Global forest management data at a 100m resolution for the year 2015
Creators
- Myroslava Lesiv1
- Dmitry Schepaschenko1
- Marcel Buchhorn2
- Linda See1
- Martina Dürauer1
- Ivelina Georgieva1
- Martin Jung1
- Florian Hofhansl1
- Katharina Schulze3
- Andrii Bilous4
- Volodymyr Blyshchyk4
- Liudmila Mukhortova5
- Carlos Luis Muñoz Brenes6
- Leonid Krivobokov7
- Stephan Ntie8
- Khongor Tsogt9
- Stephan Alexander Pietsch1
- Elena Tikhonova10
- Moonil Kim11
- Fulvio Di Fulvio1
- Yuan-Fong Su12
- Roma Zadorozhniuk13
- Flavius Sorin Sirbu14
- Kripal Pangin15
- Svitlana Bilous13
- Sergii B. Kovalevskii13
- Florian Kraxner1
- Ahmed Harb Rabia16
- Roman Vasylyshyn13
- Rekib Ahmed17
- Petro Diachuk13
- Serhii S. Kovalevskyi13
- Khangsembou Bungnamei17
- Kusumbor Bordoloi17
- Andrii Churilov13
- Olesia Vasylyshyn13
- Dhrubajyoti Sahariah17
- Anatolii P. Tertyshnyi13
- Anup Saikia17
- Žiga Malek3
- Kuleswar Singha18
- Roman Feshchenko13
- Reinhard Prestele19
- Ibrar ul Hassan Akhtar20
- Kiran Sharma17
- Galyna Domashovets13
- Seth A. Spawn-Lee21
- Oleksii Blyshchyk22
- Oleksandr Slyva13
- Mariia Ilkiv13
- Oleksandr Melnyk13
- Vitalii Sliusarchuk13
- Anatolii Karpuk13
- Andrii Terentiev13
- Valentin Bilous13
- Kateryna Blyshchyk13
- Maxim Bilous13
- Nataliia Bogovyk13
- Ivan Blyshchyk23
- Sergey Bartalev24
- Mikhail Yatskov25
- Bruno Smets2
- Piero Visconti1
- Ian Mccallum1
- Michael Obersteiner1
- Steffen Fritz1
- 1. IIASA, Austria
- 2. VITO, Belgium
- 3. Department of Environmental Geography, Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam
- 4. National University of Life and Environmental Sciences of Ukraine
- 5. V.N. Sukachev Institute of Forest, Siberian Branch of the Russian Academy of Science
- 6. Moore Center for Science, Conservation International, USA
- 7. V.N. Sukachev Institute of Forest, Siberian Branch of the Russian Academy of Science, Russia
- 8. Université des Sciences et Techniques de Masuku, Gabon
- 9. Mongolian Forest Research Association, NGO, Mongolia
- 10. Center of Forest Ecology and Productivity of the Russian Academy of Sciences, Russia
- 11. Environmental GIS/RS Center, Korea University, Republic of Korea
- 12. Department of Harbor and River Engineering, National Taiwan Ocean University, Taiwan
- 13. National University of Life and Environmental Sciences of Ukraine, Ukraine
- 14. West University of Timisoara, Romania
- 15. Department of Geography, Gauhati University,India
- 16. Natural Resources & Agricultural Engineering Department, Faculty of Agriculture, Damanhour University, Egypt
- 17. Department of Geography, Gauhati University, Jalukbari, Guwahati, Assam 781014, India
- 18. Abhayapuri College, Gauhati University, India
- 19. Institute of Meteorology and Climate Research, Germany
- 20. Department of Meteorology, COMSATS University Islamabad, Islamabad, 45550 Pakistan.
- 21. Department of Geography, University of Wisconsin-Madison, USA
- 22. State Enterprise "Sosnove Forestry", Ukraine
- 23. Berezne Forestry College of National University of Water and Environmental Engineering, Ukraine
- 24. Space Research Institute of the Russian Academy of Sciences (IKI), Russia
- 25. USDA Forest Service, PNW Research Station, Anchorage Forestry Sciences Lab, USA
Description
We provide four data records:
1.The reference data set as a comma-separated file ("reference_data_set.csv") with the following attributes:
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“ID” is a unique location identifier
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“Latitude, Longitude” are centroid coordinates of a 100m x 100m pixel.
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“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.
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“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:
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“ID” is a unique location identifier
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“pixel_center_x” , “pixel_center_y ” are centroid coordinates of a 100m x 100m pixel in lat/lon projection
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“first_landuse_class “is a land use class, as in (1).
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“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.