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(2007) 39.3, 392–402

Area

Vol. 39 No. 3, pp. 392–402, 2007ISSN 0004-0894 © The Authors.

Journal compilation © Royal Geographical Society (with The Institute of British Geographers) 2007

Blackwell Publishing Ltd

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

Kostas D Kalabokidis*, Nikos Koutsias**, Pavlos Konstantinidis† and Christos Vasilakos‡

*Department of Geography, University of the Aegean, 81100 Mytilene, Greece

Email: [email protected]

**Department of Environmental and Natural Resources Management,

University of Ioannina, 30100 Agrinio, Greece

†Forest Research Institute, NAGREF, 57006 Vasilika-Thessaloniki, Greece

‡Department of Environmental Studies, University of the Aegean, 81100 Mytilene, Greece

Revised manuscript received 27 March 2007

This paper focuses on spatial distribution of long-term fire patterns versus physical andanthropogenic elements of the environment that determine wildfire dynamics in Greece.Logistic regression and correspondence analysis were applied in a spatial database thathad been developed and managed within a Geographic Information System. Cartographicfire data were statistically correlated with basic physical and human geography factors(geomorphology, climate, land use and human activities) to estimate the degree of theirinfluence at landscape scale. Land cover types of natural and agricultural vegetationwere the most influential factors for explaining landscape wildfire dynamics inconjunction with topography and grazing.

Key words:

Greece, forest fires, multivariate statistics, physical geography, humangeography, GIS

Introduction

Spatiotemporal attributes are important characteristicsin landscape and wildfire dynamics (see Barbour

et al.

2005; Roloff

et al.

2005). Spatial analysis oflandscape wildfire may be from local to globalscales, while temporal resolution can be either short-or long-term. Consequently, wildfire and vegetationdynamics have been analysed using GeographicInformation Systems (GIS) as it offers an effectiveway 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 com-

plicated because the existence of similar plants willnot necessarily result in similar wildfire behaviour.Wildfire behaviour potential is strongly correlatedwith the quantity, size, density, moisture and quality

of vegetation as these determine the amount of fuelavailable for combustion (Pyne

et al.

1996; Andrews

et al.

2003). Vegetation interacts with topographyand weather to create conditions of fire behaviourunique in time and space. Simulation modellingcan be used to predict fire potential at broad spatialscales. The primary tool used to model firebehaviour at different landscapes is FARSITE (Finney1998). This programme integrates geospatial fueldata, climatic data and physically-based modellingof fire behaviour (BEHAVE; Andrews 1986). How-ever, owing to the quantity and quality requirementsof the data and to other constraints of working onwildfires (Peterson

et al.

2005), application of statis-tical/empirical models (as in this research study)complements simulation methods for analysis ofphysical and human impacts on landscape wildfiredynamics.

Multivariate analysis of landscape wildfire dynamics

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Vol. 39 No. 3, pp. 392–402, 2007ISSN 0004-0894 © The Authors.Journal compilation © Royal Geographical Society (with The Institute of British Geographers) 2007

The study of the interactions between wildfireand vegetation of a particular area requires essentialinformation about potential environmental factorssuch as moisture, temperature, terrain, soils, humanactivities, wildfire frequency and intensity, amongothers, that can result in numerous combinations(e.g. Chou

et al.

1993; Ryan 2002). For example,plant moisture (one of the major factors that affectsvegetation and hence wildfire occurrence) variesaccording to the time of the day or the particularlocation sampled on a plant’s crown, stem and roots(see Moroke

et al.

2005). Therefore, one might comeacross moisture values that vary in time and space(e.g. Rodríguez-Iturbe

et al.

2006). This variationmay become more complicated when soil qualityand texture are considered as well.

Consequently, evaluation of the influence of allrelevant environmental factors is practically impossible.Plant ecology studies tend to group these factors sothat their impacts can be estimated via the differentplant species that compose the vegetation of an area.Research is conducted in a subtractive manner, sinceonly a few of the enormous number of environmentalfactors can be considered for their interactions withnature and humans. Although new technologies mayincrease the capacity to evaluate more factors, theirnumber remains few compared to the real world(Michener and Brunt 2000).

