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Global mapping of potential natural vegetation: an assessment of Machine Learning algorithms for estimating land potential

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@pierreroudier @thePeerJ The link is https://t.co/Rv3yJuIX5y (I do not know what happened to the Twitter post). The color legends. I still think it is a very personal choice. You can always download our raw geotiff data and use which ever legend you prefer.
58 days ago
Global mapping of potential natural vegetation: an assessment of Machine Learning algorithms for estimating land potential https://t.co/y81ExP4Gmx
RT @tom_hengl: Our article on mapping potential natural vegetation has just been accepted for publication in the Open Access journal @thePe…
Our article on mapping potential natural vegetation has just been accepted for publication in the Open Access journal @thePeerJ The article should appear online on the journal pages in the coming weeks. In the meanwhile the most-up-to-date reprint is at: https://t.co/yelkQOgOnM
RT @tom_hengl: Earth Without People? Yes it would have been much greener. We have mapped potential natural vegetation at 1 km using variety…
RT @tom_hengl: Earth Without People? Yes it would have been much greener. We have mapped potential natural vegetation at 1 km using variety…
RT @tom_hengl: Earth Without People? Yes it would have been much greener. We have mapped potential natural vegetation at 1 km using variety…
RT @tom_hengl: Earth Without People? Yes it would have been much greener. We have mapped potential natural vegetation at 1 km using variety…
RT @tom_hengl: Earth Without People? Yes it would have been much greener. We have mapped potential natural vegetation at 1 km using variety…
RT @tom_hengl: Earth Without People? Yes it would have been much greener. We have mapped potential natural vegetation at 1 km using variety…
RT @tom_hengl: Earth Without People? Yes it would have been much greener. We have mapped potential natural vegetation at 1 km using variety…
RT @tom_hengl: Earth Without People? Yes it would have been much greener. We have mapped potential natural vegetation at 1 km using variety…
RT @tom_hengl: Earth Without People? Yes it would have been much greener. We have mapped potential natural vegetation at 1 km using variety…
176 days ago
RT @tom_hengl: Earth Without People? Yes it would have been much greener. We have mapped potential natural vegetation at 1 km using variety…
RT @tom_hengl: Earth Without People? Yes it would have been much greener. We have mapped potential natural vegetation at 1 km using variety…
Earth Without People? Yes it would have been much greener. We have mapped potential natural vegetation at 1 km using variety of data and machine learning. Read more about this work in: https://t.co/HY6mG9Vj97 To browse maps use: https://t.co/0OPEDcXk6Y
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Additional Information

Competing Interests

Tomislav Hengl and Ichsani Wheeler are employed by Envirometrix Ltd.

Author Contributions

Tomislav Hengl conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, approved the final draft.

Markus G Walsh conceived and designed the experiments, performed the experiments, analyzed the data, contributed reagents/materials/analysis tools, authored or reviewed drafts of the paper, approved the final draft.

Jonathan Sanderman conceived and designed the experiments, performed the experiments, analyzed the data, authored or reviewed drafts of the paper, approved the final draft.

Ichsani Wheeler conceived and designed the experiments, analyzed the data, contributed reagents/materials/analysis tools, prepared figures and/or tables, approved the final draft.

Sandy P Harrison analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft, cleaned up biome data set.

Iain C Prentice analyzed the data, prepared figures and/or tables, authored or reviewed drafts of the paper, approved the final draft, cleaned up biome data set.

Data Deposition

The following information was supplied regarding data availability:

http://dx.doi.org/10.7910/DVN/QQHCIK

All code used to generate the maps is available at: https://github.com/Envirometrix/PNVmaps

Funding

Funding was provided by The Nature Conservancy and the Doris Duke Charitable Foundation. SPH was supported by the ERC-funded project GC2.0 (Global Change 2.0: Unlocking the past for a clearer future, grant number 694481) and from JPI-Belmont Forum via NERC for the project "PAleao-Constraints on Monsoon Evolution and Dynamics (PACMEDY)''. This research is a contribution to the AXA Chair Programme in Biosphere and Climate Impacts and the Imperial College initiative on Grand Challenges in Ecosystems and the Environment (ICP). The funders had no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript.


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