Three types of forest stands (chestnut coppice, maritime pine stands, and poplar and willow short-rotation woody crops (SRWC)) were evaluated to determine their potential for energy production. The properties of the main aboveground biomass fractions (wood, bark and crown) and also the whole tree were analysed, thus providing data that could be used for management purposes and for evaluating potential forest, biomass energy yields and atmospheric emissions. Proximate, elemental and energetic analyses of the biomass provided important information for evaluating the fuel potential. The energetic value of the biomass derived from the maritime pine stands was higher than that of the poplar and willow clonal stands and chestnut coppice stands. The high ash content of the chestnut bark, relative to that of the wood and crown material, is also an important consideration in relation to energy production. The proportion of carbon concentration accumulated per tree was very similar in all types of material studied, although the N and S contents were higher in the maritime pine stands than in the other stands. For this reason, selection of species and fractions can help to improve fuel quality and the efficiency of the combustion processes, and to minimize atmospheric emissions.

The purpose of this study was to compare the accuracy of the Weibull, Johnson's S B and beta distributions, fitted with some of the most usual methods and with different fixed values for the location parameters, for describing diameter distributions in even-aged stands of Pinus pinaster, Pinus radiata and Pinus sylvestris in northwest Spain. A total of 155 permanent plots in Pinus sylvestris stands throughout Galicia, 183 plots in Pinus pinaster stands throughout Galicia and Asturias and 325 plots in Pinus radiata stands in both regions were measured to describe the diameter distributions. Parameters of the Weibull function were estimated by Moments and Maximum Likelihood approaches, those of Johnson's S B function by Conditional Maximum Likelihood and by Knoebel and Burkhart's method, and those of the beta function with the method based on the moments of the distribution.The beta and the Johnson's S B functions were slightly superior to Weibull function for Pinus pinaster stands; the Johnson's S B and beta functions were more accurate in the best fits for Pinus radiata stands, and the best results of the Weibull and the Johnson's S B functions were slightly superior to beta function for Pinus sylvestris stands. However, the three functions are suitable for this stands with an appropriate value of the location parameter and estimation of parameters method.

The prediction of growing stock volume is one of the commonest applications of remote sensing to support the sustainable management of forest ecosystems. In this study, we used data from the 4th Spanish National Forest Inventory (SNFI-4) and from the 1st nationwide Airborne Laser Scanning (ALS) survey to develop predictive yield models for the three major commercial tree forest species (Eucalyptus globulus, Pinus pinaster and Pinus radiata) grown in north-western Spain. Integration of both types of data required prior harmonization because of differences in timing of data acquisition and difficulties in accurately geolocating the SNFI plots. The harmonised data from 477 E. globulus, 760 P. pinaster and 191 P. radiata plots were used to develop predictive models for total over bark volume, mean volume increment and total aboveground biomass by relating SNFI stand variables to metrics derived from the ALS data. The multiple linear regression methods and several machine learning techniques (k-nearest neighbour, random trees, random forest and the ensemble method) were compared. The study findings confirmed that multiple linear regression is outperformed by machine learning techniques. More specifically, the findings suggest that the random forest and the ensemble method slightly outperform the other techniques. The resulting stand level relative RMSEs for predicting total over bark volume, annual increase in total volume and total aboveground biomass ranged from 30.8-38.3%, 34.2-41.9% and 31.7-38.3% respectively. Although the predictions can be considered accurate, more precise geolocation of the SNFI plots and coincide temporarily with the ALS data would have enabled use of a much larger and robust field database to improve the overall accuracy of estimation.

Stem taper data are usually hierarchical (several measurements per tree, and several trees per plot), making application of a multilevel mixed-effects modelling approach essential. However, correlation between trees in the same plot/stand has often been ignored in previous studies. Fitting and calibration of a variable-exponent stem taper function were conducted using data from 420 trees felled in even-aged maritime pine (Pinus pinaster Ait.) stands in NW Spain. In the fitting step, the tree level explained much more variability than the plot level, and therefore calibration at plot level was omitted. Several stem heights were evaluated for measurement of the additional diameter needed for calibration at tree level. Calibration with an additional diameter measured at between 40 and 60% of total tree height showed the greatest improvement in volume and diameter predictions. If additional diameter measurement is not available, the fixed-effects model fitted by the ordinary least squares technique should be used. Finally, we also evaluated how the expansion of parameters with random effects affects the stem taper prediction, as we consider this a key question when applying the mixed-effects modelling approach to taper equations. The results showed that correlation between random effects should be taken into account when assessing the influence of random effects in stem taper prediction.

& Key message By combining inventory data and spatially-continuous environmental information, we were able to develop models for Atlantic populations of maritime pine (Pinus pinaster Aiton) in Spain in order to predict suitable habitat and site index at a spatial resolution of 250 × 250 m. & Context Currently available, spatially continuous environmental information was used to make reliable predictions about suitable habitat and forest productivity. & Aims To develop raster-based distribution and productivity models for Atlantic populations of maritime pine in Spain to predict current and future suitable habitat and productivity. & Methods Occurrence data and site index values were obtained from the Third Spanish National Forest Inventory and research plots, respectively. After testing different algorithms, random forest were selected for modelling the relationships between maritime pine occurrence, site index and spatially continuous environmental variables. & Results The overall accuracy of the suitable habitat model was 73%, and climate (mainly thermal properties) and soil physical properties were the most important variables. The site index model explained 60% of the observed variability, and lithological properties were the most important variables. A slight increase in site index (0.46-0.51%) and a large increase in suitable habitat (50-66%) are expected for 2070 under the most pessimistic climate change scenario. & Conclusion The currently available spatial continuous information enables the development of accurate raster data models for predicting suitable habitat and site productivity without the need for fieldwork. Climate change is expected to increase the potentially suitable habitat of Atlantic maritime pine populations in Spain in the coming decades.