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Historical fire records and meteorological observations spanning over one century (1894–2010) were assembled in a database to collect long-term fire and weather data in Greece. Positive/negative events of fire occurrence on an annual basis were considered as the years where the annual values of the examined parameters were above (positive values) or below (negative values) the 95% confidence limits around the trend line of the corresponding parameter. To analyse the association of positive/negative events of fire occurrence with meteorological extremes, we proceeded with a cross-tabulation analysis based on a Monte Carlo randomization. Positive/negative values of total annual precipitation were randomly associated with the corresponding values of burned areas, and significant associations were observed for seasonal precipitation totals (spring and fire season). Fire season precipitation is the dominant factor coinciding with negative values of area burned, while years with high spring precipitation coincide with years with large areas burned. These results demonstrate the dual role of precipitation in controlling a fire's extent through fuel build-up and dryness. Additionally, there is a clear outperformance of precipitation-related variables compared with temperature-related weather revealing that, at least in Greece, total area burned at the national scale is controlled by precipitation totals rather than air temperature. This analysis improves our understanding of the underlying mechanisms of fire regimes and provides valuable information concerning the development of models relating fire activity to weather parameters, which are essential when facing a changing climate that may be associated with shifts in various aspects of the typical fire regimes of ecosystems. Our results may allow fire managers to more easily incorporate the effect of extreme weather conditions into long-term planning strategies. They contribute to the exploration of fire–climate relationships and may become more important if climate change scenarios are used to predict the occurrence of future extreme weather taking into consideration that climate change is discussed on the basis of changes of extremes rather than changes in means.

Deep-rooted plants (phreatophytes) survive the Mediterranean dry season by utilizing rainfall accumulating belowground from past rainfall events. This adaptation potentially influences their moisture content when acting as living fuel for forest fires. Other shallow-rooted herbaceous plants (therophytes) survive drought as seeds, acting as very flammable fine fuel because their biomass totally desiccates in summer. We investigated the effects that these ecological adaptations (functional-structural) may have in shaping the fire regime in the Mediterranean by: (a) separating precipitation ( P ) and temperature ( T ) to longer (entire hydrological year) and shorter (seasonal and monthly) temporal scales, (b) correlating the above with burnt area (BA) and number of fires (NF) from the thermo- and medio-Mediterranean vegetation belts (TMVB-MMVB; the most arid and flammable belts), (c) comparing correlations with those from regions of more moist climate and related vegetation. Spatial scales ranged from provinces of Greece and southern France, to combined data for Greece, Italy, France and Spain. We focused on 1985–2008 (and its sub periods); one of the driest periods for the Mediterranean, with large fire occurrence. Clearly, where the TMVB-MMVB prevail P outweighs T in determining BA, having a rather equal contribution in explaining NF. In the driest areas increased P accumulating from autumn, but mostly supplied in winter, suppressed BA the following summers, likely because it improved moisture content in pheatophytic vegetation (living fuel). This impact was reduced in more humid regions, where fires suppression was related to increased summer P that makes fuel damp. In contrast, increased late-spring P promoted BA and NF likely by increasing biomass accumulation from therophytic vegetation (fine fuel). Indications for the resistance of pheatophytic plants to fire following wet winters are already evident in the recent fire regime of northern Mediterranean countries. This function may strengthen under a drier climate and could assist early fire-danger prognosis.

Mountains of the northern Mediterranean basin face two major threats under global change. Aridity and available fuel are both expected to increase because of climatic and land-use changes, increasing fire danger. There may already be signs of such effects in the case of the Pinus nigra and Abies cephalonica forests on Mt. Taygetos (southern Greece). We reconstructed climate (mid- to late-fire-season drought) using tree-rings for the last 150 years and compared it with the mountain’s fire history reconstructed from P. nigra fire scars. Seven, out of the ten, large fires Mt. Taygetos experienced were associated with below-normal precipitation (P) or above-normal maximum temperature (T max). The largest fires occurred in late summer of 1879, 1944, 1998, and 2007. However, only the recent fires (1998 and 2007) had both low P and high T max, also confirmed from long-term meteorological data. The synergy between climate and fuel availability may explain the very high intensity of 1998 and 2007 fires that burned mostly as stand-replacing crown fires. The other two large fire events (1879 and 1944) most likely occurred under reduced availability in burning fuel and were related to above-normal T max. Our findings are among the first based on long-term and site-specific empirical data to support the prediction that Mediterranean mountainous areas will face a very large threat from wildfires in the twenty-first century, if socioeconomic changes leading to land abandonment and thus burning fuel accumulation are combined with the drought intensification projected for the region under global warming.

