Dr. Meinrad Abegg

Close-range remote sensing technologies show great potential to support national forest inventories (NFIs). Mobile laser scanning (MLS) has performed especially well in recent studies, as it is capable of fast data acquisition while still delivering accurate information on NFI-relevant variables (e.g. tree position, diameter at breast height [DBH]). However, the performance of MLS acquisition under challenging conditions (e.g. steep terrain, dense understorey vegetation) has rarely been addressed. Here, on 29 NFI plots distributed over the entire country of Switzerland and representing various terrain and forest conditions, we evaluated the use and performance of handheld MLS acquisition, using a GeoSLAM ZebHorizon laser scanner, to deliver analysis-ready (with minimal occlusion) point clouds and to estimate primary tree variables such as tree position and DBH. Data acquisition was successful for all tested Swiss NFI plots. The average time needed for point cloud acquisition of a 50 × 50 m sample plot was 13.13 min. Over all tested NFI plots, we achieved a tree detection rate of 82.6%, with a relative root mean square error (RMSE) of 7.25%, a mean absolute error of 2.0 cm and a systematic prediction error (SPE) of 1.0 cm for DBH estimation. The terrain gradient had an especially strong influence on the accuracy of DBH estimation. Occlusion, as a metric for point cloud capacity to hold forest structural information for the entire canopy volume, was minimal when the suggested acquisition pattern was followed, at 9% on average for the entire canopy volume. However, the level of occlusion increased in the upper part of the canopy (> 10 m above ground), to 15% on average.
Overall, handheld MLS effectively produced analysis-ready point clouds within the 15-minute acquisition time limitation for a 50 x 50 m area, even under challenging conditions, showing promise for future use in the Swiss NFI. However, the current accuracy of MLS-estimated forest variables like DBH and tree detection is insufficient to replace conventional measurements within an established NFI. Despite this, estimating NFI-relevant variables, even with lower accuracy, and expanding the area of measured tree variables could improve information on forest functions and services (e.g., protection and biodiversity), currently not possible to derive from NFI data due to the limited plot size. In the future, MLS-acquired point clouds could (1) shift from expert-assessed to more quantitative and reproducible variable estimation, and (2) enhance NFI with variables difficult to measure conventionally. This could open new pathways for assessing and monitoring forest structural diversity nationally and provide valuable calibration and validation data for large-scale forest assessments from airborne or space-borne acquisitions.

Im Rahmen des Projektes NaiS-LFI wurden in den Jahren 2014–2019 die Vegetationshöhenstufen und die Standorttypen auf den zugänglichen Waldprobeflächen des Schweizerischen Landesforstinventars (LFI) bestimmt. Dies geschah in der Systematik der Wegleitung «Nachhaltigkeit und Erfolgskontrolle im Schutzwald» (NaiS). So bietet sich die Möglichkeit zu analysieren, wie hoch die Übereinstimmung des Outputs der bislang im LFI verwendeten Definitionen und Modelle mit den auf den Probeflächen bestimmten NaiS-Vegetationshöhenstufen und -Vegetationseinheiten (= Gruppen von Standorttypen) ist. Bei den Vegetationshöhenstufen betrug die Übereinstimmung 76%. Bei den Vegetationseinheiten war sie mit 36% wesentlich geringer. Die NaiS-Vegetationshöhenstufen und -Vegetationseinheiten sind seit Juni 2023 als Klassifizierungsmerkmale in der LFI-Ergebnisabfrage im Internet verfügbar. Zudem werden in den Ableitungen, die sich auf Vegetationshöhenstufen oder Vegetationseinheiten stützen (z.B. zur Bestimmung der Naturnähe des Nadelholzanteils), neu die NaiS-Angaben verwendet.

Urban and forest trees provide valuable ecosystem services. However, they are increasingly threatened by invasive forest pests and pathogens. Trees in urban areas are often the first potential hosts non-native tree-feeding insects and tree pathogens (“pests”) encounter after introduction in a novel region. If the trees encountered are suitable hosts, these pests can establish and become invasive – eventually also in surrounding forests. Here, we compared tree species and genus composition between urban areas and surrounding forests and examined the implications for host availability for forest pests and potential effects on invasibility. We compiled and standardised 26 urban tree inventories, containing ∼ 500.000 individual trees. We used multivariate analyses to compare urban tree composition with forest tree composition from forests surrounding each municipality (10 km radius), derived from the Swiss National Forest Inventory. With > 1300 different tree species, species richness of urban trees was 17 times higher than species richness in surrounding forests. Linear models and multivariate analyses revealed that host availability for forest quarantine pests is significantly higher in urban areas than in forests, with large differences in host suitability for different quarantine pests between urban and forest tree assemblages. This indicates that differences in species composition in urban and forest trees can result in increased host availability, possibly facilitating the establishment of quarantine forest pests.

