Multivariate analysis of landscape wildfire dynamics in a Mediterranean ecosystem of Greece

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1 Area (2007) 39.3, Multivariate analysis of landscape wildfire dynamics in a Mediterranean ecosystem of Greece Blackwell Publishing Ltd Kostas D Kalabokidis*, Nikos Koutsias**, Pavlos Konstantinidis and Christos Vasilakos *Department of Geography, University of the Aegean, Mytilene, Greece kalabokidis@aegean.gr **Department of Environmental and Natural Resources Management, University of Ioannina, Agrinio, Greece Forest Research Institute, NAGREF, Vasilika-Thessaloniki, Greece Department of Environmental Studies, University of the Aegean, Mytilene, Greece Revised manuscript received 27 March 2007 This paper focuses on spatial distribution of long-term fire patterns versus physical and anthropogenic elements of the environment that determine wildfire dynamics in Greece. Logistic regression and correspondence analysis were applied in a spatial database that had been developed and managed within a Geographic Information System. Cartographic fire data were statistically correlated with basic physical and human geography factors (geomorphology, climate, land use and human activities) to estimate the degree of their influence at landscape scale. Land cover types of natural and agricultural vegetation were the most influential factors for explaining landscape wildfire dynamics in conjunction with topography and grazing. Key words: Greece, forest fires, multivariate statistics, physical geography, human geography, GIS Introduction Spatiotemporal attributes are important characteristics in landscape and wildfire dynamics (see Barbour et al. 2005; Roloff et al. 2005). Spatial analysis of landscape wildfire may be from local to global scales, while temporal resolution can be either shortor long-term. Consequently, wildfire and vegetation dynamics have been analysed using Geographic Information Systems (GIS) as it offers an effective way to manage the spatial and temporal information (Chou 1992; Salas and Chuvieco 1994; Kalabokidis et al. 2002; Miller et al. 2003). Vegetation mapping for fire dynamics is complicated because the existence of similar plants will not necessarily result in similar wildfire behaviour. Wildfire behaviour potential is strongly correlated with the quantity, size, density, moisture and quality of vegetation as these determine the amount of fuel available for combustion (Pyne et al. 1996; Andrews et al. 2003). Vegetation interacts with topography and weather to create conditions of fire behaviour unique in time and space. Simulation modelling can be used to predict fire potential at broad spatial scales. The primary tool used to model fire behaviour at different landscapes is FARSITE (Finney 1998). This programme integrates geospatial fuel data, climatic data and physically-based modelling of fire behaviour (BEHAVE; Andrews 1986). However, owing to the quantity and quality requirements of the data and to other constraints of working on wildfires (Peterson et al. 2005), application of statistical/empirical models (as in this research study) complements simulation methods for analysis of physical and human impacts on landscape wildfire dynamics.

2 Multivariate analysis of landscape wildfire dynamics 393 The study of the interactions between wildfire and vegetation of a particular area requires essential information about potential environmental factors such as moisture, temperature, terrain, soils, human activities, wildfire frequency and intensity, among others, that can result in numerous combinations (e.g. Chou et al. 1993; Ryan 2002). For example, plant moisture (one of the major factors that affects vegetation and hence wildfire occurrence) varies according to the time of the day or the particular location sampled on a plant s crown, stem and roots (see Moroke et al. 2005). Therefore, one might come across moisture values that vary in time and space (e.g. Rodríguez-Iturbe et al. 2006). This variation may become more complicated when soil quality and texture are considered as well. Consequently, evaluation of the influence of all relevant environmental factors is practically impossible. Plant ecology studies tend to group these factors so that their impacts can be estimated via the different plant species that compose the vegetation of an area. Research is conducted in a subtractive manner, since only a few of the enormous number of environmental factors can be considered for their interactions with nature and humans. Although new technologies may increase the capacity to evaluate more factors, their number remains few compared to the real world (Michener and Brunt 2000). Knowledge of the various environmental factors and of their impact upon the formation of the vegetation especially of their different combinations can be critical. Monitoring, mapping and evaluation of the factors and their combinations might help decisionmakers to avoid mistakes when