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Abstract In times of more frequent global change‐type droughts and associated tree mortality events, competition release is one silvicultural measure discussed to have an impact on the resilience of managed forest stands. Understanding how trees compete with each other is therefore crucial, but different measurement options and competition indices (CI) leave users with a difficult choice, as no single competition index has proven universally superior. To help users with the choice and computation of appropriate indices, we present the open‐source TreeCompR package, which handles 3D point clouds and classical forest inventory data, enabling the calculation of both innovative point cloud‐based indices and traditional distance‐dependent indices. It serves as a centralized platform for exploring and comparing different CIs, allowing users to test and select the most suitable CI for their specific research questions within a common interface. To evaluate the package, we used TreeCompR to quantify the competition situation of 307 European beech trees from 13 sites in Central Europe. Based on this dataset, we discuss the interpretation, comparability and sensitivity of the different indices to their parameterization and identify possible sources of uncertainty and ways to minimize them. The compatibility of TreeCompR with different data formats and different data collection methods makes it accessible and useful for a wide range of users, specifically ecologists and foresters. Due to the flexibility in the choice of input formats as well as the emphasis on tidy, well‐structured output, our package can easily be integrated into existing data‐analysis workflows both for 3D point cloud and classical forest inventory data.

Forests in the Himalayan region are crucial for maintaining the region's ecological balance, conserving biodiversity, and supporting the livelihoods of local people. However, because of limited accessibility and an adverse climate, scientific studies on how forest functions in this region depend on ecological drivers are rare. We used a handheld mobile laser scanner to assess the forest structural complexity (FSC) in the Annapurna Conservation Area of Nepal and related this to its potential drivers, including forest disturbances. Based on stratified sampling, we selected 69 plots across a gradient of elevations and precipitations. Other factors that might influence FSC were obtained from forest inventory data, climatic databases, the Google Earth platform, and digital elevation models. Using simple linear regression and multiple regression analysis, we tested for the dependency of FSC, measured using the box dimension (Db), on influential predictor variables. Overall, explanatory variables strongly influenced FSC (adjusted R2 = 0.60, P < 0.001), with Db being affected by the number of trees, the maximum height of the forests, species diversity, north-facing aspect, soil pH, and forest disturbance. Surprisingly, climatic variables, precipitation, and temperature did not show any effect on FSC. The LiDAR-based approach to FSC used in our study enabled rapid assessment in hard-to-access regions. It can be used to inform effective management and conservation, for example, in monitoring development over time or for benchmarking.

Global climate change, biodiversity decline, and increasing disturbances are challenging the health and resilience of forests. In this regard, forest managers have sought to promote enhanced structural complexity (ESC) which utilizes the positive correlation between structural complexity and biodiversity or resilience to inform management practices. In light of these concerns, we integrated remote sensing data from multiple platforms and sensors to test the potential for quantifying different levels of structural complexity in temperate forests. This analysis was conducted in the context of the BETA-FOR project, where silvicultural manipulations of forest structure replicate silvicultural or natural disturbances. BETA-FOR includes a wide-range of standardized treatments across representative Central European broad-leaved forests, which are sub-divided into aggregated (gap felling) and distributed treatments (selective thinning) in combination with varying deadwood structures. This study provides a novel analysis of ESC from complementary remote sensing perspectives in order to bridge scales among structural complexity indicators. Remotely sensed observations comprise in-situ measurements (mobile and terrestrial laser scanning), as well as spaceborne observations from various sensors (including Sentinel-1 radar, Sentinel-2 multispectral, and GEDI lidar). We found moderate to strong inter-platform correlations among structural complexity metrics (|r| >= 0.6) between mobile laser scanning (box dimension, canopy cover), terrestrial laser scanning (canopy openness index), Sentinel-1 (VH, cross-polarized backscatter), Sentinel-2 (NMDI, Normalized Multi-band Drought Index), and GEDI (total canopy cover). In addition, multivariate analyses revealed that ESC of gap aggregated treatments can be effectively delineated from control and distributed treatments across all considered remote sensing sensors/platforms. Therefore, the metrics from different platforms and sensors better characterize the changes in structural complexity through aggregated compared to distributed treatments. Furthermore, we identified the sensitivity of in-situ and spaceborne metrics towards the presence of standing deadwood structures. An unsupervised clustering analysis highlights distinct differences in structural complexity of aggregated treatments with snags and habitat trees compared with aggregated treatments without standing structures, as well as distributed and control treatments. Findings demonstrate the potential of various sensors and platforms for monitoring forest structural complexity. We recommend the spaceborne indicators Sentinel-1 VH cv, Sentinel-2 NMDI cv, and GEDI cover cv to monitor ESC at high spatio-temporal resolution as they show highest correlations to in-situ metrics, thus holding the potential to guide adaptive forest management.

