The role of forests in providing multiple goods and services has been recognized worldwide. In such a context, reliable spatial predictions of forest attributes such as tree volume and current increment are fundamental for conducting forest monitoring, improving restoration programs, and supporting decision-making processes. This article presents the methodology and the results of the wall-to-wall spatialization of the growing stock volume and the current annual increment measured in 273 plots of data of the Italian National Forest Inventory over an area of more than 3260 km2 in the Friuli Venezia Giulia region (Northeast Italy). To this aim, a random forest model was tested using as predictors 4 spectral indices from Sentinel-2, a high-resolution Canopy Height Model derived from LiDAR, and geo-morphological data. According to the Leave One Out cross-validation procedure, the model for the growing stock shows an R2 and an RMSE% of 0.67 and 41%, respectively. Instead, an R2 of 0.47 and an RMSE% of 57% were obtained for the current annual increment. The validation with an independent dataset further improved the models’ performances, yielding significantly higher R2 values of 0.84 and 0.83 for volume and for increment, respectively. Our results underline a relatively higher importance of LiDAR-derived metrics compared to other covariates in estimating both attributes, as they were even twice as important as vegetation indices for growing stock. Therefore, these metrics are promising for the development of a national LiDAR-based model.
Wind disturbances are one of the main drivers of forest dynamics in Europe, shaping forest stands and modifying the ecosystem services provisioning. Salvage logging is often most common strategy adopted after a high-severity disturbance in managed stands. Understanding natural regeneration dynamics including their interaction with the logging operations, is crucial to understand how forests will be changing under a climate with increasing variability and to design adequate adaptive post-disturbance management strategies. In this study, we focused on 148 stands damaged by storm Vaia (2018). The aim was to analyze natural regeneration dynamics under different logging systems and to investigate influences of site characteristics and disturbance legacies on sapling growth and seedling emergence. The sampling protocol consisted of one transect per stand, perpendicular to one of the intact forest edges, and with a length of 80 m. Along the transect, we collected soil cover, natural seedling and sapling stem density, and deadwood quantity in four sample plots of 3 m radius each at distances of 0, 20, 40, and 80 meters from the edge (592 plots in total). Regeneration species composition was mainly driven by previous stand composition, with some exceptions depending on seed dispersal strategy. Distance from the edge significantly influenced seedlings and saplings occurrence in large gaps and affected the browsing damage percentage, together with deadwood presence. According to GLM's models, distance from the edge, elevation, and logging methods influenced seedling establishment. At the same time, species characteristics, edge structure, deadwood and logging damages significantly influenced pre-storm seedlings and saplings presence and health. In conclusion site factors, disturbance legacies, and logging strategies are key points to consider in post-disturbance management for a fast forest recovery.
Abstract Gross primary productivity (GPP), the gross uptake of carbon dioxide (CO 2 ) by plant photosynthesis, is the primary driver of the land carbon sink, which presently removes around one quarter of the anthropogenic CO 2 emissions each year. GPP, however, cannot be measured directly and the resulting uncertainty undermines our ability to project the magnitude of the future land carbon sink. Carbonyl sulfide (COS) has been proposed as an independent proxy for GPP as it diffuses into leaves in a fashion very similar to CO 2 , but in contrast to the latter is generally not emitted. Here we use concurrent ecosystem‐scale flux measurements of CO 2 and COS at four European biomes for a joint constraint on CO 2 flux partitioning. The resulting GPP estimates generally agree with classical approaches relying exclusively on CO 2 fluxes but indicate a systematic underestimation under low light conditions, demonstrating the importance of using multiple approaches for constraining present‐day GPP.
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