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Hemispherical photography is a well-established method to optically assess ecological parameters related to plant canopies; e.g. ground-level light regimes and the distribution of foliage within the crown space. Interpreting hemispherical photographs involves classifying pixels as either sky or vegetation. A wide range of automatic thresholding or binarization algorithms exists to classify the photographs. The variety in methodology hampers ability to compare results across studies. To identify an optimal threshold selection method, this study assessed the accuracy of seven binarization methods implemented in software currently available for the processing of hemispherical photographs. Therefore, binarizations obtained by the algorithms were compared to reference data generated through a manual binarization of a stratified random selection of pixels. This approach was adopted from the accuracy assessment of map classifications known from remote sensing studies. Percentage correct (Pc) and kappa-statistics (K) were calculated. The accuracy of the algorithms was assessed for photographs taken with automatic exposure settings (auto-exposure) and photographs taken with settings which avoid overexposure (histogram-exposure). In addition, gap fraction values derived from hemispherical photographs were compared with estimates derived from the manually classified reference pixels. All tested algorithms were shown to be sensitive to overexposure. Three of the algorithms showed an accuracy which was high enough to be recommended for the processing of histogram-exposed hemispherical photographs: \"Minimum\" (Pc 98.8%; K 0.952), \"Edge Detection\" (Pc 98.1%; K 0.950), and \"Minimum Histogram\" (Pc 98.1%; K 0.947). The Minimum algorithm overestimated gap fraction least of all (11%). The overestimation by the algorithms Edge Detection (63%) and Minimum Histogram (67%) were considerably larger. For the remaining four evaluated algorithms (IsoData, Maximum Entropy, MinError, and Otsu) an incompatibility with photographs containing overexposed pixels was detected. When applied to histogram-exposed photographs, these algorithms overestimated the gap fraction by at least 180%.

Our knowledge of temperate broadleaf forest ecology is based mostly on the study of production forests, which lack the terminal stage of forest development and have a simpler stand structure than old-growth and primeval forests. How primeval and production forests differ in net primary production (NPP) is not well known. In three primeval and three nearby production forests of European beech (Fagus sylvatica) in the Slovakian Carpathians, we measured aboveground biomass stocks (live and dead), aboveground NPP (ANPP) and parameters characterizing canopy structural diversity (leaf area index and its spatial variation). Our study aims were (1) to explore the role of canopy structural diversity for ANPP and (2) to assess evidence of a productivity decline in the terminal stage. While aboveground live biomass stocks were on average 20% greater in the primeval forests (386 vs. 320 Mg ha⁻¹; insignificant difference at two sites), deadwood mass stocks were on average four times larger than in the production forests (86 vs. 19 Mg ha⁻¹). ANPP was similarly high in the primeval and production forests (10.0 vs. 9.9 Mg ha⁻¹ y⁻¹) and did not decrease towards the terminal stage. Production models indicate that, in the primeval forests, about 10% of ANPP (ca. 1 Mg ha⁻¹ y⁻¹) was generated by effects related to leaf area heterogeneity, evidencing a positive effect of structural diversity on forest productivity, even though species diversity was low. This study helps to better understand the impact of forest management on the productivity and carbon storage in temperate woodlands.

Automated species classification from 3D point clouds is still a challenge. It is, however, an important task for laser scanning-based forest inventory, ecosystem models, and to support forest management. Here, we tested the performance of an image classification approach based on convolutional neural networks (CNNs) with the aim to classify 3D point clouds of seven tree species based on 2D representation in a computationally efficient way. We were particularly interested in how the approach would perform with artificially increased training data size based on image augmentation techniques. Our approach yielded a high classification accuracy (86%) and the confusion matrix revealed that despite rather small sample sizes of the training data for some tree species, classification accuracy was high. We could partly relate this to the successful application of the image augmentation technique, improving our result by 6% in total and 13, 14, and 24% for ash, oak and pine, respectively. The introduced approach is hence not only applicable to small-sized datasets, it is also computationally effective since it relies on 2D instead of 3D data to be processed in the CNN. Our approach was faster and more accurate when compared to the point cloud-based “PointNet” approach.

Background Old-growth and primeval forests are passing through a natural development cycle with recurring stages of forest development. Several methods for assigning patches of different structure and size to forest development stages or phases do exist. All currently existing classification methods have in common that a priori assumptions about the characteristics of certain stand structural attributes such as deadwood amount are made. We tested the hypothesis that multivariate datasets of primeval beech forest stand structure possess an inherent, aggregated configuration of data points with individual clusters representing forest development stages. From two completely mapped primeval beech forests in Albania, seven ecologically important stand structural attributes characterizing stand density, regeneration, stem diameter variation and amount of deadwood are derived at 8216 and 9666 virtual sampling points (moving window, focal filtering). K-means clustering is used to detect clusters in the datasets (number of clusters (k) between 2 and 5). The quality of the single clustering solutions is analyzed with average silhouette width as a measure for clustering quality. In a sensitivity analysis, clustering is done with datasets of four different spatial scales of observation (200, 500, 1000 and 1500 m 2 , circular virtual plot area around sampling points) and with two different kernels (equal weighting of all objects within a plot vs. weighting by distance to the virtual plot center). Results The clustering solutions succeeded in detecting and mapping areas with homogeneous stand structure. The areas had extensions of more than 200 m 2 , but differences between clusters were very small with average silhouette widths of less than 0.28. The obtained datasets had a homogeneous configuration with only very weak trends for clustering. Conclusions Our results imply that forest development takes place on a continuous scale and that discrimination between development stages in primeval beech forests is splitting continuous datasets at selected thresholds. For the analysis of the forest development cycle, direct quantification of relevant structural features or processes might be more appropriate than classification. If, however, the study design demands classification, our results can justify the application of conventional forest development stage classification schemes rather than clustering.

