Vol. 10 No. 3 April-May 1996 Section 5 Page 333 - Semantic Scholar

int. j. geographical information systems, 1996, vol. 10, no. 3, 333± 345

Research Article Mapping the spatial distribution of forest ® re danger using GIS EMILIO CHUVIECO and JAVIER SALAS Departmento de GeografõÂ a, Universidad de AlcalaÂ de Henares Colegios, 2 28801 AlcalaÂ de Henares, Spain (Received 9 November 1993; accepted 19 July 1994) Abstract. A geographical information system (GIS) is proposed as a suitable tool for mapping the spatial distribution of forest ® re danger. Using a region severely a ected by forest ® res in Central Spain as the study area, topography, meteorological data, fuel models and human-caused risk were mapped and incorporated within a GIS. Three danger maps were generated: probability of ignition, fuel hazard and human risk, and all of them were overlaid in an integrated ® re danger map, based upon the criteria established by the Spanish Forest Service. GIS make it possible to improve our knowledge of the geographical distribution of ® re danger, which is crucial for suppression planning (particularly when hotshot crews are involved) and for elaborating regional ® re defence plans.

1. Introduction Forest ® res are a major concern in Spain, as well as in other Mediterranean countries, because of the environmental and human losses they cause. A total of 2 410916ha were a ected by ® res in Spain between 1980 and 1989, with losses totalling 443450million pesetas (more than 3000million ECU: ICONA 1991). Spain has the largest area of burned land within Europe, both because it is the most forested country within the European Mediterranean basin, and because it has the largest average burned area per ® re. However, considering the whole extent of forest land, Spain has a lower percentage of burned forest (0´9 per cent) than France (1´2 per cent), Italy (1´9 per cent) or Portugal (2´4 per cent) (VeÂlez 1992). Factors related to both climate and human activity help explain the high incidence of forest ® res in the Mediterranean region: 1. Climate produces a long period of plant stress during the summer drought, which translates as low plant moisture and, as a result, high ¯ ammability. 2. Land use patterns have changed in Spain in the last thirty years. Traditionally, land use was balanced and ® re was used to improve grassland yields, but now many people have migrated to the cities and shrubs now cover much of the former grassland, increasing the amount of available fuel. On the other hand, urban residents use forested areas for recreational purposes, which together with careless agricultural practices, causes many ® res (ICONA 1991). 2. Use of GIS in forest ® re danger studies Creating a ® re danger index involves taking into account a wide range of factors, most commonly weather, fuel, and topography (Deeming et al. 1978, Delabraze 1982). (To avoid confusion in forest ® re terminology we have used ® re danger, ® re 0269± 3798/96 $12´00 Ñ

