The aim of this study was to analyse the regeneration of Pinus pinaster after wildfire and the possible inter and intraspecific competition during the first 3 years after fire. The study area is located in a P. pinaster stand in León province (NW Spain). Three study sites (S1, S2 and S3) were established in an area burned in 1998. In each site, three permanent plots (20 · 1 m) were marked. A total of 20 quadrats of 1 m 2 were studied in each plot. The number and height of pine seedlings 1, 2 and 3 years after fire was recorded in each quadrat. The regeneration of understorey vegetation in the quadrats was analysed concurrently. The significance of linear correlations among the number and height of seedlings and understorey vegetation cover was tested by calculating Pearson correlation coefficients.Seed germination and seedling emergence took place massively during the first year after the fire and decreased through time. The height growth was constant over the 3 years at site S2, while a growth burst could be observed between years 2 and 3 at sites S1 and S2. Also, pines from site S2 reached shorter maximum heights in all years compared to pines from site S1 and S3. The understorey vegetation showed minimal regeneration during the first year but then increased greatly with time. Woody understorey cover and total vegetation cover were negatively correlated with pine seedling density in sites with a high number of seedlings (e.g. S1 and S3). When woody cover, total cover and pine seedling density were low (e.g. S2), there were no correlations. There was a positive correlation between vegetation cover and the maximum height of Pinus seedlings in all study sites.

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Multispectral imagery is a widely used source of information to address post-fire ecosystem management. The aim of this study is to evaluate the ability of remotely sensed indices derived from Landsat 8 OLI/TIRS to assess initial burn severity (overall, on vegetation and on soil) in fire-prone pine forests along the Mediterranean-Transition-Oceanic climatic gradient in the Mediterranean Basin. We selected four large wildfires which affected pine forests in a climatic gradient within the Iberian Peninsula. In each wildfire we established CBI plots to obtain field values of three burn severity metrics: site, vegetation and soil burn severity. The ability of 13 spectral indices to match these three field burn severity metrics was compared and their transferability along the climatic gradient assessed using linear regression models. Specifically, we analysed the performance of 12 indices previously used for burn severity assessments (8 reflective, 2 thermal, 2 mixed) and a new reflective index (dNBR-EVI). The results showed that Landsat spectral indices have a greater ability to determine site and vegetation burn severity than soil burn severity. We found large differences in indices performances among the three different climatic regions, since most indices performed better in the Mediterranean and Transition regions than in the Oceanic one. In general, the dNBR-EVI showed the best fit to site, vegetation and soil burn severity in the three regions, demonstrating broad transferability along the entire climatic gradient.

The increasing occurrence of large and severe fires in Mediterranean forest ecosystems produces major ecological and socio-economic damage. In this study, we aim to identify the main environmental factors driving fire severity in extreme fire events in Pinus fire prone ecosystems, providing management recommendations for reducing fire effects. The study case was a megafire (11,891 ha) that occurred in a Mediterranean ecosystem dominated by Pinus pinaster Aiton in NW Spain. Fire severity was estimated on the basis of the differenced Normalized Burn Ratio from Landsat 7 ETM +, validated by the field Composite Burn Index. Model predictors included pre-fire vegetation greenness (normalized difference vegetation index and normalized difference water index), pre-fire vegetation structure (canopy cover and vertical complexity estimated from LiDAR), weather conditions (spring cumulative rainfall and mean temperature in August), fire history (fire-free interval) and physical variables (topographic complexity, actual evapotranspiration and water deficit). We applied the Random Forest machine learning algorithm to assess the influence of these environmental factors on fire severity. Models explained 42% of the variance using a parsimonious set of five predictors: NDWI, NDVI, time since the last fire, spring cumulative rainfall, and pre-fire vegetation vertical complexity. The results indicated that fire severity was mostly influenced by pre-fire vegetation greenness. Nevertheless, the effect of pre-fire vegetation greenness was strongly dependent on interactions with the pre-fire vertical structural arrangement of vegetation, fire history and weather conditions (i.e. cumulative rainfall over spring season). Models using only physical variables exhibited a notable association with fire severity. However, results suggested that the control exerted by the physical properties may be partially overcome by the availability and structural characteristics of fuel biomass. Furthermore, our findings highlighted the potential of low-density LiDAR for evaluating fuel structure throughout the coefficient of variation of heights. This study provides relevant keys for decision-making on pre-fire management such as fuel treatment, which help to reduce fire severity.

The effects of wildfire on vegetation regeneration in communities dominated by Quercus pyrenaica and those dominated by Pinus pinaster in NW Spain were compared. In order to study changes in the composition and structure of both types of community, permanent plots were established in areas dominated by Q. pyrenaica and those dominated by P. pinaster. All were burned by wildfires at the end of summer. In each plot a permanent transect of 20 m  1 m was established. Basal cover of the plant species present was analysed. In both types of community the global cover values generally increased throughout the study period. In the oak communities the species that appear in the first years are those that will dominate in the mature stage, like Q. pyrenaica and Erica australis. Both species are typical resprouters: from shoots on the rhizome or the stem of subterranean roots in the case of Q. pyrenaica and from lignotubers in the case of E. australis. Among the other species, herbaceous perennials dominated during the first year after the fire, Luzula lactea being the most representative. The percentage of bare soil decreased very rapidly after the first year of regeneration. However, in the P. pinaster communities the species that appeared with higher cover values during the first and second year after fire were seeders, like P. pinaster and Halimium alyssoides. Other species that appeared in these communities were Chamaespartium tridentatum, and E. australis. Amongst the herbaceous perennials, the most representative species was the Liliacea Ornithogalum umbellatum, which appeared throughout the study period in all the burned plots. The percentages of bare soil were higher than in the oak communities. Structural parameters such as diversity and specific richness were much higher in the community dominated by oak than in the pine stand. In general, regeneration after wildfire in the Pinus community was slower than in oak communities.