Terrestrial laser scanning (TLS) has been successfully used for three-dimensional (3D) data capture in forests for almost two decades. Beyond the plot-based data capturing capabilities of TLS, vehicle-based mobile laser scanning (MLS) systems have the clear advantage of fast and precise corridor-like 3D data capture, thus providing a much larger coverage within shorter acquisition time. This paper compares and discusses advantages and disadvantages of multi-temporal MLS data acquisition compared to established TLS data recording schemes. In this pilot study on integrated TLS and MLS data processing in a forest, it could be shown that existing TLS data evaluation routines can be used for MLS data processing. Methods of automatic laser scanner data processing for forest inventory parameter determination and quantitative structure model (QSM) generation were tested in two sample plots using data from both scanning methods and from different seasons. TLS in a multi-scan configuration delivers very high-density 3D point clouds, which form a valuable basis for generating high-quality QSMs. The pilot study shows that MLS is able to provide high-quality data for an equivalent determination of relevant forest inventory parameters compared to TLS. Parameters such as tree position, diameter at breast height (DBH) or tree height can be determined from MLS data with an accuracy similar to the accuracy of the parameter derived from TLS data. Results for instance in DBH determination by cylinder fitting yielded a standard deviation of 1.1 cm for trees in TLS data and 3.7 cm in MLS data. However, the resolution of MLS scans was found insufficient for successful QSM generation. The registration of MLS data in forests furthermore requires additional effort in considering effects caused by poor GNSS signal.

Background and Aims In addition to terrestrial laser scanning (TLS), mobile laser scanning (MLS) is increasingly arousing interest as a technique which provides valuable 3D-data for various applications in forest research. Using mobile platforms, the 3D-recording of large forest areas is carried out within a short space of time. Vegetation structure is described by millions of 3D-points which show an accuracy in the millimeter range and offer a powerful basis for automated vegetation modelling. The successful extraction of single trees from the point cloud is essential for further evaluations and modelling at the individual-tree level, such as volume determination, quantitative structure modelling or local neighbourhood analyses. However, high-precision automated tree segmentation is challenging, and has so far mostly been performed using elaborate interactive segmentation methods. Methods Here, we present a novel segmentation algorithm to automatically segment trees in MLS point clouds, applying distance adaptivity as a function of trajectory. In addition, tree parameters are determined simultaneously. In our validation study we used a total of 825 trees from ten sample plots to compare the data of trees segmented from MLS data with manual inventory parameters and parameters derived from semi-automatic TLS segmentation. Key Results The tree detection rate reached 96 % on average for trees with distances up to 45 m from the trajectory. Trees were almost completely segmented up to a distance of about 30 m from the MLS trajectory. The accuracy of tree parameters was similar for MLS segmented and TLS segmented trees. Conclusions Besides plot characteristics, the detection rate of trees in MLS data strongly depends on the distance to the travelled track. The algorithm presented here facilitates the acquisition of important tree parameters from MLS data, as an area-wide automated derivation can be accomplished in a very short time.

The three-dimensional (3D) morphology of individual trees is critical for light interception, growth, stability and interactions with the local environment. Forest management intensity is a key driver of tree morphology, but how the long-term abandonment of silvicultural measures impacts trunk and crown morphological traits is not fully understood. Here, we take advantage of a long management intensity gradient combined with a high-resolution terrestrial laser scanning (TLS) approach to explore how management history affects the 3D structure of mature beech (Fagus sylvatica L.) trees. The management gradient ranged from long-term (>50 years) and short-term (>20 years) unmanaged to extensively and intensively managed beech stands. We determined 28 morphological traits and quantified the vertical distribution of wood volume along the trunk. We evaluated the differences in tree morphological traits between study stands using Tukey's HSD test. Our results show that 93% of the investigated morphological traits differed significantly between the study stands. Significant differences, however, emerged most strongly in the stand where forest management had ceased >50 years ago. Furthermore, we found that the vertical distribution of trunk wood volume was highly responsive between stands with different management intensity, leading to a 67% higher taper top height and 30% lower taper of beech trees growing in long-term unmanaged stands compared to those in short-term unmanaged or managed stands. These results have important implications for management intensity decisions. It is suggested that the economic value of individual beech trees from long-term unmanaged forests can be expected to be very high. This might also translate to beech forests that are extensively managed, but we found that a few decades of implementation of such a silvicultural system is not sufficient to cause significant differences when compared to intensively managed stands. Furthermore, TLS-based high-resolution analyses of trunk and crown traits play a crucial role in the ability to better understand or predict tree growth responses to the current drivers of global change.

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.

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