applying environmental policy and undertaking management. Ecological balance is a dynamic, not static, phenomenon, and nature is a field of endless changes that scientists must monitor and analyse to identify and hopefully avoid unfavourable situations for humans (Perry 2002). Nature varies constantly moment to moment and the anthropogenic dimensions must be taken into consideration (Argent 2004) when examining the practice of an economic or agricultural policy resulting in detrimental effects, for example overgrazing that can lead to land degradation and desertification (Bennet 1975; Navas et al. 2005). This research attempts to study the interactions between wildfire dynamics and the formation of vegetation under the impact of various environmental factors and human activities (Augustin et al. 2001). Wildfire and vegetation patterns were studied using multivariate data analysis techniques applied in a geographic database created and managed within a GIS environment making use of the powerful tools it supports (Kalabokidis and Koutsias 2000). Methodology Study area The peninsula of Sithonia in northern Greece (Figure 1) has been chosen as a study area for its variation in environmental factors. Despite its high biodiversity, the greater part of the vegetation in Sithonia consists exclusively of evergreen coniferous forest ecosystems, being Aleppo Pine (Pinus halepensis) and Black Pine (Pinus nigra), along with evergreen broadleaf (Quercus coccifera, Q. ilex, Pistacia lentiscus, Arbutus unedo, Phillyrea media, Erica arborea, E. Figure 1 Three-dimensional view of the Sithonia peninsula in northern Greece. The superimposed image is an RGB colour composite of Landsat-5 Thematic Mapper channels TM7, TM4 and TM3

3 394 Kalabokidis et al. manipuliflora) or garrigue-type scrublands (Cistus mospeliensis, C. salvifolius, C. incanus). The area has been developing under different socio-economic conditions over the past century, providing an excellent research opportunity for assessment of the impact of human activities (including agriculture and stock-rearing) and the environment in the formation of vegetation and the spatial distribution of wildfires. Sithonia is the middle peninsula of the Halkidiki Prefecture in Greece and covers an area of 400 km 2 (Figure 1). Its population of approximately permanent residents is distributed uniformly across the peninsula. During the prolonged summer period and holidays, around tourists in hotels and summer homes (mainly in coastal areas) cause extraordinary pressure on the environment (e.g. Briassoulis 2002; Henderson et al. 2005). Geologically, Sithonia belongs to the Servo- Macedonian massif and Circum-Rhodope belt (Mountrakis 1985). The great diversity of the terrain is highlighted by narrow ravines, wide valleys, steep coastline and steep slopes (Figure 1). Mountain Polyelaios is the highest peak at 823 m and there are few rivers and lakes on the peninsula. The igneous rocks that extend to the East result in acidic, shallow and infertile soils, while the west side is composed of limestone. According to the Köppen (1936) classification system, Sithonia belongs to climate types Csa and Csb, with Mediterranean climate (warm and dry summers; mild and moderately rainy winters) across the foothills and coastal areas, and sub-mediterranean with slightly lower temperatures in elevations over 600 m (Konstantinidis et al. 2005). Dependent response variable Generally, events may be described by a bivariate point pattern that consists of events and control points or by a marked point pattern where a variable is attached to each individual observation (Gatrell et al. 1996). Wildland fire ignition points do not correspond to either of these two types, since only the x and y coordinates are extracted from the fire records database. Multivariate statistical techniques cannot be easily applied to explore the spatial patterns, since they require the existence of one of the two point pattern types. For example, spatial prediction of fire ignition probabilities based on logistic regression modelling requires a binary dependent variable (Chou et al. 1990; Koutsias and Karteris 1998; Vasconcelos et al. 2001). To overcome these limitations, control points that correspond to no-fire must be established using a sampling scheme. To avoid creating control points that would be on the same or nearby location to fire ignition points, we applied a random sampling scheme excluding buffer zones of 1000 m around fire ignition points. The buffer zone of 1000 m has been chosen out of three alternatives tested (i.e. of 1000, 2000 and 3000 m). Using this buffer size, the control points and fire ignition points summed together are randomly distributed. The mean nearest neighbour distance of fire ignition points was used to estimate the number of control points (Koutsias et al. 2004). In total, 39 fire event records between 1985 and 1995 were retrieved from the Hellenic Forest Service database. The mean nearest neighbour distance of the 39 fire ignition points in the database was m. This means that fire ignition points represent a clumped spatial arrangement. Under a spatial random process, the mean nearest neighbour distance of m corresponds to 72 points across the study area. These 72 points include both fire ignition points and the control points. Since there were 39 fire ignition points, there should be 33 control points (i.e. 72 minus 39), so that the control points and fire ignition points summed together match the random distribution (Figure 2). Figure 2 Spatial distribution of wildfire ignition points together with the control points established by a random sampling scheme restricted by the constraint of distance to fire ignition points (i.e m)