1. In close-range remote sensing data collected in a forest, occlusion often causes incomplete or sparse point cloud representations of individual trees, impeding accurate 3D reconstruction of tree architecture and estimation of tree height and volume. Recent developments in deep learning (DL) for 3D data have produced approaches for point cloud completion, which could potentially be applied to trees.
2. We explored the potential of a DL approach to fill gaps in dense point clouds representing the main structures of deciduous trees by applying an existing transformer-based completion model (PoinTr). Complete point clouds are required as training data, but even dense terrestrial laser scanning (TLS) data sets contain gaps caused by occlusion, making it nearly impossible to acquire such data. We therefore investigated the ability of point cloud completion models trained on a range of synthetic data sets to handle occlusion patterns in real-world point clouds.
3. Despite the limited data set, we successfully fine-tuned a general pre-trained completion model to fill gaps within 1 m³ segments of tree point clouds. Fine-tuning on synthetic tree data improved the model's ability to complete tree objects compared with training on diverse artificial objects. However, the quality of the predictions was influenced by the level of sophistication of the synthetic data. Our results demonstrate that incorporating even limited real-world TLS data during training can considerably improve completion results but may introduce additional noise in the predictions.
4. 3D point cloud completion with DL has the potential to improve and fill gaps in point clouds of individual trees, facilitating further steps in the processing and analysis of 3D forest data.

Aim: To determine the relationships between the functional trait composition of forest communities and environmental gradients across scales and biomes and the role of species relative abundances in these relationships.
Location: Global.
Time period: Recent.
Major taxa studied: Trees.
Methods: We integrated species abundance records from worldwide forest inventories and associated functional traits (wood density, specific leaf area and seed mass) to obtain a data set of 99,953 to 149,285 plots (depending on the trait) spanning all forested continents. We computed community-weighted and unweighted means of trait values for each plot and related them to three broad environmental gradients and their interactions (energy availability, precipitation and soil properties) at two scales (global and biomes).
Results: Our models explained up to 60% of the variance in trait distribution. At global scale, the energy gradient had the strongest influence on traits. However, within-biome models revealed different relationships among biomes. Notably, the functional composition of tropical forests was more influenced by precipitation and soil properties than energy availability, whereas temperate forests showed the opposite pattern. Depending on the trait studied, response to gradients was more variable and proportionally weaker in boreal forests. Community unweighted means were better predicted than weighted means for almost all models.
Main conclusions: Worldwide, trees require a large amount of energy (following latitude) to produce dense wood and seeds, while leaves with large surface to weight ratios are concentrated in temperate forests. However, patterns of functional composition within-biome differ from global patterns due to biome specificities such as the presence of conifers or unique combinations of climatic and soil properties. We recommend assessing the sensitivity of tree functional traits to environmental changes in their geographic context. Furthermore, at a given site, the distribution of tree functional traits appears to be driven more by species presence than species abundance.

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.

Forests are under pressure and going through rapid changes. However, current inventorying and monitoring (IM) programs are often either disjointed, too narrow in their scope and/or do not operate at fine enough temporal resolutions, which may hinder scientific understanding, the timely supply of information, fast decision making, and may result in the sub-optimal use of resources. For these reasons, there is an urgent need for Advanced Forest Inventorying and Monitoring (AIM) programs to (i) achieve expanded relevance (by augmenting data/information across ecosystem properties and trophic levels), (ii) have increased temporal resolution (by tailored data collection frequency), and (iii) make use of technological advances (by incorporating novel tools and technologies). The Advanced Inventorying and Monitoring for Swiss Forests (SwissAIM) initiative was launched in 2020 to address these needs. SwissAIM builds upon the foundation offered by the existing programs (e.g., national forest inventory, long-term forest ecosystem research, biodiversity monitoring). It aims to offer a collaborative and adaptive framework to enable integrated data collection, evaluation, interpretation, analysis, and modeling. Ideally, it will result in a more responsive system with respect to current and predicted biotic/abiotic stressors that will challenge Swiss forests. Developing such a system implies identifying the information needs of different stakeholders (e.g., science, policy, practice), related technical requirements, and governance frameworks. Here, we present (i) the main features of the SwissAIM initiative (vision, scientific questions and variables, governance and engagement), (ii) the main outcomes of the participatory design process (measurements, sampling, and plot design), (iii) the potential transferability of AIM initiatives outside Switzerland (timing, relevance, practicability), and (iv) the key messages that emerged (i.e., need for advancement, integration and transdisciplinarity, statistical underpinning). Since similar needs related to forest inventorying and monitoring are emerging throughout Europe and elsewhere, the objective of this opinion paper is to share our experience and promote a dialog with those interested in developing AIM initiatives in other countries and regions.

