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The complexity of forest structures plays a crucial role in regulating forest ecosystem functions and strongly influences biodiversity. Yet, knowledge of the global patterns and determinants of forest structural complexity remains scarce. Using a stand structural complexity index based on terrestrial laser scanning, we quantify the structural complexity of boreal, temperate, subtropical and tropical primary forests. We find that the global variation of forest structural complexity is largely explained by annual precipitation and precipitation seasonality (R² = 0.89). Using the structural complexity of primary forests as benchmark, we model the potential structural complexity across biomes and present a global map of the potential structural complexity of the earth´s forest ecoregions. Our analyses reveal distinct latitudinal patterns of forest structure and show that hotspots of high structural complexity coincide with hotspots of plant diversity. Considering the mechanistic underpinnings of forest structural complexity, our results suggest spatially contrasting changes of forest structure with climate change within and across biomes. 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 work has been supported by the DFG Priority Program 1374 “Infrastructure-Biodiversity-Exploratories”. Field work permits were issued by the responsible state environmental offices of Baden-Württemberg, Thüringen and Brandenburg (according to § 72 BbgNatSchG). S.S. was supported by the Spanish Government under a Ramón y Cajal contract (RYC-2016-20604).

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.

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.

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.

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.

Mountain ranges comprise heterogeneous environments and high plant diversity, but little is known about the architecture and structural complexity of trees in mountain forests. We studied the relationship between tree architecture, environmental conditions, and tree structural complexity in forests of the Annapurna region in the Himalaya. We further asked whether and how tree structural complexity translates into forest stand structural complexity. The study covers 546 trees on 14 undisturbed study plots across wide ranges of elevation (1300 to 3400 m asl.) and annual precipitation (1180 to 3600 mm yr.−1). They were assessed by ground‐based mobile laser scanning. We found that tree structural complexity, expressed as box‐dimension (Db), was lowest for the needle‐leaved Pinus wallichiana and highest for the broad‐leaved Daphniphyllum himalense. A high share of the variation in Db was explained by tree architecture. In multivariate models, tree height, crown radius, and crown length explained more than 60% of the observed variation in Db. Stem density of the plot accounted for 19% of the variation in Db, and there was no influence of tree diversity. Precipitation explained l3% of the observed variation in tree Db, but elevation and slope did not have significant influences. As expected, tree height decreased with increasing elevation, but small trees often had relatively high Db values. The standard deviation of tree‐level Db within a plot explained 47% of the variation in stand‐level structural complexity among plots, surpassing the maximum tree‐level Db. This suggests that both the sole removal of small or large trees would reduce the stand‐level complexity by 36%. We conclude that in the Himalayan forests, species identity and tree architecture play a significant role in determining tree structural complexity, while environmental factors have a smaller role. Furthermore, structural variation among the trees within a plot plays a crucial role for the structural complexity at the stand level. Tree architecture and structural complexity were studied in mountain forests of the Himalaya. We assessed 546 trees belonging to six species by mobile laser scanning. The change in tree structural complexity with elevation was less pronounced than the decline in tree height. Species identity and tree architecture played a significant role in determining tree structural complexity. Structural variation among the trees determines the structural complexity at the stand level.

Tropical biodiversity is threatened by the expansion of oil-palm plantations. Reduced-impact farming systems such as agroforests, have been proposed to increase biodiversity and ecosystem functioning. In regions where oil-palm plantations already dominate the landscape, this increase can only be achieved through systematic ecological restoration. However, our knowledge about the underlying ecological and socio-economic processes, constraints, and trade-offs of ecological restoration in oil-palm landscapes is very limited. To bridge this gap, we established a long-term biodiversity enrichment experiment. We established experimental tree islands in a conventional oil-palm plantation and systematically varied plot size, tree diversity, and tree species composition. Here, we describe the rationale and the design of the experiment, the ecosystem variables (soil, topography, canopy openness) and biotic characteristics (associated vegetation, invertebrates, birds) of the experimental site prior to the establishment of the experiment, and initial experimental effects on the fauna. Already one year after establishment of the experiment, tree plantings had an overall positive effect on the bird and invertebrate communities at the plantation scale. The diversity and abundance of invertebrates was positively affected by the size of the tree islands. Based on these results, we expect a further increase of biodiversity and associated ecological functions in the future. The long-term interdisciplinary monitoring of ecosystem variables, flora, fauna, and socio-economic aspects will allow us to evaluate the suitability of tree islands as a restoration measure. Thereof, guidelines for ecologically improved and socio-economically viable restoration and management concepts could be developed.

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.

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.