4 Multivariate analysis of landscape wildfire dynamics 395 Figure 3 Examples of the independent explanatory variables used in the study Independent explanatory variables A geographical information database consisting of environmental and anthropogenic variables such as topography, geology, vegetation and meteorological data, land use cover types and human activities, has been created for the Sithonia peninsula, utilising the ArcGIS software. Part of this geographical database is illustrated in Figure 3. A description of the independent explanatory variables is given in Table 1. All independent explanatory variables were expressed in raster format using a grid resolution of 30 m to be compatible with other raster data that were available for the study area at the same resolution. Distance to roads, density of livestock areas, elevation, slope and climatic information (summer mean air temperature and relative humidity, and annual precipitation) were defined as continuous variables, while aspect, geology and vegetation types were defined as categorical (Table 1). Where the original data were provided as point observations (i.e. wildland fire ignition points, control points, livestock activities), the kernel density estimation method was applied to transform the point observations to continuous density surfaces. The method consists of placing a bivariate probability density function over each point observation and estimating the intensity at each intersection of a superimposed grid (Worton 1989). This method can also be used for control points, where there are no-fire observations or places where no livestock practices exist, with the densities of each point or control point combined on one final layer. In wildland fire management, this technique has been successfully utilised in the same study area by Koutsias et al. (2004) to transform wildland fire ignition points to continuous density surfaces. Raw data from five meteorological stations for the period were used to estimate the spatial distribution of climatic variables (i.e. summer mean air temperature, summer mean relative humidity and annual precipitation) by applying multi-linear regression models to the latitude, longitude and elevation. Correlation coefficients (R 2 ) of the multiple regression used to build the associated mean trendsurfaces averaged over 0.71, despite the fact that the Mediterranean climate is remarkably variable (Bolle 2003).

5 396 Kalabokidis et al. Table 1 Data sources and description of values for all the continuous (Cont.) and categorical (Cat.) explanatory/ independent variables in the logistic regression analysis Variable Type Source Values Legend Distance to roads Cont. Topographic map (scale 1:50 000) m Density of livestock Cont. Kernel density surfaces times m 2 Air temperature Cont. 5 weather stations ( ) o C Relative humidity Cont. 5 weather stations ( ) % Annual precipitation Cont. 5 weather stations ( ) mm mm mm Elevation (DEM) Cont. Interpolated from contours intervals of 20 m digitized from 1: scale topographic maps divided by m Slope Cont. Digital Elevation Model (DEM) % Aspect Cat. Digital Elevation Model (DEM) 1 Flat 2 North 3 Northeast 4 East 5 Southeast 6 South 7 Southwest 8 West 9 Northwest Geology Cat. Geologic map (scale 1:50 000) 1 Sedimentary rocks 2 Meta-sedimentary rocks 3 Igneous rocks Vegetation cover Cat. Forest cover map (scale 1: ) 1 Aleppo Pine 2 Black Pine 3 Evergreen scrubs 4 Agriculture Logistic regression modelling Many statistical multivariate techniques can be used for predicting a dependent response variable from a set of independent explanatory ones when the dependent and independent variables are continuous and follow a normal distribution, e.g. multiple regression or discriminant analysis (Norusis 1990). Alternatively, logistic regression is used when the multivariate normal model is not assumed or the set of independent measurements consists of continuous and categorical variables (Afifi and Clark 1990). Logistic regression is used when the dependent variable is binary and expressed as 1 or 0, reflecting the experimental question being true or false (Mendenhall and Sincich 1996). Logistic regression models the probability of an event occurring as a linear function of