1996 Taylor & Francis Ltd.

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E. Chuvieco and J. Salas

hazard and ® re risk as in Merril and Alexander, 1987). In dealing with all these factors, several variables must be considered, and their spatial integration may be improved using a GIS, where both analytical and geographical relations are taken into account. Several GIS applications have been developed in the last decade to improve management of forest ® res. Three major topics have been covered: ® re danger mapping, fuel management, and ® re e ects assessment. In the ® rst topic, di erent authors have demonstrated the capacity of GIS to improve the spatial analysis of ® re danger indices which are used for ® re-prevention and pre-suppression planning. The indices are mainly based on meteorological data (temperature, humidity, precipitation), which are usually gathered from only a few weather stations although they are used to estimate the conditions a ecting large territories. Therefore, it is assumed that the stations properly characterise the spatial diversity of weather. Unfortunately, most of the time this assumption is far from true, because the weather stations are located in urban areas, at low elevations, and consequently do not represent conditions a ecting forest land well. GIS provides tools for spatial interpolation of weather data, and so a more complete view of the geographical diversity of ® re danger can be obtained. GIS can also spatially integrate several hazard variables, such as vegetation, topography, soil and ® re history, which are only considered from sample areas in traditional ® re danger systems. This capacity has been used to generate locally oriented GIS-based danger models which cover a small area at high resolution (typically from 50 to 100 m grid size) (Cosentino et al. 1981, Brass et al. 1983, Burgan and Shasby 1984, Gum 1985, Yool et al. 1985, Root et al. 1986, van Wyngarden and Dixon, 1989). However, there is some experience with global, low resolution, ® re danger models (Werth et al. 1985; McKinley et al. 1985). The critical point of these systems is the vegetation layer. Several studies have found a close correlation between the spread and intensity of the ® re and fuel characteristics, such as size, plant moisture, compactness and density (Rothermel 1983, Burgan and Rothermel 1984). Fire behaviour models have been developed for di erent forest fuel types (Deeming et al. 1978, Andrews 1986). Several papers have explored the use of satellite remote sensing to generate data for these fuel models through digital image processing, using Landsat-MSS or TM images (Rabii 1979, Shasby et al. 1981, Salazar 1982, Agee and Pickford 1985), and NOAA-AVHRR (Miller and Johnston 1985, Sadowski and Westover 1986). Other variables frequently used for these GIS-based danger indices are weather information, topography and ® re history (Brass et al. 1983, Yool et al. 1985, Chuvieco and Congalton 1989). The following criteria are used for the combination of these variables: (i) Use of qualitative criteria for assigning danger values from the cross-relations of the di erent variables (Brass et al. 1983, Yool et al. 1985) (ii) Adaptation of standard danger indices, such as the U.S. National Fire Danger Rating System (Agee and Pickford 1985) or some modules of BEHAVE (Woods and Gossette 1992) (iii) Creation of new danger models, based upon the selective weighting of the danger variables (Chuvieco and Congalton 1989; Vliegher 1992) (iv) Creation of locally-oriented models, where danger weights for each variable are obtained from multiple regressions computed for that particular area (Chou 1992).

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Most of these models do not include the spatial distribution of human risk because of the di culty in modelling ® re-related activities, such as recreation and arson. This risk factor is particularly important for Mediterranean countries, where most of the ® res are caused by human activity. An approach to solving this di culty has been to model the spatial distribution of human risk indirectly, based upon auxiliary variables like accessibility and ® re incidence (Chuvieco and Congalton 1989, Vega-GarcõÂ a et al. 1993). GIS have also been applied to other ® re management topics. The most promising are the location of look-out towers (Pawlina et al. 1990), dispatch planning (Salazar and Power 1988), ecological evolution after ® re (Lowell and Astroch 1989), and ® re growth simulation (Davis and Burrows 1990, Vasconcelos and Guertin 1992). 3. Objectives This paper presents a methodology for ® re danger mapping using GIS. In order to simulate real conditions as closely as possible and provide an operational basis we have employed the ® re danger system presently used by the Spanish Forest Service (ICONA 1988). Our purpose was to map the spatial distribution of the di erent components used in this system, as well as to integrate them in a simple index. We chose the variables which were most permanently associated with ® re danger, such as topography, fuel types and human activity. The spatial distribution of weather data was generated taking into account the topography of the study area and using both interpolation and extrapolation techniques. This method is an improvement of a previous GIS application to ® re danger mapping (Chuvieco and Congalton 1989). The earlier version considered ® ve variables: vegetation, slope, aspect, elevation and human risk using a selective weighting structure. Some re® nements are presented here to make the model more operational. These can be summarized as follows: new variables (illumination, texture, weather data), more objective criteria for weighting danger variables, and a new scheme for integrating the variables into the synthetic ® re danger index. 4. Study area The study area is located 150km west of the city of Madrid (® gure 1). It includes 588sq km of very rough terrain. Elevations range from 400 to 2000m, and the area

Figure 1. The study area.

