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Every year approx. 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 the major knowledge gaps.

Developing adaptation measures in forestry is an urgent task because the forests regenerated today will have to cope with climate conditions that may drastically change during the life of the trees in the stand. This paper presents a comprehensive review of potential adaptation options in forestry in Europe based on three pillars: a review of the scientific literature, an analysis of current national response strategies, and an expert OPEN ACCESSForests 2011, 2 962 assessment based on a database compiled in the COST Action ECHOES (Expected Climate Change and Options for European Silviculture). The adaptation measures include responses to both risks and opportunities created by climate change and address all stages of forestry operations. Measures targeted to reduce vulnerability to climate change may either aim to reduce forest sensitivity to adverse climate change impacts or increase adaptive capacity to cope with the changing environmental conditions. Adaptation measures mitigating drought and fire risk such as selection of more drought resistant species and genotypes are crucial. For adaptation to be successful it is of the utmost importance to disseminate the knowledge of suitable adaptation measures to all decision makers from the practice to the policy level. The analysis of the ECHOES database demonstrates that this challenge is well recognized in many European countries. Uncertainty about the full extent of climate change impacts and the suitability of adaptation measures creates a need for monitoring and further research. A better understanding of how to increase adaptive capacity is also needed, as well as regional vulnerability assessments which are crucial for targeting planned adaptation measures.

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a b s t r a c tStatistically-designed inventories and biodiversity monitoring programs are gaining relevance for biological conservation and natural resources management. Mandated periodic surveys provide unique opportunities to identify and satisfy natural resources management information needs. However, this is not an end in itself but rather is the beginning of a process that should lead to sound decision-making in biodiversity conservation. Forest inventories are currently evolving towards multipurpose resource surveys and are broadening their scope in several directions: (i) expansion of the target population to include non-traditional attributes such as trees outside the forest and urban forests; (ii) forest carbon pools and carbon sequestration estimation; (iii) assessment of forest health; and (iv) inclusion of additional variables such as biodiversity attributes that are not directly related to timber assessment and wood harvesting.There is an on-going debate regarding the role of forest inventories in biodiversity assessment and monitoring. This paper presents a review on the topic that aims at providing updated knowledge on the current contribution of forest inventories to the assessment and monitoring of forest biodiversity conditions on a large scale. Specific objectives are fourfold: (i) to highlight the types of forest biodiversity indicators that can be estimated from data collected in the framework of standard forest inventories and the implications of different sampling methods on the estimation of the indicators; (ii) to outline current possibilities for harmonized estimation of biodiversity indicators in Europe from National Forest Inventory data; (iii) to show the added value for forest biodiversity monitoring of framing biodiversity indicators into ecologically meaningful forest type units; and (iv) to examine the potential of forest inventory sample data for estimating landscape biodiversity metrics.

Aim of study: We aim at (i) developing a reference definition of mixed forests in order to harmonize comparative research in mixed forests and (ii) review the research perspectives in mixed forests.Area of study: The definition is developed in Europe but can be tested worldwide.Material and Methods: Review of existent definitions of mixed forests based and literature review encompassing dynamics, management and economic valuation of mixed forests.Main results: A mixed forest is defined as a forest unit, excluding linear formations, where at least two tree species coexist at any developmental stage, sharing common resources (light, water, and/or soil nutrients). The presence of each of the component species is normally quantified as a proportion of the number of stems or of basal area, although volume, biomass or canopy cover as well as proportions by occupied stand area may be used for specific objectives. A variety of structures and patterns of mixtures can occur, and the interactions between the component species and their relative proportions may change over time.The research perspectives identified are (i) species interactions and responses to hazards, (ii) the concept of maximum density in mixed forests, (iii) conversion of monocultures to mixed-species forest and (iv) economic valuation of ecosystem services provided by mixed forests.Research highlights: The definition is considered a high-level one which encompasses previous attempts to define mixed forests. Current fields of research indicate that gradient studies, experimental design approaches, and model simulations are key topics providing new research opportunities.Keywords: COST Action; EuMIXFOR; mixed-species forests; admixtures of species.

This paper describes applications of non-parametric and parametric methods for estimating forest growing stock volume using Landsat images on the basis of data measured in the field, integrated with ancillary information. Several k-Nearest Neighbors (k-NN) algorithm configurations were tested in two study areas in Italy belonging to Mediterranean and Alpine ecosystems. Field data were acquired by the regional forest inventory and forest management plans, and satellite images are from Landsat 5 TM and Landsat 7 ETM+. The paper describes the data used, the methodologies adopted and the results achieved in terms of pixel level accuracy of forest growing stock volume estimates. The results show that several factors affect estimation accuracy when using the k-NN method. For the two test areas a total of 3500 different configurations of the k-NN algorithm were systematically tested by changing the number and type of spectral and ancillary input variables, type of multidimensional distance measures, number of nearest neighbors and methods for spectral feature extraction using the leave-one-out (LOO) procedure. The best k-NN configurations were then used for pixel level estimation; the accuracy was estimated with a bootstrapping procedure; and the results were compared to estimates obtained using parametric regression methods implemented on the same data set. The best k-NN growing stock volume pixel level estimates in the Alpine area have a Root Mean Square Error (RMSE) ranging between 74 and 96 m 3 ha − 1 (respectively, 22% and 28% of the mean measured value) and between 106 and 135 m 3 ha − 1 (respectively, 44% and 63% of the mean measured value) in the Mediterranean area. On the whole, the results cast a promising light on the use of non-parametric techniques for forest attribute estimation and mapping with accuracy high enough to support forest planning activities in such complex landscapes. The results of the LOO analyses also highlight the importance of a local empirical optimization phase of the k-NN procedure before defining the best algorithm configuration. In the tests performed the pixel level accuracy increased, depending on the k-NN configuration, as much as 100%.

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