a set of explanatory variables. The general form of logistic regression is given by: fz ( ) = ( e a βi X i) where X i are the explanatory variables and a, β i are the regression coefficients (Sharma 1996). Logistic regression has been used in studies relevant to wildland fire occurrence analysis. In a pioneer work published by Chou et al. (1990), a probabilistic model of wildfire occurrence was developed using the logistic regression model considering environmental, human and spatial factors extracted from ecological databases in California. Critical zones of fire danger were identified for each geographical unit, based on the estimated probabilities. Chuvieco et al. (1998) compared logistic regression and an Artificial Neural Network approach for estimating large fires in the Euro-Mediterranean Basin from geographical and

6 Multivariate analysis of landscape wildfire dynamics 397 statistical variables. Vasconcelos et al. (2001) also compared logistic regression and neural networks for the spatial prediction of fire ignition probabilities in central Portugal. Although neural networks demonstrated slightly better accuracy and were more robust, logistic regression allowed for more interpretation than the neural networks which provide no indication of the internal importance of each variable as the weights of variables after training are not easily interpreted (Vasconcelos et al. 2001). In this study, wildfire dynamics were analysed by developing a logistic regression model for the Sithonia peninsula in order to examine (i) the human impact on long-term wildfire patterns by including the variables dealing with proximity to human activities (roads and livestock activities), along with the influence of (ii) climate (summer mean air temperature and relative humidity, and annual precipitation) and (iii) geomorphology (elevation, slope, aspect, geology). Categorical variables were handled using the SPSS statistical package according to the DEVIATION coding scheme (Norusis 1990). Within this coding scheme, one could see the influence of each vegetation cover type on the presence of wildfire, compared to the average effect of all cover types. The logistic regression analysis utilised the Forward Stepwise Selection method with entry testing based on the significance of the score statistic, and removal testing based on the probability of the Wald statistic within SPSS (Norusis 1990). The Wald statistic shows the significance of an individual independent variable in the presence of the other variables included in the model. Although stepwise selection methods are not recommended in theory testing where a priori hypotheses exist (Menard 2001), they are used in exploratory analysis where no a priori assumptions exist about the relationships between the variables, and the objective is to discover relationships. Due to the relatively small number of actual fire events, the data size did not permit the splitting of the observations into calibration and evaluation data sets as usually practised, and the same data were used for model calibration and model evaluation. Correspondence analysis Correspondence analysis has been used to describe the relationships between two nominal variables; in this study those of vegetation cover types and zones of fire occurrence. This approach is based on the premise that the existing distribution of the cover types is representative of its response to kernel Table 2 Classification table and statistical performance of the logistic regression model Observed Predicted Percentage correct No-fire (0) Fire (1) No-fire (0) Fire (1) densities of the long-term fire patterns considered in the study (Felicísimo et al. 2002). Correspondence analysis assumes nominal variables and can describe the relationships between categories of each variable, as well as the relationship between the variables (see SPSS v for Windows). Results and discussion Overall percentage: Log Likelihood = Cox & Snell R 2 = Nagelkerke R 2 = Table 2 sets out how well the logistic regression model explained the response variable by comparing the predicted and the observed outcomes. The overall classification accuracy of the logistic model averaged values up to 81.9 per cent, identifying that the model had the majority of its predictions correct for both fire observations (i.e. 89.7% of the burned areas were correctly classified) and no-fire observations (i.e. 72.7% of the unburned areas were correctly classified) (Table 2). The goodness of fit of the model was assessed by examining the 2 times the log of the likelihood (65.586), Cox & Snell R-square (0.378) and Nagelkerke R-square (0.504) statistics (Table 2). The logistic model was considered good, since it resulted in small values of these statistics (Norusis 1990). Although stepwise regression methods have some inherent limitations, their use is widespread within environmental science (Whittingham et al. 2006) such as landscape ecology studies, where a priori interactions between the predictors and the dependent phenomenon (e.g. wildfire) are unknown. Using a full model including all effects may lead to a model that would include non-significant parameters and other well-known drawbacks (see e.g. Whittingham et al. 2006). Density of livestock activities has a statistically negative effect on wildfire occurrence in the study area (Table 3), shown by the sign of the beta regression

