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On March 10, 2025 at 7:40:26 PM UTC, Gravatar Lars Waser:

  • Updated description of Forest Type NFI from

    A series of Forest Type NFI datasets covering Switzerland have been produced, and currently available for the years 2023, 2018, and 2016. These datasets provide the fractional representation (0-100%) of the class broadleaf at the pixel level. The three datasets are based on different remote sensing data from different time spans and with different spatial resolutions: - **Forest Type NFI 2023: Sentinel-1/-2, 2021-2023, 10m (new dataset!)** - Forest Type NFI 2018: Sentinel-1/-2, 2016-2018, 10m - Forest Type NFI 2016: Aerial orthoimages, 2010-2015, 3m . --------------------- . **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). **User’s feedback:** We are very motivated to improve the dataset further and welcome any feedback or suggestions for corrections. Feel free to directly contact **lars.waser@wsl.ch** _________________ . *Detailed description:* **Forest Type NFI datasets, versions 2023 and 2018:** Both datasets use 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 all areas with vegetation height > 5m version 2018, and >3 m version 2023). 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. In addition to the original Sentinel-2 spectral bands, vegetation indices such as GEMI, MSAVI2, NDVI, CLRE and CCCI were used in the final model. The classification models were tested, trained and validated using up to 400,000 labels representing the two classes broadleaf and coniferous, derived from aerial image delineations. For the 2023 data set, the previously used labels were quality-checked for reliability and temporal consistency with the image data. Additionally, more labels were collected in the regions where the previous version of the data set performed less accurately – as reported by the users. Similar high model performances were achieved for both data sets: overall accuracy = 0.96, kappa = 0.94, F1-score =0.96, precision = 0.97, recall = 0.94 (2023), 0.95 (2018). Independent validation and plausibility check included the comparison of the predicted results with aerial image interpretations of the National Forest Inventory (NFI). For both data sets, deviations particularly occurred in mixed forest stands: over all accuracy = 0.81 (2018, 2023), kappa = 0.62 (2018), 0.59 (2023). For more details, see Waser et al. (2021). **The Forest Type NFI dataset, version 2016:** This dataset presents a countrywide map with the two classes broadleaf and coniferous in Switzerland based on digital aerial imagery. The spatial resolution of the data set is 3 m. The pixel values correspond to the fraction (0-100 %) of the class broadleaf. The classification approach incorporates a RF classifier, predictors from multispectral aerial imagery (ADS80) and the SwissAlti3D terrain model. The model was tested, trained and validated using 90,000 digitized polygons and achieved an overall accuracy of 0.99 and a kappa of 0.98. Independent validation and plausibility check included the comparison of the predicted results with aerial image interpretations of the NFI. Significant deviations were observed, primarily due to an underestimation of broadleaved trees (median underestimation of 3.17%), especially in mixed forest stands. For more details, see Waser et al. (2017; https://doi.org/10.3390/rs9080766).
    to
    A series of Forest Type NFI datasets covering Switzerland have been produced, and currently available for the years 2023, 2018, and 2016. These datasets provide the probability (0-100%) of the class broadleaf at the pixel level. While 0% probability corresponds to the pure class coniferous, 100% correspond to the class pure broadleaf. Values around 50% probability weren't assigned to one of the two classes and might belong to mixed stands. The three datasets are based on different remote sensing data from different time spans and with different spatial resolutions: - **Forest Type NFI 2023: Sentinel-1/-2, 2021-2023, 10m (new dataset!)** - Forest Type NFI 2018: Sentinel-1/-2, 2016-2018, 10m - Forest Type NFI 2016: Aerial orthoimages, 2010-2015, 3m . --------------------- . **Important note:** None of the datasets is suitable for analysis at the individual tree crown level because pixel-level probabilities 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). **User’s feedback:** We are very motivated to improve the dataset further and welcome any feedback or suggestions for corrections. Feel free to directly contact **lars.waser@wsl.ch** _________________ . *Detailed description:* **Forest Type NFI datasets, versions 2023 and 2018:** Both datasets use 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 probability (0-100%) of the class broadleaf at the pixel level (covering all areas with vegetation height > 5m version 2018, and >3 m version 2023). 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. In addition to the original Sentinel-2 spectral bands, vegetation indices such as GEMI, MSAVI2, NDVI, CLRE and CCCI were used in the final model. The classification models were tested, trained and validated using up to 400,000 labels representing the two classes broadleaf and coniferous, derived from aerial image delineations. For the 2023 data set, the previously used labels were quality-checked for reliability and temporal consistency with the image data. Additionally, more labels were collected in the regions where the previous version of the data set performed less accurately – as reported by the users. Similar high model performances were achieved for both data sets: overall accuracy = 0.96, kappa = 0.94, F1-score =0.96, precision = 0.97, recall = 0.94 (2023), 0.95 (2018). Independent validation and plausibility check included the comparison of the predicted results with aerial image interpretations of the National Forest Inventory (NFI). For both data sets, deviations particularly occurred in mixed forest stands: over all accuracy = 0.81 (2018, 2023), kappa = 0.62 (2018), 0.59 (2023). For more details, see Waser et al. (2021). **The Forest Type NFI dataset, version 2016:** This dataset presents a countrywide map with the two classes broadleaf and coniferous in Switzerland based on digital aerial imagery. The spatial resolution of the data set is 3 m. The pixel values correspond to the probabilities (0-100 %) of the class broadleaf. The classification approach incorporates a RF classifier, predictors from multispectral aerial imagery (ADS80) and the SwissAlti3D terrain model. The model was tested, trained and validated using 90,000 digitized polygons and achieved an overall accuracy of 0.99 and a kappa of 0.98. Independent validation and plausibility check included the comparison of the predicted results with aerial image interpretations of the NFI. Significant deviations were observed, primarily due to an underestimation of broadleaved trees (median underestimation of 3.17%), especially in mixed forest stands. For more details, see Waser et al. (2017; https://doi.org/10.3390/rs9080766).