This study presents a new semi-automatic method to map burned areas by using multi-temporal Land Remote Sensing Satellite Program Landsat Thematic Mapper TM and Enhanced TM Plus ETM+ images. The method consists of a set of rules that are valid especially when the post-fire satellite image has been captured shortly after the fire event. The overall accuracy of the method when applied to two case studies in Mt Parnitha and Samos Island in Greece were 95.69% and 93.98%, respectively. The commission and omission errors for Mt Parnitha were 6.92% and 10.24%, while those for Samos Island were 3.97% and 8.80%, respectively. Between the two types of error, it is preferred to minimize omission errors, since commission errors can be easily identified as part of product quality assessment and algorithm tuning procedures. The rule-based approach minimizes human interventions and makes it possible to run the mapping algorithm for a series of images that would otherwise need extensive time investment. In case of failure to capture burned areas correctly, it is possible either to make some adjustments by modifying the thresholding coefficients of the rules, or to discard some of the rules, since some editing is usually required to correct errors following the automated extraction procedures. When this method was applied to a series of US Geological Survey USGS Landsat TM and ETM+ archived satellite images covering the periods 1984–1991 and 1999–2009, a total of 1773 fires were identified and mapped from six different scenes that covered Attica and the Peloponnese in Greece. The majority of uncaptured burned areas corresponded to fires with size classes of 0–1 ha and 1–5 ha, where the loss in capturing fire scars is generally significant. This was expected since it is possible that small fires, identified and recorded by forest authorities, may not have been captured by satellite data due to limitations arising either from the spatial resolution of the sensor or imposed by the temporal series, which do not systematically cover the full period.

Climate and weather play an important role in shaping fire activity patterns by controlling fuel productivity and fire spread, respectively. Additionally, climate is a key factor controlling primary productivity while different climate zones are expected to support different vegetation formations, that on their turn, include different fuel types. The use, therefore, of an underlying phytogeographical framework would provide more comprehensive outputs in exploring fire activity patterns at national scales, instead of using administrative units that could include various types of non-ecological divisions. Within this concept, we examined the recent fire activity of the main forest formations occurring in Greece and we explored their relationships with various weather parameters and indexes. Correlations of number of fires with weather variables, especially among the vegetation formations that summarize most of the human influence were lax, yet burned area showed significant correlations mainly with fire season precipitation totals. The precipitation related variables generally outperformed the temperature-related variables in correlating with both number of fires and burned area. Climatic indexes, which embody the combined effects of precipitation and air temperature, reflected improved correlations, but not to the extent that could allow for the promotion of their use in further modeling.

Purpose of review: This paper presents a review of the current state of the art in remote sensing based monitoring of forest disturbances and forest degradation from optical Earth Observation data. Part one comprises an overview of currently available optical remote sensing sensors, which can be used for forest disturbance and degradation mapping. Part two reviews the two main categories of existing approaches: classical image-to-image change detection and time series analysis. Recent findings: With the launch of the Sentinel-2a satellite and available Landsat imagery, time series analysis has become the most promising but also most demanding category of degradation mapping approaches. Four time series classification methods are distinguished. The methods are explained and their benefits and drawbacks are discussed. A separate chapter presents a number of recent forest degradation mapping studies for two different ecosystems: temperate forests with a geographical focus on Europe and tropical forests with a geographical focus on Africa. Summary: The review revealed that a wide variety of methods for the detection of forest degradation is already available. Today, the main challenge is to transfer these approaches to high resolution time series data from multiple sensors. Future research should also focus on the classification of disturbance types and the development of robust up-scalable methods to enable near real time disturbance mapping in support of operational reactive measures., This is the Authors' accepted version only! The final version of this paper can be located at Springer.com as part of the Current Forestry Reports (2017) 3: 32. doi:10.1007/s40725-017-0047-2

The aim of our study was to explore the spectral properties of fire-scorched (burned) and non fire-scorched (vegetation) areas, as well as areas with different burn/vegetation ratios, using a multisource multiresolution satellite data set. A case study was undertaken following a very destructive wildfire that occurred in Parnitha, Greece, July 2007, for which we acquired satellite images from LANDSAT, ASTER, and IKONOS. Additionally, we created spatially degraded satellite data over a range of coarser resolutions using resampling techniques. The panchromatic (1 m) and multispectral component (4 m) of IKONOS were merged using the Gram-Schmidt spectral sharpening method. This very high-resolution imagery served as the basis to estimate the cover percentage of burned areas, bare land and vegetation at pixel level, by applying the maximum likelihood classification algorithm. Finally, multiple linear regression models were fit to estimate each land-cover fraction as a function of surface reflectance values of the original and the spatially degraded satellite images. The main findings of our research were: (a) the Near Infrared (NIR) and Short-wave Infrared (SWIR) are the most important channels to estimate the percentage of burned area, whereas the NIR and red channels are the most important to estimate the percentage of vegetation in fire-affected areas; (b) when the bi-spectral space consists only of NIR and SWIR, then the NIR ground reflectance value plays a more significant role in estimating the percent of burned areas, and the SWIR appears to be more important in estimating the percent of vegetation; and (c) semi-burned areas comprising 45–55% burned area and 45–55% vegetation are spectrally closer to burned areas in the NIR channel, whereas those areas are spectrally closer to vegetation in the SWIR channel. These findings, at least partially, are attributed to the fact that: (i) completely burned pixels present low variance in the NIR and high variance in the SWIR, whereas the opposite is observed in completely vegetated areas where higher variance is observed in the NIR and lower variance in the SWIR, and (ii) bare land modifies the spectral signal of burned areas more than the spectral signal of vegetated areas in the NIR, while the opposite is observed in SWIR region of the spectrum where the bare land modifies the spectral signal of vegetation more than the burned areas because the bare land and the vegetation are spectrally more similar in the NIR, and the bare land and burned areas are spectrally more similar in the SWIR.

