Published July 7, 2020 | Version v1
Preprint Open

Methodology for generating a global forest management layer

Creators

  • 1. International Institute for Applied Systems Analysis (IIASA)
  • 2. Flemish Institute for Technological Research (VITO)-Remote Sensing
  • 3. Department of Environmental Geography, Institute for Environmental Studies (IVM), Vrije Universiteit Amsterdam
  • 4. National University of Life and Environmental Sciences of Ukraine
  • 5. Sukachev Institute of Forest SB RAS
  • 6. Moore Center for Science, Conservation International
  • 7. Université des Sciences et Techniques de Masuku
  • 8. Mongolian Forest Research Association, NGO
  • 9. International Institute for Applied Systems Analysis (IIASA) ; Department of Forest and Soil Sciences, Institute of Silviculture, BOKU-University
  • 10. Center for Forest Ecology and Productivity of the Russian Academy of Sciences (CEPF RAS), 117997, Russian Federation
  • 11. National Science and Technology Center for Disaster Reduction
  • 12. National university of life and environmental sciences of Ukraine
  • 13. West University of Timisoara
  • 14. Gauhati University
  • 15. Scientific and Research Institute of Forestry and Landscape Park Management, National University of Life and Environmental Sciences of Ukraine
  • 16. Department of Natural Resources and Agricultural Engineering, Damanhour University, Faculty of Agriculture
  • 17. Department of Geography, Gauhati University
  • 18. National University of Life and Environmental Sciences of Ukraine, Education and Research Institute of Forestry and Landscape-Park Management, Department of Botany, Dendrology and Forest Tree Breeding
  • 19. National University of Life and Environmental Sciences of Ukraine, Separated subdivision of NULES of Ukraine "Boyarka Forestry Research Station
  • 20. Institute for Environmental Studies (IVM), Vrije Universiteit
  • 21. Abhayapuri College, Gauhati University
  • 22. Institute of Meteorology and Climate Research, Atmospheric Environmental Research (IMK-IFU)
  • 23. Department of Meteorology, COMSATS University, Islamabad; Space Applications & Research Complex, Pakistan Space and Upper Atmosphere Research Commission
  • 24. Department of Geography, University of Wisconsin-Madison; Center for Sustainability and the Global Environment (SAGE), University of Wisconsin‐Madison
  • 25. State Enterprise "Sosnove Forestry"
  • 26. Storozhynets forestry college
  • 27. Berezne Forestry College of National University of Water and Environmental Engineering

Description

The first ever global map of forest management was generated based on remote sensing data. To collect training data, we launched a series of Geo-Wiki (https://www.geo-wiki.org/) campaigns involving forest experts from different world regions, to explore which information related to forest management could be collected by visual interpretation of very high-resolution images from Google Maps and Microsoft Bing, Sentinel time series and normalized difference vegetation index (NDVI) profiles derived from Google Earth Engine. A machine learning technique was then used with the visually interpreted sample (280K locations) as a training dataset to classify PROBA-V satellite imagery. Finally, we obtained a global wall-to-wall map of forest management at a 100m resolution for the year 2015. The map includes classes such as intact forests; forests with signs of management, including logging; planted forests; woody plantations with a rotation period up to 15 years; oil palm plantations; and agroforestry. The map can be used to deliver further information about forest ecosystems, protected and observed forest status changes, biodiversity assessments, and other ecosystem-related aspects.

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