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This study proposes and evaluates a relatively new concept for fire occurrence zoning based on documented historical fire records. The proposed method creates continuous kernel density surfaces based on wildland fire ignition observations. Kernels have the advantage of directly producing density estimates that are not influenced by grid size or localization effects. Within this scheme, kernel density surfaces have been created and reclassified to construct fire occurrence zones at local to global scales in the Mediterranean Basin. Specifically, fire occurrence zones were created for the European scale (European Mediterranean Basin), national scale (Greece), regional scale (Peloponnese, Greece) and local scale (Chalkidiki, Greece). To evaluate fire occurrence zones, we compared the observed with the expected distribution of the number of fires within these zones using a Monte Carlo randomization test, finding that these numbers were statistically different in all cases. The deviations observed from the expected distributions towards the high occurrence zone indicated their successful assessment and value. In this paper, we further discuss their potential role and use for multi-scale fire management and policy in a European context.

In recent years advances in Earth Observation (EO) have increased our ability to inventory, monitor, and understand Earth's natural and artificial ecosystems. In particular, recent technological developments in the instrument characteristics of the remote sensing systems enabled large-scale usage of EO in environmental governance and protection. Remote sensing can be a potent tool employed in the field of environmental compliance. However, to extract and distribute relevant information to potential stakeholders, simple, yet efficient and robust approaches are needed. The focus of our research is the development of EO algorithms and the corresponding EO processing chains that could be used operationally for providing cost-efficient information to end-users to address environmental compliance and tackle environmental violations over specific thematic domains using freely available Copernicus data. The EO algorithms and the relevant EO processing chains have been developed as part of the EU-H2020 funded project: "EnviroLENS-Copernicus for environmental law enforcement support" where the demonstration area of our approach is in the Montenegro – Albania border. The case considers several sites along the border to Albania, from the coastal area of Velika Plaža to the North Lake Skadar National Park. All sites have been designated in the past as protected areas, due to their rich biodiversity and threatened habitats. The focus of this contribution is on the deforestation detection processing chain. The developed EO processing chains rely on the use and analysis of multi-temporal Sentinel-2 images, while it adopts an object-based perspective for detecting, quantifying, and mapping potential environmental violations. [ABSTRACT FROM AUTHOR]

Increased fire activity, related both to human activities and to climate change, necessitates effective fire prevention and suppression strategies. The main purpose of this paper is to model and map fire suppression difficulty using common approaches in three different ecosystems in Eastern Europe by employing very high-resolution satellite imagery and landscape fire behaviour modelling. The integrated approach could allow fire managers to organize prevention and management of firefighting activities more efficiently by implementing both simple and easy-to-use risk and operational fire suppression difficulty indices. The development of cross-border common methodological approaches could foster cooperation between national authorities that would also maximize the efficiency of firefighting procedures. [ABSTRACT FROM PUBLISHER]

In the Mediterranean area, rural abandonment and the rapid urbanisation after the 1950's led to complex environmental problems, such as the exploitation of natural resources, the environmental pollution, whilst the unstrained urban growth caused excessive pressure on the existing infrastructure, which affects buildings, public transportation, water quality and public health. Various studies demonstrate that the implementation of green roof technology can moderate environmental problems, through the reduction of heat flux and solar reflectivity, the minimisation of buildings’ energy consumption, the air pollution removal, the storm water runoff reduction, the air cooling and the effective management of the urban heat island effect. In this framework, the aim of this study is to assess the green roof potential and the quantification of its benefits over Thessaloniki, Greece's second largest city. In order to do so, very high spatial resolution satellite image and natural colour orthoimagery were used along with a geographical object-based image analysis approach. Carbon sequestration potential, rainwater retention and energy conservation were estimated based on coefficients adopted after a comprehensive literature review and actual dynamic energy simulations. [ABSTRACT FROM AUTHOR]