In the Mediterranean region, natural vegetation has been strongly affected by human activities for thousands of years. During the last decades, there has been a notable tendency for abandonment of marginal agricultural land that is further associated with a number of ecological consequences. In this study we recorded and mapped the temporal changes of land use/land cover (LULC) classes that were further aggregated into major process-based changes for the period 1945-2009 in Aetoloakarnania prefecture, Greece. LULC mapping was based on aerial photographs acquired in 1945, 1960, 1985-1986 and 2007-2009. Object-based image analysis allowed the classification of the region's LULC classes and, consecutively, the assessment of process-based LULC changes for each time period. The results indicated that agricultural land increased during the first post-war years while abandonment of agricultural land took place during more recent decades, especially after the period 1960-1985. The observed land abandonment is combined with a simultaneous densification of shrublands and forests. Radical socio-economic changes that took place in the 1960's include the migration of rural populations towards big cities and the beginning of the development of the tourist industry. We argue that these socio-economic changes play an important role in shaping the observed LULC changes.

In this study, we assess the performance of a self-organising neuro-fuzzy classifier for burned area mapping using multi-spectral satellite data. The proposed neuro-fuzzy model incorporates a multi-layered structure consisting of two types of nodes. The first type is a generic fuzzy neuron classifier (FNCs), whereas the second is solely a decision fusion operator. The Group Method of Data Handling algorithm is used for structure learning providing the model with self-organising attributes and feature selection capabilities. The resulting novel structure consists not only of layers of FNCs but also of layers with only decision fusion due to the nature of the burned area mapping problem. The algorithm is applied to an entire LANDSAT-5 TM multi-spectral image, acquired over central Greece shortly after the major wildfire events of the summer of 2007. In addition to the self-organising neuro-fuzzy classifier, the image data set was classified using neural networks, support vector machines and AdaBoost algorit...

Maps depicting the spatially explicit fire history of an area, including variables such as fire frequency and fire return interval, are important tools promoting the understanding of processes associated with wildfires (fire ignition and spread), the assessment of the impacts of wildland fires on landscape dynamics, and decisions on appropriate management practices. Remote sensing is a cost- and time-effective alternative to automatically assess a vast amount of spatial information and produce various thematic maps. The aim of this study was to reconstruct the recent fire history of Attica region (Greece), in a spatially explicit mode by means of remote sensing techniques using a series of Landsat images acquired from 1984 to 2011. The results show that the fire scar perimeters were captured with high accuracy. Regression modelling shows that the differences between the area burned estimated from satellite data and that recorded by the forest service can be explained (86.3% of the variance) by the number ...

During the Middle Ages and the early modern period the cultivation of the chestnut tree became dominant in various mountain regions in Western Europe. Large numbers of giant chestnut trees have been reported throughout the continent that may be considered the living heritage of this period. In this paper we used a systematic inventory of giant chestnut trees in southern Switzerland for reconstructing the eco-cultural niche related to the long-term cultivation and conservation of such remarkable trees. To this purpose we implemented a retrospective logistic modelling approach with the presence of giant chestnuts as response variable compared with 65 environmental and cultural predictors. We performed different logistic regression analyses using untransformed and transformed variables. Out of the 42 models produced, we finally selected two models, on the basis of their parsimony and accuracy. Many of the selected predictors, such as distance from the lake, abundance of small livestock or number of secondary settlements, reveal that former local and regional socio-economic conditions and environmental constraints have considerable explanatory power. The approach allows us to detect several aspects of the targeted eco-cultural niche that may have acted in the distant past to support the development of the traditional fruit chestnut culture, as well as more recently, to preserve the giant chestnut tree population during the disruption phase of this culture. Most results are in line with the historical documentation, while others go beyond the dictates of written history and reveal interesting traits of the past economic and cultural systems.

Various methods have been developed during the past three decades to improve the classification accuracy in burned area mapping using satellite data captured by different sensors. In this article, we compare ten such classification approaches using Landsat Thematic Mapper (TM) imagery on three Mediterranean test sites by evaluating the classification accuracy using (i) a traditional pixel-based approach, (ii) the concept of the Pareto boundary of efficient solution and (iii) linear regression analysis. Additionally, we make a discrimination of errors depending on their distribution and causal factor. The classification approaches compared resulted in not statistically significant differences in the accuracy of the burned area maps. Differences between the methods were also observed when considering the accuracy along the edges of the burned patches; however, again these were not statistically significant. The findings of our study in a Mediterranean environment clearly demonstrate that, for the selection ...

