Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential

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I've updated the predictions of the world biomes https://t.co/5EKyOPtYQo. Now using 40% more covariates + Ensemble ML (RF, xgboost, deepnet and glmnet). PNV methodology explained in https://t.co/schUfJD3UT. Can be used to derive soil carbon sequestration potential and similar. https://t.co/e7VW7wmw3f
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@SCaldararu Even the first part is not novel as such. Potential natural vegetation has been mapped already using (partly) the same data and algorithm. https://t.co/cW5HnQYmAU
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2439 days ago
RT @tom_hengl: @restoreforward We tested some Machine Learning algorithms for predicting forest tree species in Europe https://t.co/schUfJD…
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RT @tom_hengl: @restoreforward We tested some Machine Learning algorithms for predicting forest tree species in Europe https://t.co/schUfJD…
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RT @tom_hengl: @restoreforward We tested some Machine Learning algorithms for predicting forest tree species in Europe https://t.co/schUfJD…
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@restoreforward We tested some Machine Learning algorithms for predicting forest tree species in Europe https://t.co/schUfJD3UT and the results were promising. Hopefully soon we will be able to provide state-of-the-art species distribution data globally and support regreening of our
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RT @sandersoil: @tom_hengl on a role - also our work 'Global mapping of potential natural vegetation: an assessment of machine learning alg…
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RT @sandersoil: @tom_hengl on a role - also our work 'Global mapping of potential natural vegetation: an assessment of machine learning alg…
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RT @sandersoil: @tom_hengl on a role - also our work 'Global mapping of potential natural vegetation: an assessment of machine learning alg…
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RT @sandersoil: @tom_hengl on a role - also our work 'Global mapping of potential natural vegetation: an assessment of machine learning alg…
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2539 days ago
RT @sandersoil: @tom_hengl on a role - also our work 'Global mapping of potential natural vegetation: an assessment of machine learning alg…
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2539 days ago
RT @sandersoil: @tom_hengl on a role - also our work 'Global mapping of potential natural vegetation: an assessment of machine learning alg…
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@tom_hengl on a role - also our work 'Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential' was a top 5 most viewed #PlantScience and #SpatialScience article in @thePeerJ in 2018
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RT @sandersoil: Fun collaboration with @tom_hengl @MarkusWalsh1 @IchsaniWheeler and the great team of Sandy Harrison & Colin Prentice explo…
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RT @sandersoil: Fun collaboration with @tom_hengl @MarkusWalsh1 @IchsaniWheeler and the great team of Sandy Harrison & Colin Prentice explo…
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RT @sandersoil: Fun collaboration with @tom_hengl @MarkusWalsh1 @IchsaniWheeler and the great team of Sandy Harrison & Colin Prentice explo…
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Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential https://t.co/JS71Q1RRVu via @thePeerJ
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RT @g_fiske: A useful new dataset | Global mapping of potential natural vegetation https://t.co/ke4FjP31LB via @tom_hengl, Walsh, @sanderso…
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RT @thePeerJ: Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential ht…
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Global mapping of potential natural vegetation: an assessment of machine learning algorithms for estimating land potential https://t.co/SojCDXcXhg https://t.co/rlfEPjnN58
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2684 days ago
Classic Hengl https://t.co/z3kkmEzxoz
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RT @tom_hengl: Imagine world without us / Natural Vegetation developing without obstacles. Yes it would have been much greener planet, exce…
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RT @tom_hengl: Imagine world without us / Natural Vegetation developing without obstacles. Yes it would have been much greener planet, exce…
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RT @tom_hengl: Imagine world without us / Natural Vegetation developing without obstacles. Yes it would have been much greener planet, exce…
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2685 days ago
A useful new dataset | Global mapping of potential natural vegetation https://t.co/ke4FjP31LB via @tom_hengl, Walsh, @sandersoil et al.
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