7 398 Kalabokidis et al. Table 3 Standard errors (SE), Wald statistics (Wald) and significance levels (Sig.) for coefficients (B) of variables included in the logistic regression equation; significance levels are calculated using the score statistics (Score) for variables not in the equation Variable B SE Wald Score Sig. Distance to roads Density of livestock Summer mean air temperature Summer mean relative humidity Annual precipitation Elevation Slope Aspect Geology Vegetation cover coefficient ( 1.043). Proximities to roads appears not to have a significant fire effect in Sithonia, with its sparse and undeveloped road network along with effective preventive and pre-suppression measures taken by local authorities. Road networks and livestock activities constitute the main expression of human activities through proximity, urbanisation and grazing of wildland ecosystems. Fire and climate relationships are generally considered strong (e.g. seasonal air temperature, relative humidity during the summer and annual precipitation patterns). Nevertheless, climatic factors in the study area do not have a statistically conclusive influence on wildfire occurrence (Table 3). The relation of fire with average climatic factors studied is not significant and probably due to (i) the small size and elongated shape of the area as well as proximity to the sea resulting in less climatic variation locally and (ii) the partial correlation with the elevation gradient used to create continuous climatic layers by multiple regression (Sharma 1996). Topographic factors presented a highly significant influence on fire occurrence in the rugged terrain of Halkidiki, elevation and slope both being critical at the 5 per cent significance level (Table 3). Widespread distribution of fire in Sithonia s granite soils seems not to be influenced significantly by the aspect and geology variables, while the steep slopes and high elevations of Sithonia s topography have positive and negative effects on wildfire, respectively (i.e and B coefficients in Table 3). The overall effect of vegetation on wildfires was highly significant (0.006) with each vegetation cover type being significant at the 5 per cent statistical level compared to the average effect of all the remaining cover types in the study peninsula (Table 3). Only the Wald statistic of is calculated for the vegetation cover variable as it is categorical data. Vegetation versus wildfire correlations were studied more specifically by correspondence analysis (Figure 4). The composite vegetation cover types were defined on the basis of the dominant species and included coniferous forests of Aleppo Pine and Black Pine displaying the most association with fire (Behave- Plus fuel model 10 as described in Andrews et al. 2003); evergreen or garrigue-type scrubs displaying a medium level association with fire (BehavePlus fuel model 7); and agricultural areas (olive tree orchards and grapevines) displaying the least association with fire (BehavePlus fuel models 8 and/or 6) (Figure 4). Mediterranean scrub vegetation occurs relatively independent of the wildfire frequency and extent, implying that these plants are very welladjusted to local fire regimes. The coniferous forests of Sithonia show high levels of fire frequency. Agricultural lands experience the least influence by wildfire ignitions, being under various management regimes, controlled by humans and their practices. Human impact over the second part of the twentieth century has been significant for evergreen vegetation and agriculture lands in the study area, with less fire activity according to the analyses. Evergreen scrub appears as a result of land degradation around livestock stables (e.g. see Bakker et al. 2005), where grazing activities are intensified. This is especially true for the southern part of the Sithonia peninsula that is overused for the wintering of thousands of animals transferred there from North Greece, because of its mild climate. These relationships reflect