The aim of this study is to explore the burning pattern of the catastrophic wildfires that occurred in Peloponnisos, southern Greece, in 2007. These fires caused the loss of 67 lives and were recognized as the most extreme natural disaster in the country's recent history. We compare the 2007 fires against fuel availability across the landscape in the light of a null model using Monte-Carlo randomization and against the fire pattern for the preceding period, 2000-2006. Additionally we applied a multi-response permutation-procedure test, a data-driven method free from assumptions about the underlying distribution. The study contributes to the ongoing debate over the relative importance of fuel versus weather in explaining large and intense wildfires. While the majority of the 2007 wildfires burned low-elevation fire-prone ecosystems, a part of them moved to non-fire-prone ecosystems, indicating a departure from the burning pattern of recent history. The CORINE land-cover categories most affected by fire included agricultural lands highly interspersed with large areas of natural vegetation followed by sclerophyllous vegetation, transitional woodland shrubs, complex cultivation patterns and olive groves. These reflect greater fuel accumulation through the encroachment of natural vegetation in abandoned fields as well as changing patterns of land-use. The rising proportions of humid and sub-humid areas burned are clearly related to weather patterns. The synergistic effect between fuel and weather helps explain the unusually large 2007 wildfires in Peloponnisos. This change may imply a climatically driven alteration of the established fire-regime promoted by fuel accumulation that portends major ecological consequences. The ecological disaster foreseen is justified by the lack of specific adaptations to cope with fire in non fire-prone plant communities. (C) 2011 Elsevier B.V. All rights reserved. Agricultural and Forest Meteorology

In July 1983, a large wildfire occurred on the island of Karpathos in Greece. However, only a general sketch of the burn perimeter was available and this lacked detailed spatial information, partic...

This paper describes an integrated GIS-based methodology for conservation value assessment using the traditional species-based and a habitat-based multi-criteria evaluation. The methodology was applied to 49 small islands and islets in the East Aegean area, belonging to a SCI Natura 2000 site. The species-based (botanical approach) conservation status assessment attributes a specific weight to the parameter of endemism, while the multi-criteria evaluation (MCE) approach emphasises the criteria of diversity, rarity, naturalness, replaceability and threat on each habitat type in combination with the number of significant taxa. The conservation values attained from the two approaches are significantly correlated, but the multi-criteria approach is more effective as it includes all the habitat types within the study area in the 25% of the polygons with the highest conservation values. Furthermore, results from the multi-criteria analysis were more in line with subjective assessments of the conservation value of the habitat types than the ones obtained by the botanical approach.

Land cover/land use (LCLU) transitions and landscape changes were mapped and interpreted in a deltaic Natura 2000 habitat site in northern Greece. Multi-temporal maps created over a period of about 65 years and based on panchromatic aerial photographs and high spatial resolution satellite data, helped to assess LCLU transformations. These changes were associated with human interference and major socio-economic processes occurring in the area during the study period. In 1945, forests and wetlands dominated the delta area, occupying 63% of the total land cover. Subsequently, these land cover types severely reduced in coverage, and agricultural areas dominated the landscape as a result of policies introduced to accommodate the settlement of refugees from Asia Minor. This transition increased landscape fragmentation in 1960. Due to relative increase of swap changes, the landscape became less heterogeneous in 1992. Finally, in 2002, agricultural areas exhibit a small decrease in coverage having converted back to natural areas following the introduction of EU Common Agricultural Policy. About 77% of the deltaic ecosystem experienced LCLU changes during the first period of the analysis (1945–1960). Almost two thirds of this change is attributed to intercategorical transitions, while the rest is related to swap changes. During the second period of the analysis (1960–1992), half the study area was transformed due to net and swap changes. Finally, in the third, most recent time period (1992–2002), 85% of the landscape showed persistence while net changes were twice as frequent as swap changes. Human interference was the key factor beyond the landscape transformations and intercategorical transitions observed in the delta. During the previous century natural habitats were pressured by major socio-economic processes that transformed their composition and structure. Assessing these changes is critical to develop area-specific policies that minimize negative influences and provide a framework for restoring habitats to their natural conditions.

Soil erosion is a prominent cause of land degradation and desertification in Mediterranean countries. The detrimental effects of soil erosion are exemplified in climate (in particular climate change), topography, human activities, and natural disasters. Forest fires, which are an integral part of Mediterranean ecosystems, are responsible for the destruction of above-and below-ground vegetation that protects against soil erosion. Under this perspective, the estimation of potential soil erosion, especially after fire events, is critical for identifying watersheds that require management to prevent sediment loss, flooding, and increased ecosystem degradation. The objective of this study was to model the potential post-fire soil erosion risk following a large and intensive wildland fire, in order to prioritize protection and management actions at the watershed level in a Mediterranean landscape. Burn severity and preand post-fire land cover/uses were mapped using an ASTER image acquired two years before the f...