Knowledge of the various environmental factors andof their impact upon the formation of the vegetation– especially of their different combinations – can becritical. Monitoring, mapping and evaluation of thefactors and their combinations might help decision-makers to avoid mistakes when applying environmental

policy and undertaking management. Ecologicalbalance is a dynamic, not static, phenomenon, andnature is a field of endless changes that scientistsmust monitor and analyse to identify and hopefullyavoid unfavourable situations for humans (Perry 2002).Nature varies constantly moment to moment andthe anthropogenic dimensions must be taken intoconsideration (Argent 2004) when examining thepractice of an economic or agricultural policy result-ing in detrimental effects, for example overgrazingthat 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 ofvegetation under the impact of various environmentalfactors and human activities (Augustin

et al.

2001).Wildfire and vegetation patterns were studied usingmultivariate data analysis techniques applied in ageographic database created and managed within aGIS environment making use of the powerful tools itsupports (Kalabokidis and Koutsias 2000).

Methodology

Study area

The peninsula of Sithonia in northern Greece(Figure 1) has been chosen as a study area for itsvariation in environmental factors. Despite its highbiodiversity, the greater part of the vegetation inSithonia consists exclusively of evergreen coniferousforest ecosystems, being Aleppo Pine

(Pinus halepensis)

and Black Pine

(Pinus nigra)

, along with evergreenbroadleaf

(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

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manipuliflora)

or

garrigue

-type scrublands (

Cistusmospeliensis

,

C. salvifolius

,

C. incanus

). The area hasbeen developing under different socio-economicconditions over the past century, providing anexcellent research opportunity for assessment of theimpact of human activities (including agriculture andstock-rearing) and the environment in the formationof vegetation and the spatial distribution of wildfires.

Sithonia is the middle peninsula of the HalkidikiPrefecture in Greece and covers an area of 400 km

2

(Figure 1). Its population of approximately 13 000permanent residents is distributed uniformly acrossthe peninsula. During the prolonged summer periodand holidays, around 300 000 tourists in hotels andsummer homes (mainly in coastal areas) cause extraor-dinary pressure on the environment (e.g. Briassoulis2002; Henderson

et al.

2005).Geologically, Sithonia belongs to the Servo-

Macedonian massif and Circum-Rhodope belt(Mountrakis 1985). The great diversity of the terrainis highlighted by narrow ravines, wide valleys, steepcoastline and steep slopes (Figure 1). MountainPolyelaios is the highest peak at 823 m and there arefew rivers and lakes on the peninsula. The igneousrocks that extend to the East result in acidic, shallowand infertile soils, while the west side is composedof limestone. According to the Köppen (1936) classifi-cation system, Sithonia belongs to climate types Csaand Csb, with Mediterranean climate (warm and drysummers; mild and moderately rainy winters) acrossthe foothills and coastal areas, and sub-Mediterraneanwith slightly lower temperatures in elevations over600 m (Konstantinidis

et al.

2005).

Dependent response variable

Generally, events may be described by a bivariatepoint pattern that consists of events and controlpoints or by a marked point pattern where a vari-able is attached to each individual observation(Gatrell

et al.

1996). Wildland fire ignition points donot correspond to either of these two types, sinceonly the x and y coordinates are extracted from thefire records database. Multivariate statistical techniquescannot be easily applied to explore the spatial patterns,since they require the existence of one of the twopoint pattern types. For example, spatial prediction offire ignition probabilities based on logistic regressionmodelling 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 pointsthat would be on the same or nearby location tofire ignition points, we applied a random samplingscheme excluding buffer zones of 1000 m aroundfire ignition points. The buffer zone of 1000 m hasbeen chosen out of three alternatives tested (i.e. of1000, 2000 and 3000 m). Using this buffer size, thecontrol points and fire ignition points summedtogether are randomly distributed. The mean nearestneighbour distance of fire ignition points was usedto estimate the number of control points (Koutsias

et al.

