTP9Bushes and slash
PSPole stageFTP10Forest and slash
HFSHigh-forest FTP11Grasses, bushes, and forest
FTP1GrassesFTP12Grasses, bushes and slash
FTP2BushesFTP13Grasses, forest and slash
FTP3ForestFTP14Bushes, forests and slash
FTP4Wood slashFTP15Grasses, bushes, forest, and slash
Table 2. Database definition and number of fire observations used for model building.
Table 2. Database definition and number of fire observations used for model building.
Dependent VariablesSurface FeaturesNumber of Observations
Fire typeReforested stands (All species) (RF_FT)1524
Not reforested stands (All species) (NRF_FT)1136
Reforested Pine sp. (RFPINE_FT)122
Natural Pine stands (NRFPINE_FT)488
Reforested P. pinaster (RFPP_FT)1054
Natural stands of P. pinaster Ait. (NRFPP_FT)167
Eucalyptus sp. (RFE_FT)330
Burned areaReforestations (RF_BA)1291
Not Reforested stands (NRF_BA)923
Reforested Pine sp. (RFPINE_BA)59
Natural Pine stands (NRFPINE_BA)306
Reforested P. Pinaster (RFPP_BA)886
Natural stands of P. Pinaster (NRFPP_BA)125
Eucalyptus sp. (RFE_BA)298
Total tree burned areaReforestations (RF_TBA)924
Not reforested stands (NRF_TBA)597
Reforested Pine sp. (RFPINE_TBA)40
Natural Pine stands (NRFPINE_TBA)146
Reforested P. pinaster (RFPP_TBA)657
Natural stands of P. pinaster (NRFPP_TBA)98
Eucalyptus sp. (RFE_TBA)192
Table 3. Comparison between the number of ignition points located in reforested and natural stand by species (1998–2008) (source: adapted from Castroviejo et al. [52] and the II National Forest Inventory [53]).
Table 3. Comparison between the number of ignition points located in reforested and natural stand by species (1998–2008) (source: adapted from Castroviejo et al. [52] and the II National Forest Inventory [53]).
SpeciesTotal RF Surface (ha)N° Ignition Points in RF (N° Ig/100 ha)Total NRF (ha)N° Ignition Points in NRF (N° Ig/100 ha)
P. pinaster782,41414,908
P. sylvestris552,973663
P. halepensis466,282535
Eucalyptus sp.431,0124633
P. nigra378,542255
P. pinea212,761112
Populus sp.50,519695
Not available
Other hardwoods30,03321
Not available
P. uncinata17,3221
Table 4. Best model architecture and confusion matrix for the classification of Fire Type within reforested and not reforested stands (all species).
Table 4. Best model architecture and confusion matrix for the classification of Fire Type within reforested and not reforested stands (all species).
N° of Obs.1524Model RF_FT (network 6-0-2)
AccuracyObservedPred. Crown FirePred. Surface FireTotal
Train0.98Crown Fire4355440
0.73Surface Fire108305413
Test0.96Crown Fire1756181
0.73Surface Fire50136186
Valid0.98Crown Fire1392141
0.74Surface Fire41122163
N° of Obs. 1136Model NRF_FT (network 8-1-2)
Train0.95Crown Fire29814312
0.46Surface Fire173151324
Test0.95Crown Fire1266132
0.41Surface Fire8358141
Valid0.95Crown Fire1195124
0.40Surface Fire6142103
Table 5. Sensitivity analysis of the best models for the classification of Fire Type within reforested and not reforested stands (all species).
Table 5. Sensitivity analysis of the best models for the classification of Fire Type within reforested and not reforested stands (all species).
Input variables in the best RF_FT model
Crown fire−0.03280.03050.0218−0.5766−0.0622
Surface fire0.0328−0.0305−0.02180.57660.0622
Crown fire0.00130.00040.00020.14260.0016
Surface fire0.00130.000400.14260.0016
Frequency 0.690.650.960.940.93
Input variables in the best NRF_FT model
Crown fire−0.2050.097−0.008−0.025−0.917−0.618−0.076−0.493
Surface fire0.205−0.0970.0080.0250.9170.61820.0760.493
Crown fire0.010.0019.0137.7190.0980.044800.028
Surface fire0.010.0019.0137.7190.0980.044800.028
Frequency 0.7510.
Table 6. Best model architecture and confusion matrix for the classification of Fire Type for continental Mediterranean mountain pines in reforested and natural stands.
Table 6. Best model architecture and confusion matrix for the classification of Fire Type for continental Mediterranean mountain pines in reforested and natural stands.
N° of Obs. 122Model RFPINE_FT (network 9-0-2)
Data SubsetsAccuracyObservedPred. Crown FirePred. Surface FireTotal
Train0.82Crown fire23528
0.72Surface fire112940
Test0.7Crown fire12517
0.69Surface fire4913
