Global forest management data at a 100m resolution for the year 2015: region-specific models

Region-specific models used for the global Forest Management data, at 100m resolution, for the year 2015

The global Forest Management data map for year 2015 (see DOI 10.5281/zenodo.4541512) was produced using a set of region-specific Random Forest Classifier models.

These models are trained on and applied to each region defined in the Global Biome Cluster layer (see DOI 10.5281/zenodo.5848609). They can be run with Python's scikit-learn Random Forest Classifier and the Python joblib package.

The model information is provided in three folders:

• training data (.csv files) for each model in the right Remote Sensing band order, including lat, lon of the location, and the class [coded as number]
• training parameters: random forest classifier parameters and used PROBA-V metrics bands (.ini files) to train the model with the given training data, after the 5folder cross-validation and optimization
• models (.joblib.z files) for each biome. The model names includes the identifier code from the global Biome Cluster layer.