Aim: Ecological and anthropogenic factors shift the abundances of dominant and rare tree species within local forest communities, thus affecting species composition and ecosystem functioning. To inform forest and conservation management it is important to understand the drivers of dominance and rarity in local tree communities. We answer the following research questions: (1) What are the patterns of dominance and rarity in tree communities? (2) Which ecological and anthropogenic factors predict these patterns? And (3) what is the extinction risk of locally dominant and rare tree species?.
Location: Global.
Time period: 1990–2017.
Major taxa studied: Trees.
Methods: We used 1.2 million forest plots and quantified local tree dominance as the relative plot basal area of the single most dominant species and local rarity as the percentage of species that contribute together to the least 10% of plot basal area. We mapped global community dominance and rarity using machine learning models and evaluated the ecological and anthropogenic predictors with linear models. Extinction risk, for example threatened status, of geographically widespread dominant and rare species was evaluated.
Results: Community dominance and rarity show contrasting latitudinal trends, with boreal forests having high levels of dominance and tropical forests having high levels of rarity. Increasing annual precipitation reduces community dominance, probably because precipitation is related to an increase in tree density and richness. Additionally, stand age is positively related to community dominance, due to stem diameter increase of the most dominant species. Surprisingly, we find that locally dominant and rare species, which are geographically widespread in our data, have an equally high rate of elevated extinction due to declining populations through large-scale land degradation.
Main conclusions: By linking patterns and predictors of community dominance and rarity to extinction risk, our results suggest that also widespread species should be considered in large-scale management and conservation practices.

Understanding and monitoring the surrounding environment increasingly rely on its 3D representations. However, the often high costs of 3D data equipment limit its wide usage, and low-cost solutions are in demand. Here, we propose a novel approach based on spherical stereo videos captured with a known baseline (distance between the cameras) for a low-cost and efficient 3D point cloud reconstruction. In a forest environment, we evaluated 1) the influence of baseline length on point cloud quality and 2) the suitability of the generated point clouds for extracting primary forest attributes (tree position and diameter). Our results show that the proposed approach allows for feasible 3D reconstruction of complex forest plots. The highest point cloud quality was achieved with a baseline of 60 cm. This setup enabled the correct detection of more than 65% of the trees within the forest plots, producing an average tree position error between 30 and 50 cm and clearly outperforming other setups. A Multiscale Model-to-Model Cloud (M3C2) analysis showed signed distances between the generated point cloud and the reference data with zero mean and 1 m standard deviation. We demonstrate that the proposed approach can be a valuable low-cost solution for 3D point cloud reconstruction, facilitating forest assessment and monitoring.

The density of wood is a key indicator of the carbon investment strategies of trees, impacting productivity and carbon storage. Despite its importance, the global variation in wood density and its environmental controls remain poorly understood, preventing accurate predictions of global forest carbon stocks. Here we analyse information from 1.1 million forest inventory plots alongside wood density data from 10,703 tree species to create a spatially explicit understanding of the global wood density distribution and its drivers. Our findings reveal a pronounced latitudinal gradient, with wood in tropical forests being up to 30% denser than that in boreal forests. In both angiosperms and gymnosperms, hydrothermal conditions represented by annual mean temperature and soil moisture emerged as the primary factors influencing the variation in wood density globally. This indicates similar environmental filters and evolutionary adaptations among distinct plant groups, underscoring the essential role of abiotic factors in determining wood density in forest ecosystems. Additionally, our study highlights the prominent role of disturbance, such as human modification and fire risk, in influencing wood density at more local scales. Factoring in the spatial variation of wood density notably changes the estimates of forest carbon stocks, leading to differences of up to 21% within biomes. Therefore, our research contributes to a deeper understanding of terrestrial biomass distribution and how environmental changes and disturbances impact forest ecosystems.