In recent decades, there is an increasing need for harmonised and accurate information on the status and extent of forests. However, delineating the extent of forest areas is a complex task, since the existence of more than 100 definitions of forest worldwide causes considerable discrepancies in forested area estimates. The aim of this work was to examine the potential of geographic object based image analysis (GEOBIA) and very high spatial resolution imagery to discriminate forest areas following two different definitions of forest in northern Greece. In particular, we examined the definition of forest under the Greek law as well as the United Nations Food and Agricultural Organisation definition. Our findings suggest that the developed GEOBIA approach not only performed remarkably well for the discrimination of forest areas but also allowed to estimate rapidly and reliably forest extents when the two aforementioned forest definitions were employed. [ABSTRACT FROM PUBLISHER]

Spatial explicit knowledge regarding the quantity and the spatial distribution of forest parameters is crucial for sustainable forest management, as well as in fulfilling national reporting needs in the framework of international treaties (i.e. Kyoto Protocol, FAO, EFFIS etc). Especially, tree number which can be used for assessing forest tree density (tree number/ha), is among the most important and laboursome parameters to be measured in the field. The aim of this study is to estimate tree number based on the use of nationwide, freely available, very high spatial resolution orthophotos acquired from Greek National Cadastre and Mapping Agency during the 2007-2009 period. The study area is the University Forest of Taxiarchis, which is located in central Halkidiki, Northern Greece. The dominant species of the forest includes both broadleaves (oak, beech) and coniferous species (Black pine, Calabrian pine), which are found in both pure and mixed stands. Tree crown detection was tested on natural color orthophoto bands in several plots. The principal components and intensity-hue-saturation transformations were also applied in order to enhance tree detection accuracy. Local maxima technique was utilized for tree crown detection. Accuracy results were evaluated based on field plot data available from the official forest management plan of the area completed in 2011. Overall, the detection accuracy exceeded 50% which is deemed satisfactory considering also the heterogeneity of the Mediterranean landscape and the limited spectral resolution of the remote sensing data available. [ABSTRACT FROM AUTHOR]

A novel fuzzy decision tree is proposed in this paper (the FDT-support vector machine (SVM) classifier), where the node discriminations are implemented via binary SVMs. The tree structure is determined via a class grouping algorithm, which forms the groups of classes to be separated at each internal node, based on the degree of fuzzy confusion between the classes. In addition, effective feature selection is incorporated within the tree building process, selecting suitable feature subsets required for the node discriminations individually. FDT-SVM exhibits a number of attractive merits such as enhanced classification accuracy, interpretable hierarchy, and low model complexity. Furthermore, it provides hierarchical image segmentation and has reasonably low computational and data storage demands. Our approach is tested on two different tasks: natural forest classification using a QuickBird multispectral image and urban classification using hyperspectral data. Exhaustive experimental investigation demonstrates that FDT-SVM is favorably compared with six existing methods, including traditional multiclass SVMs and SVM-based binary hierarchical trees. Comparative analysis is carried out in terms of testing rates, architecture complexity, and computational times required for the operative phase. [ABSTRACT FROM AUTHOR]

Judicial wildland fire prevention and management requires precise information on fuel characteristics and spatial distribution of the various vegetation types present in an area. The aim of this study was to present an integrated approach to forest fire management, combining local-scale fuel-type mapping from very high spatial resolution imagery with fire behavior simulation. The specific objectives were (i) to develop a detail site-specific fuel model in a Mediterranean area that is suitable for fire behavior prediction; (ii) to produce a detailed local-scale fuel-type map with an object-based approach; and (iii) to generate accurate fire behavior maps. The spatial extent of the different fuel types of a forested landscape in northern Greece characterized by heterogeneous vegetation and topography was determined using a Quickbird image. Site-specific fuel models were created by measuring fuel parameters in representative natural fuel complexes. Following necessary preprocessing of the image to compensate for geometric errors, multiscale components of the scene were delineated through a segmentation algorithm. The resulting image objects were assigned to respective fuel types using a CART statistical model with an overall accuracy over 80%. The FARSITE fire simulation model was applied to simulate potential wildland fire growth and behavior. Utilizing the spatial database capabilities of geographic information systems, FARSITE allows the user to simulate the spatial and temporal spread and behavior of a fire burning in heterogeneous terrain, fuels, and weather. [ABSTRACT FROM PUBLISHER]

