A landslide monitoring method using data from unmanned aerial vehicle and terrestrial laser scanning with insufficient and inaccurate ground control points

Non-contact remote sensing techniques,such as terrestrial laser scanning(TLS)and unmanned aerial vehicle(UAV)photogrammetry,have been globally applied for landslide monitoring in high and steep mountainous areas.These techniques acquire terrain data and enable ground deformation monitoring.However,practical application of these technologies still faces many difficulties due to complex terrain,limited access and dense vegetation.For instance,monitoring high and steep slopes can obstruct the TLS sightline,and the accuracy of the UAV model may be compromised by absence of ground control points(GCPs).This paper proposes a TLS-and UAV-based method for monitoring landslide deformation in high mountain valleys using traditional real-time kinematics(RTK)-based control points(RCPs),low-precision TLS-based control points(TCPs)and assumed control points(ACPs)to achieve high-precision surface deformation analysis under obstructed vision and impassable conditions.The effects of GCP accuracy,GCP quantity and automatic tie point(ATP)quantity on the accuracy of UAV modeling and surface deformation analysis were comprehensively analyzed.The results show that,the proposed method allows for the monitoring accuracy of landslides to exceed the accuracy of the GCPs themselves by adding additional low-accuracy GCPs.The proposed method was implemented for monitoring the Xinhua landslide in Baoxing County,China,and was validated against data from multiple sources.

Tree species identity and interaction determine vertical forest structure in young planted forests measured by terrestrial laser scanning

Vertical forest structure is closely linked to multiple ecosystem characteristics,such as biodiversity,habitat,and productivity.Mixing tree species in planted forests has the potential to create diverse vertical forest structures due to the different physiological and morphological traits of the composing tree species.However,the relative importance of species richness,species identity and species interactions for the variation in vertical forest structure remains unclear,mainly because traditional forest inventories do not observe vertical stand structure in detail.Terrestrial laser scanning(TLS),however,allows to study vertical forest structure in an unprecedented way.Therefore,we used TLS single scan data from 126 plots across three experimental planted forests of a largescale tree diversity experiment in Belgium to study the drivers of vertical forest structure.These plots were 9–11years old young pure and mixed forests,characterized by four levels of tree species richness ranging from monocultures to four-species mixtures,across twenty composition levels.We generated vertical plant profiles from the TLS data and derived six stand structural variables.Linear mixed models were used to test the effect of species richness on structural variables.Employing a hierarchical diversity interaction modelling framework,we further assessed species identity effect and various species interaction effects on the six stand structural variables.Our results showed that species richness did not significantly influence most of the stand structure variables,except for canopy height and foliage height diversity.Species identity on the other hand exhibited a significant impact on vertical forest structure across all sites.Species interaction effects were observed to be site-dependent due to varying site conditions and species pools,and rapidly growing tree species tend to dominate these interactions.Overall,our results highlighted the importance of considering both species identity and interaction effects in choosing suitable species combinations for forest management practices aimed at enhancing vertical forest structure.

Automatic identification of rock discontinuity and stability analysis of tunnel rock blocks using terrestrial laser scanning

Local geometric information and discontinuity features are key aspects of the analysis of the evolution and failure mechanisms of unstable rock blocks in rock tunnels.This study demonstrates the integration of terrestrial laser scanning(TLS)with distinct element method for rock mass characterization and stability analysis in tunnels.TLS records detailed geometric information of the surrounding rock mass by scanning and collecting the positions of millions of rock surface points without contact.By conducting a fuzzy K-means method,a discontinuity automatic identification algorithm was developed,and a method for obtaining the geometric parameters of discontinuities was proposed.This method permits the user to visually identify each discontinuity and acquire its spatial distribution features(e.g.occurrences,spac-ings,trace lengths)in great detail.Compared with hand mapping in conventional geotechnical surveys,the geometric information of discontinuities obtained by this approach is more accurate and the iden-tification is more efficient.Then,a discrete fracture network with the same statistical characteristics as the actual discontinuities was generated with the distinct element method,and a representative nu-merical model of the jointed surrounding rock mass was established.By means of numerical simulation,potential unstable rock blocks were assessed,and failure mechanisms were analyzed.This method was applied to detection and assessment of unstable rock blocks in the spillway and sand flushing tunnel of the Hongshiyan hydropower project after a collapse.The results show that the noncontact detection of blocks was more labor-saving with lower safety risks compared with manual surveys,and the stability assessment was more reliable since the numerical model built by this method was more consistent with the distribution characteristics of actual joints.This study can provide a reference for geological survey and unstable rock block hazard mitigation in tunnels subjected to complex geology and active rockfalls.