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is divided by Sierra de Gredos in the central mountains of the Iberian peninsula. Vegetation in mountain areas is mainly formed by Pinus pinaster and Pinus pinea, with some zones where Castanea sativa and Quercus pyrenaica are more common. Several types of xerophytic shrubs cover the higher elevations: Cytisus purgans and Retama sphaerocarpa are the most frequent. Lowlands include Quercus ilex rotundi folia and resinous shrubs such as Cistus laurifolius and Cistus ladanifer. The climate of this area is characterized by mild temperatures (mean annual between 8ß and 14ß C) and medium-to-high rainfall (between 500 and 1500mm). Topography causes great variety in the area, with higher rainfall on the South slopes. Like most of central Spain, an intense summer drought a ects this area. Only 10 per cent of the annual rain falls in summer (July, August and September), and most of it is caused by severe storms. Temperatures above 30ß C are also fairly common, especially on the Southern slopes. According to these characteristics, there is a high potential danger of forest ® re in the summer season. Indeed this area has been very severely a ected by ® res. Most of them are caused by human activity, either carelessness or arson, but once started they are favoured by the steep slopes, highly ¯ ammable vegetation and intense summer drought. 5. Structure of the ® re danger index The structure of the ® re danger index used in this paper is summarized in ® gure 2. It is based in the system presently used by ICONA, the Spanish institution in charge of forest ® re defence. ICONA’s system takes into account three di erent components: 1. Weather Danger Index (WDI). The most operational of these indices is the WDI (ICONA 1988, 1990b), which is computed three times per day for a set of weather stations covering the main wildland areas of the country in order to organize suppression resources. The WDI includes two factors: probability of ignition (PI) and wind. The former estimates the likelihood of ® ne dead fuel ignition. Its computation is based on air temperature and relative humidity, plus fuel conditions (its topographic situation and exposure). Wind velocity and dryness are considered along with PI to establish three danger categories: low-moderate, high and extreme.

Figure 2. The framework of the forest ® re risk model.


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2. Fuel Hazard Component (FHC)Ð is associated with the rate of ® re-spread for the di erent vegetation fuel types. It considers four categories of fuel models, based on the BEHAVE system: (i) primary carrier of the ® re is grass, (ii) primary carrier of the ® re is brush or litter beneath the brush, (iii) primary carrier of the ® re is litter beneath a timber stand, (iv) primary carrier of the ® re is logging slash (Anderson 1982). 3. Fire Incidence Index (FII)Ð is computed from historical ® re records referenced to a 100km2 UTM grid. This resolution is not applicable to a model trying to map the geographical variability of ® re danger in a small area. Therefore it is simpli® ed here by considering a Human Risk Index (HRI) instead which indicates those areas with a higher probability of being a ected by human activities of ® re risk, such as camping or arson. 6. Variables for the GIS danger model According to the previously discussed ® re danger system, the following variables needed to be generated: air temperature and humidity, slope, aspect, fuel types, exposure of fuel types, and human ® re risk. All these variables were included in a raster GIS with a 30 Ö 30m grid resolution. Consequently, all the components of ® re danger could be mapped, as well as being integrated in a single ® re danger index which substantially help the study of the spatial distribution of ® re danger. Because high resolution DEM data is not yet available for all Spain a DEM was generated for the study area by digitizing contours from the 1550000 scale map of the study area within Idrisi GIS (Eastman 1992). Linear interpolation used to generate the elevation grid, and a set of 300 random points were used for accuracy assessment, yielding an average elevation error of 8´4m with a maximum of 32m. Slope, illumination and aspect were generated from the DEM, using a rectangular window of 3 pixels by 3 pixels. The illumination map was computed for a solar elevation angle of 55ß (® gure 3), which corresponds to the conditions of the satellite image used for the vegetation map. Only four weather stations were available within the study area. All of them provided air temperature data, but only one measured relative humidity. The four stations were located below 1200m on both sides of the central mountain, and so interpolation was inappropriate to derive an air temperature map. Instead, multiple regression techniques were employed against topographic map variables acting as independent variables. A set of 27 weather stations were chosen within a radius of 100km of the study area, a wide range of elevations being covered to compute properly thermal gradients. Mean annual and summer temperatures were regressed against elevation, slope, aspect, latitude and longitude. Stepwise multiple regressions were used, and only one variable (elevation) was ® nally retained, because it accounted for 89´78 per cent of variance while the other variables explained only an additional 2 per cent. The regression yielded residuals of 0´03± 2´65ß C. Non-linear regressions were also computed, but they did not signi® cantly improve the residuals. Therefore, equation (1) was used to generate a temperature map of the study area Tm a x =38´19Õ 0´00914673*E

