­Three-dimensional structural complexity of stands is determined by the inner and outer crown structure of individual trees

Stand structural complexity influences various forest ecosystem functions, such as carbon storage or productivity or resilience to climate extremes. However, defining and measuring stand structural complexity is not trivial, as different structural attributes can be used to describe stand structure.

We focus on a terrestrial laser scan-based stand structural complexity index (SSCI) and its components, mean fractal dimension (MeanFrac) and effective number of layers (ENL). These indices are now widely used, but there is still a lack of understanding of what exactly constitutes them. In this study, we analysed which structural attributes of individual trees determine these indices at which spatial scale. For our analysis, we used a high-resolution terrestrial laser scanning (TLS) dataset consisting of 11 structural attributes of over 1300 individual trees from 30 study plots of a young tree experiment in subtropical China.

We found that SSCI is mainly determined by inner, i.e. branching intensity and branch number, and outer crown structure, i.e. crown compactness. MeanFrac is best described by branching intensity. Thus, a higher canopy space filling, i.e. higher density, leads to a higher stand structural complexity. Tree height and diameter at breast height are the main determinants of ENL. Different structural attributes (branching intensity vs. tree height) promote different aspects of stand structural complexity (horizontal vs. vertical), providing a variety of management options to increase stand structural complexity.

Our results show that to better understand stand structural complexity, it is essential to include crown structural attributes in the assessment of stand structure.

Publication:

Koller A, Kunz M, Perles-Garcia MD, von Oheimb G (2025): 3D structural complexity of forest stands is determined by the magnitude of inner and outer crown structural attributes of individual trees. Agricultural and Forest Meteorology 363, 110424. https://doi.org/10.1016/j.agrformet.2025.110424.