Spatial validation reveals poor predictive performance of large-scale ecological mapping models

Mapping ecological variables using machine-learning algorithms based on remote-sensing data has become a widespread practice in ecology. Here, the authors use forest biomass mapping as a study case to show that the most common model validation approach, which ignores data spatial structure, leads to...

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Autores principales:	Pierre Ploton, Frédéric Mortier, Maxime Réjou-Méchain, Nicolas Barbier, Nicolas Picard, Vivien Rossi, Carsten Dormann, Guillaume Cornu, Gaëlle Viennois, Nicolas Bayol, Alexei Lyapustin, Sylvie Gourlet-Fleury, Raphaël Pélissier
Formato:	article
Lenguaje:	EN
Publicado:	Nature Portfolio 2020
Materias:	Science Q
Acceso en línea:	https://doaj.org/article/628f13be6ee34becb88ef385ebe283e3
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id	oai:doaj.org-article:628f13be6ee34becb88ef385ebe283e3
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spelling	oai:doaj.org-article:628f13be6ee34becb88ef385ebe283e32021-12-02T17:19:41ZSpatial validation reveals poor predictive performance of large-scale ecological mapping models10.1038/s41467-020-18321-y2041-1723https://doaj.org/article/628f13be6ee34becb88ef385ebe283e32020-09-01T00:00:00Zhttps://doi.org/10.1038/s41467-020-18321-yhttps://doaj.org/toc/2041-1723Mapping ecological variables using machine-learning algorithms based on remote-sensing data has become a widespread practice in ecology. Here, the authors use forest biomass mapping as a study case to show that the most common model validation approach, which ignores data spatial structure, leads to overoptimistic assessment of model predictive power.Pierre PlotonFrédéric MortierMaxime Réjou-MéchainNicolas BarbierNicolas PicardVivien RossiCarsten DormannGuillaume CornuGaëlle ViennoisNicolas BayolAlexei LyapustinSylvie Gourlet-FleuryRaphaël PélissierNature PortfolioarticleScienceQENNature Communications, Vol 11, Iss 1, Pp 1-11 (2020)
institution	DOAJ
collection	DOAJ
language	EN
topic	Science Q
spellingShingle	Science Q Pierre Ploton Frédéric Mortier Maxime Réjou-Méchain Nicolas Barbier Nicolas Picard Vivien Rossi Carsten Dormann Guillaume Cornu Gaëlle Viennois Nicolas Bayol Alexei Lyapustin Sylvie Gourlet-Fleury Raphaël Pélissier Spatial validation reveals poor predictive performance of large-scale ecological mapping models
description	Mapping ecological variables using machine-learning algorithms based on remote-sensing data has become a widespread practice in ecology. Here, the authors use forest biomass mapping as a study case to show that the most common model validation approach, which ignores data spatial structure, leads to overoptimistic assessment of model predictive power.
format	article
author	Pierre Ploton Frédéric Mortier Maxime Réjou-Méchain Nicolas Barbier Nicolas Picard Vivien Rossi Carsten Dormann Guillaume Cornu Gaëlle Viennois Nicolas Bayol Alexei Lyapustin Sylvie Gourlet-Fleury Raphaël Pélissier
author_facet	Pierre Ploton Frédéric Mortier Maxime Réjou-Méchain Nicolas Barbier Nicolas Picard Vivien Rossi Carsten Dormann Guillaume Cornu Gaëlle Viennois Nicolas Bayol Alexei Lyapustin Sylvie Gourlet-Fleury Raphaël Pélissier
author_sort	Pierre Ploton
title	Spatial validation reveals poor predictive performance of large-scale ecological mapping models
title_short	Spatial validation reveals poor predictive performance of large-scale ecological mapping models
title_full	Spatial validation reveals poor predictive performance of large-scale ecological mapping models
title_fullStr	Spatial validation reveals poor predictive performance of large-scale ecological mapping models
title_full_unstemmed	Spatial validation reveals poor predictive performance of large-scale ecological mapping models
title_sort	spatial validation reveals poor predictive performance of large-scale ecological mapping models
publisher	Nature Portfolio
publishDate	2020
url	https://doaj.org/article/628f13be6ee34becb88ef385ebe283e3
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Spatial validation reveals poor predictive performance of large-scale ecological mapping models

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