8 Multivariate analysis of landscape wildfire dynamics 399 Figure 4 Correspondence analysis plot of fire occurrence zones and vegetation cover types that wildland fires are infrequent in areas of less productive vegetation that are heavily used either for intensive animal breeding or dryland agriculture. Geomorphology has a very strong influence on the succession of vegetation and hence wildfire dynamics. Mediterranean vegetation types in lower elevation coastal areas and foothills experienced more fire activity compared to mid- to high-elevation forests with more moist and temperate climatic conditions. This follows the spatial distribution of vegetation zones encountered in the mountains of continental Greece. Increased fire occurrence in lower elevations is also due to the concentration of most of the socioeconomic activities around the coastal areas. On the steeper slopes in the rugged terrain of the study area, the higher fire occurrence and growth may also be a result of flames tilting closer to the fuel (comparable to wind effects on combustion). Wildland fire ignitions do not appear to be affected by aspect and geology in Sithonia. Nevertheless, ecosystems of the study area are intuitively influenced by the geology of the soil parent material thus, having an indirect influence on wildfire dynamics. The logistic regression model, showing high correct classification percentages and acceptable goodness of fit statistics (Table 2), was also used to map fire occurrence probabilities. Figure 5 shows a density function of fire occurrence likelihoods based on environmental hazards and anthropogenic risk criteria that can be utilized to rate fire danger in the study area. The interpolated predicted probabilities of the logistic regression model in the study area fit quite well to kernel density surfaces of wildland fire ignition and control points (Figure 5). The correlation value between the two wildland fire occurrence patterns is Moreover, most of the residual values are very low, indicating the success of the logistic model for estimating fire occurrence probabilities. Systematic fire risk assessment of hazards and vulnerability could create quantitative indices of wildfire behaviour and effects from spatial layers of meteorological, vegetative, topographic and socio-economic information that will eventually develop fire danger indices based on geography and maps (Kalabokidis 2004). Information and computing infrastructure, developed a priori, might then provide for on-time and realistic assistance in fire prevention planning and real-time fire suppression operations that will enhance public safety, maintain natural resources by keeping them physically and aesthetically intact, and improve the opportunities for people to live in natural environments.

9 400 Kalabokidis et al. Figure 5 Predicted probabilities from the logistic regression modelling and kernel densities from wildland fire ignition and control points Conclusion The logistic model that has been developed in this study presents a fairly realistic picture of the natural/ ecological and human impacts on the wildfire patterns of Halkidiki. In general, human presence and activities showed mixed statistical correlations to wildfire occurrence in the Sithonia peninsula due to the intense human pressures on the environment in the last few decades. Topography was relatively significant in the study area, while the climatic variables showed no significant differences between fire and no-fire observations. The summer temperature and humidity and the annual precipitation patterns appeared to be non-significant in the Sithonia peninsula, but it is worth noticing that their influence on wildfires was denoted by the elevation, which also relates to environmental barriers to fire expansion (e.g. by more moisture and lower temperature in higher elevations). Land cover types of natural or agricultural vegetation, in conjunction with various human impacts and geomorphologic parameters, were the most influential factors for explaining fire dynamics along the peninsula. Wildfire distributions and fire ecology have proved once again to be mainly influenced by terrain and vegetation (fuel) patterns at the landscape level. In this research study, multivariate analytical processes contributed to better understanding and explanation of landscape wildfire and vegetation dynamics. The study indicated the worth of logistic regression in environmental modelling where natural events and experimental questions can be expressed in a binary mode, presence vs absence. Finally, the critical role of GIS for the input, management, processing, spatial analysis, cartographic modelling and visualisation of complex and multi-faceted physical phenomena (sometimes very much chaotic in behaviour) and anthropogenic parameters should be acknowledged. Acknowledgements This research was partially funded by the Greek General Secretariat for Research and Technology within the 3rd European Community Support Programme/Operational Programme Competitiveness of the project SITHON on forest fires. Dr Peter F. Moore of GHD, Australia, and three anonymous referees are acknowledged for their helpful comments and constructive suggestions. References Afifi A A and Clark V 1990 Computer-aided multivariate analysis 2nd edn Van Nostrand Reinhold, New York Andrews P L 1986 BEHAVE: Fire behavior prediction and fuel modeling system-burn subsystem, part 1 Gen. Tech. Rep. INT-194 USDA Forest Service, Intermountain Research Station, Ogden UT

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