## 1 Introduction
GIS is a multidisciplinary approach that can combine methods from science, engineering, and the economy with the experience of field officers to produce robust knowledge in firefighting. GIS, besides its analytical capabilities, is the ideal platform for coordination, information exchange, and awareness provision for all involved stakeholders (all levels of authorities, fire department, police, forest services, agricultural coops, citizens, etc.). Multiple levels of spatial and nonspatial data and information related to fire risk, such as meteorological data, land cover, vegetation features, and topography, in the form of historical information is combined and evaluated to create detailed fire risk maps [24; 25; 26]. The information to be used by the fire risk model has to be reliable, the most recent, easy to obtain, and processable with reasonable H/W and S/W resources in order to produce an update fire risk map. The key for the analysis is the determination and assignment of the proper weights between all these pieces of information. Many studies, in particular, have used multi-criteria decision analysis (MCDA) in conjunction with the analytic hierarchy process (AHP), which assigns weights to the influencing parameters, so as to successfully develop fire risk maps [24; 25; 26; 14; 27; 28]. In the AHP framework, a decision is broken down into a hierarchy of criteria or alternatives, and subsequently one can evaluate the significance of each criterion to the final decision, given the relevant weights between each pair of criteria [29]. After an exhaustive review of the bibliography, we proposed seven factors to be used as criteria in AHP to estimate fire risk: the land cover (LC), the elevation, the aspect, the slope, the TWI, and the distance from roads (DfR) and settlements (DfS). Out of those seven factors, studies have shown that land cover is the most important for estimating fire risk [24; 27], especially in cases where the land cover indicates the type of vegetation that covers the area [28; 30]. It is evident that having a detailed and updated depiction of land cover is critical for estimating fire risk. In view of this fact, GIS can be used to classify land cover and vegetation from satellite imagery with the implementation of machine learning algorithms [31; 32]. Various algorithms have been used by different studies for land classification, such as k-means clustering [33], maximum likelihood classification [34], and support-vector machines (SVM) [35]. In fact, support-vector machine models have been used for land classification with promising results [31; 35]. In this paper, the SVM algorithm was applied to satellite images obtained in 2019 and 2021 in order to create detailed land cover maps for the Natura 2000 (GR1110005) zone, which includes the Dadia-Lefkimi-Soufli National Park Forest in the county of Evros. Subsequently, the use of these land cover maps in combination with six other important fire risk factors can determine the fire risk of the National Park for September 2019 and September 2021. On the basis of the fire risk maps of 2019 and 2021 in conjunction with the burned areas from the past fires of 1 st October 2020 and 9 th July 2021, we evaluated how substantial land cover changes can affect fire risk mapping. In order to make a valid comparison, we chose to include in the fire risk model factors that remain relatively constant for long periods of time. This creates a baseline fire risk map of our study area.