Laser-scanned point clouds of forests make it possible to extract valuable information for forest management. To consider single trees, a forest point cloud needs to be segmented into individual tree point clouds. Existing segmentation methods are usually based on hand-crafted algorithms, such as identifying trunks and growing trees from them, and face difficulties in dense forests with overlapping tree crowns. In this study, we propose TreeLearn, a deep learning-based approach for tree instance segmentation of forest point clouds. TreeLearn is trained on already segmented point clouds in a data-driven manner, making it less reliant on predefined features and algorithms. Furthermore, TreeLearn is implemented as a fully automatic pipeline and does not rely on extensive hyperparameter tuning, which makes it easy to use. Additionally, we introduce a new manually segmented benchmark forest dataset containing 156 full trees. The data is generated by mobile laser scanning and contributes to create a larger and more diverse data basis for model development and fine-grained instance segmentation evaluation. We trained TreeLearn on forest point clouds of 6665 trees, labeled using the Lidar360 software. An evaluation on the benchmark dataset shows that TreeLearn performs as well as the algorithm used to generate its training data. Furthermore, the performance can be vastly improved by fine-tuning the model using manually annotated datasets. We evaluate TreeLearn on our benchmark dataset and the Wytham Woods dataset, outperforming the recent SegmentAnyTree, ForAINet and TLS2Trees methods. The TreeLearn code and all datasets that were created in the course of this work are made publicly available.

Abstract Increasing forest structural complexity is becoming a common goal in forestry worldwide. However, the lack of empirical quantification clouds its implementation. Here we quantified the long-term effects (> 30 y) of partial harvest on stand structural complexity and net primary productivity using the east–west precipitation gradient (318–2508 mm, mean annual precipitation-MAP) of western Patagonian as a study system. In this gradient, pairs of 1-ha plots on 20 sites (20 plots harvested and 20 plots unharvested) were installed. In each plot terrestrial laser scanning was used to quantify the stand structural complexity index (SSCI), and Sentinel satellite images to obtain the Enhanced Vegetation Index (EVI: proxy of net primary productivity). Generalized linear mixed-effect models were used to relate SSCI to MAP and EVI to SSCI, with harvesting as indicator variable, and site as random variable (two plots nested to same precipitation). Results showed that harvested plots on mesic-to-humid sites (but not on dry sites) had higher SSCI and EVI values compared to unharvested plots, likely due to a greater vertical canopy packing. These results show the influence of precipitation on SSCI, which resulted in a more diversified stand structure and higher EVI. Such insights support site-specific management aimed to increase forest structural complexity.

Vegetation structural complexity is an important component of forest ecosystems, influencing biodiversity and functioning. Due to the heterogeneous distribution of vegetation elements, structural complexity underpins ecological dynamics, species composition, microclimate, and habitat diversity. Field measurements and Light Detection and Ranging (LiDAR) data, such as airborne (ALS) and terrestrial (TLS), can assess structural characteristics of forest and agroforestry systems at various spatial scales. This assessment is urgently needed for monitoring ecosystem restoration in degraded lands (e.g., in oil palm landscapes), where it is not well-known how structural measures derived from these different approaches relate to each other. Here, we compared the degree of correlation between individual and multivariate datasets of vegetation structural complexity metrics derived from ALS, TLS, and ground-based inventory approaches. The study was conducted in a 140 ha oil palm monoculture, enriched with 52 plots in the form of tree islands representing agroforestry systems of varying sizes and planted diversity levels in Sumatra, Indonesia. Our datasets comprised 25 ALS, five TLS, and nine ground-based inventory metrics. We studied correlations among metrics related to traditional stand summary, heterogeneity, and vertical and horizontal stand structure. We used principal component analysis for data dimensionality reduction, correlation analysis to quantify the strength of relationships between metrics, and Procrustes analysis to investigate the agreement between datasets. Significant correlations were found between ALS and TLS metrics for canopy density (r = 0.79) and maximum tree height (r = 0.58) and between ALS and ground-based inventory measures of stand heterogeneity and height diversity (r between 0.60 and −0.63). Further, we observed significant agreements between the ordinations of multivariate datasets (r = 0.56 for ALS − TLS; and r = 0.46 for ALS – ground-based inventory). Our findings underline the ability of ALS to capture structural complexity patterns, especially for canopy gap dynamics and vegetation height metrics, as captured by TLS, and for measures of heterogeneity and vertical structure as captured by ground-based inventories. Our study highlights the strength of each approach and underscores the potential of integrating ALS and TLS with ground-based inventories for a comprehensive characterization of vegetation structure in complex agroforestry systems, which can provide guidance for their management and support ecosystem restoration monitoring efforts.

