Dataset Open Access

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

Myroslava Lesiv; Dmitry Schepaschenko; Marcel Buchhorn; Linda See; Martina Dürauer; Ivelina Georgieva; Martin Jung; Florian Hofhansl; Katharina Schulze; Andrii Bilous; Volodymyr Blyshchyk; Liudmila Mukhortova; Carlos Luis Muñoz Brenes; Leonid Krivobokov; Stephan Ntie; Khongor Tsogt; Stephan Alexander Pietsch; Elena Tikhonova; Moonil Kim; Fulvio Di Fulvio; Yuan-Fong Su; Roma Zadorozhniuk; Flavius Sorin Sirbu; Kripal Pangin; Svitlana Bilous; Sergii B. Kovalevskii; Florian Kraxner; Ahmed Harb Rabia; Roman Vasylyshyn; Rekib Ahmed; Petro Diachuk; Serhii S. Kovalevskyi; Khangsembou Bungnamei; Kusumbor Bordoloi; Andrii Churilov; Olesia Vasylyshyn; Dhrubajyoti Sahariah; Anatolii P. Tertyshnyi; Anup Saikia; Žiga Malek; Kuleswar Singha; Roman Feshchenko; Reinhard Prestele; Ibrar ul Hassan Akhtar; Kiran Sharma; Galyna Domashovets; Seth A. Spawn-Lee; Oleksii Blyshchyk; Oleksandr Slyva; Mariia Ilkiv; Oleksandr Melnyk; Vitalii Sliusarchuk; Anatolii Karpuk; Andrii Terentiev; Valentin Bilous; Kateryna Blyshchyk; Maxim Bilous; Nataliia Bogovyk; Ivan Blyshchyk; Sergey Bartalev; Mikhail Yatskov; Bruno Smets; Piero Visconti; Ian Mccallum; Michael Obersteiner; Steffen Fritz

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).


NatureMap project ( Funder Norway's International Climate and Forest Initiative (NICFI): This is a similar data set on zenodo (Lesiv, M. et al. Global planted trees extent 2015. Zenodo, 2020) This is the version that was used in one of the follow up studies, which was needed as a reference. Please ignore it and instead use this zenodo record.
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