1. Introduction
One major environmental concern is the occurrence of forest fires, that affect forest preservation, create economic and ecological damage, and cause human suffering. Spain is among the top five European countries with the highest number of wildfires. In the NW region of Galicia, forest fires are one of the natural hazards. In 2019, 1676 forest fires were registered in the Galician region, covering 13.691 ha [
1].
Human caused fires are initiated by accidents, negligence, or arson. Road access is a significant contributing factor in the occurrence of human caused ignitions [
2]. The 70% of forest fires occur close to main roads, at a distance of less than 500 m [
3]. The increased ignition risk related to increasing housing and road density may be additionally modulated by the vegetation types crossed by roads [
2]. Road managers have the knowledge to control roadside vegetation through preventive operations including herb mowing, applying herbicide products, clearing, or pruning vegetation. The assessment of the ignition potential of roadside vegetation plays a cornerstone role in prevention management. The best method to mitigate the likelihood of fire ignition is decreasing the roadside flammability [
4]. The current law regarding forest firefighting in Galicia [
5] establishes a forest-to-road distance threshold between 4 and 10 m, depending on tree species and road class. Targeting fuel treatments and locating resources to areas where fire ignitions are predicted to occur may be effective in improving the probability in the fire containment [
6].
Fire risk could be defined as the probability of a forest fire occurring as well as the potential damage it could cause in a given place (vulnerability) [
7]. The susceptibility mapping or spatial fire prediction consists of locating where a forest fire will likely occur [
8]. Further, the current technology applied in the control of such natural events has three component categories: predicting, monitoring and prevention [
9]. Therefore, fire risk mapping is essential to plan the maintenance actions on vegetation.
Regarding the scale, there are studies of long-term, short-term, or in real-time. Considering spatial scales there are global, regional, or local studies. Long-term risk is linked to features of a territory that do not vary periodically (topography or vegetation types) and it is more suitable to improve overall forest management planning [
10]. On the other hand, short-term risk is linked to changeable factors (vegetation stress or climate conditions) and is updated using a time scale that can eventually be daily or hourly, with mainly operative usefulness [
11]. From a geographic point of view, global wildfires are linked to long-term approaches [
12]. Regional risk mapping is especially suitable for national and regional orientation of environment, such as forest polices and legislation [
13]. Local risk maps are refereed to extensions below those aforementioned which are especially to be included in land management, wildfire risk communication, and emergency planning, at the municipal or local community level [
14].
Fire occurrence, frequency, and intensity primarily depend on weather conditions and vegetation fuel load, which contribute to ignition of and to sustaining fire, respectively [
15]. Identification of the greatest influence variables on fire occurrence is essential for modeling fire risk [
16]. The main factors which contribute to fire ignition and propagation are topography (elevation, slope and aspect), vegetation, weather, and human factors [
17]. Several common variables, slope, aspect, elevation, distance from road, distance from settlement, and land use cover are used to generate forest fire risk maps [
7,
18,
19,
20,
21,
22]. There are studies which include other parameters such as normalized difference vegetation index (NDVI) and climate parameters (annual air temperature, annual precipitation, and wind speed) [
23]. On the other hand, there are several studies which include the fire weather index (FWI) [
24] or fire historic to map forest fire risk areas [
25] and have pointed-out the suitability of the FWI as a fire danger indicator for different Mediterranean climates and forest types [
26,
27].
Remote sensing technologies is a frequently used method to assess vegetation conditions. LiDAR (light detection and ranging) is an active remote sensing technology that can accurately measure three-dimensional vegetation structure over large areas efficiently, in comparison to other traditional methodologies. The use of LiDAR data to map fuel types is subject of current research [
28,
29]. Having up to date and accurate fuel type maps is fundamental to properly manage wildland fire risk areas [
30]. Thus, the integration of this parameter in forest fire risk maps is an innovative and interesting aspect which provides significant fuel information.
Many studies have been executed to produce forest fire maps using geographic information systems (GIS) and remote sensing (RS) techniques [
22,
31,
32,
33,
34].
Several techniques are used in the forest fire risk maps generation, such as knowledge-based methods in combination with GIS [
21,
23,
35,
36,
37], logistic regression [
38], analytic hierarchy process (AHP) [
39,
40,
41], fuzzy logic [
42], goal programming (GP) and analytical network process [
43], artificial neural networks (ANN) [
44], and random forest [
45].