Prolonged periods of extreme heat and drought are changing forest health and forest structure. Loss of vitality, signs of dieback, and mortality of trees are reported in many regions of the world. There is a need for information on the quantification of drought effects on forest structure in order to detect drought stress at an early stage. Furthermore, forest structure is linked to growth processes that result from single tree and stand dynamics. In this study, we used mobile laser scanning to objectively assess forest structure in European beech (Fagus sylvatica L.) stands differently affected by drought. We used the box-dimension as a holistic measure of structural complexity that jointly describes both distribution and amount of plant material in a forest, and canopy cover as a measure for overall defoliation. We observed large canopy gaps in heavily damaged stands, but the overall structural complexity of the forest stands remained unchanged. However, when dividing the 3D forest stand model from mobile laser scanning into 5 m height layers, we noticed a “structural flip” with reduced structural complexity in the upper parts of the forests and increased structural complexity in the lower strata. This indicates that the forests are responding to drought with increased mortality of mature trees, resulting in increased light availability in the understory and consequently increased growth of the understory trees. The rather indifferent overall structural complexity of the investigated forests was due to the reorganization of structures by shifting the major foliage layer from top-to-bottom and is interpreted as a successful ecosystem-scale response to the drought events. However, it is unclear how these forests will respond to repeated droughts. The “structural flip” should therefore be regarded as an early warning signal pointing to increased ecosystem stress, though the stress level has not yet exceeded the adaptive capacity in the investigated forest.

Forest canopies host an abundant but often neglected diversity of arthropods, which requires careful attention in times of ongoing biodiversity loss. Yet, how tree species composition interacts with canopy structure in shaping arthropod communities remains largely unknown. Here, by combining mobile laser scanning and insecticidal fogging with a machine learning algorithm, we studied which canopy architectural properties affect canopy arthropod communities in monospecific and mixed stands of broadleaved European beech and the coniferous Norway spruce and non-native Douglas fir in Germany. Evaluating the abundances and ecological guild diversity of ∼ 90,000 arthropods and 27 partly novel high-resolution structural variables, we identified vegetation volume and tree species identity as weak predictors of arthropod abundance and ecological guild diversity. In contrast, structural heterogeneity, i.e. structural complexity, vertical layering and variability of canopy gaps—which were highest in coniferous stands—were strong positive drivers. Despite this, arthropod ecological guild diversity was lower in non-native Douglas fir. Mixed stands had intermediate arthropod abundance and ecological guild diversity. Our study shows that habitat heterogeneity and tree species-identity are closely interlinked in shaping associated canopy arthropod communities. Positive effects of habitat heterogeneity on arthropod ecological guild diversity were often uncoupled from resource availability, and the key role of our novel intra-canopy gap indices suggests that they should be considered as indicators in future research on forest heterogeneity-diversity relationships. Broadleaf-conifer mixtures may be suitable to mediate negative tree-species identity effects when adapting forests to global change.