In this paper, principal component analysis (PCA), a dimensionality reduction method, has been applied successfully as an image enhancement technique to improve the spectral signal of burnt surfaces. Forward/backward PCA (F/B PCA) and image differencing, which the proposed method consists of, creates a new spectral space that preserves the original spectral patterns while enhancing particular structures of the original satellite data. Burnt surfaces constitute a spectrally enhanced feature after selective removal of spectral information from the original Landsat-7 Enhanced Thematic Mapper data. Improvement of the spectral separability of burnt surfaces is most evident in spectral channels ETM+4 and ETM+7, where burnt surfaces already compose distinct spectral objects, and channels ETM+2 and ETM+5. This improvement is reasonable since the third PC axis, which is not considered in the back-transformation, is composed mainly of the spectral information in these channels. Another benefit of the technique is a reduction of interband correlation in the satellite data. No clear differences between the standardized and non-standardized F/B PCA were identified to recommend the use of one over the other. Both methods show advances in certain aspects. Finally, an increase of the separability value between burnt areas and dry vegetated areas from 0.473 to 1.06 and 1.31 was obtained with the use of the standardized and non-standardized F/B PCA, respectively.

Based on kernel density estimation methods, this paper introduces an alternative approach of fire occurrence modeling that addresses the inherent positional inaccuracies of recorded wildland fire ignition points. These observations, recorded in longitude and latitude using only degrees and first minutes, contain positional inaccuracies of about ± 700 to ± 925 meters in x and y axes. Kernel density estimation was applied to these historical fire observations recorded between 1985 and 1995 in Halkidiki peninsula, Greece, as well as, to simulated inaccurate points into which positional inaccuracies of the same magnitude were randomly introduced. Substantial differences were observed when a regular grid of quadrants was superimposed over the two point distributions. Although, at higher grid resolution these mismatches were minimized, the problem of generalization appeared. Contrar-ily, the concept of “moving window” assisted to retain high grid resolution and minimize the effect of inaccurate point observations. In addition, the kernel approach, which considers also the relative position of points within the “moving window,” produced more realistic estimates.

The use of spatially explicit fire spread models to assess fire propagation and behaviour has several applications for fire management and research. We used the FARSITE simulator to predict the spread of a set of wildfires that occurred along an east–west gradient of the Euro-Mediterranean countries. The main purpose of this work was to evaluate the overall accuracy of the simulator and to quantify the effects of standard vs custom fuel models on fire simulation performance. We also analysed the effects of different fuel models and slope classes on the accuracy of FARSITE predictions. To run the simulations, several input layers describing each study area were acquired, and their effect on simulation outputs was analysed. Site-specific fuel models and canopy inputs were derived either from existing vegetation information and field sampling or through remote-sensing data. The custom fuel models produced an increase in simulation accuracy, and results were nearly unequivocal for all the case studies examined. We suggest that spatially explicit fire spread simulators and custom fuel models specifically developed for the heterogeneous landscapes of Mediterranean ecosystems can help improve fire hazard mapping and optimise fuel management practices across the Euro-Mediterranean region.

The fire season of 2007 was particularly devastating for Greece, achieving the new all-time record of estimated burnt area (225,734 ha) since 1980. The season was remarkably severe in Peloponnese Peninsula, in southern continental Greece, being considered the most extreme natural disaster in the recent history of Greece. Moreover during the hydrological year of 2007, Peloponnese was struck by a severe winter drought that corresponds to the second lowest annual accumulated value since 1951. However, the subsequent spring was very wet partially attenuating the effect of the previous drought. Additionally, the region was stricken by three heat heaves during summer, being the number of hot nights especially noticeable, surpassing more than 35 nights over the Southern Greece. Here we show that the central and Northern sector of Peloponnese Peninsula become the most susceptible to wildfires due to the combined effect of the two extreme meteorological events, drought and heatwaves which was confirmed by the location of the main burnt areas of 2007 fire season. Additionally, the analysis showed that during the extreme days of fire activity in 2007, strong northerly advection of very hot and dry air over the region, favored fire occurrence. The study attempts to bring new light to the synergistic effect between fuel availability and weather conditions that created extraordinary conditions for fire propagation. We focused on the largest burnt areas and the respective NDVI behavior is assessed throughout the pre fire periods. We found that vegetation dynamics are related to the extreme climatic events that occurred in these periods. Moreover, our results confirm that the higher fire incidence in areas with higher vegetation activity and density seems to indicate that the large burnt areas of 2007 fires season in Peloponnese Peninsula appear to be more sensitive to fuel availability and vegetation density than to vegetation dryness.

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.

