Forest Type NFI 2023
Important note: This dataset is NOT suitable for analysis at the individual tree crown level because probabilities at the pixel level (e.g., of the broadleaf class) are not allocated to individual trees. Analysis is recommended only for areas larger than 3x3 pixels, i.e. by calculating the mean values, except in rare cases of homogeneous forest stands (either broadleaf or coniferous class).
This dataset uses a remote sensing-based approach for a countrywide mapping of the Dominant Leaf Type (DLT) in Switzerland, classifying areas as either broadleaf or coniferous. These datasets have a spatial resolution of 10 m and provide the probabilities (0-100%) of the class broadleaf at the pixel level (covering areas with vegetation height > 3m). The classification approach is based on a Random Forest (RF) classifier, that combines predictors derived from multi-temporal Sentinel-1 and Sentinel-2 data with the SwissAlti3D terrain model. The models were calibrated using digitized training polygons and independently validated data from the National Forest Inventory (NFI). Whereas high model overall accuracies (0.97) and kappa (0.96) were achieved, the comparison of the Forest Type NFI 2023 dataset with independent NFI data revealed deviations in mixed stands.
The dataset 'Forest Type NFI 2023' is freely available on request (lars.waser@wsl.ch).
Additional Information
| Field | Value |
|---|---|
| Metadata last updated | March 14, 2025 |
| Data last updated | March 14, 2025 |
| Created | March 6, 2025 |
| Format | geotif |
| License | Other (Specified in the description) |
| DOI | |
| Access Restriction | Level: Public |
| Publication State | |
| Size | 3.10 GB |