Do different indices of forest structural heterogeneity yield consistent results?

Forest management with a focus on high structural heterogeneity is a major goal in modern forestry to increase multifunctionality. The assessment and quantification of forest structures has, therefore, gained much attention in re-cent years. However, there is no standardized approach to surveying forest heterogeneity; instead, a variety of structural indices, which have been devel-oped over past decades, are used. This makes it difficult to interpret the re-sults of different studies and to base management decisions on such data. In this study, we compared six structural indices that differ in terms of their complexity and the method of data acquisition. These included the Gini coeffi-cient of the diameter at breast height and of tree height, the Shannon index of tree species diversity, two complex indices of structural heterogeneity, one based on conventional inventory data and one on terrestrial laser scanning (TLS) data, and a simple-holistic TLS-based stand structural complexity index. For the comparison of these six indices, we used data from 84 plots in 12 dif-ferent forest stand types in two study areas in Germany. The stand types con-sisted of different dominant tree species and included different age classes. The degree of correlations among the different indices was highly variable. In addition, we did not find a clear age-dependency of the indices. We conclude that the choice of a specific index plays an important role in the evaluation and interpretation of forest structural heterogeneity. Because TLS data offer multiple benefits in terms of precision, reproducibility and comprehensive-ness, we recommend to use TLS-based indices of structural heterogeneity.

Automated summary

Forest management aiming at high structural heterogeneity is crucial for modern forestry. However, there is no standardized approach to assess forest heterogeneity, resulting in the use of various structural indices. This study compared six different structural indices and recommended the use of TLS-based indices for evaluating forest structural heterogeneity.