Natural disasters such as floods, wildfires, earthquakes and volcanic eruptions usually result in significant human losses and environmental degradation. To minimize the consequences of these catastrophic events, fire managers and national authorities are in need of accurate information regarding the geographic extent of the affected areas, both during the outbreak and shortly after the suppression of the fire event. The aim of this study was to ascertain whether analysis of Earth Observation (EO) data acquired through the International Charter 'Space and Major Disasters' can be successfully used to address the requirements of environmental disaster management. Specifically, operational information extracted through reliable and robust object-based classification models was provided on the extent of the total burned area and the environmental implications in the Peloponnese (southern Greece) following a request from the National Forest Service and the World Wide Fund for Nature (WWF) Hellas. The results not only revealed the size of the disaster but also indicated the potential operational use of remote sensing in order to provide immediate and precise information to local, national and international organizations in relation to the post-fire impact assessment. [ABSTRACT FROM AUTHOR]

The present study aims to support the Mapping and Assessment of Ecosystems and their Services (MAES) implementation in Greece, by synthesizing an indicator that could be used for abiotic attribute assessments and specifically for geodiversity. Such an indicator can be used not only for reporting obligations under EU initiatives but also for identifying "conservation hotspots". Such areas, characterized by rich geodiversity, are important for supporting biodiversity and other ecosystem services. In addition, identification and mapping of threats to those areas, due to natural or anthropogenic processes, can be used for the introduction or reformation of protective environmental legislation. The geodiversity indicator has been compiled using geological, geomorphological, climatic, pedological and hydrological data layers, while threats to geodiversity have been produced by integrating the sub-indices of erosion, protection level, land degradation, mineral and/or ore extraction activity, and the concentration of wildfire ignition sites. Finally, a bivariate map highlights geodiversity "hotspots" in Greece, which were found to correspond in most cases with locations of rich geodiversity and poor protection from adverse natural or human induced processes, mainly due to the lack of protective legislation. The study's outcomes provide a baseline for scientifically informed decisions for conservation, management and spatial planning, while simultaneously complying with EU and national legislation and strategies for nature conservation and integrated development., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier Ltd.. All rights reserved.)

We provide a map of wildland urban interface (WUI) areas at national scale in Greece, using as primary data the Corine Land Cover (CLC) 2018 product. The WUI areas distribution in Greece is calculated for all the regional units of the county. The regional units correspond to NUTS (Nomenclature of Territorial Units for Statistics) level 2 for Greece, being important for the national to regional fire risk management and mitigation within the country. Furthermore, spatially explicit information regarding the fire affected areas and the changes in WUI areas between 2006 and 2018 on the basis of the CLC data for each regional unit is also provided. This geospatial information can provide valuable, concurrent information at no-cost to all responsible and competent authorities involved in landscape fire management in Greece and represent a valuable contribution to assist in national and regional scale planning., Competing Interests: The authors declare that they have no known competing financial interests or personal relationships which have, or could be perceived to have, influenced the work reported in this article., (© 2020 The Authors.)

Fire severity is an increasingly critical issue for forest managers for estimating fire impacts. Estimating high fire severity potential and accurate classification between fire severity levels are essential for integrated fire management planning in fire prone Mediterranean pine ecosystems. This study attempts to determine the role of topography, pre-fire forest stand structure, fuel complex characteristics and fire behavior parameters on high fire severity potential and classification based on a large fire event occurred in Thasos, Greece. Within this framework, the Random Forest (RF) classification algorithm was used to model the relationship between a large set of predictors and fire severity as expressed by the differenced Normalized Burn Ratio (dNBR) spectral index, inferred from differenced pre- and post-fire Landsat 8 Operational Land Imager (OLI) at 30-m resolution. Results from the RF classifier algorithm showed that high fire severity potential and classification between fire severity levels mainly depended on topography variables and fuel complex characteristics. Assessing of factors which drive a fire to turn into high severe fire and classification into fire severity levels will substantially help land and forest managers to increase fire prevention and develop of concrete actions for successful post fire management at landscape level., (Copyright © 2019 Elsevier Ltd. All rights reserved.)