In Central Europe, anthropogenic coniferous monocultures have been subject to conversion to more diverse mixed forests since the 1990s, however, they are still abundant across many forest landscapes. Artificial and natural tree regeneration both play a key role during conversion by determining the species composition and structure of the future forests. Many abiotic and biotic factors can potentially influence the regeneration process and its specific combinations or interactions may be different among tree species and its developmental stages. Here, we aimed to identify and quantify the effect of the most important drivers on the density of the most abundant regenerating tree species (i.e., Norway spruce and European beech), as well as on species and structural diversity of the tree regeneration. We studied tree regeneration in four former monospecific coniferous stand types (i.e., Norway spruce, Scots pine, European larch, and Douglas fir) in Southwest Germany that have been under conversion to mixed forests since the 1990s. We sampled tree regeneration in four growth height classes together with a variety of potentially influencing factors on 108 sampling plots and applied multivariate analyses. We identified light availability in the understorey, stand structural attributes, browsing pressure, and diaspore source abundance as the most important factors for the density and diversity of tree regeneration. Particularly, we revealed species-specific differences in drivers of regeneration density. While spruce profited from increasing light availability and decreasing stand basal area, beech benefited either from a minor reduction or more strikingly from an increase in overstorey density. Increasing diaspore source abundance positively and a high browsing pressure negatively affected both species equally. Our results suggest that humus and topsoil properties were modified during conversion, probably due to changes in tree species composition and silvicultural activities. The species and structural diversity of the tree regeneration benefitted from increasing light availability, decreasing stand basal area, and a low to moderate browsing pressure. We conclude that forest managers may carefully equilibrate among the regulation of overstorey cover, stand basal area, and browsing pressure to fulfil the objectives of forest conversion, i.e., establishing and safeguarding a diverse tree regeneration to promote the development of mature mixed forests in the future.

Forest management has a direct influence on the structure and stability of forests. In this study, we used the 3D data from mobile laser scanning in real forest stands dominated by European beech (Fagus sylvatica L.) to simulate different silvicultural treatments and assess their impact on the structural complexity and short-term economic return. For the structural assessment, we used the box-dimension (Db), a holistic measure of structural complexity in forest. The expected net revenues of the silvicultural treatments were used as a proxy for short-term economic gain. We simulated six different treatments in 19 different real-world forest stands. The results showed that each treatment had a negative impact on the structural complexity of the stands but with varying severity. The treatments with the smallest effect on stand structural complexity showed the highest net revenue, indicating no trade-offs if a forest owner strives for small stand structural changes and high economic return. The approach used here allows quantifying the structural and economic consequences of different treatments in forest stands prior to the actual application in the real world. This holds large potential for decision making according to the forest owner's objective.

Forest structure depends both on extrinsic factors such as climate and on intrinsic properties such as community composition and diversity. Here, the authors use a dataset of stand structural complexity based on LiDAR measurements to build a global map of structural complexity for primary forests, and find that precipitation variables best explain global patterns of forest structural complexity.

The dynamics of forest structure influence forest ecosystem functions and are modified by forest management and natural disturbances. Here, we quantified the dynamics of stand structural complexity of differently managed and unmanaged European beech (Fagus sylvatica L.) forests. We determined changes of different aspects of stand structural complexity between 2014 and 2019 using terrestrial laser scanning data from 42, one hectare-sized forest plots, representing even - aged forest management, uneven - aged forest management and unmanaged stands. Unmanaged forests showed no significant changes in stand structural complexity within the time frame investigated, due to the absence of major disturbances. On the contrary, managed uneven - aged and even - aged forest stands showed more pronounced dynamics in stand structural complexity than the unmanaged forests. In this context, uneven - aged stands with higher initial canopy openness showed a higher increase in structural complexity than stands with lower canopy openness, which could be attributed to growth responses of understory vegetation in lower strata due to improved light availability at the beginning of the observed time period. Dynamics of structural complexity under even - aged forest management strongly differed between different developmental stages, with young thickets and mature timber stands showing highest increases in stand structural complexity. Overall, we did not observe significant decreases in stand structural complexity within the observed time frame. Our findings need to be viewed in the context of long-term dynamics of forest structure and contribute to the understanding of how forest management can affect short - term structural dynamics in beech forests.

