From Trees to Forest Inventories: Do End‑to‑End LiDAR Pipelines Really Work?

There is an ever-growing toolshed of processing solutions for close-range LiDAR scans of forests. But these tools often cover only a fraction of the workflow from a point cloud to a full forest inventory, which includes tree position, diameter at breast height (DBH), tree height, and species. Many tools either perform single steps, such as segmenting individual trees, or extract only geometric information, such as DBH and tree height, but not species information, while others do just this. Even though many of these tools are validated, and first benchmarks exist for individual tasks, it remains unclear whether a pipeline can be conducted across multiple tools to generate a full inventory and how errors propagate through such pipelines. Therefore, we validate a pipeline that includes single-tree segmentation (SegmentAnyTree), species classification (DetailView), and geometric parameter extraction (CspStandSegmentation) against manual full inventories of two contrasting forests in south-west Germany. The first forest is a flat, mature mixed forest dominated by Fagus sylvatica (approx. 1500 trees), while the second forest is on steep terrain with a diverse age structure, mostly dominated by coniferous species like Picea abies and Abies alba (approx. 750 trees). Therefore, these forests depict a strong gradient in structural complexity. We illustrate how reproducible, easily usable and scalable pipelines can be implemented across programming languages using the Galaxy platform. We clearly depict how errors propagate from the segmentation to the subsequent processes and how this influences the overall performance of forest inventory tasks.