Valid0.62Crown fire10616
0.12Surface fire718
N° of Obs. 488Model NRFPINE_FT (network 7-0-2)
Train0.67Crown fire8642128
0.55Surface fire6481145
Test0.67Crown fire422062
0.55Surface fire253156
Valid0.64Crown fire351954
0.6Surface fire172643
Table 7. Sensitivity analysis of the best models for the classification of Fire Type within reforested and not reforested stands (Pine Group).
Table 7. Sensitivity analysis of the best models for the classification of Fire Type within reforested and not reforested stands (Pine Group).
Input variables in the best RFPINE_FT model
Crown fire0.8290.2280.207−0.234−1.0550.0450.096
Surface fire−0.829−0.228−0.2070.2341.055−0.045−0.096
Crown fire0.2360.0060.0050.0060.11900.001
Surface fire0.2360.0060.0050.0060.11900.001
Frequency 0.920.8510.520.830.350.45
Input variables in the best NRFPINE_FT model
Crown fire0.1980.003−0.007−0.3180.0910.1050.066
Surface fire−0.198−0.0030.0070.318−0.091−0.105−0.066
Crown fire0.001000.002000
Surface fire0.001000.002000
Frequency 0.930.380.67110.710.71
Table 8. Best model architecture and confusion matrix for the classification of Fire Type within reforested and not reforested stands of P. pinaster.
Table 8. Best model architecture and confusion matrix for the classification of Fire Type within reforested and not reforested stands of P. pinaster.
N° of Obs. 1053Model RFPP_FT (network 6-0-2)
Data SubsetsAccuracyObservedPred. Crown FirePred. Surface FireTotal
Train0.98Crown Fire2705275
0.79Surface Fire66249315
Test1Crown Fire1280128
0.81Surface Fire23102125
Valid0.97Crown Fire1033106
0.77Surface Fire2381104
N° of Obs. 166Model NRFPP_FT stands (network 5-2-2)
Train0.98Crown fire57158
0.71Surface fire102535
Test0.9Crown fire19221
0.73Surface fire51419
Valid0.83Crown fire15318
0.6Surface fire6915
Table 9. Sensitivity analysis of the best models for the classification of Fire Type within reforested and not reforested stands (P. pinaster).
Table 9. Sensitivity analysis of the best models for the classification of Fire Type within reforested and not reforested stands (P. pinaster).
Input variables in the best RFPP_FT model
AverageCrown fire−0.092−0.0120.0210.0220.001−0,499
Surface fire0.0920.012−0.021−0.022−0.0010,499
VarianceCrown fire0.0047.183001.6110,122
Surface fire0.0047.183001.6110,122
Frequency 0.640.860.210.320.360,57
Input variables in NRFPP_FT model
AverageCrown fire−0.997−0.213−0.578−1.273−1.069
Surface fire0.9970.2130.5781.2731.069
VarianceCrown fire0.3030.0130.1020.4950.348
Surface fire0.3030.0130.1020.4950.348
Frequency 10.310.920.540.66
Table 10. Best model architecture and confusion matrix for the classification of Fire Type in Eucalyptus sp.
Table 10. Best model architecture and confusion matrix for the classification of Fire Type in Eucalyptus sp.
N° of Obs. 330Model RFE_FT (network 6-0-2)
Data SubsetsAccuracyObservedPred. Crown FirePred. Surface FireTotal
Train0.95Crown fire83487
0.77Surface fire227597
Test0.92Crown fire38341
0.84Surface fire63339
Valid0.96Crown fire30131
0.74Surface fire92635
Table 11. Sensitivity analysis of the best model for the classification of Fire Type within reforested stands of Eucalyptus sp.
Table 11. Sensitivity analysis of the best model for the classification of Fire Type within reforested stands of Eucalyptus sp.
Input Variables in the Best RFE_FT Model
AverageCrown fire−0.004−0.0970.3610.3480.514−0.05
Surface fire0.0040.097−0.361−0.348−0.5140.05
VarianceCrown fire0.0020.0060.0780.0720.1580.001
Surface fire0.0020.0060.0780.0720.1580.001

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Da Ponte, E.; Costafreda-Aumedes, S.; Vega-Garcia, C. Lessons Learned from Arson Wildfire Incidence in Reforestations and Natural Stands in Spain. Forests 2019, 10, 229.

AMA Style

Da Ponte E, Costafreda-Aumedes S, Vega-Garcia C. Lessons Learned from Arson Wildfire Incidence in Reforestations and Natural Stands in Spain. Forests. 2019; 10(3):229.

Chicago/Turabian Style

Da Ponte, Emmanuel, Sergi Costafreda-Aumedes, and Cristina Vega-Garcia. 2019. "Lessons Learned from Arson Wildfire Incidence in Reforestations and Natural Stands in Spain" Forests 10, no. 3: 229.

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