When assessing fire danger, interpolation of the dependent variable—historic fire occurrence—is required in order to statistically compare and analyze it with human factors, environmental parameters and census statistics. To confirm the compatibility between the distinct data types, occasionally, for this kind of spatial analysis, historical observations of the primary wildland fire (given as x and y coordinates) must be transformed either to continuous surfaces or to area data. The simple overlay approach converts single point observations to area data. However, this procedure assumes lack of spatial uncertainties that would otherwise result in serious errors caused by the positional inaccuracies of the original point observations. Here, we used kernel density interpolation to convert the original data on wildland fire ignition into an expression of areal units, defined by a raster grid and, subsequently, by the administrative borders of the municipalities in two study areas in Spain. By overlaying a normal bivariate probability density function (kernel) over each point observation, each ignition point was considered an uncertain point location rather than an exact one.

An autologistic regression model, which takes into account neighbouring associations, was developed and applied for burned land mapping using Landsat-5 Thematic Mapper data. The integration of the autocovariate component (estimated using a moving window of 3 @ 3 pixels) into the ordinary logistic regression model increased significantly the overall accuracy from 88.18% to 92.44%. In contrast, the accuracy derived with application of post-classification majority filters, which follow the same principles, were not significantly different to that derived with ordinary logistic regression.

Logistic regression modeling was applied, as an alternative classification procedure, to a single post-fire Landsat-5 Thematic Mapper image for burned land mapping. The nature of the classification problem in this case allowed the structure and application of logistic regression models, since the dependent variable could be expressed in a dichotomous way. The two logistic regression models consisted of the TM 4, TM 7, TM 1 and TM 4, TM 7, TM 2 presented an overall accuracy of 97.37% and 97.30%, respectively and proved to be the most well performing three-channel color composites. The discriminator ability in respect to burned area mapping of each one of the six spectral channels of Thematic Mapper, which was achieved by applying six logistic regression models, agreed with the results taken from the separability indices Jeffries-Matusita and Transformed Divergence.

This study focused on the development of a logistic regression model for burned area mapping using two Landsat-5 Thematic Mapper (TM) images. Logistic regression models were structured using the spectral channels of the two images as explanatory variables. The overall accuracy of the results and other statistical indications denote that logisticregression modelling can be usedsuccessfully for burned area mapping. The model that consisted of the spectral channels TM4, TM7 and TM1 and had an overall accuracy of 97.62%, proved to be the most suitable. Moreover, the study concluded that the spectral channel TM4 was the most sensitive to alterations of the spectral response of the burned category pixels, followed by TM7.

The effect of climate change on wildfires constitutes a serious concern in fire-prone regions with complex fire behavior such as the Mediterranean. The coarse resolution of future climate projections produced by General Circulation Models (GCMs) prevents their direct use in local climate change studies. Statistical downscaling techniques bridge this gap using empirical models that link the synoptic-scale variables from GCMs to the local variables of interest (using e.g. data from meteorological stations). In this paper, we investigate the application of statistical downscaling methods in the context of wildfire research, focusing in the Canadian Fire Weather Index (FWI), one of the most popular fire danger indices. We target on the Iberian Peninsula and Greece and use historical observations of the FWI meteorological drivers (temperature, humidity, wind and precipitation) in several local stations. In particular, we analyze the performance of the analog method, which is a convenient first choice for this problem since it guarantees physical and spatial consistency of the downscaled variables, regardless of their different statistical properties. First we validate the method in perfect model conditions using ERA-Interim reanalysis data. Overall, not all variables are downscaled with the same accuracy, with the poorest results (with spatially averaged daily correlations below 0.5) obtained for wind, followed by precipitation. Consequently, those FWI components mostly relying on those parameters exhibit the poorest results. However, those deficiencies are compensated in the resulting FWI values due to the overall high performance of temperature and relative humidity. Then, we check the suitability of the method to downscale control projections (20C3M scenario) from a single GCM (the ECHAM5 model) and compute the downscaled future fire danger projections for the transient A1B scenario. In order to detect problems due to non-stationarities related to climate change, we compare the results with those obtained with a Regional Climate Model (RCM) driven by the same GCM. Although both statistical and dynamical projections exhibit a similar pattern of risk increment in the first half of the 21st century, they diverge during the second half of the century. As a conclusion, we advocate caution in the use of projections for this last period, regardless of the regionalization technique applied. © 2013 Springer Science+Business Media Dordrecht., This work was partly funded by European Union's Seventh Framework Programme (FP7/2007-2013) under grant agreements 243888 (FUME Project) and from Spanish Ministry MICINN under grant EXTREMBLES (CGL2010-21869).