Quantification of occlusions influencing the tree stem curve retrieving from single-scan terrestrial laser scanning data

Background: The stem curve of standing trees is an essential parameter for accurate estimation of stem volume.This study aims to directly quantify the occlusions within the single-scan terrestrial laser scanning(TLS) data,evaluate its correlation with the accuracy of the retrieved stem curves, and subsequently, to assess the capacity of single-scan TLS to estimate stem curves.Methods: We proposed an index, occlusion rate, to quantify the occlusion level in TLS data. We then analyzed three influencing factors for the occlusion rate: the percentage of basal area near the scanning center, the scanning distance and the source of occlusions. Finally, we evaluated the effects of occlusions on stem curve estimates from single-scan TLS data.Results: The results showed that the correlations between the occlusion rate and the stem curve estimation accuracies were strong(r = 0.60–0.83), so was the correlations between the occlusion rate and its influencing factors(r = 0.84–0.99). It also showed that the occlusions from tree stems were the main factor of the low detection rate of stems, while the non-stem components mainly influenced the completeness of the retrieved stem curves.Conclusions: Our study demonstrates that the occlusions significantly affect the accuracy of stem curve retrieval from the single-scan TLS data in a typical-size(32 m × 32 m) forest plot. However, the single-scan mode has the capacity to accurately estimate the stem curve in a small forest plot(< 10 m × 10 m) or a plot with a lower occlusion rate, such as less than 35% in our tested datasets. The findings from this study are useful for guiding the practice of retrieving forest parameters using single-scan TLS data.

Geostructural stability assessment of cave using rock surface discontinuity extracted from terrestrial laser scanning point cloud

The use of terrestrial laser scanning（TLS） in the caves has been growing drastically over the last decade.However, TLS application to cave stability assessment has not received much attention of researchers.This study attempted to utilize rock surface orientations obtained from TLS point cloud collected along cave passages to（1） investigate the influence of rock geostructure on cave passage development, and（2）assess cave stability by determining areas susceptible to different failure types. The TLS point cloud was divided into six parts（Entry hall, Chamber, Main hall, Shaft 1, Shaft 2 and Shaft 3）, each representing different segments of the cave passages. Furthermore, the surface orientation information was extracted and grouped into surface discontinuity joint sets. The computed global mean and best-fit planes of the entire cave show that the outcrop dips 290° with a major north-south strike. But at individual level, the passages with dip angle between 26° and 80° are featured with dip direction of 75°-322°. Kinematic tests reveal the potential for various failure modes of rock slope. Our findings show that toppling is the dominant failure type accounting for high-risk rockfall in the cave, with probabilities of 75.26%, 43.07%and 24.82% in the Entry hall, Main hall and Shaft 2, respectively. Unlike Shaft 2 characterized by high risk of the three failure types（32.49%, 24.82% and 50%）, the chamber and Shaft 3 passages are not suffering from slope failure. The results also show that the characteristics of rock geostructure considerably influence the development of the cave passages, and four sections of the cave are susceptible to different slope failure types, at varying degrees of risk.

A new approach to retrieve leaf normal distribution using terrestrial laser scanners

Leaf normal distribution is an important structural characteristic of the forest canopy. Although terrestrial laser scanners（TLS） have potential for estimating canopy structural parameters, distinguishing between leaves and nonphotosynthetic structures to retrieve the leaf normal has been challenging. We used here an approach to accurately retrieve the leaf normals of camphorwood（Cinnamomum camphora） using TLS point cloud data.First, nonphotosynthetic structures were filtered by using the curvature threshold of each point. Then, the point cloud data were segmented by a voxel method and clustered by a Gaussian mixture model in each voxel. Finally, the normal vector of each cluster was computed by principal component analysis to obtain the leaf normal distribution. We collected leaf inclination angles and estimated the distribution, which we compared with the retrieved leaf normal distribution. The correlation coefficient between measurements and obtained results was 0.96, indicating a good coincidence.

