Wind and snow-induced damage have been analyzed at stand level for three pine forests in the Central-Eastern Pyrenees (Pinus nigra Arn. salzmanii, Pinus sylvestris L. and Pinus uncinata Ram.). Stand-level models have been then developed for the most affected two species, Pinus sylvestris L. and Pinus uncinata Ram., to describe damage severity. The models were based on data from national forest inventory plots. They included variables related to the spatial location and structure of the stands, being validated using a sub-set of the database (25% of the plots randomly selected). Mountain pine forests (Pinus uncinata Ram.) were the most heavily affected by wind and snow disturbances. For both mountain and Scots pine species, topographic exposure and the severity of the local storm regime had an important effect on the degree of damage. Stand’s resistance to wind and snow was found to be dependent on the combined effect of basal area and mean slenderness of the dominant trees. For a given slenderness ratio, damage increased strongly in lower-density stands, particularly in stands with basal areas below 15 m/ha. Stand structure was particularly important to define the resistance of Scots pine stands, which presented a higher vulnerability to wind and snow under higher degree of even-agedness. The models presented in this study provide empirically-based information that can be used to implement silvicultural practices to minimize the risk of those forests to suffer wind and snow-related damages.2

Forest aboveground biomass (AGB) estimation over large extents and high temporal resolution is crucial in managing Mediterranean forest ecosystems, which have been predicted to be very sensitive to climate change effects. Although many modeling procedures have been tested to assess forest AGB, most of them cover small areas and attain high accuracy in evaluations that are difficult to update and extrapolate without large uncertainties. In this study, focusing on the Region of Murcia in Spain (11,313 km²), we integrated forest AGB estimations, obtained from high-precision airborne laser scanning (ALS) data calibrated with plot-level ground-based measures and bio-geophysical spectral variables (eight different indices derived from MODIS computed at different temporal resolutions), as well as topographic factors as predictors. We used a quantile regression forest (QRF) to spatially predict biomass and the associated uncertainty. The fitted model produced a satisfactory performance (R² 0.71 and RMSE 9.99 t·ha⁻¹) with the normalized difference vegetation index (NDVI) as the main vegetation index, in combination with topographic variables as environmental drivers. An independent validation carried out over the final predicted biomass map showed a satisfactory statistically-robust model (R² 0.70 and RMSE 10.25 t·ha⁻¹), confirming its applicability at coarser resolutions.

Wildfires play a major role in driving vegetation changes and can cause important environmental and economic losses in Mediterranean forests, especially where the dominant species lacks efficient postfire regeneration mechanisms. In these areas, postdisturbance vegetation management strategies need to be based on detailed, spatially continuous inventories of the burned area. Here, we present a methodology in which we combine airborne LiDAR and multispectral imagery to assess postfire regeneration types in a spatially continuous way, using a Mediterranean black pine (Pinus nigra Arn ssp. salzmannii) forest that burned in 1998 as a case study. Five postfire regeneration types were obtained by clustering field-plot data using Ward’s method. Two of the five regeneration types presented high tree cover (one clearly dominated by hardwoods and the other dominated by pines), a third type presented low to moderate tree cover, being dominated by hardwoods, and the remaining two types matched to areas dominated by soil–herbaceous or shrub layers with very low or no tree cover (i.e., very low to no tree species regeneration). These five types of regeneration were used to conduct a supervised classification of remote sensing data using a nonparametric supervised classification technique. Compared with independent field validation points, the remote sensing based assessment method resulted in a global classification accuracy of 82.7%. Proportions of regeneration types in the study area indicated a general shift from the former pine-dominated forest toward hardwood dominance and showed no serious problems of regeneration failure. Our methodological approach appears to be appropriate for informing postdisturbance vegetation management strategies over large areas.