Capabilities of GIS and multi criteria decision analysis (MCDA) have been combined to solve a wide range of spatial problems [
46,
47]. The integration of MCDAs with the capabilities of GIS provides a smart spatial modelling methodology for identifying the relative significance of indicators. The AHP method approaches decision-making by arranging the important components of a problem into a hierarchical structure like a family tree. The AHP method reduces complex decisions into a series of simple comparisons, called pairwise comparison, between elements of the decision hierarchy [
17].
In general, expert knowledge plays a fundamental role in fire risk modelling. In this context, the criteria weights determined by AHP and GIS software are used as a support to determine the fire risk areas. Furthermore, the opinion of five experts were considered to derive criteria weights who collaborate in the present research. The purpose of this study is to define and implement a forest fire risk assessment for fire risk mapping, by combining several attributes. The vegetation management could be optimized by development of fire risk maps around roads.
The specific objectives of this study are to:
Investigate and determine the main factors affecting fire risk in the study area.
Determine the weight for each factor influencing forest fire risk.
Improve existing information about the generation of fuel and flammability models by LiDAR data analysis, generate vegetation continuity covers, and apply forest fire risk weather index based on the weather conditions in the area.
Develop a methodology to automatically calculate main factors involved in forest fire risk map.
Report on fire risks obtained and establish the recommendations to road managers focusing on mitigation measures or actions.
3. Results
3.1. Base Layers
As defined in
Table 2, base layer classification ranges from 1-very low, 2-low, 3-moderate, 4-high to 5-very high (value 5). The null values were one of the results obtained in the process of raster generation and correspond with pixels assignment of zero value which matched with road and buildings. Elevation parameter ranges from 461 to 582 m above mean see level (MSL) in the study area A, while in the study area B ranges from 477 to 551 m above MSL. The classified DTM map shows a 3-moderate index for both areas. The study area A presents a maximum slope of 51° and 65° of slope in study area B, that corresponds to the embankment roads. Both study areas have a predominant northeast orientation that receive less hours of sunshine in the energy balance of the year comparing to the rest of orientations.
Figure 8 shows the result of classification for both factors, where we can highlight a 5-very high value for slope layer in study area A, while study area B exhibits the higher risk in the edges of the motorway. However, the mean value consists of 3-moderate risk.
Regarding vegetation characterization and fuel models, the most representative models for study area A are models 3 and 5, which represent a total of 1.3 ha (42%) and 1.5 ha (48%), respectively. There is also a presence, but to a lesser extent, of fuels models 4 (0.02 ha) and 6 (0.27 ha). In study area B, fuel models 3–6 are present, with a total of 4.22 ha (36%), 1.11 ha (9%), 5.04 ha (43%), and 1.30 ha (11%). In addition, there is a presence of fuel model type 2 (0.01 ha) in the margins of AG-31 motorway. An important issue to be highlighted is the presence of fuel type 5 very near the roadsides, a focus item for prevention.
NDVI values range between 0.6 and 0.80 for forest areas and 0.15–0.16 for rocks and low vegetation. According with NDVI results, the highest fire risk is in the shrub area in study area A. The road surroundings in study area B includes pixels with a wide range from 1-very low to 5-very high, where the average value is 2 and mode is 1.
Figure 9 shows the results of the fuel model types and NDVI layers classification. It is important to highlight that NDVI values for road and building areas were catalogued as null to avoid overestimation.
Temperature, wind, and moisture conditions for the dates used in the study, 14 and 15 of August, were not extreme the value of temperature being high (30 °C), the wind speed is low (9–11 km/h), and the relative humidity of combustible does not present a danger value in case of forest fire with a value between 40% and 50%.
Table 3 shows the results of the meteorological raster data obtained in both study areas and FWI calculated components.
Meteorologic data is the basis for FWI calculation where the codes provide insightful partial results. The FFMC value indicates the moisture content in fine fuels, showing an average value of 89, a high score that exhibits a low ignition probability. This fact is coherent with the average value of 6 for the ISI factor, that indicates a low ignition probability. DMC and DC average values of 10 and 21, respectively, indicate a low amount of fuel available for combustion. These partial results support a FWI value of 7.01 for study area A and 7.17 for study area B, ensuing a FWI layer rated with a 2-low risk factor for both areas.
Regarding the fire historical layers analysis,
Table 4 shows the forest fires registered, the burnt area (ha), and the recurrence years of fires in the Lobios municipality since 2001.
