Nikos Koutsias

Landscape – wildfire interactions in southern Europe: Implications for landscape management

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.

Burned area mapping using logistic regression modeling of a single post-fire Landsat-5 Thematic Mapper image

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.

Logistic regression modelling of multitemporal Thematic Mapper data for burned area mapping

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.

On the relationships between forest fires and weather conditions in Greece from long-term national observations (1894–2010)

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.

SVM-Based Fuzzy Decision Trees for Classification of High Spatial Resolution Remote Sensing Images

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.

Do Factors Causing Wildfires Vary in Space? Evidence from Geographically Weighted Regression

This paper describes the results of a geo-statistical analysis carried out at the provincial level in Southern Europe to model wildfire occurrence from socio-economic and demographic indicators together with land cover and agricultural statistics. We applied a classical ordinary least squares (OLS) linear regression together with a geographically weighted regression (GWR) to explain long-term wild-fire occurrence patterns (mean annual density of >1 ha fires). The explanatory power of the OLS model increased from 52% to 78% as a result of the non-constant relationships between fire occurrence and the underlying explanatory variables throughout the Mediterranean Basin. The global model we developed (i.e., OLS regression) was not sufficient to fully describe the underlying causal factors in wildfire occurrence modeling. Indeed, local approaches (i.e., GWR) can complement the global model in overcoming the problem of non-stationarity or missing variables. Our results confirm the importance of agrarian activities, land abandonment, and development processes as underlying factors of fire occurrence. The identification of regions with spatially varying relationships can contribute to the better understanding of the fire problem, especially over large geographic areas, while at the same time recognizing its local character. This can be very important for fire management and policy.

Decreasing fires in Mediterranean Europe

Forest fires are a serious environmental hazard in southern Europe. Quantitative assessment of recent trends in fire statistics is important for assessing the possible shifts induced by climate and other environmental/socioeconomic changes in this area. Here we analyse recent fire trends in Portugal, Spain, southern France, Italy and Greece, building on a homogenized fire database integrating official fire statistics provided by several national/EU agencies. During the period 1985-2011, the total annual burned area (BA) displayed a general decreasing trend, with the exception of Portugal, where a heterogeneous signal was found. Considering all countries globally, we found that BA decreased by about 3020 km2 over the 27-year-long study period (i.e. about -66% of the mean historical value). These results are consistent with those obtained on longer time scales when data were available, also yielding predominantly negative trends in Spain and France (1974-2011) and a mixed trend in Portugal (1980-2011). Similar overall results were found for the annual number of fires (NF), which globally decreased by about 12600 in the study period (about -59%), except for Spain where, excluding the provinces along the Mediterranean coast, an upward trend was found for the longer period. We argue that the negative trends can be explained, at least in part, by an increased effort in fire management and prevention after the big fires of the 1980’s, while positive trends may be related to recent socioeconomic transformations leading to more hazardous landscape configurations, as well as to the observed warming of recent decades. We stress the importance of fire data homogenization prior to analysis, in order to alleviate spurious effects associated with non-stationarities in the data due to temporal variations in fire detection efforts.

Robust projections of Fire Weather Index in the Mediterranean using statistical downscaling

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.

Classification analyses of vegetation for delineating forest fire fuel complexes in a Mediterranean test site using satellite remote sensing and GIS

If fuel, weather and topography are considered to be the most important determinants of wildfire occurrence, it is evident that only fuel can be kept under human control and modified to reduce fire potential. In the present study, forest fuel mapping is considered from a remote sensing perspective by the assessment and mapping of general vegetation complexes. The purpose is to delineate forest types which present a particular fire behaviour and to explore the use of Landsat TM data for their mapping. The spectral classes were derived by considering as key elements of the classification scheme the main species that prevail in the overstory layer, as well as meaningful mixtures of them, discriminated by their degree of density as indicated from vegetation indices. The study area, Halkidiki, Greece, which has strong spatial heterogeneity in both the composition and structure of its ecosystems, as well as of their spatial distribution and arrangement, is a characteristic area and representative of the majority of landscape types found across Greece. The overall classification accuracy of the original Landsat TM image (85.30%) was not improved significantly when other synthetic spectral channels or the digital elevation model were integrated with the satellite data, possibly because the detailed classification scheme adopted was determined using the overall spectral discrimination offered by the original satellite data.

Comparing ten classification methods for burned area mapping in a Mediterranean environment using Landsat TM satellite data

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 of the most suitable classification approach, other factors could be given more weight, such as computational resources, imagery characteristics, availability of ancillary data, available software and the analysts experience. Maybe the most important finding of our work is that the variance imposed by the methods is less than the variance imposed by factors differentiated locally in the three study sites since the between-group variance of the overall accuracy is higher than that of the within groups.