Forest fires severity has increased in Portugal in the last decades. Climate change scenarios suggest the reinforcement of this severity. Forest ecosystem managers and policy-makers thus face the challenge of developing effective fire prevention policies. The characterization of forest fires is instrumental for meeting this challenge. An approach for characterizing fire occurrence in Portugal, combining the use of geographic information systems and statistical analysis techniques, is presented. Emphasis was on the relationships between ecological and socioeconomic features and fire occurrence. The number and sizes of wildfires in Portugal were assessed for three 5-year periods (1987-1991, 1990-1994, and 2000-2004). Features maps were overlaid with perimeters of forest fires, and the proportion of burned area for each period was modeled using weighted generalized linear models (WGLM). Descriptive statistics showed variations in the distribution of fire size over recent decades, with a significant increase in the number of very large fires. Modeling underlined the impact of the forest cover type on the proportion of area burned. The statistical analysis further showed that socioeconomic features such as the proximity to roads impact the probability of fires occurrence. Results suggest that this approach may provide insight needed to develop fire prevention policies.

While sustainable forestry in Europe is characterized by the provision of a multitude of forest ecosystem services, there exists no comprehensive study that scrutinizes their sensitivity to forest management on a pan-European scale, so far. We compile scenario runs from regionally tailored forest growth models and Decision Support Systems (DSS) from 20 case studies throughout Europe and analyze whether the ecosystem service provision depends on management intensity and other co-variables, comprising regional affiliation, social environment, and tree species composition. The simulation runs provide information about the case-specifically most important ecosystem services in terms of appropriate indicators. We found a strong positive correlation between management intensity and wood production, but only weak correlation with protective and socioeconomic forest functions. Interestingly, depending on the forest region, we found that biodiversity can react in both ways, positively and negatively, to increased management intensity. Thus, it may be in tradeoff or in synergy with wood production and forest resource maintenance. The covariables species composition and social environment are of punctual interest only, while the affiliation to a certain region often makes an important difference in terms of an ecosystem service's treatment sensitivity.

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© iForest -Biogeosciences and Forestry IntroductionFire is a major disturbance impacting the Mediterranean landscape (Rundel 1998). In recent decades its incidence has increased dramatically in southern Europe (Rego 1992, Moreno 1999, Pausas et al. 2008. The Portuguese territory is characterized by a Mediterranean climate and a rugged topography. Moreover, the Portuguese vegetation cover is mostly evergreen and drought resistant. The country is thus prone to vegetation fires. Recent demographic, socio-economic and climatic trends (e.g., Mather & Pereira 2006, Pereira et al. 2002 have further contributed to the country's vulnerability to wildfires. In Portugal, wildfire is the most important agent of land cover change (Pereira & Santos 2003). In fact, in the period extending from 1975 to 2007 the total burned area approximated 3.8 · 10 6 hectares, representing 40% of the country's area (Marques et al. 2011a).In Portugal, around 90% of the total forest land is managed by private landowners (DGRF 2006) and most stands are monospecific or dominated by one species. Eucalypt is the most important forest species in terms of area as it extends over 8.12 · 10 3 ha, corresponding to 26% of the country's forestland (ICNF 2013). Further, it provides key raw material to the export driven pulp and paper industry (about 5.75 million m 3 of pulpwood per year -DGRF 2007). Wildfires constrain the economic viability of eucalypt in commercial forestry and the competitiveness of this industry (Nogueira 1990, Moreira et al. 2001. The development of forest plans that may mitigate wildfire impacts on the profitability of eucalypt management scheduling is thus a key factor to the sustainability of this forestry sub-sector. This prompted the research of models to assess wildfire occurrence probability in eucalypt plantations as a function of variables that may be controlled by forest managers. The forestry literature has associated the term risk with the probability of occurrence of a natural hazard (González et al. 2006, Jactel et al. 2009. In this research, we will refer to risk as the probability of a stand to be affected by a wildfire (i.e., probability of burning) if an ignition exists ). Thus, rather than modeling fire ignition probability, the focus of this research is on modeling at stand level the probability of wildfire occurrence at stand level. This is understood as a spatial process related to forest structure as potential fire spread is impacted by fuel presence/composition (Fernandes 2009).In Portugal, former studies have focused on the characterization of wildfire ignition or of wildfire risk as a function of environmental or socioeconomic variables (Vasconcelos et al. 2001, Pereira & Santos 2003, Nunes et al. 2005, Catry et al. 2008, Marques et al. 2011a. It was demonstrated that in general wildfire impacts depend on the forest cover types where they occur (Moreira et al. 2001, Godinho-Ferreira et al. 2005, Nunes et al. 2005. The characterization of these impacts on eucalypt plantations was addressed recently by Fernande...

Maritime pine (Pinus pinaster Ait) is a very important timber-producing species in Portugal with a yield of ~67.1 million m 3 year 21 . It covers ~22.6 per cent of the forest area (710.6 × 10 3 ha). Fire is the most significant threat to maritime pine plantations. This paper discusses research aiming at the development of post-fire mortality models for P. pinaster Ait stands in Portugal that can be used for enhanced integration of forest and fire management planning activities. Post-fire mortality was modelled using biometric and fire data from 2005/2006 National Forest Inventory plots and other sample plots within 2006-2008 fire perimeters. A three-step modelling strategy based on logistic regression methods was used. Firstly, the probability of mortality to occur after a wildfire in a stand is predicted and secondly, the degree of mortality caused by a wildfire on stands where mortality occurs is quantified. Thirdly, mortality is distributed among trees. The models are based on easily measurable tree characteristics so that forest managers may predict post-fire mortality based on forest structure. The models show that relative mortality decreases when average d.b.h. increases, while slope and tree size diversity increase the mortality.