2004). In total, 39 fire event records between1985 and 1995 were retrieved from the HellenicForest Service database. The mean nearest neighbourdistance of the 39 fire ignition points in the data-base was 1206.73 m. This means that fire ignitionpoints represent a clumped spatial arrangement.Under a spatial random process, the mean nearestneighbour distance of 1206.73 m corresponds to72 points across the study area. These 72 pointsinclude both fire ignition points and the controlpoints. 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 pointssummed 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. 1000 m)


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Independent explanatory variables

A geographical information database consisting ofenvironmental and anthropogenic variables such astopography, geology, vegetation and meteorologicaldata, land use cover types and human activities, hasbeen created for the Sithonia peninsula, utilising theArcGIS software. Part of this geographical databaseis illustrated in Figure 3. A description of the in-dependent explanatory variables is given in Table 1.All independent explanatory variables were expressedin raster format using a grid resolution of 30 m to becompatible with other raster data that were available forthe study area at the same resolution. Distance to roads,density of livestock areas, elevation, slope and climaticinformation (summer mean air temperature and relativehumidity, and annual precipitation) were defined ascontinuous variables, while aspect, geology andvegetation types were defined as categorical (Table 1).

Where the original data were provided as pointobservations (i.e. wildland fire ignition points, con-trol points, livestock activities), the kernel densityestimation method was applied to transform thepoint observations to continuous density surfaces.

The method consists of placing a bivariate prob-ability density function over each point observationand estimating the intensity at each intersection of asuperimposed grid (Worton 1989). This method canalso be used for control points, where there are no-fireobservations or places where no livestock practicesexist, with the densities of each point or controlpoint combined on one final layer. In wildland firemanagement, this technique has been successfullyutilised in the same study area by Koutsias

et al.

(2004) to transform wildland fire ignition points tocontinuous density surfaces.

Raw data from five meteorological stations for theperiod 1970–1995 were used to estimate the spatialdistribution of climatic variables (i.e. summer meanair temperature, summer mean relative humidityand annual precipitation) by applying multi-linearregression models to the latitude, longitude and ele-vation. Correlation coefficients (R

2

) of the multipleregression used to build the associated mean trend-surfaces averaged over 0.71, despite the fact thatthe Mediterranean climate is remarkably variable(Bolle 2003).

Figure 3 Examples of the independent explanatory variables used in the study

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Logistic regression modelling

Many statistical multivariate techniques can be usedfor predicting a dependent response variable froma set of independent explanatory ones when thedependent and independent variables are continuousand follow a normal distribution, e.g. multipleregression or discriminant analysis (Norusis 1990).Alternatively, logistic regression is used when themultivariate normal model is not assumed or the setof independent measurements consists of continuousand categorical variables (Afifi and Clark 1990).Logistic regression is used when the dependentvariable is binary and expressed as 1 or 0, reflectingthe experimental question being true or false(Mendenhall and Sincich 1996). Logistic regressionmodels the probability of an event occurring as alinear function of a set of explanatory variables. Thegeneral form of logistic regression is given by:

where

X

i

are the explanatory variables and

a

,

β

i

arethe regression coefficients (Sharma 1996).

Logistic regression has been used in studies relevantto wildland fire occurrence analysis. In a pioneer workpublished by Chou

et al

. (1990), a probabilistic modelof wildfire occurrence was developed using thelogistic regression model considering environmental,human and spatial factors extracted from ecologicaldatabases in California. Critical zones of fire dangerwere identified for each geographical unit, based onthe estimated probabilities. Chuvieco

et al

. (1998)compared logistic regression and an Artificial NeuralNetwork approach for estimating large fires in theEuro-Mediterranean Basin from geographical and

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) 0–3457.4 0–3457.4 mDensity of livestock Cont. Kernel density surfaces 0–0.727 times 10 0–0.727 m−2

Air temperature Cont. 5 weather stations (1970–1995) 20–25 20–25oCRelative humidity Cont. 5 weather stations (1970–1995) 61–81 61–81%Annual precipitation Cont. 5 weather stations (1970–1995) 550 537.5–562.4 mm