The application of vegetation indices is a very common approach in remote sensing of burned areas to either map the fire scar or estimate burn severity since they minimize the effect of exogenous factors and enhance the correlation with the internal parameters of vegetation. In a recent study we found that the original spectral channels, based on which these indices are estimated, are sensitive to external parameters of the vegetation as for example the spectral reflectance of the background soil. In such cases, the influence of the soil in the reflectance values is different in the various spectral regions depending on its type. These problems are further enhanced by the non-homogeneous pixels, as created from fractions of different types of land cover. Parnitha (Greece), where a wildfire occurred on July 2007, was established as test site. The purpose of this work is to explore the sensitivity of vegetation indices when used to estimate and map different fractions of fire-scorched (burned) and non fire-scorched (vegetated) areas. IKONOS, a very high resolution satellite imagery, was used to create a three-class thematic map to extract the percentages of vegetation, burned surfaces, and bare soil. Using an overlaid fishnet we extracted samples of completely “burned”, completely “vegetated” pixels and proportions with different burn/vegetation ratios (45%-55% burned – 45%-55% vegetation, 20%-30% burned – 70%- 80% vegetation, 70%-80% burned – 20%-30% vegetation). Vegetation indices were calculated (NDVI, IPVI, SAVI) and their values were extracted to characterize the mentioned classes. The main findings of our recent research were that vegetation indices are less sensitive to external parameters of the vegetation by minimizing external effects. Thus, the semi-burned classes were spectrally more consistent to their different fractions of scorched and non-scorched vegetation, than the original spectral channels based on which these indices are estimated.

Vegetation indices have been widely used in remote sensing literature for burned land mapping and monitoring. In the present study we used satellite data (IKONOS, LANDSAT, ASTER, MODIS) of multiple spectral (visible, near, shortwave infrared) and spatial (1-500 meters) resolutions, acquired shortly after a very destructive fire occurred in the mountain of Parnitha in Attica, Greece the summer of 2007. The aim of our study is to examine and evaluate the performance of some vegetation indices for burned land mapping and also to characterize the relationships between vegetation indices and the percent of fire-scorched (burned) and non fire-scorched (vegetated) areas. The available satellite images were processed geometrically, radiometrically and atmospherically. The very high resolution IKONOS imagery was served as a base to estimate the percent of cover of burned areas, bare soil and vegetation by applying the maximum likelihood classification algorithm. The percent of cover for each type was then correlated to vegetation indices for all the satellite images, and regression models were fit to characterize those relationships. In total 57 versions of some classical vegetation indices were computed using LANDSAT, ASTER and MODIS data. Most of them were modified by replacing Red with SWIR channel, as the latter has been proved sensitive to burned area discrimination. IPVI and NDVI showed a better performance among the indices tested to estimate the percent of vegetation, while most of the modified versions of the indices showed highest performance to estimate the percent of burned areas.

Humans are responsible for most forest fires in Europe, but anthropogenic factors behind these events are still poorly understood. We tried to identify the driving factors of human-caused fire occurrence in Spain by applying two different statistical approaches. Firstly, assuming stationary processes for the whole country, we created models based on multiple linear regression and binary logistic regression to find factors associated with fire density and fire presence, respectively. Secondly, we used geographically weighted regression (GWR) to better understand and explore the local and regional variations of those factors behind human-caused fire occurrence. The number of human-caused fires occurring within a 25-yr period (1983–2007) was computed for each of the 7638 Spanish mainland municipalities, creating a binary variable (fire/no fire) to develop logistic models, and a continuous variable (fire density) to build standard linear regression models. A total of 383 657 fires were registered in the study dataset. The binary logistic model, which estimates the probability of having/not having a fire, successfully classified 76.4% of the total observations, while the ordinary least squares (OLS) regression model explained 53% of the variation of the fire density patterns (adjusted R2 = 0.53). Both approaches confirmed, in addition to forest and climatic variables, the importance of variables related with agrarian activities, land abandonment, rural population exodus and developmental processes as underlying factors of fire occurrence. For the GWR approach, the explanatory power of the GW linear model for fire density using an adaptive bandwidth increased from 53% to 67%, while for the GW logistic model the correctly classified observations improved only slightly, from 76.4% to 78.4%, but significantly according to the corrected Akaike Information Criterion (AICc), from 3451.19 to 3321.19. The results from GWR indicated a significant spatial variation in the local parameter estimates for all the variables and an important reduction of the autocorrelation in the residuals of the GW linear model. Despite the fitting improvement of local models, GW regression, more than an alternative to "global" or traditional regression modelling, seems to be a valuable complement to explore the non-stationary relationships between the response variable and the explanatory variables. The synergy of global and local modelling provides insights into fire management and policy and helps further our understanding of the fire problem over large areas while at the same time recognizing its local character.

Deltaic ecosystems are natural ecosystems of particular ecological and economical value. Satellite imagery can provide an excellent framework for fast and reliable capture of data, which is essential for thorough eco-environmental analysis in valuable ecosystems. The recent availability of very high spatial resolution satellite data reconsiders the digital methods used for information extraction and thematic mapping. The strength of object-based analysis lies on the fact that individual pixels, composing real world objects, can be identified in the satellite imagery and classified using semantic and heuristic knowledge. Multi-sensor satellite data of multiple spectral and spatial resolutions from IKONOS and ASTER sensors, coupled with object-based analysis, were used for mapping a rural NATURA 2000 deltaic area in Western Greece.