(1)

where Tm a x is average maximum July temperature (in degrees Celsius) and E is the elevation (in metres). Only maximum temperatures for July are used, because ® res are most frequent then. This maximum July temperature map shows a broad thermal

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Figure 3. Illumination image at 55ß solar elevation angle. Mountain range of Sierra de Gredos is evident in the centre of the study area.

contrast between the valleys and the highest areas (® gure 4) of as much as 14ß C from the elevation range of 1600m. Relative air humidity, could not be generated by regression techniques, because few of the weather stations measured this variable. Consequently, they were extrapolated from simple bioclimatological models (Hungerford et al. 1989) which require elevation data for the whole study area, water pressure at dew point temperature

Figure 4. Average July maximum temperature of the study area estimated with linear regression.

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and current temperature from a base weather station. Relative humidity is estimated for the other cells from the thermal gradient and the hydrostatic equation. A uniform atmosphere and a constant absolute humidity are assumed. Estimation errors for summer air humidity using one test weather station were under 1 per cent. The third variable to be considered in the ICONA’s danger index was vegetation. From a forest ® re danger perspective, vegetation must be classi® ed according to fuel types. ICONA has developed a photographic key of fuel models for the Spanish landscapes (ICONA 1990a), which adapts the ones developed for the BEHAVE programme (Anderson 1982, Burgan and Rothermel 1984). As this programme mainly considers surface ® res, fuel types are de® ned by understorey vegetation, instead of vegetation canopy and this greatly complicates their discrimination from remote sensing data. The study area contained the following fuel models: (1) short grass, (2) grass and litter understorey, (4) tall shrub, (5) brush, (6) dormant brush, (7) brush beneath a timber stand, and (9) litter. The fuel-type map was generated from six non-thermal bands of a Landsat Thematic Mapper image from July 1988 which were geometrically corrected to match the topographic variables using 20ground control points and cubic convolution. Because of the di culties in discriminating among the fuel types on the basis of spectral information only (Rabii 1979), elevation slope and illumination were integrated with the Landsat spectral bands as well as two texture images to account for the spatial variability of vegetation types. Both supervised and unsupervised classi® cation strategies were adopted. Finally, a mixed procedure was selected for generating the fuel-type map. A set of 144test ® elds were veri® ed on the ® eld for accuracy assessment. The overall accuracy was 75per cent, with a kappa value of 0´7. Two approaches can be taken to deriving the human risk index (HRI). The spatial distribution of hazardous human activities can be examined. Some factors of human risk are spatially concrete, such as recreation and dry grass burning, which tend to be associated with particular areas. However, some of the most signi® cant human-related factors, such as arson, do not have a clear spatial pattern. Therefore, complete mapping of human risk factors is very complex. Alternatively the spatial pattern of human risk can be derived from the location of ® re ignition sites, because most of the ® res in the study area are man caused. Here, therefore HRI is found by computing a contingency table between ® re ignition sites, as described by ® re records, and the total area of these places (table 1). Unfortunately, ® re records use very general terms to locate the ® re source, for example, `near a road’ or `close to a trail’. This is taken to be within a 30 m corridor from any road, Table 1. Location of 286 ® re ignition sources in the past 15 years.

Number of Fires Surface covered (in percentage) Expected ® res*

Roads

Trails

Recreational areas

Others

Total

77 1´9 5´6

45 8´7 25´8

13 0´5 1´5

161 88´9 263´1

296 100 296

(*) Expected ® res were calculated as a proportion of the total number of ® res and the area covered in each location. Computed chi-squared is 189´7 which is signi® cant at 99 per cent con® dence level.