575 562.5–587.4 mm. . . . . .850 837.5–862.4 mm

Elevation (DEM) Cont. Interpolated from contours intervals of 20 m digitized from 1:50 000-scale topographic maps

0–823divided by 102

0–823 m

Slope Cont. Digital Elevation Model (DEM) 0–100 0–100%Aspect Cat. Digital Elevation Model (DEM) 1 Flat

2 North3 Northeast4 East5 Southeast6 South7 Southwest8 West9 Northwest

Geology Cat. Geologic map (scale 1:50 000) 1 Sedimentary rocks2 Meta-sedimentary rocks3 Igneous rocks

Vegetation cover Cat. Forest cover map (scale 1:200 000)

1 Aleppo Pine2 Black Pine3 Evergreen scrubs4 Agriculture

f ze

a Xi i( )

( )

=+ ∑− +

1

1β


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statistical variables. Vasconcelos

et al.

(2001) alsocompared logistic regression and neural networksfor the spatial prediction of fire ignition probabilitiesin central Portugal. Although neural networks demon-strated slightly better accuracy and were more robust,logistic regression allowed for more interpretationthan the neural networks which provide no indica-tion of the internal importance of each variable asthe weights of variables after training are not easilyinterpreted (Vasconcelos

et al.

2001).In this study, wildfire dynamics were analysed by

developing a logistic regression model for the Sithoniapeninsula in order to examine (i) the human impacton long-term wildfire patterns by including the vari-ables dealing with proximity to human activities(roads and livestock activities), along with the influ-ence of (ii) climate (summer mean air temperatureand relative humidity, and annual precipitation) and(iii) geomorphology (elevation, slope, aspect, geology).Categorical variables were handled using the SPSSstatistical package according to the DEVIATIONcoding scheme (Norusis 1990). Within this codingscheme, one could see the influence of each vegeta-tion cover type on the presence of wildfire, comparedto the average effect of all cover types. The logisticregression analysis utilised the Forward StepwiseSelection method with entry testing based on thesignificance of the score statistic, and removal test-ing based on the probability of the Wald statisticwithin SPSS (Norusis 1990). The Wald statisticshows the significance of an individual independentvariable in the presence of the other variablesincluded in the model. Although stepwise selectionmethods are not recommended in theory testingwhere a priori hypotheses exist (Menard 2001), theyare used in exploratory analysis where no a prioriassumptions exist about the relationships betweenthe variables, and the objective is to discoverrelationships. Due to the relatively small number ofactual fire events, the data size did not permit thesplitting of the observations into calibration andevaluation data sets as usually practised, and thesame data were used for model calibration andmodel evaluation.

Correspondence analysis

Correspondence analysis has been used to describethe relationships between two nominal variables; inthis study those of vegetation cover types and zonesof fire occurrence. This approach is based on thepremise that the existing distribution of the covertypes is representative of its response to kernel

densities of the long-term fire patterns considered inthe study (Felicísimo

et al

. 2002). Correspondence ana-lysis assumes nominal variables and can describethe relationships between categories of each variable,as well as the relationship between the variables(see SPSS v. 12.0.1 for Windows).

Results and discussion

Table 2 sets out how well the logistic regressionmodel explained the response variable by comparingthe predicted and the observed outcomes. Theoverall classification accuracy of the logistic modelaveraged values up to 81.9 per cent, identifying thatthe model had the majority of its predictions correctfor both fire observations (i.e. 89.7% of the burnedareas were correctly classified) and no-fire observa-tions (i.e. 72.7% of the unburned areas were correctlyclassified) (Table 2). The goodness of fit of themodel was assessed by examining the

−

2 times thelog 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 methodshave some inherent limitations, their use is widespreadwithin environmental science (Whittingham

et al

.2006) such as landscape ecology studies, where apriori interactions between the predictors and thedependent phenomenon (e.g. wildfire) are unknown.Using a full model including all effects may lead toa model that would include non-significant para-meters 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 studyarea (Table 3), shown by the sign of the

beta

regression

Table 2 Classification table and statistical performance of the logistic regression model