Every year approximately half a million hectares of land are burned by wildfires in southern Europe, causing large ecological and socio-economic impacts. Climate and land use changes in the last decades have increased fire risk and danger. In this paper we review the available scientific knowledge on the relationships between landscape and wildfires in the Mediterranean region, with a focus on its application for defining landscape management guidelines and policies that could be adopted in order to promote landscapes with lower fire hazard. The main findings are that (1) socio-economic drivers have favoured land cover changes contributing to increasing fire hazard in the last decades, (2) large wildfires are becoming more frequent, (3) increased fire frequency is promoting homogeneous landscapes covered by fire-prone shrublands; (4) landscape planning to reduce fuel loads may be successful only if fire weather conditions are not extreme. The challenges to address these problems and the policy and landscape management responses that should be adopted are discussed, along with major knowledge gaps. L'articolo è disponibile sul sito dell'editore www.elsevier.com

Protection against natural disasters is an increasing challenge for society. In many areas the frequent occurrence of natural hazards such as, for example, floods, landslides and forest fires has become a constant threat for inhabitants, the habitat itself and its resources. The overall magnitude of damaging events in the last decade clearly shows the need for comprehensive and innovative risk management. It is absolutely necessary to implement controlling mechanisms for the protection of population, land use areas, infrastructure and the natural space in endangered areas. Therefore monitoring tools and appropriate simulation procedures are indispensable. They have to provide necessary information for risk evaluation, successful risk management and communication. These are the main tasks the trans-national INTERREG IIIB Cadses Project is dealing with. The report gives an insight into the particular activities and will present some results of MONITOR.

Historical fire records and meteorological observations, spanning more than 1 century (1894–2010), were gathered and assembled in a database, to provide long-term fire–weather associations. We investigated the relationships between forest fire activity and meteorological parameters and sought to find temporal patterns and trends in these historical records and to identify any linkages between meteorological parameters and fire occurrence in the eastern Mediterranean region. Trend analysis of the time series revealed a statistically significant increase in the number of fires and air temperature, particularly after the mid-1970s. Fire occurrence, expressed as the annual number of fires and total burnt area, was strongly correlated with the mean maximum and the absolute maximum air temperature which, in turn, was related to the occurrence of summer heat waves. Total burnt area was also strongly negatively correlated with fire-season precipitation, and positively correlated with 2-year-lagged annual and summer precipitation, underlying the effect of precipitation in controlling fuel production and moisture. These findings support the argument that although annually lagged precipitation totals may have a marginal effect on fire risk by influencing biomass production and accumulation, the lag0 weather parameters are the main drivers of fire spread by directly controlling fuel moisture.

Analyses of distribution patterns and genetic structures of forest stands can address distinct family structures and provide insights into the association of genetic and phenotypic variation patterns. In this study, point pattern analysis and spatial autocorrelation were used to examine the spatial and genetic structures in two naturally generated beech stands, which differ in age, trunk morphology, and stand management. Significant tree clumping was observed at distances up to 20 m in the young forest stand, whereas dispersion at distances under 10 m was observed in the old stand. The spatial analysis based on Ripley’s k function of the two different groups of trees showed that the non-forked trees match in both stands the spatial pattern of all trees while the forked were randomly distributed. Additionally, according to the bivariate analysis, forked trees in both stands were randomly distributed as related to non-forked tree positions. Finally, Moran’s I values were not very high, though significant genetic autocorrelation was identified at distances up to 20 m in the young stand, suggesting the existence of distinct family structures. However, no significant genetic structuring was observed in the old stand. Our findings suggest that spatial genetic patterns are impacted by stand age, environmental factors and human activities. The spatial distribution of forked trees was not clearly associated to family structures. Random effects and also micro-environmental variation could be additional factors explaining forking of beech individuals. peerReviewed

Summarization: Land use/land cover (LULC) is a fundamental concept of the Earth’s system intimately connected to many phases of the human and physical environment. Earth observation (EO) technology pro- vides an informative source of data covering the entire globe in a spatial and spectral resolution appropriate to better and easier classify land cover than traditional or conventional methods. The use of high spatial and spectral resolution imagery from EO sen- sors has increased remarkably over the past decades, as more and more platforms are placed in orbit and new applications emerge in different disciplines. The aim of the present review work is to provide all-inclusive critical reflection on the state of the art in the use of EO technology in LULC mapping and change detection. The emphasis is placed on providing an overview of the different EO datasets, spatial-spectral-temporal characteristics of satellite data and classification approaches employed in land cover classification. The review concludes providing recommendations and remarks on what should be done in order to overcome hurdle faced using above-mentioned problems in LULC mapping. This also provides information on using classifier algorithms depending upon the data types and dependent on the regional ecosystems. One of the main messages of our review is that in future, there will be a need to assemble techniques specifically used in LULC with their merit and demerits that will enable more comprehensive understanding at regional or global scale and improve understanding between different land cover relationship and variability among them and these remains to be seen. Presented on