Forest Type NFI 2018
Important note: None of the datasets is suitable for analysis at the individual tree crown level because pixel-level fractions (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 fractional representation (0-100%) of the class broadleaf at the pixel level (covering areas with vegetation height > 5m). 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 tree type map with independent NFI data revealed deviations in mixed stands.
The dataset 'Forest Type NFI 2018' is available on request (lars.waser@wsl.ch).
Additional Information
| Field | Value |
|---|---|
| Metadata last updated | March 6, 2025 |
| Data last updated | March 6, 2025 |
| Created | April 23, 2021 |
| Format | geotiff |
| License | Other (Specified in the description) |
| DOI | 10.16904/1000001.6 |
| Access Restriction | Level: Restricted to Same Organization Members |
| Publication State | |
| Size | 182.20 MB |