R script for large-scale mapping of forest canopy layering. The approach combines tree-based and area-based methods, using airborne laser scanning point clouds together with individual tree detection (ITD) information (tree height and position) to distinguish between single- and multi-layered forests. The output is a raster layer at 10m x 10m resolution, encoded as follows: 1: single-layered (class 1): Both point cloud and ITD approaches classify the forest as single-layered. 2: probably single-layered (class 2): ITD classifies the forest as single-layered, while the point cloud suggests multi-layered. 3: probably multi-layered (class 3): ITD classifies the forest as multi-layered, while the point cloud suggests single-layered. 4: multi-layered (class 4): Both point cloud and ITD approaches classify the forest as multi-layered.

Next to the R script, we also provide an example data set. The LAZ files in the example dataset are from the nationwide ALS data and are provided by the Federal Office of Topography swisstopo in accordance with its open data policy (swisstopo, 2022a, 2019). The detected individual trees were identified using Dalponte and Coomes' (2016) algorithm on a spike-free vegetation height model (cell size 0.5m). The provided forest mask corresponds to the NFI forest layer definition by Waser et al. (2015). For details and mentioned references, we refer to the related publication by Bast et al. (under revision; JAG).