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E. Chuvieco and J. Salas Table 2. Weights assigned to the Human Risk Index (HRI). Location Roads Recreational areas Trails No pathways

Risk weight 23 14 3 1

trail or recreational area, which had previously been digitized from the 1550000 topographic maps. The Chi-squared value was found to be signi® cant at 99 per cent probability, with a contingency coe cient (cÃ ) of 0´894. Therefore, the spatial association between human-caused ® res and accessibility to wildland areas was proven. Our HRI was based on this observation. Weights were obtained from the relations between the percentage of ® re number versus percentage of total area. Zones without roads and trails had a weight of 1, while the other extreme, road corridors were assigned a value of 23, because ® res started near these zones are 23 times more frequent (table 2). 7. Fire danger map The Fire Danger map is derived from integration of these variables began with deriving the WDI. As stated above, the WDI has two components: (i) probability of ignition (PI), and (ii) wind speed and direction. Wind data have not been included in the present research because, (i) no direct data are available for the zone, (ii) models of wind propagation are still quite limited (Zack 1989), and (iii) wind is very dynamic over time, and therefore it is very di cult to select signi® cant average values. The process for the PI map started with the air temperature and relative air humidity maps using a set of tables derived from ICONA (1988), base fuel moisture was computed for every cell of the study area. This base moisture was calculated from air temperature and relative humidity. Afterwards, this base fuel moisture was corrected by considering local topography and fuel exposure (proportion of shade), again using the ICONA tables. This correction depends on the fuel moisture base, and considers two categories of slope (under 30 per cent and above 30 per cent), four classes of aspect (North, South, West and East), and two fuel exposures (exposed and covered). To apply these corrections within the GIS, slope and aspect maps were recoded to the intervals proposed by ICONA and fuel exposure was obtained from the fuel type map by considering the presence or absence of tree cover. Final calculation of PI needed another table provided by ICONA, which required the corrected fuel moisture, air temperature and fuel exposure. Final values of PI in the study area ranked from 100per cent to 50per cent. High probability values indicate the most dangerous situation. The predominance of these high values is obviously related to the speci® c weather conditions used in calculating the base fuel moisture. The spatial distribution of PI is clearly related to topographic conditions, which also in¯ uence vegetation. A strong contrast between the valleys and Sierra de Gredos is evident. It is quite important to note that GIS techniques have made it possible to take into account the topographic and vegetation diversity of the study area for computing PI values. Within a conventional framework, the base and corrected fuel moisture would have been computed from the characteristics of just a single point (the weather

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Mapping the spatial distribution of forest ® re danger Table 3. Estimation of rate of spread for di erent fuel types.

Fuel model 1 2 4 5 6 7 9

Anderson (1982)

Main and Haines (1983)

AMA and Tragsatec (1993)

Local conditions (1993)*

Rank

ROS

Rank

ROS

Rank

ROS

Rank

ROS

1 3 2 6 4 5 7

39´0 17´5 37´5 9´0 15´0 10´0 3´7

6 4 1 2 5 3 7

10´5 12´9 86´4 27´9 12´0 15´0 2´4

2 3 1 6 4 5 7

91´0 51´0 138´0 24´0 34´0 29´0 7´0

2 3 1 6 4 5 7

39´0 14´0 51´0 9´0 14´0 11´0 3´0

ROS = Rate of Spread (metres/minutes). For fuel model description, see text. (*) Local conditions are based on BEHAVE calculations for our study area and the weather data used in this project.

station), and as a result a single PI value would have de® ned the whole study area. Using GIS, we can obtain a basic insight into the geographical distribution of both the danger variables and the PI, even when a single weather station is used as the primary data source. The second component of the Danger Index is the Fuel Hazard Component (FHC). The FHC is derived from the fuel types map. In order to calculate a synthetic danger index, this map had to be converted from a nominal to an interval scale. This conversion is based on the estimated rate of spread of the di erent fuel types. The most generally used model for calculating rate of spread is based on the BEHAVE programme (Rothermel 1983). In table 3 di erent rates of spread estimations are presented for the fuel types of our study area under diverse weather conditions. We have also included the results of running BEHAVE on our fuel types with the previously reported weather data. The results are fairly similar to other research with the exception of the work by Main and Haines (1983). Since these authors operated in very di erent environmental conditions from ours, this deviation is probably not signi® cant. Therefore, our results are good indicators of the rate of spread for the di erent fuel types in our climate. Final weights for each fuel type were calculated proportionally to its rate of spread, scaling down from the most dangerous, model 4 (shrub), which was assigned a value of 10 (table 4). The Human Risk Index (HRI) was obtained using corridors of 30 meters from any road and trail in the study area and combined with all recreational areas within Table 4. Weights assigned to the Fuel Hazard Component (FHC). Fuel model

