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New insights into tree architecture from mobile laser scanning and geometry analysis

Cite this dataset

Seidel, Dominik et al. (2021). New insights into tree architecture from mobile laser scanning and geometry analysis [Dataset]. Dryad.


The structure and dynamics of a forest are defined by the architecture and growth patterns of its individual trees. In turn, tree architecture and growth result from the interplay between the genetic building plans and environmental factors. We set out to investigate whether (i) latitudinal adaptations of the crown shape occur due to characteristic solar elevation angles at a species’ origin, (ii) architectural differences in trees are related to seed dispersal strategies, and (iii) tree architecture relates to tree growth performance. We used Mobile Laser Scanning (MLS) to scan 473 trees and generated three-dimensional data of each tree. Tree architectural complexity was then characterized by fractal analysis using the box-dimension approach along with a topological measure of the top-heaviness of a tree. The tree species studied originated from various latitudinal ranges but were grown in the same environmental settings in the arboretum. We found that trees originating from higher latitudes had significantly less top-heavy geometries than those from lower latitudes. Therefore, to a certain degree, the crown shape of tree species seems to be determined by their original habitat. We also found that tree species with wind-dispersed seeds had a higher structural complexity than those with animal-dispersed seeds (p < 0.001). Furthermore, tree architectural complexity was positively related to the growth performance of the trees (p < 0.001). We conclude that the use of 3D data from MLS in combination with geometrical analysis, including fractal analysis, is a promising tool to investigate tree architecture.


A ground-based mobile laser scanning (MLS) system was used to obtain 3D point cloud data for accurate measurement and mapping of the environment. The scanning was carried out in February 2020 when the trees were leafless, to ensure free sight on the tree crowns. All 473 trees were scanned carrying the scanner in the hand at around breast height with the arm outstretched while moving at a slow walking pace. We walked in a zig-zag route around the trees and covered two planting rows at a time in each scan by following the direction of the row and finally ending at the exact point where the scan started (up and down the row). We made sure to close the loop every time. By zig-zagging every other tree on the way back, we covered all trees from both sites. All MLS data was processed using GeoSLAM Hub 6 software and subsampled (0.01 m) in Cloud Compare.

We obtained records of the periodical circumference measurements for 391 of the individual trees since the time of plantation from the Bavarian State Institute for Viticulture and Horticulture (LWG). Tree circumference was measured using calipers. We calculated the difference between the initial plantation radial measurement and the present radius of the tree individuals as a measure of tree growth and expressed it as annual radial increment.

We used an algorithm written in Mathematica (Wolfram Research, Champaign, USA) to determine the structural complexity of each tree individual as shown in Seidel (2018).

We used the relative height of maximum horizontal crown area (Rel.Hmaxarea) to describe the top-heaviness of a tree’s geometry. It was calculated based on the height of the maximum horizontal crown area in relation to the total tree height. Therefore, it is a relative measure corrected for tree height and given in percent. The underlying parameter “height of maximum crown area” was calculated as described in Seidel et al. (2011). In short, the tree point clouds were split into horizontal layers of 10 cm in thickness and the area of the convex-hull polygon enclosing all points in each horizontal layer was calculated. The height of the layer with the largest area is considered Hmaxarea (or ‘HCPA’ (height of maximum crown projection area) in earlier studies). The relative Hmaxarea was then given in percent of the total tree height. Tree height was derived from the point cloud as the difference between highest point and lowest point in the point cloud of a tree (zmax – zmin).

The secondary data for the places of species origin and latitudinal range were obtained from the database of the European Forest Genetic Resources Program (EUFORGEN 1994) and Van Den Berk Nurseries (Vdberk 2020).

Information regarding seed dispersal strategy was obtained from the Royal Botanic Gardens Kew Seed Information Database (SID 2020) as well as from additional literature (Howe and Smallwood 1982; Clark et al. 1999; Loewer 2005; Oyama et al. 2018).

Usage notes

The mid-point of the maximum and minimum latitudinal distribution of each species was only estimated. We are aware that this mid-point latitude is of limited accuracy since highly detailed geographical information on every species’ natural distribution would be needed for an exact mid-point determination. This is, however, unavailable for many species. We used the absolute values of the latitudes in order to analyze both hemispheres together since we do not assume an effect on tree architecture based on the hemisphere (average solar elevation angles are the same). This analysis was performed for 431 trees from 83 species since we could not find exact origins for some of the cultivars.


Deutsche Forschungsgemeinschaft, Award: SE2383/5-1

Landwirtschaftliche Rentenbank, Award: 844732