These fires did not strictly affect study area A, that is catalogued as Null in the map, but it is crucial to remark the anthropogenic fire risk index due to fire recurrence.
The Celanova municipality located in the study area B only presents a registered forest fire in 2005, with a total 243 ha burned that occurred far away from the study area B, resulting in a Null classification.
Regarding anthropogenic issues, the study areas are within the first described road buffer of 300 m and were classified as 5-very high risk. By taking settlements distances into account, study area B is located within the 500 m buffer while study area A is located within the buffer settlement of 1000 m. The layers were classified as 5-very high-risk index for the study area B, and a 4-high risk index in the study area A.
3.2. Forest Fire Risk Mapping
Table 5 and
Table 6 show the results of the AHP, where
Table 5 allocates the results of weighting criteria factors and
Table 6 shows weights of groups. The lowest weight corresponds to the fire historical variable, while the highest weight corresponds to vegetation parameters.
The CR of the confusion matrix calculated is 0.080, and thus, the matrix is consistent enough [
65]. The weights vector derived from the comparison matrix are used to obtain the equation for the fire risk as given in Equation (5).
where FR is fire risk, V is vegetation type, NDVI is normalized difference vegetation index, FMT is fuel model types, T is topography, A is aspect, S is slope, E is elevation, AI is anthropogenic issues, DR is road distance, DS is distance settlement distance, FWI is fire weather index, and FH is fire historical.
Figure 10 shows the final risk map where each pixel has a value between 1 and 5 according to the previous classification.
The forest fire risk map layer was created merging layers of different resolution. Aspect, slope, elevation, fuel types, distance from roads, and settlement layers have 2 m of resolution, while NDVI has 20 m and FWI and fire historic layers have 500 m of resolution. The resolution of 2 m was determined for the output layer. Layers with different resolution were resampled according to the nearest neighbors algorithm.
The forest fire risk map shows that the vegetation factor played a key role. Slope is an important factor in forest fire risk being very high in the surroundings of road, which is visible in the study area B, with areas classified as high possibility of forest fire with a 4 value. FWI obviously decreases the susceptibility of forest fire ignition.
The Study area A map resulted in two classes, low and moderate forest fire risk, which represent a total area of 2.25 and 2.77 ha, respectively. In study area B, forest fire risk map resulted, low, moderate, and high, which represent a total area of 6.81, 12.96, and 0.23 ha, respectively.
4. Discussion
This study presented a workflow for forest fire risk mapping around roads based on several data sources. Results show fire key variables and their weight in an AHP decision support system. The contribution of LiDAR allows the analysis of the vegetation structure and its integration on the forest fire risk map. The fuel information provides an important weight of 0.750 in the forest fire risk map calculation. Moreover, this methodology can be applied to other areas with higher dimensions, for example, when LiDAR data are available in a public spatial data infrastructure (SDI).
Most fire incidents happen in low elevation areas, due to numerous factors such as human presence, decrease in relative humidity, and the increase in the temperature [
63]. Due to this, topography, anthropogenic, and meteorological parameters are fundamental in the study being the meteorological parameter considered the most important of the three with a weight of 0.298 and the topography the lower weight assignment with 0.108. The anthropogenic parameter was considered more important than topography with an assignment of 0.180.
In residential areas and near roads, more human activities are witnessed, and the human activity is the most significant factor in the fire outbreak [
67]. In this study, anthropogenic parameters have a value of 0.180, of which road distances has a weight of 0.750, while settlement distances have a weight of 0.250. The weights indicated that the factor of vegetation group has the highest significance and affectation. This statement is in accordance with the results achieved by Valdrevu et al. [
47] and Rassoli et al. [
63].
In contrast, the historical fires parameter is less important than other factors with a weight assignment of 0.055. Historical fires were used as fire risk parameter in this study. The study of Yathish et al. [
15] used the fire occurrence layer for calibration and validation, considering land surface temperature as the only meteorological parameter. In this paper, the FWI integration was investigated as a parameter in the fire risk mapping which allow detailed information about the moisture effects in fuel and wind in the behavior of fire being of great importance in the fire risk prevention and providing more concrete information than meteorological data.
The results of Gigović et al. [
40] also showed that land use is an important parameter in forest fire modelling, which is consistent with the results of our research. The land use parameter is comparable to the vegetation fuel type parameter used in the present study. However, the results of Gigović et al. [
40] showed the high importance of distance between roads and settlement, which have more importance than climate, in contrast to the present study. The weights obtained for the FWI parameter and the human factors were 0.298 and 0.180, respectively. FWI provides meteorological information directly related to forest fire risk and its probability of ignition and propagation, while the meteorological conditions do not provide information on fuels state. This fact supports the application of FWI as an influence parameter in forest fire risk mapping.