The adaptation of forest management to changing environmental conditions due to climate change relies on information on the current forest and tree vitality. In common practice, the percentage of crown defoliation is used as a proxy for tree vitality, an approach that has several drawbacks. By performing laser scanning on a forest plot in the Hainich National Park, we tested a new approach to quantifying tree vitality. Based on the difference in structural complexity measured between summer and winter, assessed via the box-dimension, the new approach provides an objective and reproducible method that does not only allow the quantification of the defoliation, but also includes the effects of branch diebacks. In addition, we assessed the change in box-dimension and branch lengths between 2013 and 2021 and studied the overall structural development of the trees to analyze the alteration of their vitality. On average, we found a decrease in overall structural complexity, as well as in branch lengths for most trees in the investigated forest. Further, the mean difference in box-dimension (summer minus winter) decreased with ascending vitality class. We conclude that the vitality of the trees was negatively affected over the period of 8 years, and we argue the newly proposed method based on the box-dimension holds potential to be used as a measure for tree vitality in deciduous forests.

Today, creating or maintaining forest structural complexity is a management paradigm in many countries due to the positive relationships between structural complexity and several forest functions and services. In this study, we tested whether the box-dimension (Db), a holistic and objective measure to describe the structural complexity of trees or forests, can be used to quantify the structural complexity of 14 European beech (Fagus sylvatica L.) dominated forest plots by means of mobile laser scanning (MLS). The goal of this study was to explore the potential of this approach for quantifying the effect of leaves (summer vs winter) and management (lately unmanaged vs managed) on forest structural complexity. The findings suggest that repeated measurements on the same site and at the same time yielded consistent results if the measuring scheme is standardized. The results also showed that standardized measurement protocols allowed quantifying differences in forest structural complexity due to season. The highest stand structural complexity was found in leaf-on condition during summer, with the complexity being significantly higher than in winter condition. Also, in case of our beech-dominated plots, managed forests were more complex in structure than formerly managed but now unmanaged forests. This study illustrates the potential of MLS for monitoring the changes in forest structural complexity and allows correcting stand structural information for seasonality.

Laser scanning has revolutionized the ability to quantify single-tree morphologies and stand structural variables. In this study, we address the issue of occlusion when scanning a spruce (Picea abies (L.) H.Karst.) and beech (Fagus sylvatica L.) forest with a mobile laser scanner by making use of a unique study site setup. We scanned forest stands (1) from the ground only and (2) from the ground and from above by using a crane. We also examined the occlusion effect by scanning in the summer (leaf-on) and in the winter (leaf-off). Especially at the canopy level of the forest stands, occlusion was very pronounced, and we were able to quantify its impact in more detail. Occlusion was not as noticeable as expected for crown-related variables but, on average, resulted in smaller values for tree height in particular. Between the species, the total tree height underestimation for spruce was more pronounced than that for beech. At the stand level, significant information was lost in the canopy area when scanning from the ground alone. This information shortage is reflected in the relative point counts, the Clark–Evans index and the box dimension. Increasing the voxel size can compensate for this loss of information but comes with the trade-off of losing details in the point clouds. From our analysis, we conclude that the voxelization of point clouds prior to the extraction of stand or tree measurements with a voxel size of at least 20 cm is appropriate to reduce occlusion effects while still providing a high level of detail.

Abstract Aboveground tree architecture is neither fully deterministic nor random. It is likely the result of mechanisms that balance static requirements and light‐capturing efficiency. Here, we used terrestrial laser scanning data to investigate the relationship between tree architecture, here addressed using the box‐dimension (Db), and the architectural benefit‐to‐cost ratio, the light availability, and the growth of trees. We detected a clear relationship between Db and the benefit‐to‐cost ratio for the tested three temperate forest tree species (Fagus sylvatica L., Fraxinus excelsior L., and Acer pseudoplatanus L.). In addition, we could also show that Db is positively related to the growth performance of several tropical tree species. Finally, we observed a negative relationship between the strength of competition enforced on red oak (Quercus rubra L.) trees and their Db. We therefore argue that Db is a meaningful and integrative measure that describes the structural complexity of the aboveground compartments of a plant as well as its relation to structural efficiency (benefit‐to‐cost ratio), productivity, and growing conditions (competition or availability of light).

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.