Dass die Artenvielfalt von der Flächengrösse abhängt, ist ein bekanntes Phänomen. Mit zunehmender Fläche steigt im Allgemeinen die Anzahl festgestellter Arten. Dieser Zusammenhang ist jedoch nicht linear. Die Baumartenvielfalt von Wäldern unterschiedlicher Grösse zu vergleichen, stellt deshalb oftmals eine Herausforderung dar. Auf der Grundlage von Daten des Schweizerischen Landesforstinventars wurde nun eine Methodik entwickelt, mit der Schätzungen zur erwarteten Baumartenvielfalt auf beliebige Flächengrössen skaliert werden können. Die Methodik wurde in erster Linie zur Verbesserung der internationalen Berichterstattung entwickelt. Nun soll sie auch der Schweizer Forstpraxis zugänglich gemacht werden – mittels einer öffentlichen Web-App.

The emergence of alternative stable states in forest systems has significant implications for the functioning and structure of the terrestrial biosphere, yet empirical evidence remains scarce. Here, we combine global forest biodiversity observations and simulations to test for alternative stable states in the presence of evergreen and deciduous forest types. We reveal a bimodal distribution of forest leaf types across temperate regions of the Northern Hemisphere that cannot be explained by the environment alone, suggesting signatures of alternative forest states. Moreover, we empirically demonstrate the existence of positive feedbacks in tree growth, recruitment and mortality, with trees having 4–43% higher growth rates, 14–17% higher survival rates and 4–7 times higher recruitment rates when they are surrounded by trees of their own leaf type. Simulations show that the observed positive feedbacks are necessary and sufficient to generate alternative forest states, which also lead to dependency on history (hysteresis) during ecosystem transition from evergreen to deciduous forests and vice versa. We identify hotspots of bistable forest types in evergreen-deciduous ecotones, which are likely driven by soil-related positive feedbacks. These findings are integral to predicting the distribution of forest biomes, and aid to our understanding of biodiversity, carbon turnover, and terrestrial climate feedbacks.

Tree volume is a key feature in forest monitoring, delivering information, such as wood availability or forest carbon balance. To date, tree volume, i.e. the total volume of the above ground woody parts of a tree, cannot be measured directly with conventional tools. Terrestrial laser scanning (TLS) offers the potential to directly measure tree volume. However, its application in forest monitoring requires a profound understanding of the precision and accuracy of retrieval approaches. In this study, we present a simulation environment for evaluating TLS application in forest inventories. We investigate the influence of understorey density, scanner placement and TLS sensor type on volume estimation of tree parts of varying diameters. Using information from 30 sample plots from the Swiss NFI to simulate 197 sample trees, we evaluate three understorey densities, five scanner locations and their combinations and three realistic and one hypothetic (geometric scanning) TLS sensor types. We show that tree volume estimates from point clouds are biased to certain extent: from about 25% for small trees to a few percent for larger trees above 40 cm diameter at breast height (DBH). Especially small tree parts (diameters 7 cm) lack accurate and precise estimation. In small trees with 12 cm DBH they are overestimated by 110% in average with a high variation, whereas they are underestimated in large trees, i.e. with DBH 75 cm, by 50% in average. Volume estimation of small tree parts is subject to physical limits of TLS, however the estimation of volume of large tree parts could be feasible with appropriate TLS settings and field protocols. Nevertheless, tree volume estimation using TLS must be understood in greater depth before it can be applied regularly in forest inventories.

Accurate estimates of above-ground tree biomass within forest inventories are essential for calibration and validation of biomass mapping products based on Earth observation data. Terrestrial laser scanning (TLS) enables detailed and non-destructive volume estimation of individual trees, which can be converted to biomass with wood basic density. Existing TLS-based approaches range from simple geometrical features to virtual 3D reconstruction of entire trees. Validating such approaches with weight measurements is a key step before the integration of TLS or other close-range technologies into operational applications such as forest inventories. In this study, we firstly evaluate individual tree volume estimation approaches based on 3D reconstruction through quantitative structure models (QSM) against destructive reference data of 60 trees and compare them to operational allometric scaling models (ASM). Secondly, we determine the explanatory power of TLS-derived geometric parameters regarding total wood, stem, coarse wood and fine branch volume. We observe similar accuracy in merchantable (>7 cm) wood compartments for ASMs (NRMSE = 25 %) and QSMs (NRMSE = 29 %), with QSMs showing better results for broadleaves than conifers and generally overestimating fine branch volume. Feature selection shows that a combination of stem diameters and volume of convex hulls around tree crowns has the most potential to model the entire tree volume including branches, especially for conifers. In cases where the quality of available point clouds is insufficient for QSMs, 3D information can thus still be utilised by deriving geometric parameters. The integration of crown dimension parameters into new allometric models could substantially improve the estimation of branch wood volume.