Observed Predicted Percentagecorrect

No-fire (0) Fire (1)

No-fire (0) 24 9 72.7Fire (1) 4 35 89.7

Overall percentage: 81.9−2 Log Likelihood = 65.586Cox & Snell R2 = 0.378 Nagelkerke R2 = 0.504

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coefficient (

−

1.043). Proximities to roads appearsnot to have a significant fire effect in Sithonia, withits sparse and undeveloped road network along witheffective preventive and pre-suppression measurestaken by local authorities. Road networks and live-stock activities constitute the main expression of humanactivities through proximity, urbanisation and grazingof wildland ecosystems. Fire and climate relation-ships are generally considered strong (e.g. seasonalair temperature, relative humidity during the summerand annual precipitation patterns). Nevertheless, climaticfactors in the study area do not have a statisticallyconclusive influence on wildfire occurrence (Table 3).The relation of fire with average climatic factorsstudied is not significant and probably due to (i) thesmall size and elongated shape of the area as wellas proximity to the sea resulting in less climatic varia-tion locally and (ii) the partial correlation with theelevation gradient used to create continuous climaticlayers by multiple regression (Sharma 1996). Topo-graphic factors presented a highly significant influenceon fire occurrence in the rugged terrain of Halkidiki,elevation and slope both being critical at the 5 percent significance level (Table 3). Widespread distri-bution of fire in Sithonia’s granite soils seems not tobe influenced significantly by the aspect and geologyvariables, while the steep slopes and high elevationsof Sithonia’s topography have positive and negativeeffects on wildfire, respectively (i.e. 0.111 and

−

0.494B coefficients in Table 3).

The overall effect of vegetation on wildfires washighly significant (0.006) with each vegetation covertype being significant at the 5 per cent statistical levelcompared to the average effect of all the remaining

cover types in the study peninsula (Table 3). Onlythe Wald statistic of 12.444 is calculated for thevegetation cover variable as it is categorical data.Vegetation versus wildfire correlations were studiedmore specifically by correspondence analysis (Figure 4).The composite vegetation cover types were definedon the basis of the dominant species and includedconiferous forests of Aleppo Pine and Black Pinedisplaying the most association with fire (

Behave-Plus

fuel model 10 as described in Andrews

et al.

2003); evergreen or garrigue-type scrubs displayinga medium level association with fire (

BehavePlus

fuel model 7); and agricultural areas (olive treeorchards and grapevines) displaying the least associ-ation with fire (

BehavePlus

fuel models 8 and/or 6)(Figure 4). Mediterranean scrub vegetation occursrelatively independent of the wildfire frequency andextent, implying that these plants are very well-adjusted to local fire regimes. The coniferous forestsof Sithonia show high levels of fire frequency. Agri-cultural lands experience the least influence bywildfire ignitions, being under various managementregimes, controlled by humans and their practices.

Human impact over the second part of the twentiethcentury has been significant for evergreen vegetationand agriculture lands in the study area, with less fireactivity according to the analyses. Evergreen scrubappears as a result of land degradation around live-stock stables (e.g. see Bakker

et al.

2005), wheregrazing activities are intensified. This is especiallytrue for the southern part of the Sithonia peninsulathat is overused for the wintering of thousands ofanimals transferred there from North Greece,because of its mild climate. These relationships reflect

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 0.011 0.918Density of livestock −1.043 0.501 4.328 0.037Summer mean air temperature 1.037 0.308Summer mean relative humidity 1.054 0.305Annual precipitation 0.905 0.341Elevation −0.494 0.238 4.328 0.037Slope 0.111 0.042 6.875 0.009Aspect 5.582 0.694Geology 1.193 0.551Vegetation cover – – 12.444 0.006


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that wildland fires are infrequent in areas of lessproductive vegetation that are heavily used eitherfor intensive animal breeding or dryland agriculture.