ROS

Hazard value

1 2 4 5 6 7 9

39 14 51 9 14 11 3

7´6 2´7 10´0 1´8 2´7 2´2 0´6

ROS= Rate of Spread (metres/minutes)

342


forest land. The HRI was organized into four groups (table 2) with weights proportional to frequency of ® re starts versus area covered for each accessibility category. Final integration of the three components (PI, FHC and HRI) to generate the Fire Danger Index (FDI) was performed using (2) FDI = PI/10*FHC+ HRI

(2)

The ® rst two factors, the PI and the FHC are multiplicative, following the ICONA criteria. We decided to include HRI, which is not presently used by ICONA, as an additive factor, because it is less spatially continuous than the other two. Forestry personnel with ICONA agree with this formula. The FDI was calculated on a cell by cell basis, and generated a danger map over the entire study area. The geographical distribution of ® re danger shows the spatial relationships of the main variables associated with the starting or spreading of the ® re. The whole range of ® re danger values was recoded to four categories (extreme, high, moderate and low) to make map production easier (® gure 5). Higher values are found in the valleys of the study area, because they include the most critical meteorological conditions (higher temperatures, lower relative humidity) and present the most dangerous fuel models (pastures and tall shrub). Road and path corridors are also evident, especially on the pine covered southern slopes of Sierra de Gredos, where the associated corridors increase the danger from moderate to high. This map shows the spatial distribution of ® re danger when a high hazard scenario is considered, because of the temperature and air humidity values we used as a basis for calculations. Similar maps might be generated for speci® c days or for longer periods. In these cases, daily or average danger maps respectively might also be produced. 8. Accuracy assessment As we did not try to introduce a new ® re danger model, validation of results is related to the performance of the model itself. According to ICONA’s statistics,

Figure 5. Final Fire risk map: (1) Low, (2) Moderate, (3) High and (4) Extreme risk.


343

during the summer of 1993 more than 96 per cent of ® res larger than 500ha have occurred in high to extreme danger conditions (levels 2 and 3 of the ICONA’s ® re danger system). These data refer to the WDI, which is one of the main components of the model presented in this paper. No validation data are available on the reliability of FHC and the HRI to predict spatial distribution of ® re danger. However, as the FHC is based on BEHAVE calculations, which are generally accepted as good predictors of rate of spread, and the HRI relies on ® re statistics, both can be assumed to be closely related to ® re danger. Unfortunately for the validation process, but luckily for the landscape itself, the study area has not been a ected by large forest ® res recently and consequently no spatial accuracy assessment could be done. 9. Conclusions The methodology outlined in this paper o ers a simple approach to obtain ® re danger maps, by considering the spatial distribution of factors a ecting the starting or spreading of a ® re. The actual implementation of this method may be done in a few weeks, using low-cost software. Fire danger maps are quite relevant for prevention and suppression purposes. They can help to design regional ® re defense plans, which include fuel management practices and vigilance controls, such as ® re-break design, dispatch, prescribed burning, look-out tower location, etc. The method is cost e ective and makes it possible to cover a whole territory with good spatial resolution, something that is not feasible with traditional ® re danger methods. Also the GIS generated for this model can also be used for other purposes, such as training hotshot crews when they are not familiar with the ® re area. Acknowledgments This research was funded by the Servicio Nacional de Defensa contra Incendios Forestales of the Spanish Forest Service (ICONA), institution in charge of forest ® re defense. The authors wish to thank Ricardo VeÂlez for his suggestions for the model design. Partial funding was also obtained from the MINERVE-1 project, funded by the Environment program of the European Community (DG-XII). We also wish to thank C.F. Warren of the I.C.E. at the University of AlcalaÂ de Henares for her linguistic assistance. References Agee, J. K. and Pickford, S. G. 1985, Vegetation and fuel mapping of North Cascades

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