Determining the drivers of non-native plant invasions is critical for managing native ecosystems and limiting the spread of invasive species. Tree invasions in particular have been relatively overlooked, even though they have the potential to transform ecosystems and economies. Here, leveraging global tree databases, we explore how the phylogenetic and functional diversity of native tree communities, human pressure and the environment influence the establishment of non-native tree species and the subsequent invasion severity. We find that anthropogenic factors are key to predicting whether a location is invaded, but that invasion severity is underpinned by native diversity, with higher diversity predicting lower invasion severity. Temperature and precipitation emerge as strong predictors of invasion strategy, with non-native species invading successfully when they are similar to the native community in cold or dry extremes. Yet, despite the influence of these ecological forces in determining invasion strategy, we find evidence that these patterns can be obscured by human activity, with lower ecological signal in areas with higher proximity to shipping ports. Our global perspective of non-native tree invasion highlights that human drivers influence non-native tree presence, and that native phylogenetic and functional diversity have a critical role in the establishment and spread of subsequent invasions.

1. Biodiversity is an important component of natural ecosystems, with higher species richness often correlating with an increase in ecosystem productivity. Yet, this relationship varies substantially across environments, typically becoming less pronounced at high levels of species richness. However, species richness alone cannot reflect all important properties of a community, including community evenness, which may mediate the relationship between biodiversity and productivity. If the evenness of a community correlates negatively with richness across forests globally, then a greater number of species may not always increase overall diversity and productivity of the system. Theoretical work and local empirical studies have shown that the effect of evenness on ecosystem functioning may be especially strong at high richness levels, yet the consistency of this remains untested at a global scale.
2. Here, we used a dataset of forests from across the globe, which includes composition, biomass accumulation and net primary productivity, to explore whether productivity correlates with community evenness and richness in a way that evenness appears to buffer the effect of richness. Specifically, we evaluated whether low levels of evenness in speciose communities correlate with the attenuation of the richness–productivity relationship.
3. We found that tree species richness and evenness are negatively correlated across forests globally, with highly speciose forests typically comprising a few dominant and many rare species. Furthermore, we found that the correlation between diversity and productivity changes with evenness: at low richness, uneven communities are more productive, while at high richness, even communities are more productive.
4. Synthesis. Collectively, these results demonstrate that evenness is an integral component of the relationship between biodiversity and productivity, and that the attenuating effect of richness on forest productivity might be partly explained by low evenness in speciose communities. Productivity generally increases with species richness, until reduced evenness limits the overall increases in community diversity. Our research suggests that evenness is a fundamental component of biodiversity–ecosystem function relationships, and is of critical importance for guiding conservation and sustainable ecosystem management decisions.

Understanding what controls global leaf type variation in trees is crucial for comprehending their role in terrestrial ecosystems, including carbon, water and nutrient dynamics. Yet our understanding of the factors influencing forest leaf types remains incomplete, leaving us uncertain about the global proportions of needle-leaved, broadleaved, evergreen and deciduous trees. To address these gaps, we conducted a global, ground-sourced assessment of forest leaf-type variation by integrating forest inventory data with comprehensive leaf form (broadleaf vs needle-leaf) and habit (evergreen vs deciduous) records. We found that global variation in leaf habit is primarily driven by isothermality and soil characteristics, while leaf form is predominantly driven by temperature. Given these relationships, we estimate that 38% of global tree individuals are needle-leaved evergreen, 29% are broadleaved evergreen, 27% are broadleaved deciduous and 5% are needle-leaved deciduous. The aboveground biomass distribution among these tree types is approximately 21% (126.4 Gt), 54% (335.7 Gt), 22% (136.2 Gt) and 3% (18.7 Gt), respectively. We further project that, depending on future emissions pathways, 17-34% of forested areas will experience climate conditions by the end of the century that currently support a different forest type, highlighting the intensification of climatic stress on existing forests. By quantifying the distribution of tree leaf types and their corresponding biomass, and identifying regions where climate change will exert greatest pressure on current leaf types, our results can help improve predictions of future terrestrial ecosystem functioning and carbon cycling.