A 20m Pan-European Wildfire 12-class Fuel Map

Fuel map 12 classes methodology Input susceptibility map:A Pan-European susceptibility is generated by training a Machine Learning model based on the Random Forest algorithm using topographic, land cover, climate data, and fire presence/absence labels. The model infers the wildfire susceptibility for each geo-spatial pixel of the domain. the native map resolution is at 100m and it has been resampled to 20m using bilinear interpolation. Susceptibility map is categorized in 3 classes using as thresholds the 1st and 10th percentiles in the burned area distribution from 2008 to 2022, (retreived from the European Forest Fires Information System). 1 = Low2 = Medium3 = High Input vegetation maps: a) CLCplus backbone for European domain (10m resampled to 20 with nearest method) b) Global Copernicus land cover 100m for areas outside Eu (N Africa, middle east, Ukraine) - resample to 20m (nearest neighbor)c) Global Copernicus land cover 10m for areas outside Eu (N Africa, middle east, Ukraine) - resample to 20m (nearest neighbor) the 3 maps have been aggregated in a 4 type macro-veg classes: 0 = not burnable1 = Grasslands and croplands 2 = Broadleaves forest3 = Shrublands4 = Conifers forests Fuel map generation: the resulted Pan-EU map is built with the following logic: for area outside Europe, the map at the 10m 'wins' always besides when tree forest is present.In the forest class (2), the 100m map override with class 2 or 4. (Because the 100m map has broadleaves/conifer discrimination).In the European domain, the CLCplus backbone always wins. the merged veg map is then combined with the categorized susceptibility map using the 12-class contingency matrix. FT 1 2 3 4S1 1 4 7 102 2 5 8 113 3 6 9 12 the dataset is present in HDF5 (.h5) format (lightweight) and a script (.py) for converting it in Geotiff with COG optimization is provided. The file size of the Geotiff can sensibly increase during the convertion.