A new index of forest structural heterogeneity using tree architectural attributes measured by terrestrial laser scanning

The maintenance or enhancement of structural heterogeneity (SH) of forest stands is considered a key element of sustainable forest management in central Europe, making the monitoring of stand structure an important task. However, existing approaches that enable the quantification of SH usually neglect the three-dimensional (3D) representation of forest structures. To overcome this limitation, terrestrial laser scanning (TLS) can be applied to provide an efficient, non-destructive and high resolution record of 3D structures. In this study, we present a new structural heterogeneity index (SHITLS) at the plot level which is based on 3D point clouds of individual trees that were derived from multiple scans. Because tree crowns are of particular importance for the 3D SH of forests, we focus particularly on outer and inner crown attributes. To test the robustness and the explanatory power of our index we sampled data on 84 plots across 12 different forest stand types in two study areas of Germany. We analyzed whether our index is able to explain differences between the different stand types. In addition, we compared our SHITLS to an existing stand structural complexity index (SSCI) that is based purely on single TLS scans. We found different trends of SHITLS between stand types dominated by Fagus sylvatica compared to those dominated by coniferous tree species (mainly Picea abies and Pinus sylvestris). Correlation analyses showed a significant, but weak, correlation with SSCI. The advantage of the SHITLS is the use of multiple scans from several scan positions. Thus, a better and very high resolution coverage of the forest structures is achieved. Due to its TLS-based nature this method provides many benefits in terms of reproducibility, objectivity and simplicity of data evaluation.

Automated summary

In this study, a new structural heterogeneity index (SHITLS) based on 3D point clouds was introduced for quantifying the 3D features of forest structures. By using multiple scans from different positions, a better and higher resolution coverage of forest structures was achieved. Compared to existing stand structural complexity indices based on single scans, SHITLS showed a better ability to explain differences between different stand types.