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

Hengl, Tomislav; Walsh, Markus G; Sanderman, Jonathan; Wheeler, Ichsani; Harrison, Sandy P; Prentice, Iain C

Hengl, Tomislav; Walsh, Markus G; Sanderman, Jonathan; Wheeler, Ichsani; Harrison, Sandy P; Prentice, Iain C.

Afiliación

Hengl T; Envirometrix Ltd, Wageningen, Netherlands.
Walsh MG; The Earth Institute, Columbia University, New York, NY, USA.
Sanderman J; Selian Agricultural Research Institute, Arusha, Tanzania.
Wheeler I; Woods Hole Research Center, Falmouth, MA, USA.
Harrison SP; Envirometrix Ltd, Wageningen, Netherlands.
Prentice IC; School of Archeology, Geography and Environmental Science, University of Reading, Reading, UK.

PeerJ ; 6: e5457, 2018.

Article en En | MEDLINE | ID: mdl-30155360

Palabras clave

Biome; FAPAR; Forest species; Global; Land potential; Machine learning; Natural vegetation; Random forest

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Texto completo: 1 Colección: 01-internacional Banco de datos: MEDLINE Tipo de estudio: Prognostic_studies Idioma: En Revista: PeerJ Año: 2018 Tipo del documento: Article País de afiliación: Países Bajos

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