Estimating growing stock is one of the main objectives of forest inventories. It refers to the stem volume of individual trees which is typically derived by models as it cannot be easily measured directly. These models are thus based on measurable tree dimensions and their parameterization depends on the available empirical data. Historically, such data were collected by measurements of tree stem sizes, which is very time- and cost-intensive. Here, we present an exceptionally large dataset with section-wise stem measurements on 40’349 felled individual trees collected on plots of the Experimental Forest Management project. It is a revised and expanded version of previously unpublished data and contains the empirically derived coarse (diameter ≥7 cm) and fine branch volume of 27’297 and 18’980, respectively, individual trees. The data were collected between 1888 and 1974 across Switzerland covering a large topographic gradient and a diverse species range and can thus support estimations and verification of volume functions also outside Switzerland including the derivation of whole tree volume in a consistent manner.

Cultivation of non‐native tree species is a promising option to adapt managed forests to climate change. However, consequences of non‐native tree species on flora, fauna, and microorganisms that occur in forest ecosystems (forest associates) need to be considered when managing forests. We lack a solid understanding of how cultivating non‐native tree species in pure stands and in mixture with native tree species impacts abundance and species diversity of forest associates. We compared abundance, alpha‐, beta‐ and gamma‐diversity of eight forest‐floor‐associated taxa that are relevant for ecosystem functioning (including fungi, plants, arthropods, and small mammals) between different forest ecosystems. We addressed pure stands of non‐native coniferous Douglas‐fir (Pseudotsuga menziesii) and two native species, broad‐leaved European beech (Fagus sylvatica) and coniferous Norway spruce (Picea abies), as well as mixed stands of European beech with each conifer in two regions with differing site conditions in temperate Northwest Germany. Diversity indices revealed differences among species groups. Gamma‐diversity and alpha‐diversity of forest associates in Douglas‐fir and spruce stands were usually higher than in beech stands. Estimates of species diversity of mixed stands are intermediate between estimates for the respective pure stands. Differences in the diversity between the two study regions were highly taxon specific with no clear support for a general trend toward a higher or lower species diversity of forest associates depending on site quality. Abundance values show a pattern similar to our diversity estimates, but with a higher statistical uncertainty. Non‐native Douglas‐fir stands provided habitats to support associated species communities of equally high or higher diversity than stands of native beech or spruce. Mixed stands of non‐native and native tree species may be a management option to achieve different goals, that is, to provide habitats for species that are strictly associated with beech and to increase resilience to climate change. However, the overall diversity of forest‐floor‐associated biota is not improved by cultivating Douglas‐fir or spruce in mixture with beech.

The frequency and size of canopy gaps largely determine light transmission to lower canopy strata, controlling structuring processes in the understory. However, quantitative data from temperate virgin forests on the structure of regeneration in gaps and its dynamics over time are scarce. We studied the structure and height growth of tree regeneration by means of sapling density, shoot length growth and cumulative biomass in 17 understory gaps (29 to 931 m2 in size) in a Slovakian beech (Fagus sylvatica L.) virgin forest, and compared the gaps with the regeneration under closed-canopy conditions. Spatial differences in regeneration structure and growth rate within a gap and in the gap periphery were analyzed for their dependence on the relative intensities of direct and diffuse radiation (high vs. low). We tested the hypotheses that (i) the density and cumulative biomass of saplings are higher in gaps than in closed-canopy patches, (ii) the position in a gap influences the density and height growth of saplings, and (iii) height growth of saplings increases with gap size. Sapling density and biomass were significantly higher in understory gaps than under closed canopy. Density of saplings was positively affected by comparatively high direct, but low diffuse radiation, resulting in pronounced spatial differences. In contrast, sapling shoot length growth was positively affected by higher levels of diffuse radiation and also depended on sapling size, while direct radiation intensity was not influential. Conclusively, in this forest, regeneration likely becomes suppressed after a short period by lateral canopy expansion in small gaps (<100 m2), resulting in a heterogeneous understory structure. In larger gaps (≥100 m2) saplings may be capable even at low plant densities to fill the gap, often forming a cohort-like regeneration layer. Thus, gaps of different sizes imprint on the resulting canopy structure in different ways, enhancing spatial heterogeneity.

In the analysis of old-growth forest dynamics, the continuous process of tree aging and forest structural change is split up into several distinct forest development stages. The criteria for distinguishing the stages vary among the different approaches. In most of them, vertical canopy heterogeneity is only coarsely addressed and horizontal forest structure is quantified at spatial scales far exceeding the size of conventional forest inventory plots. In order to describe and analyze the complex mosaic structure of temperate old-growth forests with objective and quantitative measures in the context of forest inventories, we propose the Development Stage Index IDS. It employs two easily measured stand structural parameters (stem density and basal area) for quantifying the abundance of trees in three conventionally recognized tree diameter classes (premature  < 40 cm; mature 40–70 cm; and over-mature ≥ 70 cm) in plots of 500 m2 size, systematically distributed in the forest. This allows quantifying the spatial extension of the Initial, Optimum and Terminal stages of forest development at plot, stand and landscape levels. Based on thorough stand structural analyses in three virgin beech (Fagus sylvatica) forests in Slovakia, we demonstrate that IDS is a promising tool for (1) quantifying the proportion of the three stages on different scales, (2) visualizing the complex mixing of stages, and (3) analyzing dynamic changes in old-growth forest structure. We conclude that the Development Stage Index has the potential to improve the empirical foundation of forest dynamics research and to allow this discipline to proceed to more rigorous hypothesis testing.