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dc.contributor.authorGuirado Hernández, Emilio
dc.contributor.authorAlcaraz Segura, Domingo
dc.contributor.authorCabello Piñar, Francisco Javier
dc.contributor.authorPuertas Ruíz, Sergio
dc.contributor.authorHerrera, Francisco
dc.contributor.authorTabik, Siham
dc.date.accessioned2020-02-19T12:39:43Z
dc.date.available2020-02-19T12:39:43Z
dc.date.issued2020-01-21
dc.identifier.issn2072-4292
dc.identifier.urihttp://hdl.handle.net/10835/7706
dc.description.abstractAccurate tree cover mapping is of paramount importance in many fields, from biodiversity conservation to carbon stock estimation, ecohydrology, erosion control, or Earth system modelling. Despite this importance, there is still uncertainty about global forest cover, particularly in drylands. Recently, the Food and Agriculture Organization of the United Nations (FAO) conducted a costly global assessment of dryland forest cover through the visual interpretation of orthoimages using the Collect Earth software, involving hundreds of operators from around the world. Our study proposes a new automatic method for estimating tree cover using artificial intelligence and free orthoimages. Our results show that our tree cover classification model, based on convolutional neural networks (CNN), is 23% more accurate than the manual visual interpretation used by FAO, reaching up to 79% overall accuracy. The smallest differences between the two methods occurred in the driest regions, but disagreement increased with the percentage of tree cover. The application of CNNs could be used to improve and reduce the cost of tree cover maps from the local to the global scale, with broad implications for research and management.es_ES
dc.language.isoenes_ES
dc.publisherMDPIes_ES
dc.rightsAttribution-NonCommercial-NoDerivatives 4.0 Internacional*
dc.rights.urihttp://creativecommons.org/licenses/by-nc-nd/4.0/*
dc.subjectconvolutional neural networkses_ES
dc.subjectdata augmentationes_ES
dc.subjectdeep learninges_ES
dc.subjectdry forestes_ES
dc.subjectforest mappinges_ES
dc.subjectlarge-scale datasetses_ES
dc.subjecttransfer learninges_ES
dc.titleTree Cover Estimation in Global Drylands from Space Using Deep Learninges_ES
dc.typeinfo:eu-repo/semantics/articlees_ES
dc.relation.publisherversionhttps://www.mdpi.com/2072-4292/12/3/343es_ES
dc.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES


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Attribution-NonCommercial-NoDerivatives 4.0 Internacional
Except where otherwise noted, this item's license is described as Attribution-NonCommercial-NoDerivatives 4.0 Internacional