The purpose of this study was to assess spatial wildfire risk in a typical Mediterranean wildland-urban interface (WUI) in Greece and the potential effect of three different burning condition scenarios on the following four major wildfire risk components: burn probability, conditional flame length, fire size, and source-sink ratio. We applied the Minimum Travel Time fire simulation algorithm using the FlamMap and ArcFuels tools to characterize the potential response of the wildfire risk to a range of different burning scenarios. We created site-specific fuel models of the study area by measuring the field fuel parameters in representative natural fuel complexes, and we determined the spatial extent of the different fuel types and residential structures in the study area using photointerpretation procedures of large scale natural color orthophotographs. The results included simulated spatially explicit fire risk components along with wildfire risk exposure analysis and the expected net value change. Statistical significance differences in simulation outputs between the scenarios were obtained using Tukey's significance test. The results of this study provide valuable information for decision support systems for short-term predictions of wildfire risk potential and inform wildland fire management of typical WUI areas in Greece.

The aims of this study were to map and analyze land use/land cover transitions and landscape changes in the Parnitha and Penteli mountains, which surround the Athens metropolitan area of Attica, Greece over a period of 62 years. In order to quantify the changes between land categories through time, we computed the transition matrices for three distinct periods (1945-1960, 1960-1996, and 1996-2007), on the basis of available aerial photographs used to create multi-temporal maps. We identified systematic and stationary transitions with multi-level intensity analysis. Forest areas in Parnitha remained the dominant class of land cover throughout the 62 years studied, while transitional woodlands and shrublands were the main classes involved in LULC transitions. Conversely, in Penteli, transitional woodlands, along with shrublands, dominated the study site. The annual rate of change was faster in the first and third time intervals, compared to the second (1960-1996) time interval, in both study areas. The category level analysis results indicated that in both sites annual crops avoided to gain while discontinuous urban fabric avoided to lose areas. At the transition level of analysis, similarities as well as distinct differences existed between the two areas. In both sites the gaining pattern of permanent crops with respect to annual crops and the gain of forest with respect to transitional woodland/shrublands were stationary across the three time intervals. Overall, we identified more systematic transitions and stationary processes in Penteli. We discussed these LULC changes and associated them with human interference (activity) and other major socio-economic developments that were simultaneously occurring in the area. The different patterns of change of the areas, despite their geographical proximity, throughout the period of analysis imply that site-specific studies are needed in order to comprehensively assess the driving forces and develop models of landscape transformation in Mediterranean areas., (Copyright © 2014 Elsevier B.V. All rights reserved.)

Improvement of satellite sensor characteristics motivates the development of new techniques for satellite image classification. Spatial information seems to be critical in classification processes, especially for heterogeneous and complex landscapes such as those observed in the Mediterranean basin. In our study, a spectral classification method of a LANDSAT-5 TM imagery that uses several binomial logistic regression models was developed, evaluated and compared to the familiar parametric maximum likelihood algorithm. The classification approach based on logistic regression modelling was extended to a contextual one by using autocovariates to consider spatial dependencies of every pixel with its neighbours. Finally, the maximum likelihood algorithm was upgraded to contextual by considering typicality, a measure which indicates the strength of class membership. The use of logistic regression for broad-scale land cover classification presented higher overall accuracy (75.61%), although not statistically significant, than the maximum likelihood algorithm (64.23%), even when the latter was refined following a spatial approach based on Mahalanobis distance (66.67%). However, the consideration of the spatial autocovariate in the logistic models significantly improved the fit of the models and increased the overall accuracy from 75.61% to 80.49%.