Speaker
Description
Forests with a high overall biodiversity provide a variety of ecosystem functions and service and are associated with greater ecosystem stability and resilience to disturbance events. To identify and preserve intact forest ecosystems across the country, monitoring forest biodiversity on national scale is essential. Large-scale remote sensing datasets offer the potential to develop novel monitoring strategies on a national level.
Within this context, we used high resolution area-wide LiDAR derived tree canopy height models and a national tree species classification map of Germany based on Sentinel-2 satellite data with 20 m spatial resolution to investigate the links between tree height heterogeneity and tree species diversity on different spatial scales and across forest types. Height heterogeneity was derived from the canopy height models whereas biodiversity metrics were calculated from the tree species map. Topographic variables and canopy cover were included in the analysis. First results suggest that the relation between tree height heterogeneity and tree species diversity varies between geographic regions and is partly driven by forest composition. We analysed and evaluated the impact of spatial scale and resolution, as well as forest type specific structural features.
We compared our satellite-based findings to results generated from forest inventory data in exemplary regions to provide a comprehensive picture of the potential and the constraints of using remote sensing data products such as canopy height models for forest biodiversity assessment. This study contributes to the development of remotely sensed forest biodiversity indicators and facilitates the integration of remote sensing datasets into large-scale forest assessments to improve the detection of biodiversity changes in forest ecosystems over time and space.
| Status Group | Doctoral Researcher |
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