Coupling biomass models with nutrient concentrations can provide sound estimations of carbon and nutrient contents, enabling the improvement of carbon and nutrient balance in forest ecosystems. Although nutrient concentrations are often assumed to be constant for some species and specific tree components, at least in mature stands, the concentrations usually vary with age, site index and even with tree density. The main objective of this study was to evaluate the sources of variation in nutrient concentrations in biomass compartments usually removed during harvesting operations, covering a range of species and management conditions: semi-natural forest, conventional forest plantations and short rotation forestry (SRF). Five species (Betula pubescens, Quercus robur, Eucalyptus globulus, Eucalyptus nitens and Populus spp.) and 14 genotypes were considered. A total of 430 trees were sampled in 61 plots to obtain 6 biomass components: leaves, twigs, thin branches, thick branches, bark and wood. Aboveground leafless biomass was pooled together for poplar. The concentrations of C, N, K, P, Ca, Mg, S, Fe, Mn, Cu, Zn and B were measured and the total biomass of each sampled tree and plot were determined. The data were analysed using boosted regression trees and conventional techniques. The main sources of variation in nutrient concentrations were biomass component > > genotype (species) ≈ age > tree diameter. The concentrations of Ca, Mg and K were most strongly affected by genotype and age. The concentrations of P, K, Ca, Mg, S and Cu in the wood component decreased with age, whereas C concentrations increased, with a trend to reach 50% in the older trees. In the SRF, interamerican poplar and P. trichocarpa genotypes were comparatively more efficient in terms of Ca and K nutrient assimilation index (NAI) (+ 65–85%) than eucalypts, mainly because leafless biomass can be removed. In the conventional eucalypt plantations (rotation 15 years), debarking the wood at logging (savings of 225% of Ca and 254% of Mg for E. globulus) or the use of selected genotypes (savings of 45% of P and 35% of Ca) will provide wood at a relatively lower nutrient cost. Considering all the E. globulus genotypes together, the management for pulp with removal of debarked wood shows NAI values well above (× 1.7–× 3.9) the ones found for poplar or eucalypt SRF and also higher (× 1.6–× 4.0) than the ones found for oak and birch managed in medium or long rotations. The annual rates of nutrient removal were low in the native broadleaved species but the rates of available soil nutrients removed were high as compared to poplar or eucalypts. Management of native broadleaved species should consider nutrient stability through selection of the biomass compartments removed. The nutrient assimilation index is higher in poplar grown under short rotation forestry management than in the other systems considered. Nutrient management of fast growing eucalyptus plantations could be improved by selecting efficient genotypes and limiting removal of wood. The values of the nutrient assimilation index are lower in the natural stands of native broadleaved species than in the other systems considered.

We used data from Pinus pinaster Ait. and Pinus sylvestris L. trees growing in single-species even-aged stands in northwestern Spain to develop crown profile models. Such models are key components of growth and yield models, and they are also important for assessing competitive level, microclimate, tree vigour, mechanical stability, biological diversity, fire susceptibility and behaviour under wind stress, amongst other features. The equations used in crown profile estimation (i.e. those including crown radius, largest crown radius, height to the largest crown radius and height to the base of the live foliage) were fitted simultaneously to take into account correlations between the different variables. The fitting technique also enabled us to consider an autocorrelated, heteroscedastic error structure and to use a different number of observations for the different variables involved in the fitting process. The fitted models explained between 66 and 94 per cent of the variability in crown radius, with a mean error between 0.15 and 0.64 m. The crown profile models developed can be used to estimate the crown profile when only diameter at breast height, total tree height and height to the base of the live foliage are measured. Inclusion of more variables (such as largest crown radius) provides more accurate results.

The installation of research or permanent plots is a very common task in growth and forest yield research. At young ages, tree height is the most commonly measured variable, so the location of individuals is necessary when repeated measures are taken and if spatial analysis is required. Identifying the coordinates of individual trees and re-measuring the height of all trees is difficult and particularly costly (in time and money). The data used comes from three Pinus pinaster Ait. and three Pinus radiata D. Don plantations of 0.8 ha, with an age ranging between 2 and 5 years and mean heights between 1 and 5 m. Five individual tree detection (ITD) methods are evaluated, based on the Canopy Height Model (CHM), where the height of each tree is identified, and its crown is segmented. Three CHM resolutions are used for each method. All algorithms used for individual tree detection (ITD) tend to underestimate the number of trees. The best results are obtained with the R package, ForestTools and rLiDAR. The best CHM resolution for identifying trees was always 10 cm. We did not detect any differences in the relative error (RE) between Pinus pinaster and Pinus radiata. We found a pattern in the ITD depending on the height of the trees to be detected: the accuracy is lower when detecting trees less than 1 m high than when detecting larger trees (RE close to 12% versus 1% for taller trees). Regarding the estimation of tree height, we can conclude that the use of the CHM to estimate height tends to underestimate its value, while the use of the point cloud presents practically unbiased results. The stakeout of forestry research plots and the re-measurement of individual tree heights is an operation that can be performed by UAV-based LiDAR scanning sensors. The individual geolocation of each tree and the measurement of heights versus pole and/or hypsometer measurement is highly accurate and cost-effective, especially when tree height reaches 1–1.5 m.

Tree height is a key variable in forest monitoring studies and for forest management. However, tree height measurement is time consuming, and the recommended procedure is to use estimates from height-diameter models. Here, we compare height-diameter model forms and approaches for predicting tree height (h) as a function of tree diameter at breast height (d) and additional stand level covariates. Four model forms were evaluated: (i) basic models (which only used d as predictor variable); (ii) generalized models (which used d and stand-level predictor variables); (iii) a mixed-effects model based on the best basic model; and (iv) a mixed-effects model based on the best generalized model. Several alternatives aimed at minimizing height measurement effort were tested in terms of accuracy and applicability. From a practical point of view, the selected generalized model is recommended for estimating the height of maritime pine in Portugal. The results also show that a calibrated basic mixed model provides more accurate results than a basic model locally fitted when the number of h-d observations is limited.