Assessment of handheld mobile terrestrial laser scanning for estimating tree parameters

Sustainable forest management heavily relies on the accurate estimation of tree parameters.Among others,the diameter at breast height(DBH) is important for extracting the volume and mass of an individual tree.For systematically estimating the volume of entire plots,airborne laser scanning(ALS) data are used.The estimation model is frequently calibrated using manual DBH measurements or static terrestrial laser scans(STLS) of sample plots.Although reliable,this method is time-consuming,which greatly hampers its use.Here,a handheld mobile terrestrial laser scanning(HMTLS) was demonstrated to be a useful alternative technique to precisely and efficiently calculate DBH.Different data acquisition techniques were applied at a sample plot,then the resulting parameters were comparatively analysed.The calculated DBH values were comparable to the manual measurements for HMTLS,STLS,and ALS data sets.Given the comparability of the extracted parameters,with a reduced point density of HTMLS compared to STLS data,and the reasonable increase of performance,with a reduction of acquisition time with a factor of5 compared to conventional STLS techniques and a factor of3 compared to manual measurements,HMTLS is considered a useful alternative technique.

Applicability of an ultra-long-range terrestrial laser scanner to monitor the mass balance of Muz Taw Glacier,Sawir Mountains,China

Glacier mass balance is a key component of glacier monitoring programs. Information on the mass balance of Sawir Mountains is poor due to a dearth of in-situ measurements. This paper introduces the applicability of an ultra-long-range terrestrial laser scanner(TLS) to monitor the mass balance of Muz Taw Glacier, Sawir Mountains, China. The Riegl VZ?-6000 TLS is exceptionally well-suited for measuring snowy and icy terrain. Here, we use TLS to create repeated high spatiotemporal resolution DEMs, focusing on the annual mass balance(June 2, 2015 to July 25, 2016). According to TLS-derived high spatial resolution point clouds, the front variation(glacier retreat) of Muz Taw Glacier was 9.3 m. The mean geodetic elevation change was 4.55 m at the ablation area. By comparing with glaciological measurements, the glaciological elevation change of individual stakes and the TLS-derived geodetic elevation change of corresponding points matched closely, and the calculated balance was-3.864±0.378 m w.e.. This data indicates that TLS provides accurate results and is therefore suitable to monitor mass balance evolution of Muz Taw Glacier.

Study on the LAI Single Tree Model Based on Terrestrial Laser Scanning

Leaf area index (LAI) is a key parameter for studying global terrestrial ecology and environment and has great ecological significance. How to accurately measure and calculate structural parameters of trees has become an urgent matter. This paper reports the use of terrestrial laser scanning (TLS) as a measurement tool to achieve accurate LAI estimation through point cloud preprocessing measures, the LeWos algorithm, and voxel methods. The accuracy and feasibility of this indirect measurement method were explored. It is found that the single wood structure parameters extracted from TLS have a good linear relationship with manual measurement, and the extraction errors meet the requirements of real-scene conversion. The study also found when the voxel size is consistent with the minimum distance of the point cloud set by TLS instrument, it has a strong correlation with the measured value of canopy analyser. These results lay the foundation for conveniently and quickly obtaining structural parameters of trees, tree growth state detection, and canopy ecological benefit assessment.

Applications of terrestrial laser scanning for tunnels: a review

In recent years, the use of terrestrial laser scanning （TLS） technique in engineering surveys is gaining an increasing interest due to the advantages of non-contact, rapidity, high accuracy, and large scale. Millions of accurate 3D points （mm level accuracy） can be delivered by this technique with a high point density in a short time （up to 1 million points per second）, which makes it a poten- tial technique for large scale applications in engineering environments such as tunnels, bridges, and heritage buildings. Tunnels, in particular those with long lengths, create great challenges for surveyors to obtain the satisfactory scanned data. This paper presents a short history of TLS techniques used for tunnels. A general overview of TLS techniques is given, followed by a review of several applications of TLS for tunnels. These applications are classified as： detecting geological features of drilling tun- nels, monitoring the geometry of tunnels during excavation, making deformation measurements, and extracting features. The review emphasizes how TLS techniques can be used to measure various aspects of tunnels. It is clear that TLS techniques are not yet a common tool for tunnel investigations, but there is still a huge potential to excavate.

