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dc.contributor.authorGuirado, Emilio
dc.contributor.authorAlcaraz Segura, Domingo 
dc.contributor.authorCabello, Javier
dc.contributor.authorPuertas-Ruíz, Sergio
dc.contributor.authorHerrera Triguero, Francisco 
dc.contributor.authorTabik, Siham 
dc.date.accessioned2020-03-25T13:06:48Z
dc.date.available2020-03-25T13:06:48Z
dc.date.issued2020-01-21
dc.identifier.citationGuirado, E.; Alcaraz-Segura, D.; Cabello, J.; Puertas-Ruíz, S.; Herrera, F.; Tabik, S. Tree Cover Estimation in Global Drylands from Space Using Deep Learning. Remote Sens. 2020, 12, 343. [doi:10.3390/rs12030343]es_ES
dc.identifier.urihttp://hdl.handle.net/10481/60633
dc.descriptionWe are grateful to Javier Montes for providing help with the Python scripts to access Google Maps’ API.es_ES
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.description.sponsorshipS.T., E.G., D.A.-S., and F.H. were supported by the project DeepSCOP-Ayudas Fundación BBVA a Equipos de Investigación Científica en Big Data 2018. E.G. is supported by the European Research Council grant agreement 647038 (BIODESERT). S.T. is supported by the Ramón y Cajal Program of the Spanish Government (RYC-2015-18136). S.T., E.G., and F.H. were supported by the Spanish Ministry of Science under the project TIN2017-89517-P. D.A.-S., E.G., and J.C. received support from project ECOPOTENTIAL, funded by European Union Horizon 2020 Research and Innovation Program, under grant agreement No. 641762, and from European LIFE Project ADAPTAMED LIFE14 CCA/ES/000612. D.A-S, S.T. and E.G. received support from Programa Operativo FEDER-Andalucía 2014-2020 under project DETECTOR A-RNM-256-UGR18. D.A-S. received support from NASA’s Work Program on Group on Earth Observations—Biodiversity Observation Network (GEOBON) under grant 80NSSC18K0446.es_ES
dc.language.isoenges_ES
dc.publisherMDPIes_ES
dc.relationEC/H2020/641762es_ES
dc.rightsAtribución 3.0 España*
dc.rights.urihttp://creativecommons.org/licenses/by/3.0/es/*
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.rights.accessRightsinfo:eu-repo/semantics/openAccesses_ES
dc.identifier.doi10.3390/rs12030343


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Atribución 3.0 España
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