Work

Drivers of remotely sensed tree height heterogeneity across spatial scales: Tree species diversity effects depend on local conditions and forest type

Year: 2025

Type: article

Abstract: Forests with high structural complexity provide a variety of ecosystem functions and services. They are further associated with greater ecosystem stability. Area-wide canopy height models with high spatial resolution are a powerful tool for deriving forest structural attributes. However, the drivers of forest structural heterogeneity as captured by canopy height models, such as tree height heterogeneity, are not fully understood across spatial scales. Here, we analyzed the relationship of tree species diversity and tree height heterogeneity (1) in a tree diversity experiment, (2) among three forest inventory sites across Germany and (3) on the German national scale. We show that the effect of tree species diversity on tree height heterogeneity depends on the spatial scale and local conditions. Depending on canopy height model resolution, effects of topography, tree species composition and data acquisition parameters of canopy height models have a larger influence on tree height heterogeneity compared to tree species diversity. Moreover, canopy cover fraction is highly correlated with height heterogeneity. By combining our findings from different inventory sites and examining national patterns, this study provides new insights for the application and interpretation of remotely sensed forest biodiversity indicators. • We quantify drivers of height heterogeneity derived from canopy height models. • Tree species diversity is not the main driver of height heterogeneity. • Tree species diversity effects differ between sites and spatial scales. • Forest type mediates tree species diversity effects.

Source: Ecological Indicators

DOI: 10.1016/j.ecolind.2025.114245