Continuous cover forestry is often considered a management alternative to age-class forestry, in closer compliance with economic as well as societal demands. It is further thought to provide forest stands of high stability and resilience under conditions of climate change. The guiding principle for the stand structure of continuous cover forestry systems is to create managed forest stands that are multi-layered and hence of high structural diversity. Past studies of both these characteristics have been mostly qualitative. Here we used data from terrestrial laser scanning (TLS) to quantify differences in stand structure between forests managed for decades according to the continuous cover concept and forests managed otherwise. We found that the vertical distribution of plant material in the continuous cover stands was relatively homogeneous and similar to the vertical distribution found in primary European beech forests. We also found that the structural complexity of continuous cover forests was significantly higher than that of even-aged monocultures of Scots pine and Norway spruce. Based on these findings, a scaled index was developed that quantifies structural attributes of TLS point clouds and can significantly distinguish continuous cover forests from even-aged forests. This index may be a useful tool to quantify the difference in structure of a given continuous cover forest stand from a “target structure”, meaning the theoretical structure describing an ideal continuous cover forest.

Abstract The three‐dimensional forest structure affects many ecosystem functions and services provided by forests. As forests are made of trees it seems reasonable to approach their structure by investigating individual tree structure. Based on three‐dimensional point clouds from laser scanning, a newly developed holistic approach is presented that enables to calculate the box dimension as a measure of structural complexity of individual trees using fractal analysis. It was found that the box dimension of trees was significantly different among the tested species, among trees belonging to the same species but exposed to different growing conditions (at gap vs. forest interior) or to different kinds of competition (intraspecific vs. interspecific). Furthermore, it was shown that the box dimension is positively related to the trees’ growth rate. The box dimension was identified as an easy to calculate measure that integrates the effect of several external drivers of tree structure, such as competition strength and type, while simultaneously providing information on structure‐related properties, like tree growth.

Mixed forests make up the majority of natural forests, and they are conducive to improving the resilience and resistance of forest ecosystems. Moreover, it is in the crown of the trees where the effect of inter- and intra-specific interaction between them is evident. However, our knowledge of changes in crown morphology caused by density, competition, and mixture of specific species is still limited. Here, we provide insight on stand structural complexity based on the study of four response crown variables (Maximum Crown Width Height, MCWH; Crown Base Height, CBH; Crown Volume, CV; and Crown Projection Area, CPA) derived from multiple terrestrial laser scans. Data were obtained from six permanent plots in Northern Spain comprising of two widespread species across Europe; Scots pine (Pinus sylvestris L.) and sessile oak (Quercus petraea (Matt.) Liebl.). A total of 193 pines and 256 oaks were extracted from the point cloud. Correlation test were conducted (ρ ≥ 0.9) and finally eleven independent variables for each target tree were calculated and categorized into size, density, competition and mixture, which was included as a continuous variable. Linear and non-linear multiple regressions were used to fit models to the four crown variables and the best models were selected according to the lowest AIC Index and biological sense. Our results provide evidence for species plasticity to diverse neighborhoods and show complementarity between pines and oaks in mixtures, where pines have higher MCWH and CBH than oaks but lower CV and CPA, contrary to oaks. The species complementarity in crown variables confirm that mixtures can be used to increase above ground structural diversity.

Many Indonesian forests have been cleared and replaced by fast-growing cash crops (e.g., oil palm and rubber plantations), altering the vegetation structure of entire regions. Complex vegetation structure provides habitat niches to a large number of native species. Airborne laser scanning (ALS) can provide detailed three-dimensional information on vegetation structure. Here, we investigate the potential of ALS metrics to highlight differences across a gradient of land-use management intensities in Sumatra, Indonesia. We focused on tropical rainforests, jungle rubber, rubber plantations, oil palm plantations and transitional lands. Twenty-two ALS metrics were extracted from 183 plots. Analysis included a principal component analysis (PCA), analysis of variance (ANOVAs) and random forest (RF) characterization of the land use/land cover (LULC). Results from the PCA indicated that a greater number of canopy gaps are associated with oil palm plantations, while a taller stand height and higher vegetation structural metrics were linked with rainforest and jungle rubber. A clear separation in metrics performance between forest (including rainforest and jungle rubber) and oil palm was evident from the metrics pairwise comparison, with rubber plantations and transitional land behaving similar to forests (rainforest and jungle rubber) and oil palm plantations, according to different metrics. Lastly, two RF models were carried out: one using all five land uses (5LU), and one using four, merging jungle rubber with rainforest (4LU). The 5LU model resulted in a lower overall accuracy (51.1%) due to mismatches between jungle rubber and forest, while the 4LU model resulted in a higher accuracy (72.2%). Our results show the potential of ALS metrics to characterize different LULCs, which can be used to track changes in land use and their effect on ecosystem functioning, biodiversity and climate.

