Global forest management data at a 100m resolution for the year 2015

We provide four data records:

1.The reference data set as a comma-separated file ("reference_data_set.csv") with the following attributes: 

• “ID” is a unique location identifier 

• “Latitude, Longitude” are centroid coordinates of a 100m x 100m pixel. 

• “Land_use_ID “is a land use class: 

  • 11 – Naturally regenerating forest without any signs of human activities, e.g., primary forests.  
  • 20 – Naturally regenerating forest with signs of human activities, e.g., logging, clear cuts etc.  
  • 31 – Planted forest.  
  • 32 – Short rotation plantations for timber.  
  • 40 – Oil palm plantations.  
  • 53 – Agroforestry. 

• “Flag” identifies a data origin:  1- the crowdsourced locations, 2- the control data set, 0 – the additional experts' classifications following the opportunistic approach.

2. The 100 m forest management map in a geoTiff format with the classes presented – "FML_v3.2.tif ".

3. The predicted class probability from the Random Forest classification in a geoTiff format – "ProbaV_LC100_epoch2015_global_v2.0.3_forest-management–layer-proba_EPSG-4326.tif"

4. Validation data set as a comma-separated file ("validation_data_set.csv) with the following attributes: 

• “ID” is a unique location identifier 

• “pixel_center_x” , “pixel_center_y ” are centroid coordinates of a 100m x 100m pixel  in lat/lon projection 

• “first_landuse_class “is a land use class, as in (1). 

• “second_landuse_class “is a second possible land use class, as in (1), identified in case it was difficult to assign one class with high confidence. 

5. Original crowdsourced data set as a .csv table.

6. Compiled FAO FRA forest statistics and mapped classes by countries into one table (.csv format).