Suryabhagavan et al. [
37] in their study, showed that the most important parameter in the fire risk is the vegetation, followed by slope. The lower values of weight parameters are for settlement and road distances. The attribution of weight of the parameters are similar with this study. The lower values were also attributed to the anthropogenic issues and the higher weight was assigned to the vegetation parameter. The difference in both studies regardless of the FWI parameter used in the present study is in line with the research done.
Vallejo et al. [
7] remarked that spatial cadastral information of buildings and roads could address the main limitations in their study and improve the classification of fuel types along with the analysis of transport networks. In the present work, these topics were covered with the use of cadaster layers to adapt fuel condition based on NDVI and, hence, buildings and roads were clipped to filter misleading or wrong values. The vegetation information used by Vallejo et al., was obtained from Landsat- 7 images with a resolution of 15 m. In comparison, the LiDAR data used in this study has a mean spacing between points of 0.05 and 2 m of resolution in raster transformation which allows for a more detailed study of the vegetation structure parameters.
In the study by Gheshalghi [
21], a GIS-based analytical network process was used to provide a fire risk map using slope, aspect, altitude, land cover, NDVI, annual rainfall, temperature, distance to settlements, and distance to roads as input layers. The classification of the parameters and their weights is very similar to that followed in the present study. The highest weight consists of the climatic group, giving an annual rainfall weight of 0.17713 followed by NDVI with 0.16445 value. Lowest values were obtained for distance to roads and distance to settlements with 0.05499 and 0.07056 values, respectively. The conclusions include the consideration that the most important parameters were vegetation and climate, which is coherent with the results presented in this manuscript.
The study of Coelho et al. [
36] showed the importance of rainfall and temperature parameters in a forest fire. The land use parameter had the higher weight assigned to pasture and planted forest, as the vegetation parameter in the present study, being considered fuel types 5 and 7 as the most dangerous regarding forest fire risk.
The methodology used by Eskandari [
39] used fuzzy AHP and GIS to estimate the weights of the parameters affecting forest fire mapping, including distances to farmland, roads, settlement, and rivers in addition to slope, aspect, and elevation. The results of fuzzy weighting show that human factor, with a value of 0.301, and biologic factors were the most important parameters to be considered. In the study of Eskandari, the topography group has a value 0.2517 being the least considered parameter. Additionally, in the present study, topography group was considered the second least important parameter with a weight of 0.108.
Kayet et al. [
19] in their recent study, showed a comparative of frequency ratio (FR) and AHP models for forest fire risk mapping. The results from FR and AHP showed similar trends (accuracy of 81 and 79%, respectively). In the present study, vegetation group has a value of 0.359, of which NDVI has a weight of 0.250, and FWI parameter has a value of 0.298. These parameters have the highest significance in the fire risk map to the contrary of results achieved by Kayet et al. [
19] where land surface temperature was considered the most important parameter with a value of 0.25 and NDVI parameter has a value of 0.11.
The major difference found with regards to other investigations lies in the size of the study area. This study is based on fire risk around roads, while others are done at the municipally or provincial level. Due to the use of dense point cloud, a detailed analysis of vegetation is obtained in comparison with other studies.
In general terms, the proposed method, and the availability of the required data, allows its straightforward application to a wide range of regions.
5. Conclusions
In this paper, a methodology based on GIS and AHP was developed to determine forest fire risk areas around roads in Northwest Spain. According to the results, vegetation and FWI parameters have a strong influence on forest fire ignition, whereas parameters like historical fire, topography, and humans have a lower weight in risk calculation. The final risk model shows that approximately 50% of study area A and 65% of study area B are classified as moderate fire risk (value 3 out of 5).
Fire risk mapping is a suitable tool to identify and locate areas around roads that are vulnerable and supports roadside vegetation management. For example, herbicidal products application and pruning in areas of special risk like slopes, embankments, and clearing areas which could help to reduce the propagation and intensity of forest fire. At the same time, fire risk maps help transport managers and practitioners to develop and adopt fire emergency plans. As a future trend, sensitivity analysis of AHP could be integrated with other multi criteria decision analysis (MCDA) techniques to improve the results.