Tree saplings are exposed to a competitive growth environment in which resources are limited and the ability to adapt determines general vitality and specific growth performance. In this study we analyzed the aboveground spatial neighborhood of oak [Quercus petraea (Matt.) Liebl.] and beech (Fagus sylvatica L.) saplings growing in Germany, by using hemispherical photography and terrestrial laser scanning as proxy for the competitive pressure saplings were exposed to. The hemispherical images were used to analyze the light availability and the three-dimensional (3D) point clouds from the laser scanning were used to assess the space and forest structure around the saplings. The aim was to increase the precision with which the biomass allocation, growth, and morphology of the saplings could be predicted by including more detailed information of their environment. The predictive strength of the models was especially increased through direct neighborhood variables (e.g., relative space filling), next to the light availability being the most important predictor variable. The biomass allocation patterns within the more light demanding oak were strongly driven by the space availability around the saplings. Diameter and height growth variables of both species reacted significantly to changes in light availability, and partly also to the neighborhood variables. The leaf morphology [as leaf-area ratio (LAR)] was also driven by light availability and decreased with increasing light availability. However, the branch morphology (as mean branch weight) could not be explained for oak and the model outcome for beech was hard to interpret. The results could show that individuals of the same species perform differently under constant light conditions but differing neighborhoods. Assessing the neighborhood of trees with highly precise measurement devices, like terrestrial laser scanners, proved to be useful. However, the primary response to a dense neighborhood seemed to be coping with a reduction of the lateral light availability aboveground, rather than responding to an increase of competition belowground. The results suggest continuing efforts to increase the precision with which plant environments can be described through innovative and efficient methods, like terrestrial laser scanning.

Urban forests are part of the global forest network, providing important benefits to human societies. Advances in remote-sensing technology can create detailed 3D images of trees, giving novel insights into tree structure and function. We used terrestrial laser scanning and quantitative structural models to provide comprehensive characterizations of the woody surface area allometry of urban trees and relate them to urban tree anatomy, physiology, and structural complexity. Fifty-six trees of three species (Gleditsia triacanthos L., Quercus macrocarpa Michx., Metasequoia glyptostroboides Hu & W.C. Cheng) were sampled on the Michigan State University campus. Variations in surface area allocation to non-photosynthesizing components (main stem, branches) are related to the fractal dimension of tree architecture, in terms of structural complexity (box-dimension metric) and the distribution of “path” lengths from the tree base to every branch tip. The total woody surface area increased with the box-dimension metric, but it was most strongly correlated with the 25th percentile of path lengths. These urban trees mainly allocated the woody surface area to branches, which changed with branch order, branch-base diameter, and branch-base height. The branch-to-stem area ratio differed among species and increased with the box-dimension metric. Finally, the woody surface area increased with the crown surface area of the study trees across all species combined and within each species. The results of this study provide novel data and new insights into the surface area properties of urban tree species and the links with structural complexity and constraints on tree morphology.

Trees have a fractal-like branching architecture that determines their structural complexity. We used terrestrial laser scanning technology to study the role of foliage in the structural complexity of urban trees. Forty-five trees of three deciduous species, Gleditsia triacanthos, Quercus macrocarpa, Metasequoia glyptostroboides, were sampled on the Michigan State University campus. We studied their structural complexity by calculating the box-dimension (Db) metric from point clouds generated for the trees using terrestrial laser scanning, during the leaf-on and -off conditions. Furthermore, we artificially defoliated the leaf-on point clouds by applying an algorithm that separates the foliage from the woody material of the trees, and then recalculated the Db metric. The Db of the leaf-on tree point clouds was significantly greater than the Db of the leaf-off point clouds across all species. Additionally, the leaf removal algorithm introduced bias to the estimation of the leaf-removed Db of the G. triacanthos and M. glyptostroboides trees. The index capturing the contribution of leaves to the structural complexity of the study trees (the ratio of the Db of the leaf-on point clouds divided by the Db of the leaf-off point clouds minus one), was negatively correlated with branch surface area and different metrics of the length of paths through the branch network of the trees, indicating that the contribution of leaves decreases as branch network complexity increases. Underestimation of the Db of the G. triacanthos trees, after the artificial leaf removal, was related to maximum branch order. These results enhance our understanding of tree structural complexity by disentangling the contribution of leaves from that of the woody structures. The study also highlighted important methodological considerations for studying tree structure, with and without leaves, from laser-derived point clouds.

