Methodology for generating a global forest management layer

Lesiv, M. ORCID: https://orcid.org/0000-0001-9846-3342, Shchepashchenko, D. ORCID: https://orcid.org/0000-0002-7814-4990, Buchhorn, M., See, L. ORCID: https://orcid.org/0000-0002-2665-7065, Dürauer, M., Georgieva, I. ORCID: https://orcid.org/0000-0002-5556-794X, Jung, M., Hofhansl, F. ORCID: https://orcid.org/0000-0003-0073-0946, Schulze, K., Bilous, A., Blyshchyk, V., Mukhortova, L., Muñoz Brenes, C., Krivobokov, L.V., Ntie, S., Tsogt, K., Pietsch, S. ORCID: https://orcid.org/0000-0001-6431-2212, Tikhonova, E., Kim, M., Su, Y.-F., et al. (2020). Methodology for generating a global forest management layer. Zenodo 10.5281/zenodo.3933966. (Submitted)

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Abstract

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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