Development of a 3D modeling algorithm for tunnel deformation monitoring based on terrestrial laser scanning

Deformation monitoring is vital for tunnel engineering.Traditional monitoring techniques measure only a few data points,which is insufficient to understand the deformation of the entire tunnel.Terrestrial Laser Scanning(TLS)is a newly developed technique that can collect thousands of data points in a few minutes,with promising applications to tunnel deformation monitoring.The raw point cloud collected from TLS cannot display tunnel deformation;therefore,a new 3D modeling algorithm was developed for this purpose.The 3D modeling algorithm includes modules for preprocessing the point cloud,extracting the tunnel axis,performing coordinate transformations,performing noise reduction and generating the 3D model.Measurement results from TLS were compared to the results of total station and numerical simulation,confirming the reliability of TLS for tunnel deformation monitoring.Finally,a case study of the Shanghai West Changjiang Road tunnel is introduced,where TLS was applied to measure shield tunnel deformation over multiple sections.Settlement,segment dislocation and cross section convergence were measured and visualized using the proposed 3D modeling algorithm.

MDC-Net:a multi-directional constrained and prior assisted neural network for wood and leaf separation from terrestrial laser scanning

Wood-leaf separation from terrestrial laser scanning(TLS)is a crucial prerequisite for quantifying many biophysical properties and understanding ecological functions.In this study,we propose a novel multi-directional collaborative convolutional neural network(MDC-Net)that takes the original 3D coordinates and useful features from prior knowledge(prior features)as input,and outputs the semantic labels of TLS point clouds.The MDC-Net contains two key units:(1)a multi-directional neighborhood construction(MDNC)unit to obtain more representative neighbors and enable directionally aware feature encoding in the subsequent local feature extraction,to mitigate occlusion effects;(2)a collaborative feature encoding(CFE)unit is introduced to incorporate useful features from prior knowledge into the network through a collaborative cross coding to enhance the discrimination for thin structures(e.g.small branches and leaf).The MDC-Net is evaluated onfive plots from forests in Guangxi,China,with different branch architectures and leaf distributions.Experimental results showed that the MDC-Net achieved an OA of 0.973 and a mIoU of 0.821 and outperformed other related methods.We believe the MDC-Net would facilitate the usage of TLS in ecology studies for quantifying tree size and morphology and thus promote the development of relevant ecological applications.

Fast registration of forest terrestrial laser scans using key points detected from crowns and stems

Registration of TLS data is an important prerequisite to overcome the limitations of occlusion.Most existing registration methods rely on stems to determine the transformation parameters.However,the complexity of the registration problem increases dramatically as the number of stems grows.It is tricky to reduce the stems and determine the valid ones that can provide reliable registration transformation without a knowledge of the two scans.This paper presents an automatic and fast registration of TLS point clouds in forest areas.It reduces stems by selecting from the overlap areas,which are recovered from the mode-based key points that are detected from crowns.The proposed method was tested in a managed forest in Finland,and was compared with the stem-based registration method without reducing stems.The experiments demonstrated that the mean rotation error was 2.09′,and the mean errors in horizontal and vertical translation were 1.13 and 7.21 cm,respectively.Compared with the stem-based method,the proposed method improves the registration efficiency significantly(818 s vs 96 s)and achieves similar results in terms of the mean registration errors(1.94′for rotation error,0.83 and 7.38 cm for horizontal and vertical translation error,respectively).

Usingvery long-range terrestriallaser scanner to analyze the temporal consistency of the snowpack distribution in a high mountain environment

This study demonstrated the usefulness of very long-range terrestrial laser scanning(TLS) for analysis of the spatial distribution of a snowpack, to distances up to 3000 m, one of the longest measurement range reported to date. Snow depth data were collected using a terrestrial laser scanner during 11 periods of snow accumulation and melting,over three snow seasons on a Pyrenean hillslopecharacterized by a large elevational gradient, steep slopes, and avalanche occurrence. The maximum and mean absolute snow depth error found was 0.5-0.6 and 0.2-0.3 m respectively, which may result problematic for areas with a shallow snowpack, but it is sufficiently accurate to determine snow distribution patterns in areas characterized by a thick snowpack. The results indicated that in most cases there was temporal consistency in the spatial distribution of thesnowpack, even in different years. The spatial patterns were particularly similar amongst thesurveys conducted during the period dominated by snow accumulation(generally until end of April), or amongst those conducted during the period dominated by melting processes(generally after mid of April or early May). Simple linear correlation analyses for the 11 survey dates, and the application of Random Forests analysis to two days representative of snow accumulation and melting periods indicated the importance of topography to the snow distribution. The results also highlight that elevation and the Topographic Position index(TPI) were the main variables explaining the snow distribution, especially during periods dominated by melting. The intra-and inter-annual spatial consistency of the snowpack distribution suggests that the geomorphological processes linked to presence/absence of snow cover act in a similar way in the long term, and that these spatial patternscan be easily identifiedthrough several years of adequate monitoring.