Global Ecosystem Dynamics Investigation (GEDI) satellite mission is expanding the spatial bounds and temporal resolution of large-scale mapping applications. Integrating the recent GEDI data into Airborne Laser Scanning (ALS)-derived estimations represents a global opportunity to update and extend forest models based on area based approaches (ABA) considering temporal and spatial dynamics. This study evaluates the effect of combining ALS-based aboveground biomass (AGB) estimates with GEDI-derived models by using temporally coincident datasets. A gradient of forest ecosystems, distributed through 21,766 km2 in the province of Badajoz (Spain), with different species and structural complexity, was used to: (i) assess the accuracy of GEDI canopy height in five Mediterranean Ecosystems and (ii) develop GEDI-based AGB models when using ALS-derived AGB estimates at GEDI footprint level. In terms of Pearson’s correlation (r) and rRMSE, the agreement between ALS and GEDI statistics on canopy height was stronger in the denser and homogeneous coniferous forest of P. pinaster and P. pinea than in sparse Quercus-dominated forests. The GEDI-derived AGB models using relative height and vertical canopy metrics yielded a model efficiency (Mef) ranging from 0.31 to 0.46, with a RMSE ranging from 14.13 to 32.16 Mg/ha and rRMSE from 38.17 to 84.74%, at GEDI footprint level by forest type. The impact of forest structure confirmed previous studies achievements, since GEDI data showed higher uncertainty in highly multilayered forests. In general, GEDI-derived models (GEDI-like Level4A) underestimated AGB over lower and higher ALS-derived AGB intervals. The proposed models could also be used to monitor biomass stocks at large-scale by using GEDI footprint level in Mediterranean areas, especially in remote and hard-to-reach areas for forest inventory. The findings from this study serve to provide an initial evaluation of GEDI data for estimating AGB in Mediterranean forest.

Maritime pine (Pinus pinaster Ait.) is an important conifer from the western Mediterranean Basin extending over 22% of the forest area in Portugal. In the last three decades nearly 4% of Maritime pine area has been burned by wildfires. Yet no wildfire occurrence probability models are available and forest and fire management planning activities are thus carried out mostly independently of each other. This paper presents research to address this gap. Specifically, it presents a model to assess wildfire occurrence probability in regular and pure Maritime pine stands in Portugal. Emphasis was in developing a model based on easily available inventory data so that it might be useful to forest managers. For that purpose, data from the last two Portuguese National Forest Inventories (NFI) and data from wildfire perimeters in the years from 1998 to 2004 and from 2006 to 2007 were used. A binary logistic regression model was build using biometric data from the NFI. Biometric data included indicators that might be changed by operations prescribed in forest planning. Results showed that the probability of wildfire occurrence in a stand increases in stand located at steeper slopes and with high shrubs load while it decreases with precipitation and with stand basal area. These results are instrumental for assessing the impact of forest management options on wildfire probability thus helping forest managers to reduce the risk of wildfires.Key words: forest management; risk; fire occurrence model; Pinus pinaster Ait. Resumen Evaluación de la probabilidad de ocurrencia de fuegos en rodales de Pinus pinaster en PortugalEl articulo presenta un modelo para evaluar la probabilidad de ocurrencia de incendios en masas regulares y puras de Pinus pinaster en Portugal. Se desarrolla un modelo basado en datos de inventario fácilmente disponibles de tal forma que pueda ser una herramienta útil para los gestores forestales. Los datos proceden de los dos Inventarios Nacionales de Portugal (NFI) y de los datos de los parámetros de incendios forestales durante los años 1998-2004 y de 2006 a 2007. Se ha utilizado un modelo de regresión logística binarias utilizando datos biométricos del NFI. Los datos biométricos incluyen indicadores que puedan ser cambios en las operaciones prescritas en los planes forestales. Los resultados muestran que la probabilidad de ocurrencia de incendios en un rodal aumenta en rodales localizados en grandes pendientes y con una carga alta de matorrales, mientras que decrece con la precipitación y con el área basimétrica. Estos resultados son instrumentos para evaluar el impacto de las opciones de gestión forestal en la probabilidad de incendios ayudando por tanto a los gestores a reducir el riesgo de incendio.Palabras clave: gestion forestal, riesgo, modelo de ocurrencia de incendios, Pinus pinaster Ait.

Forest and fire management planning activities are carried out mostly independently of each other. This paper discusses research aiming at the development of methods and tools that can be used for enhanced integration of forest and fire management planning activities. Specifically, fire damage models were developed for Eucalyptus globulus Labill stands in Portugal. Models are based on easily measurable forest characteristics so that forest managers may predict post-fire mortality based on forest structure. For this purpose, biometric data and fire-damage descriptors from 2005/2006 National Forest Inventory plots and other sample plots within 2006, 2007 and 2008 fire areas were used. A three-step modelling strategy based on logistic regression methods was used. In the first step, a model was developed to predict whether mortality occurs after a wildfire in a eucalypt stand. In the second step the degree of damage caused by wildfires in stands where mortality occurs is quantified (i.e. percentage of mortality). In the third step this mortality is distributed among trees. Data from over 85 plots and 1648 trees were used for modeling purposes. The damage models show that relative damage increases with stand basal area. Tree level mortality models indicate that trees with high diameters, in dominant positions and located in regular stands are less prone to die when a wildfire occurs.