Geomorphology has a very strong influence on thesuccession of vegetation and hence wildfire dynamics.Mediterranean vegetation types in lower elevationcoastal areas and foothills experienced more fireactivity compared to mid- to high-elevation forestswith more moist and temperate climatic conditions.This follows the spatial distribution of vegetationzones encountered in the mountains of continentalGreece. Increased fire occurrence in lower elevationsis also due to the concentration of most of the socio-economic activities around the coastal areas. Onthe steeper slopes in the rugged terrain of the studyarea, the higher fire occurrence and growth mayalso be a result of flames tilting closer to the fuel(comparable to wind effects on combustion). Wild-land fire ignitions do not appear to be affected byaspect and geology in Sithonia. Nevertheless, eco-systems of the study area are intuitively influencedby the geology of the soil parent material thus, havingan indirect influence on wildfire dynamics.

The logistic regression model, showing high correctclassification percentages and acceptable goodnessof fit statistics (Table 2), was also used to map fire

occurrence probabilities. Figure 5 shows a densityfunction of fire occurrence likelihoods based onenvironmental hazards and anthropogenic risk criteriathat can be utilized to rate fire danger in the studyarea. The interpolated predicted probabilities of thelogistic regression model in the study area fit quitewell to kernel density surfaces of wildland fire ignitionand control points (Figure 5). The correlation valuebetween the two wildland fire occurrence patternsis 0.763. Moreover, most of the residual values arevery low, indicating the success of the logistic modelfor estimating fire occurrence probabilities. Systematicfire risk assessment of hazards and vulnerability couldcreate quantitative indices of wildfire behaviour andeffects from spatial layers of meteorological, vegeta-tive, topographic and socio-economic informationthat will eventually develop fire danger indices basedon geography and maps (Kalabokidis 2004). Infor-mation and computing infrastructure, developed apriori, might then provide for on-time and realisticassistance in fire prevention planning and real-timefire suppression operations that will enhance publicsafety, maintain natural resources by keepingthem physically and aesthetically intact, and improvethe opportunities for people to live in ‘natural’environments.

Figure 4 Correspondence analysis plot of fire occurrence zones and vegetation cover types

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Conclusion

The logistic model that has been developed in thisstudy presents a fairly realistic picture of the natural/ecological and human impacts on the wildfirepatterns of Halkidiki. In general, human presenceand activities showed mixed statistical correlationsto wildfire occurrence in the Sithonia peninsula dueto the intense human pressures on the environmentin the last few decades. Topography was relativelysignificant in the study area, while the climaticvariables showed no significant differences betweenfire and no-fire observations. The summer temperatureand humidity and the annual precipitation patternsappeared to be non-significant in the Sithoniapeninsula, but it is worth noticing that their influenceon wildfires was denoted by the elevation, whichalso relates to environmental barriers to fire expansion(e.g. by more moisture and lower temperature inhigher elevations). Land cover types of natural oragricultural vegetation, in conjunction with varioushuman impacts and geomorphologic parameters,were the most influential factors for explaining firedynamics along the peninsula. Wildfire distributionsand fire ecology have proved once again to be mainlyinfluenced by terrain and vegetation (fuel) patternsat the landscape level.

In this research study, multivariate analyticalprocesses contributed to better understanding and

explanation of landscape wildfire and vegetationdynamics. The study indicated the worth of logisticregression in environmental modelling where naturalevents and experimental questions can be expressedin a binary mode, presence vs absence. Finally, thecritical role of GIS for the input, management,processing, spatial analysis, cartographic modellingand visualisation of complex and multi-facetedphysical phenomena (sometimes very much chaoticin behaviour) and anthropogenic parameters shouldbe acknowledged.

Acknowledgements

This research was partially funded by the Greek GeneralSecretariat for Research and Technology within the 3rdEuropean Community Support Programme/OperationalProgramme ‘Competitiveness’ of the project ‘SITHON’ onforest fires. Dr Peter F. Moore of GHD, Australia, and threeanonymous referees are acknowledged for their helpfulcomments and constructive suggestions.

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Multivariate analysis of landscape wildfire dynamics 401

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