Leading directions and effective distance of larch offspring dispersal at the upper treeline in the Northern and Polar Urals, Russia

Climate has changed sufficiently over the last 150 years and forced out upper treeline advance at the most studied sites around the world.The rate of advance has been extremely variable–from tens to hundreds meters in altitude.This is because the degree at which tree frontal populations respond to climate change depends on the complex interaction of biological and physical factors.The resulting stand pattern is the consequence of the interaction between dispersal and survival functions.A few publications have addressed the question of how this pattern is generated.In order to understand how the spatial structure of tree stands was formed at the upper limit of their distribution in the Ural Mountains,we assessed the distance and direction of dispersal of offspring from maternal individuals.We found that in frontal Larix sibirica Ledeb.populations,‘effective’dispersal of offspring ranges from 3 to 758 m(with a median of 20–33 m in open forest and 219 m in single-tree tundra in the Polar Urals and 107 m in open forest in the Northern Urals).We revealed that most of the offspring effectively dispersed not only in the direction of the prevailing winds,but also in the opposite direction up the slope,and the distance can reach 500–760 m.The data obtained can be used to develop an individual-based model which is capable of simulating in detail the dynamics of tree stands at the upper limit of their growth and reliably predicting the future position and pattern of treeline ecotone as growth conditions continue to improve in the face of observed climate change.

Reconstructing DEM using TLS point cloud data and NURBS surface

Underground coal mining inevitably results in land surface subsidence.Acquiring information on land surface subsidence is important in the detection of surface change.However,conventional data acquisition techniques cannot always retrieve information on whole subsidence area.This study focuses on the reconstruction of a digital elevation model（DEM） with terrestrial laser scanning（TLS） point cloud data.Firstly,the methodology of the DEM with terrestrial 3-dimensional laser scanning is introduced.Then,a DEM modeling approach that involves the application of curved non-uniform rational B-splines（NURBS） surface is put forward.Finally,the performance of the DEM modeling approach with different surface inverse methods is demonstrated.The results indicate that the DEM based on the point cloud data and curved NURBS surface can achieve satisfactory accuracy.In addition,the performance of the hyperbolic paraboloid appears to be better than that of the elliptic paraboloid.The reconstructed DEM is continuous and can easily be integrated into other programs.Such features are of great importance in monitoring dynamic ground surface subsidence.

Point-cloud segmentation of individual trees in complex natural forest scenes based on a trunk-growth method

Forest resource management and ecological assessment have been recently supported by emerging technologies.Terrestrial laser scanning(TLS)is one that can be quickly and accurately used to obtain three-dimensional forest information,and create good representations of forest vertical structure.TLS data can be exploited for highly significant tasks,particularly the segmentation and information extraction for individual trees.However,the existing single-tree segmentation methods suffer from low segmentation accuracy and poor robustness,and hence do not lead to satisfactory results for natural forests in complex environments.In this paper,we propose a trunk-growth(TG)method for single-tree point-cloud segmentation,and apply this method to the natural forest scenes of Shangri-La City in Northwest Yunnan,China.First,the point normal vector and its Z-axis component are used as trunk-growth constraints.Then,the points surrounding the trunk are searched to account for regrowth.Finally,the nearest distributed branch and leaf points are used to complete the individual tree segmentation.The results show that the TG method can effectively segment individual trees with an average F-score of 0.96.The proposed method applies to many types of trees with various growth shapes,and can effectively identify shrubs and herbs in complex scenes of natural forests.The promising outcomes of the TG method demonstrate the key advantages of combining plant morphology theory and LiDAR technology for advancing and optimizing forestry systems.