The structural complexity of the understory layer of forests or shrub layer vegetation in open shrublands affects many ecosystem functions and services provided by these ecosystems. We investigated how the basal area of the overstory layer, annual and seasonal precipitation, annual mean temperature, as well as light availability affect the structural complexity of the understory layer along a gradient from closed forests to open shrubland with only scattered trees. Using terrestrial laser scanning data and the understory complexity index (UCI), we measured the structural complexity of sites across a wide range of precipitation and temperature, also covering a gradient in light availability and basal area. We found significant relationships between the UCI and tree basal area as well as canopy openness. Structural equation models (SEMs) confirmed significant direct effects of seasonal precipitation on the UCI without mediation through basal area or canopy openness. However, annual precipitation and temperature effects on the UCI are mediated through canopy openness and basal area, respectively. Understory complexity is, despite clear dependencies on the available light and overall stand density, significantly and directly driven by climatic parameters, particularly the amount of precipitation during the driest month.

The three-dimensional forest structure is an important driver of several ecosystem functions and services. Recent advancements in laser scanning technologies have set the path to measuring structural complexity directly from 3D point clouds. Here, we show that the box-dimension (Db) from fractal analysis, a measure of structural complexity, can be obtained from airborne laser scanning data. Based on 66 plots across different forest types in Germany, each 1 ha in size, we tested the performance of the Db by evaluating it against conventional ground-based measures of forest structure and commonly used stand characteristics. We found that the Db was related (0.34 < R < 0.51) to stand age, management intensity, microclimatic stability, and several measures characterizing the overall stand structural complexity. For the basal area, we could not find a significant relationship, indicating that structural complexity is not tied to the basal area of a forest. We also showed that Db derived from airborne data holds the potential to distinguish forest types, management types, and the developmental phases of forests. We conclude that the box-dimension is a promising measure to describe the structural complexity of forests in an ecologically meaningful way.

One of the main goals of modern silviculture is to emulate the structural complexity of old-growth forests. In this context, it is of advantage to identify a target state of structural complexity at the stand level and to analyze the spatial characteristics that led to the desired complexity of forest structures in primary forest references. In this study, we used 3D forest scenes captured by terrestrial laser scanning (TLS) to identify spatial patterns of structural complexity of differently managed and unmanaged European forests dominated by beech (Fagus sylvatica L.). We scanned in managed even-aged and uneven-aged stands, as well as in formerly managed forests (National Parks) and primary forests. For three different forest strata, representing the understory, the midstory, and the overstory of a forest stand, we determined the structural complexity mathematically using fractal analysis. Beyond that, we analyzed the density, as well as the horizontal and vertical distribution of plant material. For all three forest strata, we observed differences in structural complexity between the different forest types. Within the lower and middle strata, the investigated primary forests showed a random to regular distribution of plant material, as well as a complex understory structure as a result of pronounced natural decay. Compared to the primary forests, the managed uneven-aged stands showed quite similar spatial patterns of distribution of plant material, but on average a higher space occupation in the lower and middle forest stratum. Our results suggest that single tree or group selection cutting is a useful management tool to imitate old-growth structures of undisturbed beech-dominated forests.

Understory vegetation influences several ecosystem services and functions of European beech (Fagus sylvatica L.) forests. Despite this knowledge on the importance of understory vegetation, it is still difficult to measure its three-dimensional characteristics in a quantitative manner. With the recent advancements in terrestrial laser scanning (TLS), we now have the means to analyze detailed spatial patterns of forests. Here, we present a new measure to quantify understory complexity. We tested the approach for different management types, ranging from traditionally and alternatively managed forests and national parks in Germany to primary forests of Eastern Europe and the Ukraine, as well as on an inventory site with more detailed understory reference data. The understory complexity index (UCI) was derived from point clouds from single scans and tested for its relationship with forest management and conventional inventory data. Our results show that advanced tree regeneration is a strong driver of the UCI. Furthermore, the newly developed index successfully measured understory complexity of differently managed beech stands and was able to distinguish scanning positions located on and away from skid-trails in managed stands. The approach enables a deeper understanding of the complexity of understory structures of forests and their drivers and dependents.