Exploring the factors affecting carbon and nutrient concentrations in tree biomass components in natural forests,forest plantations and short rotation forestry

Background: 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 forpoplar.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.Results: 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 of225% 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(x 1.7-x 3.9) the ones found for poplar or eucalypt SRF and also higher(x 1.6-x4.0) 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.(Continued on next page)(Continued from previous page)Conclusions: 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.

Assessing a novel modelling approach with high resolution UAV imagery for monitoring health status in priority riparian forests

Background:Black alder(Alnus glutinosa)forests are in severe decline across their area of distribution due to a disease caused by the soil-borne pathogenic Phytophthora alni species complex(class Oomycetes),“alder Phytopththora”.Mapping of the different types of damages caused by the disease is challenging in high density ecosystems in which spectral variability is high due to canopy heterogeneity.Data obtained by unmanned aerial vehicles(UAVs)may be particularly useful for such tasks due to the high resolution,flexibility of acquisition and cost efficiency of this type of data.In this study,A.glutinosa decline was assessed by considering four categories of tree health status in the field:asymptomatic,dead and defoliation above and below a 50% threshold.A combination of multispectral Parrot Sequoia and UAV unmanned aerial vehicles-red green blue(RGB)data were analysed using classical random forest(RF)and a simple and robust three-step logistic modelling approaches to identify the most important forest health indicators while adhering to the principle of parsimony.A total of 34 remote sensing variables were considered,including a set of vegetation indices,texture features from the normalized difference vegetation index(NDVI)and a digital surface model(DSM),topographic and digital aerial photogrammetry-derived structural data from the DSM at crown level.Results:The four categories identified by the RF yielded an overall accuracy of 67%,while aggregation of the legend to three classes(asymptomatic,defoliated,dead)and to two classes(alive,dead)improved the overall accuracy to 72% and 91% respectively.On the other hand,the confusion matrix,computed from the three logistic models by using the leave-out cross-validation method yielded overall accuracies of 75%,80% and 94% for four-,three-and two-level classifications,respectively.Discussion:The study findings provide forest managers with an alternative robust classification method for the rapid,effective assessment of areas affected and non-affected by the disease,thus enabling them to identify hotspots for conservation and plan control and restoration measures aimed at preserving black alder forests.