The importance of proleptic branch traits in biomass production of poplar in high-density plantations

Branch phenotypic traits determine tree crown architecture,which in turn governs leaf display,light interception,and biomass production.Sylleptic and proleptic branches are the obviously different branch phenotypes in the poplar crown.Many studies have focused on the influence of sylleptic branch numbers(SBN)on biomass production,but the research on the influence of proleptic branch phenotypes was only a few.To explore the relationship between proleptic branch traits and biomass generation production in a high-density poplar plantation,we investigated the branch phenotypic traits of three poplar genotypes,all of which have high survival rates in forests(>95%)and significantly different crown architecture and biomass performance in the high-density plantations(1667 stems ha−1).The plantation site was established in 2007.A terrestrial laser scanner was used to measure branch characteristics such as length,angle of origin and termination,and azi-muth angle.A hierarchical cluster analysis performed on branch characteristics showed that SBN,crown depth,and proleptic branch curvature(PBC)were clustered with bio-mass production and leaf area index(LAI).Among all of the monitored traits,PBC played the second most important role in biomass production after SBN and was significantly correlated with SBN,LAI,and biomass production.The positive correlation between PBC and SBN indicated that a larger PBC was associated with more sylleptic branches within the monitored genotypes planted in the high-density plantation,providing greater leaf area and biomass produc-tion.The results of this study will improve the identification of high-production poplar varieties for cultivation in high-density plantations for biofuel production.

Multiple forest structural elements are needed to promote beetle biomass, diversity and abundance

Background:Retention forestry is a management strategy aiming to mitigate biodiversity loss by retaining structural elements such as dead trees that would otherwise be removed.Here we analyze the biomass,diversity and abundance among forest beetles collected using window traps on 1281-ha forest sites reflecting gradients in the amount of structural elements in southwestern Germany.Results:We found that beetle biomass increased with mean diameter at breast height(a measure of tree size),and decreased with stand structural complexity.Biomass of individual feeding guilds responded differently to forest structural elements,namely lying deadwood,understory complexity,tree basal area and stand structural complexity.Beetle family diversity increased with the effective number of layers,i.e.1-m forest strata occupied by vegetation assessed via terrestrial laser scanning.Abundance of feeding guilds responded to only elevation and share of deciduous trees.Community composition in terms of biomass was structured by forest elements similar to biomass of individual feeding guilds,with the addition of lying deadwood.This differed from community composition in terms of abundance of feeding guilds,which was structured by primarily standing deadwood volume and share of deciduous trees.Conclusions:Our results show that biomass,diversity and abundance respond differently to forest structural elements.This suggests that the concurrent prioritization of multiple forest elements is needed to promote forest beetles,with more focus placed on the differing resource needs among feeding guilds.In addition,retention strategies should also consider the varying responses of beetle biodiversity metrics when assessing the importance of forest structural elements.

Effects of local neighbourhood diversity on crown structure and productivity of individual trees in mature mixed-species forests

Background:Species-specific genotypic features,local neighbourhood interactions and resource supply strongly influence the tree stature and growth rate.In mixed-species forests,diversity-mediated biomass allocation has been suggested to be a fundamental mechanism underlying the positive biodiversity-productivity relationships.Empirical evidence,however,is rare about the impact of local neighbourhood diversity on tree characteristics analysed at a very high level of detail.To address this issue we analysed these effects on the individual-tree crown architecture and tree productivity in a mature mixed forest in northern Germany.Methods:Our analysis considers multiple target tree species across a local neighbourhood species richness gradient ranging from 1 to 4.We applied terrestrial laser scanning to quantify a large number of individual mature trees(N=920)at very high accuracy.We evaluated two different neighbour inclusion approaches by analysing both a fixed radius selection procedure and a selection based on overlapping crowns.Results and conclusions:We show that local neighbourhood species diversity significantly increases crown dimension and wood volume of target trees.Moreover,we found a size-dependency of diversity effects on tree productivity(basal area and wood volume increment)with positive effects for large-sized trees(diameter at breast height(DBH)>40 cm)and negative effects for small-sized(DBH<40 cm)trees.In our analysis,the neighbour inclusion approach has a significant impact on the outcome.For scientific studies and the validation of growth models we recommend a neighbour selection by overlapping crowns,because this seems to be the relevant scale at which local neighbourhood interactions occur.Because local neighbourhood diversity promotes individual-tree productivity in mature European mixed-species forests,we conclude that a small-scale species mixture should be considered in management plans.